Physiological mechanisms underlying growth and nitrogen productivity in rice

Buddhima Chathuri

Kariyawasam Batuwaththagamage

A thesis submitted for the degree of

Doctor of Philosophy

of

The Australian National University

Division of Plant Sciences

Research School of Biology

College of Medicine, Biology & Environment

The Australian National University

Canberra, ACT 2601

AUSTRALIA

August 2016

© Copyright by Buddhima Chathuri Kariyawasam Batuwaththagamage 2016

All Rights Reserved

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Declaration

The entire research mentioned in this thesis was carried out by me with the

following exceptions:

Chapter 2: The analysis of leaf sugar and starch concentration was conducted by

Ms. Lucy Hayes at the Australian National University. Growth analysis data were

collected in collaboration with Dr. Shujuan Zhang. The LaChat Quikchem 8500

series 2 flow injection analysis system (Lachat Instruments, Milwaukee, WI,

USA) was operated by Mr. Andrew Higgins at the Australian National University;

Chapter 3: Growth analysis data were collected in collaboration with Dr.

Shujuan Zhang and the LaChat Quikchem 8500 series 2 flow injection analysis

system (Lachat Instruments, Milwaukee, WI, USA) was operated by Mr. Andrew

Higgins at the Australian National University;

Chapter 4: Photosynthesis data were collected in collaboration with Dr. Shujuan

Zhang and the CPBP-binding assay (to quantify the number of Rubisco sites)

was done by Ms. Soumi Bala at the Australian National University; and

Chapter 5: Respiration data were collected in collaboration with Dr. Shujuan

Zhang. Carbon concentration was measured using isotope ratio mass

spectrometry (IRMS) by Dr. Hilary Stuart-Williams at the Australian National

University.

Furthermore, I certify that the contents of this thesis were not previously

submitted for a degree in any university. It does not contain any materials

previously published except where due reference is given.

Buddhima Chathuri Kariyawasam Batuwaththagamage

01/08/2016

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Acknowledgements

Foremost, I would like to express my deepest gratitude to my supervisor, Prof.

Owen Atkin for giving me this opportunity, and for his enthusiasm, guidance,

encouragement and tremendous support towards successful completion of this

PhD research and the thesis, as well as for supporting this research in many

instances via grants.

I wish to express my sincere gratitude to my co-supervisor Prof. John

Evans for his invaluable support, guidance and encouragement. Also, I would

like to thank my advisor Prof. Harvey Miller for providing insights and

comprehensive suggestions at various stages of this project.

I owe a debt of gratitude to the Australian Government “Endeavour

Awards” programme for providing me a fully-funded scholarship, enabling me

to study in one of the leading universities in the world.

Special thanks are extended to Dr. Shujuan Zhang for her excellent

cooperation given in numerous ways during this research study. I greatly

appreciate the generosity of Drs. Peter Snell and Ben Ovenden at the

Department of Primary Industries, Yanco, NSW, in providing rice seeds for this

research. My appreciation is also extended to Profs. Marilyn Ball, Eldon Ball,

Patrick Meir and Spencer Whitney for nurturing me and my peer students in an

inspiring atmosphere. I would like to thank Drs. Lasantha Weerasinghe, Ben

Long, Keith Bloomfield, Clarissa Negrini, Hilary Stuart-Williams, Andrew Scafaro

and Brenden O’Leary for their generous support during this research. I would

like to extend my sincere gratitude to Jack Egerton, Stephanie McCaffery, Lucy

Hayes, Jen Xiang, Soumi Bala, Andrew Higgins, Rosemary Birch and Prue Kell for

their technical assistance with laboratory work. Sincere appreciation is

extended to the plant services staff for their support during glasshouse

experiments and the IT staff for their valuable assistance over the past years. I

am grateful to Nur, Lingling, Hoa, Zara, You Zhang and all other members of

Atkin/Ball labs for being wonderful office/lab-mates. Motivational discussions

with Drs. Thy Truong, Bratati Mukherjee and Arun Yadev kept me on track. My

appreciation is extended to Phil and Olga for their encouragement throughout

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this research effort. I am grateful to my mentors Dr. and Mrs. Ananda

Samarakoon for many inspirational discussions.

Further, I am grateful to my teachers, Profs. W.A.P. Weerakkody, W.A.J.M

de Costa, Drs. Gamini Hitinayake and Janak Vidanaarachchi at the Faculty of

Agriculture, University of Peradeniya, Sri Lanka, and Dr. K.H. Sarananda, at the

Department of Agriculture, Sri Lanka for paving the path to higher education.

Also, I would like to express my sincere appreciation to my English teacher

Audrey Cornish for her continued support.

I sincerely dedicate my utmost appreciation to my husband, Upul, for his

understanding, patience, everlasting support and unconditional love, and

especially, for being there with me throughout the journey thus far and

excitedly waiting to share what life will bring next. I am incredibly indebted to

my parents (Daisy and Sarath), uncle (Raja), sister (Buddhini), brothers (Isuru

and Pandula), brothers-in-law (Madura and Chaminda), sisters-in-law (Gagani,

Madhushika and Nishadi), little nieces (Lana and Dahami) and nephews (Dulan

and Akane), cousins (Prasad, and Lasath and family) and my beautiful friends

Dilini, Niz, Sal, Alan, Buddhie, Widi, Bosco, Indranee, Udeni, Salem and Chris for

making me feel cherished and inspired.

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Dedication

I dedicate this work to the four great pillars of my life: my mother, Daisy

Lecamwasam, for being my science teacher during my primary and secondary

education, whose many little experiments provoked my curiosity towards science;

my father, Sarath Kariyawasam, for the trust he placed in me, and for his patience

and support given throughout my life; my uncle Raja Kulapala, without whose

financial support, through the generous provision of a monthly stipend to support

my secondary and tertiary education for nearly ten years, and his great

encouragement towards my higher education, this journey would not have been

accomplished; and my husband Upul Vithanage, for reflecting rich qualities of

human life - strong moral codes, trust, overcoming hardship, sacrifice, persistence,

patience, deep love, respect, and kindness towards all living beings and nature.

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Abstract

Nitrogen (N) is one of the most important determinants of crop growth and

yield. Associated with increasing global population pressures and food demand,

N has become one of the most essential, costly inputs in modern crop

production and a major environmental pollutant throughout the world. Thus,

identifying crop genotypes with better nitrogen use efficiency has become a

prioritized research theme to minimize crop dependency on N inputs and

reduce the environmental footprint of agriculture. Nitrogen productivity (NP)

can be considered as a useful parameter in measuring the efficiency of N use to

produce new biomass. NP has been extensively studied in the field of plant eco-

physiology; however, less is known about how NP varies among crop genotypes.

The overall aim of my PhD research was to evaluate natural variation in whole-

plant growth and NP of rice genotypes bred for contrasting habitats under

steady state and limited N supply. To explore how rice genotypes interact with

N supply, I first established what N concentrations are needed to create

phenotypic variation, using a dose-response experiment. Two N treatments

were identified as limiting (0.06 mM) and optimum (2 mM) based on growth

performances of a single genotype. Next, I assessed genotypic variation in ten

rice genotypes for their capacity to grow under N-limiting conditions by

performing a functional growth analysis during early vegetative growth. Based

on above approach, three rice candidates (Takanari, IR 64 and Milyang 23) were

identified for their ability to maintain growth and NP under N limited

conditions. Thereafter, I explored what mechanisms account for their improved

performance under low N conditions. The key components defining growth

were the efficiency of carbon (C) and N use within plant tissues (leading to

higher NP) rather than differences in C and N allocation among above and below

ground organs. The extent to which changes in photosynthesis and respiration

could explain the natural variability in growth and NP was also investigated.

There were no statistically significant differences in leaf-level photosynthetic N

use efficiency (PNUE) among the ten genotypes and N levels. However, when

considering all genotypes, there were strong correlations for PNUE [as indicated

by carboxylation capacity and net assimilation rate (N basis)] with whole plant

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NP at low N. Further, there was tendency for higher PNUE in the three selected

genotypes at low N due to maintenance of photosynthetic capacity at low N

along with partitioning more N to photosynthesis (particularly Rubisco and

electron transport components) under N limited conditions. There was no

consistent pattern in the three key performers for the fraction of C loss at

whole-plant level. Further work is needed to investigate to what extent

variations in leaf PNUE contribute to differences in whole-plant NP in the three

genotypes that performed well under N limiting conditions. My results highlight

how understanding genotypic variation for shoot PNUE and radiation use

efficiency are likely to be important for understanding variations in NP of rice.

The results also highlight the need for future work to better understand the

genetic and biochemical basis for enhanced NP under low N, as doing so could

be beneficial for producing rice varieties that are more efficient under low N

environments.

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Table of contents

Acknowledgements .............................................................................................................. v

Dedication ............................................................................................................................... vii

Abstract ..................................................................................................................................... ix

Table of contents .................................................................................................................. xi

List of Tables .......................................................................................................................... xv

List of Figures ...................................................................................................................... xvii

Chapter 1 – General introduction .................................................................................. 1

1.1 Towards sustainable agriculture: increasing crop productivity using

minimum inputs of nitrogen fertilizer .............................................................................. 1

1.2 Exploring genotypic variation for the efficiency of N use and productivity 2

1.3 Importance of the vegetative stage ............................................................................. 3

1.4 Common indices used to assess rice growth in the past ..................................... 5

1.5 Using an eco-physiological approach to analyse plant growth and

productivity ................................................................................................................................. 6

1.6 Importance of phenotypic plasticity ........................................................................... 8

1.7 Importance of ontogeny .................................................................................................. 9

1.8 Thesis objectives and outline ........................................................................................ 9

Chapter 2 – Effects of nitrogen supply on plant growth and its

components in rice ............................................................................................................. 11

2.1 Summary .............................................................................................................................11

2.2 Introduction .......................................................................................................................12

2.3 Materials and methods ..................................................................................................16

2.3.1 Plant growth ..............................................................................................................16

2.3.2 Measurements ...........................................................................................................18

2.3.3 Statistics ......................................................................................................................21

2.4 Results ..................................................................................................................................22

2.4.1 Effect of N supply on N concentration and percentage of inorganic N

to total N in organs .............................................................................................................22

2.4.2 Effect of N supply on leaf chlorophyll content..............................................27

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2.4.3 Effect of nitrogen supply on plant growth and underlying components

....................................................................................................................................................28

2.4.4 Effect of nitrogen supply on sugars and starch profile in organs .........38

2.5 Discussion ...........................................................................................................................40

2.5.1 What level of N supply is needed to create a N-deficient phenotype? 40

2.5.2 Impact of N supply on growth and its underlying components in rice

....................................................................................................................................................42

2.5.3 Which components of growth respond more dynamically to N supply?

....................................................................................................................................................46

2.6 Conclusions .........................................................................................................................47

Chapter 3 – Genotypic variation in carbon and nitrogen economy of rice

at whole plant level ........................................................................................................... 49

3.1 Summary .............................................................................................................................49

3.2 Introduction .......................................................................................................................49

3.3 Materials and methods ..................................................................................................53

3.3.1 Plant growth ..............................................................................................................53

3.3.2 Plant harvesting .......................................................................................................54

3.3.3 Calculation of growth parameters .....................................................................55

3.3.4 Chemical analysis .....................................................................................................58

3.3.5 Statistics ......................................................................................................................58

3.4 Results ..................................................................................................................................59

3.4.1 Is there evidence of genotype- nitrogen interaction (G x N) for RGR

and its underlying components? ...................................................................................59

3.4.2 Do the findings of section 3.4.1 hold when assessing at a common

mass? .......................................................................................................................................71

3.4.3 To what extent do the factors underlying variation in RGR differ under

low and high N? ...................................................................................................................75

3.5 Discussion ...........................................................................................................................79

3.5.1 Did the factors that account for variations in RGR among the 10

genotypes vary with N supply? .....................................................................................79

3.5.2 Genotypic variation in ability to grow on low N supply ...........................80

3.5.3 What underlying components account for differential responses to

low N supply? .......................................................................................................................81

3.5.4 The effect of ontogeny............................................................................................83

3.6 Conclusions .........................................................................................................................84

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3.7 Future directions ..............................................................................................................84

Chapter 4 - Genotypic variation and the effect of N supply on leaf

photosynthetic nitrogen use efficiency (PNUE) of rice .................................... 85

4.1 Summary .............................................................................................................................85

4.2 Introduction .......................................................................................................................86

4.3 Materials and methods ..................................................................................................91

4.3.1 Plant growth ..............................................................................................................91

4.3.2 Measurements ...........................................................................................................92

4.3.3. Statistics .................................................................................................................. 101

4.4 Results ............................................................................................................................... 101

4.4.1 Effect of N supply and genotypic differences on leaf chemistry,

structure, gas exchange parameters and leaf PNUE .......................................... 101

4.4.2 Rapid estimation of Rubisco via Western blotting using standards pre-

determined with [14C]CPBP Rubisco content assay ........................................... 114

4.5 Discussion ........................................................................................................................ 116

4.5.1 N mediated changes in chemical and gas exchange properties .......... 116

4.5.2 N mediated changes in leaf PNUE and N partitioning to

photosynthesis and within the photosynthetic apparatus .............................. 119

4.5.3 Rapid estimation of Rubisco via Western blotting using standards pre-

determined with [14C]CPBP Rubisco content assay ........................................... 123

4.6 Conclusions ...................................................................................................................... 124

4.7 Future directions ........................................................................................................... 125

Chapter 5 – Effect of N supply on respiratory characteristics of rice ...... 127

5.1 Summary .......................................................................................................................... 127

5.2 Introduction .................................................................................................................... 128

5.3 Materials and methods ............................................................................................... 133

5.3.1 Plant growth ........................................................................................................... 133

5.3.2 Measurements ........................................................................................................ 134

5.3.3 Statistics ................................................................................................................... 139

5.4 Results ............................................................................................................................... 140

5.4.1 Effect of nitrogen supply on dark respiration of leaves and roots of a

single genotype ................................................................................................................. 140

5.4.2 Effect of nitrogen supply on dark respiration of leaves and roots of 10

genotypes ............................................................................................................................ 147

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5.4.3 How does N availability influence the degree of light inhibition of leaf

R? ............................................................................................................................................ 156

5.4.4 How does N supply influence the proportion of daily fixed CO2

released by R ..................................................................................................................... 161

5.5 Discussion ........................................................................................................................ 164

5.5.1. To what extent do leaves and roots differ in their respiratory

response to variations in N supply? ......................................................................... 164

5.5.2. How N mediated changes in relative contribution of each organ to

daily whole-plant R ......................................................................................................... 166

5.5.3. How robust are R-N scaling relationships? ................................................ 167

5.5.4 N mediated changes and genotypic differences in RL and the degree of

light inhibition of leaf R ................................................................................................. 169

5.5.5. The effect of N supply on the balance between respiration and

photosynthesis at organ and whole-plant level ................................................... 171

5.6 Conclusions ...................................................................................................................... 172

5.7 Future directions ........................................................................................................... 173

Chapter 6 –Concluding remarks and future directions .................................. 175

6.1 Overview of the thesis ................................................................................................. 175

6.2 Agronomic considerations ......................................................................................... 178

6.3 Potential targets of interest to improve nitrogen productivity in rice ..... 182

6.4 Significance of the study ............................................................................................. 184

6.5 Future directions to improve nitrogen productivity in rice ......................... 184

List of References .............................................................................................................. 187

Appendix - RGR and underlying components of 10 genotypes of rice grown

under two N treatments over six

harvests.....................................................................................................................................213

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List of Tables

Table 2.1 Results of a two-way analysis of variance (ANOVA) with factors

time (T) and nitrogen treatment (N) .........................................................................25

Table 2.2 The results of tests for differences among N treatments at each

time point for chemical and growth parameters ................................................26

Table 2.3 The results of tests for differences among time points at each N

treatment for chemical and growth parameters. ................................................27

Table 3.1 Results of a three-way analysis of variance (ANOVA) for growth

parameters related to carbon economy considering time (T), genotype

(G) and N treatment (N) as factors with the three-way interaction term is

shown as T x G x N ............................................................................................................61

Table 3.2 RGR and underlying growth components for 10 genotypes of rice

at 2 (HN) and 0.06 (LN) mM N supply average across six harvests ............63

Table 3.3 Results of a three-way analysis of variance (ANOVA) for growth

parameters related to nitrogen economy considering time (T), genotype

(G) and N treatment (N) as factors with the three-way interaction term is

shown as T x G x N ............................................................................................................71

Table 3.4 Results of Hierarchical multiple regression analysis to assess time,

N supply and genotypic dependent changes in growth parameters while

controlling for plant size .................................................................................................73

Table 4.1 Leaf chemical, structural and gas exchange characteristics of 10

genotypes of rice under high and low N supply .............................................. 103

Table 4.2 Results of two-way ANOVA for variables presented in Tables 4.1

and 4.4 ................................................................................................................................. 104

Table 4.3 An independent samples t-test was carried out to determine if

there were significant differences for chemical, structural and gas

exchange parameters among N levels at each genotype group .............. 106

Table 4.4 Average values for the fractions of N partitioned to photosynthesis

in 10 genotypes of rice under high and low N supply .................................. 108

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Table 5.1 Results of a two-way analysis of variance (ANOVA) with factors

nitrogen treatment and cessation of N supply for chemical, structural

and physiological parameters ................................................................................... 142

Table 5.2 Results of an analysis of covariance (ANCOVA) with organ N

concentration (Nm) as the covariate and time (T) as the

grouping/independent variable ............................................................................... 144

Table 5.3 Leaf chemical, structural and physiological characteristics of rice

variety Nipponbare at seven N treatments during the dose response

experiment (Chapter 2) ................................................................................................ 149

Table 5.4 Leaf and root chemical, structural and physiological characteristics

of 10 genotypes under high and low N supply ................................................ 150

Table 5.5 Results of a two-way analysis of variance (ANOVA) for leaf and root

gas exchange, chemical and structural parameters considering N

treatment (N) and genotype (G) as factors with the two-way interaction

term is shown as N x G ................................................................................................. 152

Table 5.6 Results of a three-way analysis of variance (ANOVA) for Root RD, a

and % RShoot/ RPlant considering Time (T), genotype (G) and N treatment

(N) as factors with the three-way interaction term is shown as T x G x N

................................................................................................................................................. 153

Table 5.7 Results of linear regression analyses to explore relationships

among leaf and root respiratory, chemical and metabolic parameters. 160

Table 6.1 Background of the genotypes used in the present study…………..180

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List of Figures

Figure 2.1 An overview of plant culture during the dose-response experiment

with single genotype ‘Nipponbare’ ...........................................................................17

Figure 2.2 N concentration in leaves (leaf Nm) (green), roots (root Nm)

(brown) and percentage of inorganic N to total N are plotted against N

supply - log10 scale at two time points ....................................................................24

Figure 2.3 Leaf chlorophyll content (estimated using a SPAD meter)

measured on the most recently fully expanded leaf versus N supply at

two time points ...................................................................................................................28

Figure 2.4 Growth and developmental parameters are plotted against time

(A, C, E) and N supply (log10 scale) at three time points i.e. 33, 43, 53 days

after transplanting during the steady state (B, D, F) ..........................................29

Figure 2.5 Effect of nitrogen supply on total dry mass of rice variety

‘Nipponbare’ at two time points .................................................................................31

Figure 2.6 Growth parameters (RGR, mg g-1 d-1, NAR, g m-2 d-1 and LAR, m2

kg-1) are plotted against N supply (log10 scale) and plant dry mass...........33

Figure 2.7 Growth parameters (SLA, m2 kg-1, LMR, gg-1, SMR, gg-1 and RMR,

gg-1) are plotted against N supply (log10 scale) and plant dry mass ..........34

Figure 2.8 Growth parameters (NP, g gN-1 d-1 and PNC, mg g-1) are plotted

against N supply (log10 scale) and plant dry mass .............................................35

Figure 2.9 Growth parameters relative to the optimum (1 mM) N treatment

versus N supply (log10 scale) .........................................................................................37

Figure 2.10 Effect of N supply on carbohydrate profile of leaves and roots of

rice variety ‘Nipponbare’ at two time points .........................................................39

Figure 3.1 An overview of plant culture under glasshouse conditions during

the study with 10 genotypes of rice under 0.06 and 2 mM ...........................55

Figure 3.2 Effect of N supply on (A) plant dry mass across time; (B) relative

growth rate (RGR, mg g-1 d-1) across time; (C) RGR as a function of plant

dry mass in log10 scale .....................................................................................................60

Figure 3.3 Percentage of mean RGR and its underlying parameters under

0.06 mM (LN) N supply compared to 2 mM (HN) i.e. LN:HN% for (A)

relative growth rate (RGR); (B) net assimilation rate (NAR); (C) leaf area

xviii

ratio (LAR); (D) nitrogen productivity (NP) and (E) plant nitrogen

concentration (PNC) .........................................................................................................62

Figure 3.4 Effect of N supply on growth parameters (LAR, m2 kg-1 and NAR, g

m-2 d-1) across time (A and C) and plant dry mass (B and D) ........................64

Figure 3.5 Effect of N supply on growth parameters (SLA, m2 kg-1, LMR, gg-1,

SMR, gg-1 and RMR, gg-1) across time (A, C, E, G) and plant dry mass (B,

D, F, H) ....................................................................................................................................67

Figure 3.6 Effect of N supply on growth parameters (NP, g gN-1 d-1 and PNC,

mg g-1) across time and as a function of plant dry mass - log10 scale ......70

Figure 3.7 Path diagrams showing the relationship between RGR and its

underlying components of 10 genotypes of rice (averaged across six

harvests) from both C and N economy perspective under two N levels..76

Figure 3.8 Path diagram showing the relationship between RGR and its

underlying components (NAR, LMR and SLA) of 10 genotypes of rice

(averaged across six harvests) from C economy perspective at 0.06 mM N

level ..........................................................................................................................................78

Figure 4.1 Measuring leaf gas exchange characteristics of 10 genotypes of

rice grown under two N levels (2 and 0.06 mM) using Licor6400 gas

exchange system…………………………………………………………………………………… 91

Figure 4.2 A schematic diagram to illustrate the steps 1- 5 followed for the

rapid estimation of Rubisco via Western blotting using standards

calibrated with [14C]CPBP……………………………………………………………………… 96

Figure 4.3 Western blot analysis for rice Rubisco extracted from frozen leaf

samples of rice genotype Takanari grown under 2 mM and 0.06 mM N

supply…………………………………………………………………………………………………… 97

Figure 4.4 Leaf chlorophyll (a+b) content is plotted against leaf nitrogen per

unit area (Na)……………………………………………………………………………………… 102

Figure 4.5 Bar graphs showing genotypic variation in light-saturated net

photosynthesis measured at 400 µmol mol-1 atmospheric [CO2] (A) per

unit area (A400, a); (B) per unit mass (A400, m); (C) per unit N (A400, N) under

high and low N supply………………………………………………………………………… 105

Figure 4.6 Bar graphs presenting (A) maximum rate of electron transport per

unit area normalised to 25°C ( Jmax, a25); (B) maximum carboxylation

velocity of Rubisco per unit area normalised to 25°C (Vcmax, a25); (C) Jmax,

a25:Vcmax, a

25 ratio (both normalised to 25°C) and (D) maximum

carboxylation velocity of Rubisco per unit leaf N normalised to 25°C

(Vcmax, N25) for 10 genotypes of rice under high and low N supply……… 110

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Figure 4.7 Relationships between N per unit leaf area (Na) and (A) maximum

rate of electron transport normalised to 25°C on area basis ( Jmax, a25) ; (B)

maximum carboxylation velocity of Rubisco on area basis normalised to

25°C (Vcmax, a25) ; (C) Jmax, a

25:Vcmax, a25 ratio (both normalised to 25°C); (D)

maximum carboxylation velocity of Rubisco on leaf N basis normalised to

25°C (Vcmax, N25) and (E) light-saturated net photosynthesis measured at

400 µmol mol-1 atmospheric [CO2] on N basis (A400, N)……………………….. 111

Figure 4.8 The relationships between leaf mass per unit leaf area, Ma and (A)

leaf N per unit area, Na; (B) total fraction of leaf N invested in

photosynthetic metabolism, nA (C) maximum carboxylation velocity of

Rubisco on leaf N basis normalised to 25°C (Vcmax, N25)………………………. 113

Figure 4.9 (A) The amount of Rubisco sites observed per lane estimated from

Western blotting (WB) approach versus actual amount of Rubisco sites

loaded per lane quantified from [14C]CPBP binding assay; (B) Rubisco

carboxylation rate per unit leaf area normalized to 25°C (Vcmax, a25) versus

Rubisco sites per m2……………………………………………………………………………. 115

Figure 4.10 Pie charts show the percentage of leaf N in pigment-protein

complexes, nP; percentage of leaf N in electron transport components,

nE; percentage of leaf N in Rubisco; nR, for each N treatment (2 and 0.06

mM) when averaged (A) the three genotypes (Takanari, IR 64 and

Milyang 23) that maintained growth and nitrogen productivity (see

Chapter 3); (B) the other seven genotypes (Opus, Dular, BG 34-8,

Koshihikari, Akihikari, Azucena and Nipponbare)………………………………... 122

Figure 4. 11 Nitrogen productivity (NP) at 0.06 mM (LN) is plotted against (A)

net assimilation rate (N basis) i.e. A400, N at LN and (B) carboxylation

capacity (N basis) i.e. Vcmax, N25 at LN………………………………………………………

123

Figure 5.1 The ‘Kok’ effect is illustrated in the plot of net CO2 exchange rate

(Anet, µmol CO2 m-2 s-1) versus irradiance (µmol photons m-2 s-1) ............. 136

Figure 5.2 Leaf (green) and root (brown) respiration expressed on (A) organ

dry mass basis (leaf RD, m and root RD, m) and (B) N basis (leaf RD, N and

root RD, N) are plotted against N supply at two time points ........................ 141

Figure 5.3 Leaf (green) and root (brown) respiration on organ dry mass basis

are plotted against N concentration in organs at two time points ......... 143

Figure 5.4 Combined daily leaf and root respiration expressed on plant dry

mass basis is plotted against N supply - log10 scale at two time points145

xx

Figure 5.5 Dose response bar graphs showing daily leaf and root R as a

percentage of combined daily leaf and root R (A) before cessation of N

supply; (B) following cessation of N supply and (C) values averaged

across genotypes (as there was no significant genotypic effect or G x N

interaction as suggested by the three way ANOVA, Table 5.6) for daily

shoot and root R as a percentage of daily whole plant R ............................ 146

Figure 5.6 Bar graphs showing genotypic variation in area based rates of leaf

respiration in the dark (RD, a) ...................................................................................... 148

Figure 5.7 Relationship between estimated shoot R and measured shoot R

for the same replicate during 10 genotypes experiment described in

Chapter 3 ............................................................................................................................ 154

Figure 5.8 Relationships between (A) leaf respiration in the darkness (Leaf RD,

m) on mass basis versus leaf N per unit mass (Nm); (B) leaf respiration in

the dark (Leaf RD, a) on area basis versus leaf N per unit leaf area (Na); (C)

leaf respiration in the light (Leaf RL, m) on mass basis versus leaf N per

unit mass (Nm); (D) leaf respiration in the light (Leaf RL, a) on area basis

versus leaf N per unit leaf area (Na) and (E) root respiration in the

darkness (Root RD, m) on mass basis versus root N per unit mass (Nm). 155

Figure 5.9 Relationship between Leaf respiration in the light (RL, a) and leaf

respiration in the darkness (RD, a) both are expressed on area during 10

genotypes experiment described in Chapter 3 ................................................. 157

Figure 5.10 Combined data from both experiments previously described in

Chapter 2 (shown in diamonds) and Chapter 3 (shown in circles). Leaf

respiration in the light (RL, a, open symbols), leaf respiration in the

darkness (RD, a, closed symbols) and RL/RD, ratio of respiration in the light

to that in the dark are plotted against leaf N per unit leaf area (Na),

Rubisco oxygenation velocity at light saturated irradiance 1500 µmol

photons m-2 s-1 (Vo, 1500); Rubisco carboxylation velocity at light

saturated irradiance 1500 µmol photons m-2 s-1 (Vc, 1500) ............................. 158

Figure 5.11 Bar graphs showing genotypic variation in daily whole plant

respiration (whole plant R), daily whole shoot photosynthesis (whole

plant A), and daily whole plant respiration as a fraction of daily whole

shoot photosynthesis (whole plant R/A) at second (H2) and fifth (H5)

harvests of 10 genotypes experiment described in Chapter 3 .................. 163

1

Chapter 1 – General introduction

1.1 Towards sustainable agriculture: increasing crop

productivity using minimum inputs of nitrogen fertilizer

Nitrogen (N) is one of the most important determinants of crop growth and

yield, with N deficiencies often resulting in poor growth and reductions in

harvestable yields and quality. Today N has become one of the most essential,

but costly inputs in modern crop production, as well as a major environmental

pollutant throughout the world. The demand for N fertilizers is growing

worldwide, with N fertilizers being produced via the Haber-Bosch process that

consumes considerable amounts of energy, which in turn requires combustion

of fossil fuels and greenhouse gas emissions (Beatty, 2009). Current global N

fertilizer consumption is 112 million metric tonnes (MMt) (Heffer, 2015), and is

predicted to increase to 240 MMt by the year 2050 (Tilman, 1999).

Unfortunately, plant N uptake efficiency is less than 40% (Glass, 2003), with 50-

70% of the applied N being lost back to the environment (Hodge et al., 2000,

Good et al., 2012), mainly via leaching, de-nitrification and volatilization (Shi et

al., 2010); in turn, such factors result in detrimental effects to soils, water

bodies and the atmosphere. For example, heavy use of N fertilizer creates

economic and environmental issues in some regions, such as China (Zhu and

Chen, 2002). By contrast, the affordability of N fertilizer limits crop production

in other regions e.g. Africa (Morris, 2007). Given the growing need to increase

crop production [including 60-70% increase in rice (Takai et al., 2013)] to feed

a growing world population [currently 7 billion, and predicted to reach 9.7

billion by 2050 (UN, 2015)], it seems likely that the demand/use of N fertilizers

will continue to grow. Ideally, increased crop production needs to be attained in

a sustainable manner – that is, via more efficient use of N fertilizers. Achieving

this will require a better understanding of the underlying factors that control

the efficiency of N use in plants, particularly in cereal crops.

2

1.2 Exploring genotypic variation for the efficiency of N use and

productivity

One way of reducing the economic and environmental costs associated with N

fertilizer inputs is to identify or produce plant genotypes with high N

productivity [NP, the increase in plant mass per unit tissue nitrogen content and

time (Ågren, 1985)] and nitrogen use efficiency [NUE, the grain yield per unit of

available N in the soil (Moll et al., 1982) or the total biomass or grain yield

produced per unit of applied N fertilizer (Xu et al., 2012)]. Such genotypes

would be capable of producing higher yields or sustaining existing yields by

taking up, assimilating and remobilizing available N more efficiently under N-

limited conditions. The approach chosen to quantify NUE (physiological, genetic

or agronomic) will depend on the trait of interest, and the extent to which a

researcher is interested in establishing the physiological processes

underpinning variations in NUE (Good et al., 2004). Agronomic NUE (dry mass

per unit N applied) is considered as the product of absorption NUE (N absorbed

per unit N applied) and physiological NUE (dry mass per unit N absorbed)

(Nguyen, 2014). NUE depends on the efficiency of inorganic N uptake (Glass,

2003), assimilation of ammonium (NH4+) and/or nitrate (NO3-) into organic N

and remobilization of organic N during later stages of plant growth (Masclaux-

Daubresse et al., 2010).

Genotypic variation for NUE in rice has been widely studied from an

agronomic perspective (Fageria and Barbosa Filho, 2001, Fageria et al., 2010,

Mae, 2011, Singh et al., 1998, Mahajan et al., 2012, Koutroubas and Ntanos,

2003, Inthapanya et al., 2000, Fukai et al., 1999, Ju et al., 2006). Recently, several

quantitative trait loci (QTL) were identified for NUE (Nguyen, 2014, Senthilvel

et al., 2008, Wei et al., 2011, 2012), giving attention to traits such as the number

of leaves, tillers, dry mass in organs and whole plant and N concentration in

organs. Several authors emphasized the importance of exploring existing

germplasm (Nguyen, 2014) for variations in physiological processes leading to

genotypic variation of NUE in rice (Hirel et al., 2007, Namai et al., 2009, Ida et

al., 2009, Lawlor, 2002). Improving sink capacity per unit N was identified as a

prospect to increase NUE (Mae et al., 2005). Basic knowledge of physiological

and molecular mechanisms underpinning variation in NUE is needed if we are

3

to more rapidly select genotypes with improved NUE; however, at present,

relatively little is known about the components underpinning variation in NUE

and the way such components interact with other factors to ultimately control

growth, development and yield (Garnett et al., 2015). While NUE is readily

quantified at the reproductive stage, knowledge of factors influencing the

efficiency of N use is limited for cereals during the vegetative stage of

development. Variations in the efficiency of N use prior to anthesis are likely to

be crucial in determining how much N fertilizer needs to be added to cereal

crops.

NP during vegetative growth provides a valuable addition to yield-

focussed measures of NUE. NP is an indicator of the efficiency of N use for

biomass production (Ingestad, 1979, Hirose, 1988, Garnier et al., 1995, Atkin et

al., 1996b, Lambers, 2008). In plants with high NP, high rates of growth can be

sustained without large demand for soil N, with NP during vegetative growth

influencing yield-based measures of NUE. To date few studies have investigated

quantified whole-plant NP of crop species, or the factors that influence variation

in crop NP values. Moreover, to my knowledge, no study has investigated a

potential link between NP (during vegetative growth) and yield-based measures

of NUE. The lack of understanding of how NP varies among crop genotypes

during vegetative growth, the factors influencing variation in NP values (of

crops), and linkages between NP and NUE, limits the utility of NP as a potential

trait when screening for N efficient crops in breeding programmes. Further, the

benefits of improved NUE and NP may vary markedly depending on the extent

to which individual genotypes experience abiotic stress, both now and under

future climatic scenarios. Thus far, only a few studies have investigated the

extent to which NP of crops vary in response to imposition of abiotic stress such

as low N supply. Thus, the challenge for modern agriculture is to

select/generate genotypes with high NP, thereby maximizing vegetative growth

and seed yields with lower requirements for N fertilizer.

1.3 Importance of the vegetative stage

Grain yield is tightly linked with biomass at anthesis (Horie et al., 2003, Takai et

al., 2006). Despite this, the contribution of vegetative growth for biomass

4

production and yield is often ignored. Resource availability for the mother plant

is a key criterion for seed production (Roach and Wulff, 1987) and such

reserves accumulated during early growth are used to support the reproductive

phase. For instance, in Arctium tomentosum growth rate and carbon balance

determine the size of the store i.e. hypocotyl (Roach and Wulff, 1987) and more

than 70% of the total N in the plant is recycled into seed production at the seed

filling stage (Chapin et al., 1990). To exploit the yield potential of rice, together

with efficient N use, it is essential to expand our understanding of the

components underlying rice growth during the vegetative stage of development.

For instance, N nutrition from early to mid-tillering (in the lead up to anthesis)

is crucial in determining the number of tillers and potential panicles (Tanaka et

al., 1959). More than 50% of genotypic variation in grain yield is thought to be

due to the variation in number of panicles per unit land (Koutroubas and

Ntanos, 2003), highlighting the importance of tillering ability. Further, N uptake

rate during first 35 days of growth is crucial for the accumulation of internal

pools of N, as the ability to take up N gradually declines during later growth due

to accumulation of unproductive roots (Vinod and Heuer, 2012). According to

Wada et al. (1986), 39% of the total N in a rice plant is absorbed during the

vegetative stage of growth, with N supply during vegetative growth being

critical in controlling growth rate and total biomass at anthesis, with the latter

being crucial for yields. Further, healthy leaves and culms that have been built

up during early growth become a source of N for developing organs during the

grain filling stage. About 70-90% of total panicle N is remobilized from

vegetative organs and 60% of that represents N derived from leaf blades (Mae,

1997). Finally, Rebolledo et al. (2012) emphasised the importance of early

vigour (i.e. the rapid leaf expansion for resource acquisition and dry matter

accumulation during early development) for crop improvement. Thus, attention

needs to be given to the factors controlling crop growth during early vegetative

development, particularly with respect to factors influencing the efficiency of N

use. Analysing early growth of small plants has the advantage that large

number of plants can be grown in a small area.

5

1.4 Common indices used to assess rice growth in the past

From an agronomic point of view, rice growth has been widely studied in terms

of absolute growth rate (AGR, increase of total dry mass per plant and time),

crop growth rate (CGR, increase in total dry mass per unit land area of a crop

and time), and the growth efficiency [GE, equal to W/(W+R) where W is the

amount of dry matter produced or CGR and R is that of substances respired in a

given period of plant growth or the proportion of the amount of growth in a

given amount of substrates] mainly during the post-anthesis stage of crop

development (Yamaguchi, 1978, Saitoh et al., 1998, Cock and Yoshida, 1973,

Ying et al., 1998, Hasegawa and Horie, 1996, Horie et al., 2003, Takai et al., 2006,

Taylaran et al., 2009). In one study, GE of rice plants during early vegetative

growth was 60% (Tanaka and Yamaguchi, 1968). Saitoh et al. (2000) compared

the GE, CGR and underlying components of CGR [i.e. leaf area index (LAI,

amount of one sided leaf area of a crop per unit land area) and net assimilation

rate (NAR, the increase of total dry mass per unit leaf area and time)] of two

contrasting rice varieties Nipponbare and Takanari. They observed a GE of 64%

which declined over time due to increased respiratory losses, with higher CGR

in Takanari due to greater LAI rather NAR. While providing useful information,

the above indices have limitations that restrict the level of insights into

mechanisms underpinning variation in growth rates and NP that can be derived

from their quantification. For example, while AGR and CGR both quantify

biomass increment in absolute terms, they ignore variations in original biomass

investment; moreover, while quantifying the rate of biomass production, they

do not quantify the efficiency of biomass accumulation and, as such, do not give

insights to underlying physiological mechanisms (Costa, 2004). Similarly, while

GE is useful for gaining insights into the significance of respiration in

determining biomass production, it is not sufficient to define all underlying

components (e.g. photosynthesis, biomass and N allocation etc.) involved in the

accumulation of biomass. By contrast, many of the above issues can be

elucidated via quantification of the relative growth rate (RGR, increase of total

dry mass per unit existing total dry mass and time) and its underlying

components; RGR quantifies the efficiency of existing dry mass in producing

new dry mass (Costa, 2004). RGR during early crop growth could also be

6

considered an important indicator of early vigour which is often judged visually

by crop breeders (Rebolledo et al., 2012). While Evans (1996) found no

evidence that crop yields had been improved via selection for higher pre-

anthesis CGR or RGR, the potential for increased RGR during vegetative growth

to impact of biomass at anthesis, and thus yields, remains. More studies are

needed to elucidate such links, with only a few studies (Tanaka and Yamaguchi,

1968, Cook and Evans, 1983b, Amin et al., 2002) having characterized RGR in

rice, with even less attention having been given to the response of RGR to N

supply in rice.

1.5 Using an eco-physiological approach to analyse plant

growth and productivity

RGR and its underlying components are well defined and extensively studied in

the field of plant eco-physiology. RGR is used as an important tool to

characterize growth and its underlying components in grasses, herbaceous and

other species from natural habitats (Poorter et al., 1990, Poorter and Lambers,

1991, Lambers and Poorter, 1992, Atkin et al., 1996a, Atkin et al., 1996b), and in

responses of such plants to N supply (Van der Werf et al., 1992b, van der Werf et

al., 1993a, Ryser and Lambers, 1995, Van Arendonk et al., 1998, Nagel et al.,

2001, Poorter et al., 1995). In such studies, RGR is often characterized in two

ways – carbon (C) and N economy approaches – by performing a growth

analysis and linking such analyses to measurements of C and N fluxes. From a C

economy perspective, RGR is the product of physiological (i.e. NAR, increase in

plant mass per unit leaf area and time) and morphological (i.e. LAR, ratio of leaf

area to whole plant mass) components (van der Werf et al., 1993a). NAR

indicates the balance between C gain (i.e. the rate of photosynthesis per unit leaf

area basis) in intact shoots, and C losses (i.e. the rate of whole-plant respiration

per unit leaf area), divided by the carbon concentration of the newly formed

biomass (Lambers and Poorter, 1992). Given that carbon concentrations are

relatively similar across life forms and treatments, variation in NAR are largely

the result of changes in photosynthesis and/or respiration (Poorter and Van der

Werf, 1998). In studies comparing growth characteristics of inherently fast and

slow growing species, LAR is often the key component that accounts for

7

differences in RGR of contrasting species (Lambers and Poorter, 1992); LAR is

the product of biomass allocation to leaves – being a product of the leaf mass

ratio (LMR, ratio of leaf dry mass to whole plant dry mass) and specific leaf area

(SLA, ratio of leaf area to leaf dry mass). SLA is influenced by lamina thickness,

leaf dry matter content and leaf density (Vernescu and Ryser, 2009). From a N

economy perspective, RGR is the product of plant nitrogen concentration (PNC,

nitrogen concentration of the plant) and NP (Ingestad, 1982, Lambers et al.,

1990, Atkin and Cummins, 1994, Atkin et al., 1996b, Lambers, 2008), with

variations in N allocation and the efficiency of N use in metabolic processes

influencing variations in NP and thus RGR. This approach has the potential to

reveal details about the factors underpinning biomass production and the

efficiency of N use during early growth which would then lead to variations in

grain yield and NUE at the reproductive stage.

From whole plant perspective, factors determining the variation in NP

are described by the formula stated below:

NP = (𝑃𝑁𝑈𝐸 ∗ 𝐿𝑁𝑅)− [𝑆ℎ𝑜𝑜𝑡 𝑅 (𝐿𝑁𝑅+ 𝑆𝑁𝑅)]−(𝑅𝑜𝑜𝑡 𝑅∗ 𝑅𝑁𝑅)

𝐶𝐶 (Eqn. 1.1)

where PNUE is the photosynthetic N use efficiency (rate of photosynthesis per

unit organic leaf N and day) and CC is the whole plant C concentration. Shoot R

and root R are respiration rates per unit organic N in the shoots and roots

respectively. LNR, SNR and RNR are the proportions of whole-plant organic N

that are located to the leaves, stems and roots respectively [adapted from Atkin

et al. (1996b)]. Garnier et al. (1995) informed a tight correlation of PNUE with

leaf and plant NP. Underpinning the PNUE-NP relationship is the possibility

that genotypes with high PNUE allocate a greater fraction of N to photosynthetic

processes (particularly Rubisco) and/or exhibit better light interception.

Depending on the patterns of N investment, higher PNUE can result in increased

NP, with likely positive consequences for RGR, whereas higher rates of CO2

release by respiration (per unit plant N) (e.g. that might arise from higher costs

associated with net NO3- uptake) will often result in reduced NP, and thereupon

may have a negative impact on RGR. By quantifying growth and tissue N content

of different genotypes during vegetative stage, one can seek to understand

whether variations in grain yield (i.e. NUE) are associated with differences in NP

8

during vegetative growth and N allocation within vegetatively growing plants.

Thus, from a physiological perspective, identifying genotypes with improve

efficiency of N use requires selection/generation of genotypes with high NAR

and NP; doing this could help maximize vegetative growth and seed yields while

also reducing requirements for N fertilizer. Plant growth analysis (during the

vegetative growth phase of development) along with gas exchange

measurements (to measure rates of photosynthesis and respiration) and tissue

N analysis will allow a determination of whether N-mediated or genotypic-

mediated differences in efficiency of N use are due to differences in allocation of

N to above- and below-ground tissues, and/or differences in rates of

photosynthesis and respiration (expressed per unit tissue N).

1.6 Importance of phenotypic plasticity

Being sessile organisms, plants often have to adjust their physiological and/or

morphological traits to cope with changes that occur in the surrounding

environment (Schlichting, 1986). For example, a given genotype might create a

range of phenotypes depending on its genetic makeup and N availability. This

ability to adjust the displayed phenotype is called ‘phenotypic plasticity’, which

in turn can ensure maintenance of resource acquisition, growth, survival and

reliability of crop yields (Bradshaw, 2006, Ryser and Eek, 2000, Nicotra et al.,

2010). While some consider plasticity as a useful trait for survival in a changing

climate (Schlichting, 1986, Nicotra et al., 2010) others (Weiner, 2004, Bradshaw,

2006) see some disadvantages of plasticity in crops at an individual level

compared to population level (e.g. investing resources in competitive structures

at the expense of yield). When comparing multiple genotypes in response to a

decrease in N availability, genotypes may exhibit different phenotypic

responses. Some genotypes may reduce growth and underlying components,

while others may remain insensitive to N supply. Identifying genotypes that

maintain growth under low N supply (relative to high N supply), and elucidating

the mechanisms underpinning such responses, would be the first step towards

enabling breeders to select lines capable of growing in low N soils with minimal

inputs of fertilizers.

9

1.7 Importance of ontogeny

One issue that needs to be taken into account in any study of growth is the

confounding effect of ontogeny on plant traits. Growth parameters often change

in value as plants develop (Poorter and Pothmann, 1992). For example, age-

dependent increases in shoot to root ratio have been observed in fast

developing new varieties of wheat (Siddique et al., 1989) and some herbaceous

species (Wilson, 1988). Further, stem mass ratio (SMR, ratio of stem mass to

whole-plant mass) increased at the expense of roots and/or leaves as plants

increased in size (Poorter et al., 2012). Documenting the extent of such

ontogeny-dependent changes in growth traits is important when assessing the

impact treatments such as low N supply, as comparing growth traits at a

common age can lead to erroneous conclusions on the direct effect of N supply

on a given trait (Evans, 1972). When correcting for ontogeny, such confusions

caused by differences in plant size and/or stage of development can be avoided

by conducting multiple harvests rather than just one (Coleman et al., 1994) and

examining biomass fractions as a function of total plant size (Poorter and Sack,

2012).

1.8 Thesis objectives and outline

Although there are some natural eco-system based studies available dealing

with how N availability influences plant growth, few studies have characterized

plant growth based on dose-response type experiments and most past work on

natural ecosystems has been focused on plants which are phylogenetically

distinct. What is less clear, however, is whether there is genetic diversity and

divergence in responses within closely related plants or within a species.

Further, there is limited knowledge available at the equivalent level for any crop

and it is unclear the extent to which NP of a given crop is affected by N supply in

a way may be similar or dissimilar from natural ecosystems. There is also little

knowledge on how genotypes might differ systematically to low N supply, their

ability to grow under low N and mechanisms underpinning such performances.

These issues are highly important for crops such as rice which heavily relies on

N additions and create nitrification problems around the globe. My thesis

provides the opportunity to identify rice genotypes that are efficient in N use

10

under low N environments. Therefore, the overall objective of my PhD thesis

was to evaluate natural variation in whole-plant growth and nitrogen

productivity of rice genotypes bred for contrasting habitats under steady-state

and limited N supply, and to explore what physiological mechanisms drive

faster growth in rice, particularly under low N conditions.

To address these objectives, I first established what N concentrations are

needed to create N-deficient phenotypes using a dose-response experiment

(Chapter 2); the results of Chapter 2 thus formed a road map for subsequent

experiments. Chapter 2 also enabled me to evaluate the impacts of N supply on

the C and N economy (and their underlying components) of rice. Thereafter, in

Chapter 3 I screened ten rice genotypes for their capacity to grow and use N

efficiently on low and high N supply, during early vegetative growth. Further, I

investigated what mechanisms account for observed faster growth of several

rice genotypes under low N conditions. In Chapter 4, I analysed how leaf-level

PNUE varies among the 10 selected rice genotypes, particularly at low N

conditions. Then, in Chapter 5, I examined how root and leaf respiratory

characteristics of rice vary in response to N availability and the extent to which

such changes in respiration could explain variability in whole-plant growth and

NP. Finally, in Chapter 6, I summarized the key findings of my thesis research

and provide insights for future work.

11

Chapter 2 – Effects of nitrogen supply on plant growth and its components in rice

2.1 Summary

The effect of nitrogen (N) availability on whole-plant growth and its underlying

components were investigated using hydroponically-grown rice plants. Plants

were initially grown for 53 days under steady-state supplies of seven N

concentrations (0.06-4 mM); thereafter, plants were exposed to a 10-day N

cessation period to investigate how further depletion of N from plant organs

impact on components of plant growth. Non-destructive growth parameters

were recorded through time. Destructive measurements were taken at two time

points: 53 and 63 days after transplanting (DAT). Relative growth rate (RGR)

and its underlying components were examined for the steady-state period of N

supply (i.e. 0-53 DAT). Rice exhibited N-deficient phenotypes over the 0.06-0.12

mM range of N supply. Plant growth and developmental parameters (e.g. plant

height, leaf number and tiller number) were optimal at 1 mM N treatment, while

chlorophyll and organ N concentrations were optimum around 2 mM N. Growth

traits exhibited their minimum or maximum around 0.5-1 mM. There was no

significant difference among plant dry masses between 1-2 mM N. Hence, 2 mM

was considered as the optimum N treatment for subsequent experiments.

Underpinning the biomass responses was the fact that RGR was highest at

moderate N levels (0.25-2 mM N), and lower in plants grown on very low and

high steady-state N supply. Importantly, when compared at common plant sizes,

data generally fell on one trait – plant mass relationship reflecting the impact of

N supply on plant size, not direct effects of N per se. This finding highlights the

importance of controlling for plant size when assessing the effect of N on

growth traits. Finally, analysis of phenotypic plasticity of growth traits (relative

to the optimum N treatment) suggested that traits were more affected by N

deficiency than N toxicity. Collectively, the study provides insights into the

mechanisms underpinning N-mediated changes in biomass accumulation in rice,

with the results providing a road-map to what N levels are needed to create

optimal growth and N-limited phenotypes in rice.

12

2.2 Introduction

The global population of humans is projected to reach 9.7 billion by 2050 (UN,

2015), placing increasing demands to increase crop productivity, particularly

that of rice as it is the staple food for more than half of the global population.

One way of achieving high productivity is by providing plants with adequate

amounts of nitrogen (N) fertiliser. N is vital for plant growth and survival, being

a major constituent of amino acids, proteins (enzymes), chlorophyll, nucleic

acids, and several plant hormones (Shi et al., 2010) and the key limiting factor of

plant growth and development (Kraiser et al., 2011). Importantly, only 30-40%

of applied N is taken up by crop plants (Glass, 2003, Fischer, 1998), with the

remainder of applied N being leached to the environment causing

environmental deterioration and economic losses (Masclaux-Daubresse et al.,

2010, Good et al., 2007). Rice plants in the field can experience varying levels of

N supply, including toxicity at the time of fertilization (Chen et al., 2013) and

deficiency during later growth (Dobermann and Fairhurst, 2000). Given these

factors, a thorough understanding of the physiological processes underpinning

growth and the efficiency of N use is crucial to improve the ability of rice to take

up, assimilate and remobilize nitrogen efficiently under optimal and N deficient

conditions.

Rice growth has previously been studied in terms of absolute growth

rate (AGR), crop growth rate (CGR) and growth efficiency (GE); by contrast, less

attention has been given to the relative growth rate (RGR) and its underlying

components in rice (see Chapter 1). From a carbon (C) economy perspective,

RGR is a combination of the net assimilation rate (i.e. physiological processes

regulating net C uptake), specific leaf area (i.e. morphology controlling leaf area

display) and the leaf mass ratio (i.e. biomass allocation to leaves) while plant N

concentration (PNC) together with NP (nitrogen productivity - the efficiency of

using N in metabolic processes) has the potential to explain variations in RGR

from a N economy point of view. Thus far, few studies have focussed on the

effects of N deficiency on rice, reflecting the complexity of N dynamics in paddy

soils and the difficulty of obtaining reliable and repeatable levels of N deficiency

in field-grown plants (Lafitte, 1998). Moreover, little effort has been made to

13

characterize growth and its underlying components in rice, particularly in

relation to N supply during the vegetative stage.

What is known about how variations in N supply affect RGR and its C and

N economy components in rice? In other plant systems (i.e. excluding rice), and

consistent with functional equilibrium model (Poorter and Nagel, 2000, Poorter

et al., 2012, Reich et al., 2002, van der Werf and Nagel, 1996), it is known that

under N deficiency conditions, plants allocate a greater fraction of whole-plant

biomass to roots [i.e. the root mass fraction (RMR – ratio of root mass to whole

plant mass)] and less to leaves (i.e. reduced LMR – ratio of leaf mass to whole-

plant mass) (Poorter and Nagel, 2000, Poorter et al., 2012). Such changes e.g.

increased root length (Poorter and Ryser, 2015) improve the ability of plants to

access nutrients, but limit rates of photosynthetic C fixation. As a consequence,

under severe N deficiency, RGR (Pavlik, 1983, Poorter et al., 1995), biomass

(Walker et al., 2001) and crop yield (Greenwood, 1982) often decline. By

contrast, variations in N supply result in less consistent changes in SLA, with

past studies reporting an increase, decrease or no change in SLA (Aerts, 1994).

Similarly, there are reports of NAR decreasing (Poorter et al., 1995) and

increasing (Pavlik, 1983) when plants experience reduced N supply; by contrast,

LAR usually decreases (Pavlik, 1983, Aerts, 1994) under N deficiency. N

concentration in leaves and roots is often decreased (Zhao et al., 2005, Luo et al.,

2013) under conditions of low N supply. As N supply declines, NP is reported to

initially increase and then decrease as the severity of N deficiency further

increases (van der Werf et al., 1993b, Ingestad, 1977). However, the underlying

factors responsible for these dynamic changes in NP remain unclear,

particularly for crop species such as rice. It is also unclear to what extent shifts

in RMR, LMR and NP enable rates of RGR to be maintained across a range of N

supply levels.

Available literature on growth components of rice in relation to N supply

remains limited. The contribution of respiratory CO2 release to dry matter

production in terms of growth efficiency (GE, W/W+R where W is the amount of

dry matter produced or CGR and R is that of substances respired in a given

period of plant growth or the proportion of the amount of growth in a given

14

amount of substrates) in rice variety Peta was studied by Tanaka and

Yamaguchi (1968) under two N levels; they reported a GE of 60% during early

growth and a gradual decline over time due to respiration involved in processes

that are not directly link to growth (e.g. mutual shading and elongated

internodes). Further, they observed a decline in RGR, NAR, LAR and respiratory

rates irrespective of the level of N fertilization over time. Similarly, Saitoh et al.

(2000) compared two rice varieties (Takanari and Nipponbare) under two N

levels and reported a GE of 64% during early growth and a decline over time in

both varieties independent from N fertilization due to increased respiratory

losses. In another study that compared rice grown on three levels of N supply,

root growth was stimulated over the shoot growth under limited N supply

(Murata, 1969), again consistent with functional equilibrium model (Poorter

and Nagel, 2000). Cook and Evans (1983a) compared area-based rates of

photosynthesis, leaf N content, and specific leaf weight (i.e. ratio of leaf dry mass

to leaf area) of wild-type and cultivated-type rice species; they reported that

there was no difference among cultivated and wild-type species regarding N-

induced reductions in growth and photosynthesis under low N concentrations.

Both photosynthetic rate and specific leaf weight were correlated with area-

based N content where high biomass allocation and reduction in leaf area per

plant were observed under low N. Recently, Amin et al. (2002) studied rice

growth in soil at two N levels and reported faster growth at high N supply was

associated with increased NAR values and N uptake rates. Taken together, it

thus appears that N-supply mediated variations in rice growth are linked to

concomitant changes in leaf chemistry, structure and net carbon gain. What is

less clear, however, is the level of N supply required to create N-deficient

phenotypes in rice.

In a majority of studies assessing the impact of N supply on plant growth

and physiology (using crop and non-crop species), growth components were

compared under only two N levels (Pavlik, 1983, van der Werf et al., 1993a,

Reich et al., 2003, Poorter et al., 1995). Such studies do not give insights on how

underlying components of growth adjust to fine-scale differences in N supply.

To address this issue, dose response curves can be used to characterize the

response (i.e. strength, sign and form; (Poorter et al., 2012)) of a given trait in a

15

continuous manner over a detailed range of N supply. Such an attempt was

taken by Ehara et al. (1990) who compared varietal differences of 35 rice

varieties grown up to 8.5 leaf stage at seven N levels and found a positive

correlation between NAR and RGR. What is unclear, however, is how trade-offs

in biomass allocation among leaves, stems and roots contributed to the

variation in RGR responses, or whether NP increased under low N supply.

Changes in N availability is often used as a stimulus to induce plasticity

(see Chapter 1) of growth traits (Useche and Shipley, 2009); an example of

plasticity is the dynamic increase in biomass allocation to roots under N

deficiency. Some traits are highly plastic in response to variable environments;

these include SLA, LAR and RMR (Ryser and Lambers, 1995, Ryser and Eek,

2000). Plasticity of growth traits can allow plants to maintain homeostasis of

growth under varying N availabilities, except when experiencing extremely low

or high N levels. Plasticity of traits is measured either as an absolute or relative

response. For the latter, various metrics are used, including the variation of a

given trait relative to its value under optimum or the highest N supply (Van de

Vijver et al., 1993) or the variation of the trait between a fixed time interval

(Useche and Shipley, 2009). Given that N availability in a paddy field at any

given stage of rice growth is variable, both temporally and spatially (e.g. due to

mineralization of soil organic matter and N fertilizer, N removal via crop N

demand, leaching, denitrification and volatilization), it is important that the

degree of plasticity of key growth traits be quantified in rice.

The overall objective of this chapter was to understand the impacts of N

supply on the C and N economy of rice, with particular focus on the response of

whole-plant growth and its underlying components to an increasing logarithmic

supply of N during early vegetative stage. The study addressed the following

specific objectives: (1) what level of N supply is needed to create a N deficient

phenotype (in terms of plant growth) in rice; (2) how does whole-plant growth

of rice, and its underlying components, respond to N supply; (3) which

components of growth respond more dynamically to N supply; and, (4) does

cessation of N supply alter biomass accumulation and source-sink relationships

in rice, when compared to plants grown on steady-state low-to-high levels of N?

16

2.3 Materials and methods

2.3.1 Plant growth

Seeds of rice variety Nipponbare were placed on petri-dishes, double layered

with moistened filter papers and allowed to germinate by leaving under dark

conditions in an incubator at 30 °C for 5 days. Following germination, petri

dishes with seedlings were exposed to natural light by placing them near a

window at about 20-24 °C for 2 days until the first set of leaves had expanded.

The seedlings were then transferred to moistened vermiculite and allowed to

acclimate to glasshouse conditions (temperature controlled to deliver a

day/night cycle of 28/22 °C) for 5 days. The glasshouse was surrounded by

Plexiglas cladding that allows UV radiation through. To ensure constant

photoperiod through the experiment (12 hours), additional lamps were

provided by incandescent bulbs. The study was done during the months May -

July 2013 (i.e. winter in Canberra). Average mid-day irradiance at leaf level was

640 µmol photons m-2 s-1 while midday irradiance outside the glasshouse was

1540 µmol photons m-2 s-1. At the three leaf stage, seedlings were transferred to

a hydroponic system (Fig. 2.1 A), with individual plants being placed on plastic

netting attached within PVC tubes (Fig. 2.1 B) that had a diameter of 3.7 cm and

a height of 13 cm (Fig. 2.1 C). The tubes with seedlings were then placed on a

light-proof lid with holes (3.7 diameter) and lids were used to hold 12 plants in

place above the light-proof containers of 22 L capacity filled with 16 L of

nutrient solution with one of seven different N levels (0.06, 0.12, 0.25, 0.5, 1, 2

and 4 mM N), with N being provided as both NH4+ and NO3- (Hubbart et al.,

2007); for each solution, de-ionized water was used to make up a solution

containing a range of concentrations 0.03, 0.06, 0.125, 0.25, 0.5, 1 and 2 mM of

NH4NO3, 0.6 mM NaH2PO4.2H2O, 0.5 mM K2SO4, 0.8 mM MgSO4, 0.2 mM

CaCl2.6H2O, 0.07 mM Fe-EDTA, 9 M MnCl2.4H2O, 0.1 M (NH4)6Mo7O24.4H2O,

37 M H3BO3, 0.3 M CuSO4.5H2O, 0.138 M NH4VO3, 0.75 M ZnSO4.7H2O, and

0.2 g l-1 potassium silicate solution (VWR Chemicals, No. 296546S). Solution pH

was monitored and adjusted daily to 5.8 - 6 using 1 M H2SO4 or 1 M NaOH. The

solution was continuously aerated and replaced weekly. Initially plants were

placed in half-strength nutrient solution for 1 week then transferred to full-

17

strength solution and left there for 6.5 weeks before beginning measurements

at 53 days after transplanting.

After the first round of measurements on day 53, all plants (belonging to

7 treatments) were transferred to new nutrient solutions that were identical

A

B C

Figure 2.1 An overview of plant culture during the dose-response

experiment with single genotype ‘Nipponbare’ (A) The hydroponic

system used in the experiment. The nutrient solution was stored in the

22 l light-proof containers. (B) Rice plants growing inside PVC tubes on

the top of plastic tubs that were filled in with nutrient solution (C)

Details of the seedlings growing inside the PVC tubes.

18

with the exception that all were now without nitrogen; this was also done to

evaluate whether cessation of N supply alters biomass accumulation and

source-sink relationships in the selected rice cultivar. Plants were left on the N-

less treatment for 10 days, with measurements (see next section) being done at

two harvesting points (on the day of shifting to N-less treatments and after 10

days of N cessation). These two time points will be termed as ‘before’ and ‘after’

throughout the rest of this chapter and the thesis.

2.3.2 Measurements

2.3.2.1 Growth analysis

The initial dry mass of six seedlings on day zero (i.e. when seedlings were first

shifted to the seven N treatments) was determined. Thereafter, two harvests

were made, one at 53 days after transplanting (i.e. 6.5 weeks after seedlings

were placed on steady-state N treatments) and 63 days after transplanting (i.e.

10 days after cessation of N supply). Six plants were then harvested from each

treatment at the 53- and 63-day harvest time points. At each harvest, three

plants per N treatment were separated into leaf blades, leaf sheaths (leaf sheath

will be termed as ‘stem’ throughout the rest of the thesis) and roots, and the

fresh mass of each organ (Mettler-Toledo Ltd., Port Melbourne, Victoria,

Australia) and the leaf area recorded (leaf area meter, LI-3000C, Li-Cor Inc.,

Lincoln, NE, USA). Dry mass was determined on oven dried material (70 °C for

48 hours). The following growth parameters were calculated as average values

over the 0-53 day period: relative growth rate (RGR; mg g-1 d-1), net assimilation

rate (NAR, g m-2 d-1) and nitrogen productivity (NP, g gN-1 d-1). For non-rate

parameters such as leaf area ratio (LAR; m2 kg-1plant), specific leaf area (SLA; m2

kg-1leaf), leaf mass ratio (LMR; gleaf g-1plant), stem mass ratio (SMR; gstem g-1plant),

root mass ratio (RMR; groot g-1plant) and plant N concentration (PNC, mg g-1),

values were determined at the 53 day time-point alone, as N deficient

phenotypes were not apparent at the beginning of the experiment.

RGR = 𝑙𝑛 DMt2 − 𝑙𝑛 DMt1

t2−t1 (Eqn. 2.1)

LAR and PNC were calculated by using following equations:

LAR = SLA x LMR (Eqn. 2.2)

19

PNC = (LNC x leaf DM + SNC x stem DM+ RNC x root DM)

plant DM (Eqn. 2.3)

where LNC, SNC, RNC and PNC are the leaf, stem, root and plant N

concentrations. Leaf DM, stem DM, root DM, plant DM denote leaf, stem, root

and plant dry mass.

When calculating average NAR values for the 0-53 DAT period, LAR values from

the 53 DAT harvest were used.

NAR=RGR

LAR (Eqn. 2.4)

Similarly, when calculating NP for the 0-53 DAT period, PNC values from the 53

DAT harvest were used.

NP = RGR

PNC (Eqn. 2.5)

2.3.2.2 Leaf chlorophyll content

A measure of leaf chlorophyll content in the most recently fully expanded leaf of

individual plants was taken in terms of the SPAD value using the SPAD-502

(Minolta Co. Ltd, Osaka, Japan), a commercial hand-held chlorophyll meter.

Three separate measurements were taken close to the tip, middle and at the

base of each recently fully expanded leaf.

2.3.2.3 Total N and nitrate analyses

The total N concentration in dried leaves, roots and stems were separately

extracted using the Kjeldahl method (Allen et al., 1974) and determined using a

LaChat Quikchem 8500 series 2 flow injection analysis system (Lachat

Instruments, Milwaukee, WI, USA). Nitrate was extracted from a subset of the

above leaves, roots and stems (using pooled samples of multiple replicates, due

to limited individual sample masses), using a hot water extraction method

described by Cataldo et al. (1975). The resulting supernatant was analysed for

nitrates by using the above flow-injection analysis system (Lachat Instruments,

Milwaukee, WI, USA) using a protocol (USEPA, 1991) where by nitrates in the

sample are quantitatively reduced to nitrite when samples are passed via a

copperized cadmium column. Nitrite was then quantified by diazotizing with

20

sulphanilamide followed by coupling with N-(1-naphthyl) ethylenediamine

dihydrochloride. The resulting water soluble magenta coloured dye is measured

colourimetrically at 520 nm. The difference between total N and total nitrite

was considered as the leaf organic N content.

2.3.2.4 Carbohydrate analyses

Concentration of soluble sugars (glucose, fructose, sucrose), starch and total

non-structural carbohydrates (TNC) were determined on dried, ground and

pooled leaf and root material. Sugars were extracted from 5-10 mg of ground

material where 500 µl of 80% ethanol was added to the sample and spun for 20

seconds. The suspension was kept in a thermomixer at 80°C, shaken at 500 rpm

for 20 minutes and centrifuged at 12000 rpm for 5 minutes. The supernatant

was transferred into a new tube and the above extraction process was repeated

to the pellet in the original tube for a total of three times. The resulting pellet

was frozen at -20°C for subsequent starch analysis. The supernatant containing

soluble sugars along with soluble sugar assay standards was dried overnight on

a heat block at 50-55°C. Once samples were dried the remaining residual was

resuspended in 200µl of deionized water and spun. Finally, this re-suspended

solution was frozen at -20°C for subsequent sugar analysis.

Soluble sugars were determined following the instructions provided in

Sigma Fructose Assay Kit FA20-KT1. Frozen soluble sugar samples were fully

thawed at 35°C and spun. 10µl of deionized water (in triplicate) as blanks for

assay reagents, 10µl of sugar standards (in triplicate) and 10µl of samples (in

duplicate) were added to a 96 well flat bottom plate. 200µl of the Glucose assay

reagent solution was added to every well (including blank, standard or sample)

and incubated at 18-35°C for 30 minutes. 340nm was read on a plate reader

(Infinite® M1000 Pro; Tecan US, Morrisville, NC) until the reaction reaches its

end point. The data for absorption at 340nm were recorded at the end point.

This value is used to calculate the glucose concentration. For fructose, 2µl of the

phosphoglucose isomerase (PGI) (included as part of the fructose assay kit) was

added to every well used and incubated at 18-35°C for 30 minutes. 340nm was

read until the reaction reaches its end point. The data for absorption at 340nm

were recorded at the end point for the fructose value. For sucrose, 10µl of

invertase solution (Solution C) was added to every well used and incubated at

21

18-35°C for 30 minutes. 340nm was read until the reaction reaches its end

point. The data for absorption at 340 nm were recorded at the end point. This

will be used for the sucrose value.

Starch was determined using the Megazyme Total Starch Assay Kit (K-

TSTA). Samples and starch standard were placed in the heat-block at 55°C for

15 minutes to evaporate the excess ethanol. The heat-block was then turned up

to 100°C, 195 µl of diluted α-amylase (Solution 1) was added, spun vigorously

and incubated at 100°C for 3 minutes. Samples were spun vigorously ensuring

complete homogeneity of the samples and incubated at 100°C for 2 minutes in

the heat-block and spun vigorously. Samples were incubated at 100°C again for

2 minutes in heat-block and 200 µl of 200mM sodium acetate buffer (Reagent II)

was added. 5 µl of amyloglucosidase (Bottle 2) was added and spun vigorously.

The tubes were closed and incubated at 50°C for 30 minutes. The heat block was

turned up to 55°C and set up for microtitre plate. The samples were centrifuge

for 10 minutes at 5000 rpm, 15°C. 10µl D-Glucose standards (in triplicate), 10µl

starch standard (in triplicate) and 10µl of each sample (in triplicate) were

added to a 96 well flat bottom plate. 290 µl of GOPOD reagent (Solution 3) was

added to every well used (standards and samples), incubated the plate at 55°C

for 30 minutes and covered the plate with parafilm to stop evaporation. The

plate was kept at room temperature for 5 minutes and read absorption at

515nm.

2.3.2.5 Calculation of phenotypic plasticity

Phenotypic plasticity was quantified as the degree of plasticity in response to

the optimum treatment, which was calculated by dividing the change at each N

level relative to the optimum N treatment (1 mM) by the value obtained under

the optimum (1 mM) N treatment during the steady-state N supply. This index

indicates the degree of change as well as the direction of change.

2.3.3 Statistics

All data were tested for normality and homogeneity of variance. A two-way

ANOVA procedure (general linear model) was conducted using SPSS (version

21, SPSS, Chicago, IL, USA) considering time and N treatment as factors for N

concentration in leaves (Leaf Nm) and roots (Root Nm), SPAD reading, plant

22

height, leaf number and tiller number. Differences among N treatments at each

time point or differences among time points at each N treatment were analysed

using one-way analysis of variance (ANOVA) and Tukey’s post-hoc test for

chemical and growth parameters. In situations where the assumption of

homogeneity of variance was violated, Welch ANOVA (i.e. the robust test of

equality of means) along with Games Howell post-hoc test was considered.

Slopes of ln DM vs time were tested using one-way ANOVA to verify differences

in RGR among N treatments during the steady state of N supply. For the

parametric analysis plant dry mass (DM) was ln transformed. When data

remained non-parametric or when the homogeneity of variances violated and

Welch ANOVA remained non-significant, Kruskal-Wallis tests were performed.

2.4 Results

2.4.1 Effect of N supply on N concentration and percentage of

inorganic N to total N in organs

I first consider how N concentration in leaves (leaf Nm) and roots (root Nm) of

rice change in response to changes in external N availability during steady-state

supply and after a short-term cessation of N supply. Leaf Nm was higher than

root Nm at any given N treatment (Fig. 2.2A and B). At the ‘before’ harvest (i.e.

53 DAT), leaf Nm (Fig. 2.2A) and root Nm (Fig. 2.2B) gradually increased with

increasing N supply, reaching a maximum around 2 mM and then declining

when N supply increased further to 4 mM. A two-way ANOVA revealed a

significant interaction term between time and N supply for root Nm, indicating

that the effect of N supply on root Nm differed for plants sampled on days 53 and

63 (Table 2.1).

Given that the response to time differed among the N treatments, a one-

way ANOVA and its non-parametric equivalent Kruskal Wallis test were used to

assess whether there were significant differences in root Nm among N

treatments at given time points (Table 2.2) and any temporal changes in root Nm

within each N treatment (Table 2.3). This revealed that there were significant

differences in organ N concentrations among N treatments at both 53 and 63

days after transplanting (DAT) (Table 2.2). There was a significant reduction of

N concentration in organs after cessation of N supply, except for roots at 0.25

23

and 4 mM N treatment which had statistically equivalent N concentrations (Fig.

2.2A, B and Table 2.3). The percentage of inorganic N to total N was further

examined across N treatments, to elucidate whether the above observed trends

of N concentrations were due to nitrate accumulation in organs, especially

under high N supply. Inorganic N appeared to be slightly accumulated only in

roots during steady-state growth on different N treatments, reaching a peak

around 0.5 to 1 mM N supply (Fig. 2.2C). Leaves did not accumulate nitrate (less

than 1%).

24

Figure 2.2 N concentration in leaves (leaf Nm) (green), roots (root Nm)

(brown) and percentage of inorganic N to total N are plotted against N

supply - log10 scale at two time points. The harvest conducted 53 days

after transplanting (DAT) is defined as ‘Before’ (i.e. following growth on

steady-state N levels). All plants were then subjected to ‘zero’ N for 10

days, after which plants were harvested 63 DAT – this harvest is defined

as ‘After’. (A) N concentration in leaves (Leaf Nm) versus N supply; (B) N

concentration in roots (Root Nm) versus N supply; (C) percentage of

inorganic N to total N versus N supply (n = 3; ± SE). Inorganic N was

determined in pooled samples (n = 3). See Materials and Methods for

more details on how inorganic and total N was determined.

25

Table 2.1 Results of a two-way analysis of variance (ANOVA) with factors

time (T) i.e. two time points before at 53 days after transplanting (DAT) and

after at 63 DAT and nitrogen treatment (N) for N concentration in leaves

(leaf Nm) and roots (root Nm), SPAD reading, plant height, leaf number and

tiller number. F-values and their significance are presented where degrees of

freedom is shown within parenthesis. *p < 0.05, **p < 0.01, ***p < 0.001.

Dependent variable T N T x N

Leaf Nm 244.0*** (1) 45.7*** (6) 1.65 (6)

Root Nm 109.0*** (1) 21.8*** (6) 4.70** (6)

SPAD reading 1.40 (1) 20.3*** (6) 0.56 (6)

ln DM 45.0*** (1) 9.7*** (6) 1.60 (6)

Plant height 86.7*** (9) 66.6*** (6) 1.72** (54)

Leaf number 76.3*** (9) 80.6*** (6) 8.51*** (54)

Tiller number 119.2*** (9) 129.0*** (6) 6.40*** (6)

26

Table 2.2 The results of tests for differences among N treatments at each

time point for chemical and growth parameters. Data were primarily

analysed using one-way analysis of variance (ANOVA). If the assumption of

homogeneity of variance was violated, Welch ANOVA (i.e. the robust test of

equality of means) along with Games Howell post-hoc test was considered.

Slopes of ln DM vs. time for time period 0-53 days after transplanting (DAT)

were tested to verify differences in RGR among N treatments. When data

remained non-parametric or when the homogeneity of variances violated

and Welch ANOVA remained non-significant, Kruskal-Wallis test was

performed. One way ANOVA, Welch ANOVA or Kruskal-Wallis p-values: *p <

0.05, **p < 0.01, ***p < 0.001. n.s. indicates non- significant.

Dependent variable 33

DAT

43

DAT

53

DAT

63

DAT

0-53

DAT

Leaf Nm - - ** *** -

Root Nm - - ** * -

SPAD reading - - n.s. *** -

Plant height *** *** *** - -

Leaf number *** *** *** - -

Tiller number n.s. *** *** - -

ln DM - - *** n.s. -

(ln DM at 53 DAT- ln DM at 0 DAT)/ (53-0) DAT - - - - ***

LAR - - n.s. - -

SLA - - ** - -

PNC - - ** - -

LMR - - * - -

SMR - - n.s. - -

RMR - - ** - -

27

2.4.2 Effect of N supply on leaf chlorophyll content

Did the above changes in leaf Nm across time and among N treatments (Fig. 2.3)

result in changes in leaf chlorophyll content? Figure 2.3 shows that SPAD

readings were highest in plants grown on 1 to 2 mM N supply. There was about

two-fold variation in SPAD readings when comparing the lowest with the

optimum N treatment. However, despite these patterns, there were no highly

significant differences in SPAD readings among the N treatments in the ‘before’

sampled leaves (p = 0.056). By contrast, highly significant differences were

found among N treatments following 10-day cessation of N supply (Table 2.2).

There was no significant difference in SPAD readings between two time points

at any given N treatment (Table 2.3).

Table 2.3 The results of tests for differences among time points at each N

treatment for chemical and growth parameters. Primarily, data were analysed

using one-way analysis of variance (ANOVA). If the assumption of

homogeneity of variance is violated, results of robust tests of equality of

means i.e. Welch ANOVA along with Games Howell post-hoc test was

considered. None of the data were transformed. When data remained non-

parametric or when the homogeneity of variances violated and Welch ANOVA

remained non-significant, Kruskal-Wallis test was performed. One way

ANOVA, Welch ANOVA or Kruskal-Wallis p-values: *p < 0.05, **p < 0.01, ***p

< 0.001. n.s. indicates non-significant.

Dependent variable N treatment (mM of N)

0.06 0.12 0.25 0.5 1 2 4

Leaf Nm ** ** ** * *** *** *

Root Nm ** ** n.s. * * *** n.s.

SPAD reading n.s. n.s. n.s. n.s. n.s. n.s. n.s.

ln DM * *** n.s. n.s. n.s. n.s. *.

Plant height *** ** *** *** *** *** ***

Leaf number *** *** *** *** *** *** ***

Tiller number n.s. *** *** *** *** *** ***

28

2.4.3 Effect of nitrogen supply on plant growth and underlying

components

2.4.3.1 Plant height, leaf number and tiller number

I now consider the extent to which plant height, leaf number and tiller number

were influenced by N supply (Fig. 2.4). There were significant interactions

between time and N treatments for plant height, leaf number and tiller number

(Table 2.1). This suggests that the effect of N supply on above parameters varied

through time. A one-way ANOVA and its non-parametric equivalent Kruskal

Wallis test revealed significant differences in growth and developmental

parameters among N treatments at given time points (Table 2.2) except 33 DAT,

and significant temporal changes within each N treatment (Table 2.3) except at

0.06 mM. Collectively, these results suggest that the effect of N treatment on

plant height and other parameters increased over time, and that the optimal N

supply level for growth increased as plants grew.

Figure 2.3 Leaf chlorophyll content (estimated using a SPAD

meter) measured on the most recently fully expanded leaf versus

N supply at two time points: ‘before’ (i.e. 53 DAT following

growth on steady-state N treatments) and ‘after’ (i.e. 10-days

after cessation of N supply, 63 DAT) (n = 6; ± SE).

29

Figure 2.4 Growth and developmental parameters are plotted against

time (A, C, E) and N supply (log10 scale) at three time points i.e. 33, 43, 53

days after transplanting during the steady state (B, D, F). All plants

belong to seven N treatments were subjected to ‘zero’ N for 10 days

when plants are at the age of 53 days after transplanting. Short-dashed

line indicates the time point immediately after N cessation (A) plant

height across time; (B) plant height versus N supply; (C) leaf number

across time; (D) leaf number versus N supply; (E) tiller number across

time; (F) tiller number versus N supply (n = 3; ± SE).

30

Plant height was significantly higher at 0.5 mM (p < 0.001) compared to

other N treatments (except 0.25 and 1 mM) during early stages of growth (Fig.

2.4A and B). However, the 0.5-2 mM range became more favourable for plant

elongation at 53 DAT (Fig. 2.4B). Leaf number was maintained around five up

until plants reach 39 DAT and there onwards plants prompted proliferating

leaves depending on N availability (Fig. 2.4C). Leaf number was greater at 1 mM

N supply (p < 0.001) in comparison with other N treatments at 43 DAT and

thereafter (Fig. 2.4C and 2.4D). Plants began producing tillers once they reached

a certain developmental stage (35 DAT) and tiller number was largely reduced

under low N supply (Fig. 2.4E). The highest number of tillers was observed at 1

mM N supply (p < 0.001, Fig. 2.4F). Even after the cessation of N supply at 53

DAT, in general plants kept elongating except when grown on 0.06, 0.5 and 2

mM N supply (Fig. 2.4A). This was more prominent in plants exposed to 1 and 4

mM N treatments. Leaf number initially continued increasing till 58 days after

transplanting and then decreased in most N treatments except at 0.5 and 4 mM

N supply (Fig. 2.4C). Such a change was not apparent under 0.06 and 0.12 mM N

supply where, leaf number was continuously reduced since cessation. Tiller

number was also continually increased till 58 days after transplanting and then

gradually decreased in the majority of N treatments (Fig. 2.4E).

31

2.4.3.2 Plant dry mass

Figure 2.5 exhibits how whole plant dry mass of rice vary during steady state N

supply and after cessation. The effect of N supply on plant ln dry mass (ln DM)

did not vary through time (Table 2.1).

Plant dry mass was significantly different among N treatments during

steady state of N supply; by contrast, there were no significant differences in

plant dry mass after cessation of N supply (Table 2.2). The Tukey’s post-hoc test

for mean comparisons revealed that there were no significant differences

among 0.25, 0.5, 1 and 2 mM during the steady-state N supply while none of the

pairs of N treatments were significantly different after the cessation. There was

no significant difference in plant dry mass between two time points i.e. before

and after from 0.25-2 mM (Table 2.3).

Figure 2.5 Effect of nitrogen supply on total dry mass of rice variety

‘Nipponbare’ at two time points: ‘before’ (i.e. 53 DAT following

growth on steady-state N treatments) and ‘after’ (i.e. 10-days after

cessation of N supply, 63 DAT) (n = 3; ± SE).

32

2.4.3.3 Relative growth rate and underlying components

2.4.3.3.1 Growth parameters vs N supply

There was a significant difference (p < 0.001) among N treatments for RGR as

tested by one-way ANOVA for slopes of ln dry mass (ln DM) vs. time during

steady-state N supply (Table 2.2). Tukey’s post-hoc tests confirmed that slopes

of ln DM vs time at 0.25, 0.5, 1 and 2 mM were not significantly different from

each other, but were significantly higher (p < 0.05, 0.001, 0.001 and 0.01

respectively) than 0.06 and 0.12 mM N supply. Slopes of ln DM –time were not

significantly different across 0.06, 0.12 and 4 mM N supply. Thus, RGR was

statistically greater over the moderate range of N levels, and lower in plants

grown on very low and high N supply during the steady state of N supply.

Variation in RGR in response to N supply appeared to be largely due to

variation in NAR as there was no significant difference in LAR values among the

seven N treatments (Table 2.2). Thus, the lower RGR at 0.06, 0.12 and 4 mM

were largely due to lower rates of whole plant net carbon gain per unit leaf area

and time [i.e. lower NAR (Fig. 2.6C)], with higher RGR from 0.25 - 2 mM was

associated with higher NAR values. Homeostasis in LAR across N treatments

(Fig. 2.6E) was achieved via interplay between SLA and LMR (Fig. 2.7A, C). SLA

at 4 mM N treatment was not significantly different from 0.06, 0.12 and 0.25

mM N supply. SLA exhibited a bi-modal response, being significantly lower at

0.25, 0.5, and 1 mM N than at 0.06 mM (Fig. 2.7A). Similarly, SLA values at 0.5

mM were significantly lower (p < 0.05) than at 0.12 and 4 mM, with there being

no significant difference between plants at 0.06 and 0.12 mM N supply. There

were also significant differences among N treatments for LMR (Table 2.2), with

LMR being significantly higher at 1 mM than 0.06 mM (p < 0.05). Importantly,

increasing N supply had broadly different effects of SLA and LMR values,

optimal N supply being associated with lower SLA values while LMR was

highest at those N levels. In general, the increase in LMR with N supply was

associated with a decline in RMR; while there was no significant difference for

RMR among 0.06, 0.12, 0.25 and 4 mM N treatments, RMR values were

significantly lower at 0.5 and 2 mM than at 0.06 and 0.12 mM N supply. There

was no significant difference among N treatments for SMR (Table 2.2). Taken

together, these results pointed to a trade-off in biomass allocation from roots to

33

leaves as N supply increases to an optimum, with supra-optimum N supply

leading to an increase in allocation to roots.

Figure 2.6 Growth parameters are plotted against N supply (log10 scale)

and plant dry mass. (A) relative growth rate (RGR, mg g-1 d-1) vs. N

supply (B) RGR vs. plant dry mass; (C) net assimilation rate (NAR, g m-2

d-1) vs. N supply; (D) NAR vs. plant dry mass (E) leaf area ratio (LAR, m2

kg-1) vs. N supply; (F) LAR vs. plant dry mass (n = 3; ± SE for growth traits

except RGR and NAR).

34

Figure 2.7 Growth parameters are plotted against N supply (log10

scale) and plant dry mass. (A) specific leaf area (SLA, m2 kg-1) vs. N

supply; (B) SLA vs. plant dry mass; (C) leaf mass ratio (LMR, gg-1) vs. N

supply; (D) LMR vs. plant dry mass; (E) stem mass ratio (SMR, gg-1) vs.

N supply; (F) SMR vs. plant dry mass; (G) root mass ratio (RMR, gg-1) vs.

N supply; (H) RMR vs. plant dry mass (n = 3; ± SE).

35

RGR could also be described as a product of the N concentration within

the plant (Fig. 2.8C) and the efficiency of N use by physiological processes i.e.

nitrogen productivity (Fig. 2.8A). PNC increased significantly with increasing N

supply, reaching a maximum at 2 mM N (Table 2.2). While this points to the N-

dependence of RGR (Fig. 2.6a) being linked to whole-plant N concentration, the

N-dependent changes in PNC did not fully mirror those of RGR; this reflected the

fact that there were also N-dependent changes in NP, which was greatest at 0.06

mM and lowest at 4mM N (Fig. 2.8A and C). Thus, the RGR response to different

steady-state N levels was linked, generally, to opposing patterns of PNC and NP,

with increasing N supply increasing the former and decreasing the latter.

Figure 2.8 (A) nitrogen productivity (NP, g gN-1 d-1) vs. N supply; (B) NP vs.

plant dry mass; (C) plant nitrogen concentration (PNC, mg g-1) vs. N

supply; (D) PNC vs plant dry mass (n = 3; ± SE except NP).

36

When considering how N supply affected RGR and its underlying

components, plants were harvested at day 53 and trait values compared across

the N treatments. The results pointed to N-dependent changes in several key

traits, including LAR and its underlying components (SLA and LMR). Given that

such traits can vary during plant development (i.e. they are linked to plant

ontogeny), each growth parameter was plotted against plant dry mass (Fig.

2.6B, D and F, Fig. 2.7B, D, F and H and Fig. 2.8B and D). When compared at

common plant sizes, data points belong to each of above parameters generally

fell on one trait-plant mass relationship, with trait values varying as a function

of plant size. This raises the possibility that above observed variations in growth

parameters may have been due to differences in plant size caused by N

availability, rather than to the direct effect of N supply per se on each trait. Later

sections in this thesis use multiple harvests to further explore this issue (see

Chapter 3).

2.4.3.3.2 Plasticity of growth traits

To assess which traits exhibited greater proportional changes in response to N

treatments, the degree of change of a given growth trait relative to the 1 mM N

treatment (representing optimal N supply) was plotted against N supply (Fig.

2.9A, B and C). For all traits, the greatest change relative to the optimum when

grown on 0.06 mM and to a lesser extent when grown on 0.5 and 4 mM N. LAR

(Fig. 2.9B) and SMR (Fig. 2.9C) were generally less plastic in comparison with

other traits overall; that said, LAR still exhibited some plasticity, increasing

under N deficiency as well as under N toxicity. At low N supply, plants increased

biomass allocation to roots at the expense of leaves, with RMR being around

20% higher at the lowest N level compared to plants grown at 1 mM (Fig. 2.9C).

37

Figure 2.9 Growth parameters relative to the optimum (1 mM) N

treatment versus N supply (log10 scale). (A) net assimilation rate

(NAR) and nitrogen productivity (NP) versus N supply; (B) specific

leaf area (SLA), leaf area ratio (LAR) and plant nitrogen

concentration (PNC) versus N supply; (C) organ mass ratios i.e.

leaf mass ratio (LMR), stem mass ratio (SMR) and root mass ratio

(RMR) versus N supply. Dotted line indicates the ‘zero’.

38

2.4.4 Effect of nitrogen supply on sugars and starch profile in organs

For several of the traits described above (e.g. SLA), changes in the concentration

of non-structural carbohydrates could influence traits values. Given this, I

explored how N supply affected carbohydrates profiles of rice leaves and roots

(Fig. 2.10A to F). Leaves consistently exhibited greater sugars (Fig. 2.10A, B and

C) and starch (Fig. 2.10D) concentrations at any given N supply than roots.

Sucrose was the most abundant sugar in leaves and roots followed by fructose

and glucose through the entire range of N supply. The concentration of fructose

and glucose slightly increased towards low N supply, whereas sucrose content

was mostly independent of N supply. Similarly, total sugar, starch and total

non-structural carbohydrate concentrations were little affected by steady state

N supply. Cessation of N supply reduced sucrose, fructose and glucose

concentrations in leaves under a majority of N levels except 0.24 and 1 mM.

Starch accumulated in leaves as a result of N cessation under 0.06 mM N supply.

Sugar or starch profile of roots was not affected by the cessation of N supply.

Total sugar (Fig. 2.10E) and non-structural carbohydrate (Fig. 2.10F) content

were largely maintained in both organs when increasing N supply. Taken

together, these results point to similar carbohydrate profiles in plants growing

on low to high steady state N (suggesting similar source-sink relationships),

whereas carbohydrates are somewhat depleted following complete cessation of

N availability.

39

Figure 2.10 Effect of N supply on carbohydrate profile of leaves and

roots of rice variety ‘Nipponbare’ at two time points. The harvest

conducted 53 days after transplanting (DAT) is defined as ‘Before’ (i.e.

following growth on steady-state N levels). All plants were then

subjected to ‘zero’ N for 10 days, after which plants were harvested 63

DAT – this harvest is defined as ‘After’. (A) glucose content versus N

supply; (B) fructose content versus N supply; (C) sucrose content versus

N supply; (D) starch content versus N supply; (E) total sugar content

versus N supply; (F) total non-structural carbohydrate content versus N

supply. Values are pooled samples of 3 replicates.

40

2.5 Discussion

For this thesis, I grew rice variety Nipponbare which is one of the most

commonly used japonica type reference varieties (Cho et al., 2000, Soejima et

al., 1995, Ida et al., 2009, Miyabayashi et al., 2007) over a wide range of

available N levels, ranging from 0.06 to 4 mM N. The resultant data set of

present study provided an opportunity to elucidate the underlying C and N

economy mechanisms via which growth of rice- during early vegetative growth -

is affected by N availability. Unlike most past studies comparing a limited

number of N levels, the current study compared growth responses to a

logarithmic series of seven levels of N supply. By assessing the dose-response

to a wide range of N levels, I was also able to identify levels of N supply needed

to create N-deficient plants, with information from this chapter being used in

subsequent multi-genotype comparisons in later chapters. Collectively the

results point to N-mediated changes in growth rate, with severe N deficiency

reducing RGR largely via reductions in the net assimilation rate (NAR), with the

latter being linked to reduced leaf and whole-plant concentrations of tissue

nitrogen. While the efficiency of N use varied with N supply, increases in

nitrogen productivity (NP) at low N supply were insufficient to overcome the

inhibitory effect of low tissue N concentrations.

2.5.1 What level of N supply is needed to create a N-deficient

phenotype?

When assessing what concentration of N supply is needed to create N deficient

phenotypes, a range of traits need to be considered. Leaf nitrogen

concentration exhibited a positive correlation with N supply with increasing N

supply, as previously reported (Hilbert, 1990), with mass-based leaf N

decreasing when N supply exceeded 2 mM (Fig. 2.2A). A similar pattern was

seen in roots (Fig. 2.2B) and whole-plants (Fig. 2.8C). Thus, from the

perspective of maximal tissue N concentrations in the selected rice variety, 2

mM supply was optimal, with 4 mM N supply leading to reduced tissue N

concentrations in above- and below-ground organs. Interestingly, I found

negligible accumulation of inorganic N (i.e. nitrate) in leaves, irrespective of N

supply, with roots increasing nitrate concentrations up to 0.5 mM N supply (Fig.

41

2.2C). Plants are known to accumulate excess N as nitrates in vacuoles, with

stored nitrate being the first source of N depleted when plants experience

cessations in N supply, thereafter organic N forms are used (Zhen and Leigh,

1990, Andrews et al., 2006). Indeed, when rice plants were shifted to an N-

deficient media, all nitrate was exhausted from roots following cessation of N

supply for 10 days (Fig. 2.2c). Given this, growth that took place after cessation

(Fig. 2.5) will have been achieved via depletion of nitrate storage (in roots) and

remobilization of organic N, in particular Rubisco which accounts for 15-30% N

in leaves (Makino, 2005). The lack of any change in SPAD readings in the most

recently fully expanded leaf following cessation of N supply (Fig. 2.3) suggests

that growth after N-cessation was not associated with a reduction in chlorophyll

content of newly formed leaves, but rather by remobilizing N available within

the plant (Walker et al., 2001) to newly formed leaves to support photosynthetic

capacity.

While the absence of nitrate accumulation in leaves is unexpected given

that nitrate often accumulates in leaf vacuoles of species other than rice (Zhen

and Leigh, 1990, Veen and Kleinendorst, 1985, Martinoia et al., 1981), past

studies have reported negligible accumulation nitrate in rice leaves [Murata

(1969) and Makino (2003)], suggesting that negligible leaf nitrate may be a

general phenomenon for this species. The absence of nitrate accumulation in

leaves could reflect dominance of roots for nitrate reduction and assimilation,

with reduced N being transported from roots to shoots as amino acids (Wallace

and Pate, 1967, Smirnoff and Stewart, 1985). Alternatively, nitrate might

indeed be transported from roots to leaves (Wallace and Pate, 1967), but with

all nitrate arriving in leaves being rapidly assimilated into organic forms (Pate,

1973), with no accumulation in vacuoles. Quantification of xylem nitrate and

amino acid composition would enable these hypotheses to be tested.

While organ/whole-plant N concentrations and SPAD readings suggest

an optimum around 2 mM N, plant height was highest in the 0.25-1 mM N range

during early stages of growth, shifting towards 0.5-2 mM during later growth.

Thus, any N level less than 0.5 mM was limiting during later stages of vegetative

growth, with the N supply threshold for where N-depletion likely to increase as

42

plants increased in size (reflecting the fact that larger plants more rapidly

deplete available N supply than smaller plants). For other traits, such as leaf

and tiller numbers, 1 mM N supply was the optimum, reflecting the stimulatory

effect of that N supply on initiation of tillers and leaves during early growth.

Growth rate is known to be positively correlated with tillering ability

(Rebolledo et al., 2012), and yet dry mass at the day 53 harvest was similar in

plants grown on 0.5-2.0 mM N (Fig. 2.5); thus, the optimal N supply for tillering

and leaf number is not necessarily that needed to maximize whole-plant

biomass accumulation. This suggests that at 0.5 and 2.0 mM N supply, high dry

matter accumulation is achieved via mechanisms not directly dependent on

number of tillers/leaves, such as via improved rates of net CO2 uptake per unit

leaf area and/or mass, which in turn may reflect more optimal allocation of N to

metabolic processes (e.g. improved photosynthetic N use efficiency – see

Chapter 4).

Given the above observations, what N supply levels should be provided

to create N-deficient and optimal N phenotypes? Taking into account the above

observations, 2 mM appears the best single N level to provide if the aim is to

ensure that – for the hydroponic system used in my thesis - N is not limiting,

both for small and larger plants. For N-deficient plants, any N level between

0.06-0.25 mM N supply would appear sufficient.

2.5.2 Impact of N supply on growth and its underlying components in

rice

In the present study, whole-plant dry mass at day 53 was maximal across a

broad range of N levels (0.5-2.0 mM), pointing to an ability of the selected rice

variety to maintain homeostasis of growth over a wide N supply range. Below

and above that optimal N range, RGR values declined. Viewed from a carbon

economy perspective, declines in RGR were mediated largely by decreases in

NAR when plants were supplied at sub- or supra-optimal N levels (Fig. 2.6), with

N-dependent declines in LMR being offset by increases in SLA (Fig. 2.7), with the

result that the LAR (Fig. 2.6) was relatively constant across the range of N

supply levels. From a N economy perspective, declines in RGR were the result of

reductions in the concentration of tissue N (i.e. low PNC). The latter would also

43

have played a role in mediating NAR values, given the crucial role of N in

determining the capacity for photosynthetic CO2 uptake (Evans, 1989). At

supra-optimal N (i.e. > 2 mM N), the causes of reduced NAR also appear linked

to reduced tissue N levels, albeit with the decline in NAR at 4 mM N being

proportionally greater than the decline in plant N concentration. Thus, other

factors must play a role in accounting for the decline in NAR (and thus RGR)

under supra-optimal N supply (e.g. reduced NP, perhaps reflecting allocation of

N to processes other than carbon metabolism). Given the close relationship

between N and Rubisco (Evans, 1989), one could expect photosynthetic capacity

and NAR, as well as carbohydrate profiles to change across N treatments.

However, carbohydrates remained homeostatic regardless of steady-state N

supply. This could be due to increased Rubisco activity despite lower Rubisco

content, particularly under low N conditions (see Chapter 4) with fewer ensuing

fluctuations in carbohydrate profiles. Rice is known to tolerate ammonium

(Britto et al., 2001); however, excessive supply lead to growth suppression even

when co-provided with nitrate ions that has a synergistic effect on growth

(Britto and Kronzucker, 2002). Thus, suppression of growth rate and dry mass

accumulation under 4 mM could be due to N toxicity.

When assessing whether N supply had a direct effect on traits that

influence RGR, consideration needs to be given to how low N supply is expected

to influence biomass allocation among and within organs, and whether trait

values vary with plant size. Here, I first discuss what effects low N supply is

expected to have on biomass allocation. Consistent with the functional

equilibrium model, several studies have reported cytokinin/sucrose-mediated

increases in root biomass (at the expense of leaves) (Van Arendonk et al., 1998).

According to the cytokinin-sucrose hypothesis (van der Werf and Nagel, 1996),

N deficiency reduces cytokinin production and export from roots to the shoot,

leading to reduced sink demand in young leaves. As a consequence, sugar

accumulates in young leaf cells and in the phloem. Similarly, reduced cytokinin

levels in the roots reduce the inhibitory effects of cytokinin on root growth and

increase the effect of auxin (Hermans et al., 2006); collectively, these changes

favouring cell division in roots and accelerate relative sink demand in roots. In

turn, such changes encourage more C export from leaves to roots leading to high

44

RMR. While beneficial for nutrient uptake, the increase in RMR can result in

greater respiratory losses (Poorter et al., 1995) which offset the positive effects

of higher nutrient uptake on photosynthesis. A reduction in SLA can occur

when leaf mass density increases (Ryser and Lambers, 1995, Ryser and Eek,

2000), and/or when leaf thickness increases (Arendonk et al., 1997, Wilson et

al., 1999). Moreover, low N concentration in leaves has also been associated

with high starch and cell wall material accumulation, leading to a low SLA (van

der Werf et al., 1993b). Low SLA leaves also exhibit lower photosynthetic

capacity (Evans and Poorter, 2001, Wilson, 1988). N-deficiency mediated

increases in the fraction of non-veinal sclerenchyma cells and leaf mass density

(Arendonk et al., 1997) are thought to also contribute to lower SLA values.

Consistent with the above, I found that RMR was indeed higher in plants grown

at low N, with a trade-off occurring with LMR (Fig. 2.7). Yet, the variations in

RMR was relatively minor when compared to that of previous studies for plants

grown on low N supply (van der Werf et al., 1993a, van der Werf and Nagel,

1996). This suggests that rice is not increasing N uptake under low N via greater

mass fraction in roots and points to some other factors being responsible, such

as: the presence of efficient roots with more capacity to taking up and

assimilating N; proliferating more finer roots with herringbone type branching

pattern (Izumi et al., 1996, Fitter and Stickland, 1991); and/or, high surface area

to volume ratio (Miller and Cramer, 2005). Further, rice is efficient in NH4+

assimilation via activities of glutamine synthase and glutamate dehydrogenase

in roots (Magalhäes and Huber, 1989) and this together with the presence of

specific NH4+ transporters (Sonoda et al., 2003) might have favoured the

efficiency of N uptake in rice under mixed N nutrition. There is evidence that

presence of NO3− can influence the number and location of lateral root initiation

sites (Malamy and Ryan, 2001) and NH4+ is known to increase shoot mass at the

expense of root mass in rye grass when provided with NH4+-N rather than NO3—

N (Jarvis, 1987). Further work is needed to elucidate any link between the

above observation and the availability of NH4+ in the medium. Finally, SLA

values of rice were higher in low-N grown rice (Fig. 2.7), while starch

concentrations remained unaffected by N supply (Fig. 2.10). Thus, the response

45

of rice to low N supply is not fully consistent with changes observed in other

plant systems.

To understand why traits such as SLA and RMR varied with N supply as

shown in Figure 2.7, consideration needs to be given to how such traits change

with increasing plant size. In my study, plants were harvested 53 days after

transplanting and growth on steady-state N supply, with plant sizes differing

markedly among the N levels. For N supply to have a direct effect on key traits,

then N-mediated changes in trait values should be apparent, irrespective of the

size of a plant. When compared at common plant sizes, data generally fell on

one trait – plant mass relationship for majority of traits (e.g. SLA, LMR, LAR,

SMR and RMR) suggesting trait variation was caused not by a direct effect of N

supply per se, but rather via each trait varying in response to N-mediated

changes in plant size (Figs 2.6 & 2.7). A similar result was found for SLA of

several Acacia species grown under ambient and elevated atmospheric CO2

(Atkin et al., 1998d); in such cases, SLA of whole canopies decreases as plants

increase in size, with N (or CO2) treatment unlikely to have a direct on SLA at

any given plant size. Thus, there was no evidence that N availability per se

altered key growth parameters in rice, such as RMR and SLA. Rather, variations

in these traits appear to result largely from N-mediated changes in plant size,

brought about by N-mediated changes in PNC and NAR (Figs 2.6 & 2.8). These

findings question the universality of the functional equilibrium model, at least

as it applies to the above traits of rice grown under a range of different N levels.

Even though I found no evidence of a direct effect of N supply on biomass

allocation, there was a trade-off between LMR and RMR for plants varying in

size on day 53. Larger plants grown on optimum N supply allocated relatively

more biomass to leaves and less to roots. Reduction in RMR in bigger plants may

be due to a change in the efficiency of N uptake and increased root surface area

to volume ratio. Aerts (1994) suggests that a reduction in RMR can be

compensated by finer roots with increased specific root length (i.e. the ratio of

root length to root dry mass (Reich et al., 1998b)). The fact that smaller plants

exhibited lower LMR values may reflect the fact that there is less internal

shading within the canopies of smaller plants, resulting in greater light

46

interception and light use efficiency. High SLA values in smaller plants would

further promote efficient light capture and use. As a result, relatively less

biomass needs to be allocated to leaves in small plants (Aerts, 1994).

Central to the N-supply mediated changes in RGR was the decline in PNC

in plants grown on N-deficient conditions. Importantly however, RGR did not

decline by the same proportion as PNC, reflecting the fact that the efficiency of N

use (i.e. NP ) increased with decreasing N levels (Fig. 2.8), suggesting that the

efficiency use of N for physiological processes (e.g. photosynthesis and

respiration) increases under sub-optimal N supply (Negrini, 2016). Past studies

on Lolium perenne reported a decrease in NP under higher relative addition

rates of N (Macduff et al., 2002), consistent with the results of my study.

Consistent with the finding that variations in RGR were strongly associated with

N-mediated changes in NAR rather than with biomass allocation traits, Garnier

(1991) emphasized that specific activities of organs (C and N uptake by leaves

and roots respectively) contribute more for differences in growth rates than

biomass allocation when plants are under optimum conditions. Higher NAR

values under optimum N supply were linked to increased tissue N

concentrations and increased leaf thickness (i.e. low SLA), suggesting that rates

of photosynthesis may have been higher as a result of greater investment in

palisade cell layers, more N concentration in leaves/canopy, more chlorophyll

and photosynthetic enzymes. Low RMR might also have led to low respiratory

losses, further enhancing NAR. High PNC (despite low NP) has led to a higher

RGR under optimum N supply in consistent with Poorter et al. (1990) who

observed a correlation between RGR and PNC. Collectively, the above

mentioned factors (i.e. high biomass allocation to leaves, increase in

photosynthesis due to thicker leaves, and high N concentration and reduced

respiratory losses in roots) together result in faster growth of rice under

optimum N supply.

2.5.3 Which components of growth respond more dynamically to N

supply?

How does growth of rice respond to N deficiency relative to the optimum N

treatment during the steady state of N supply? And which traits are more

47

plastic? As indicated above, past studies have suggested that plants often

optimize above- and below-ground resource acquisition via plasticity of the

components of LAR and root length ratio (root length per total plant dry mass,

RLR) (Ryser and Eek, 2000). In the current study, there was a trade-off between

LMR and RMR as a function of N supply under low N (with each exhibiting

similar degrees of plasticity, but in opposing directions), agreeing with past

studies which acknowledge that biomass allocation between above and below

ground constrain each other when optimizing resource acquisition (Ryser and

Eek, 2000). By contrast, investment in stems was largely insensitive to N supply.

Similarly, LAR varied little among the N treatments, reflecting the trade-off in

direction of plastic responses of SLA and LMR. Importantly, the greatest degree

of plasticity was exhibited by NAR and PNC, further highlighting the importance

of changes in tissue chemistry for rates of net C-gain, and ultimately RGR.

2.6 Conclusions

In the present study, 0.06-0.12 mM was identified as the N range that is capable

of creating N deficient phenotypes in rice. The growth and developmental

parameters (e.g. plant height, leaf number and tiller number) were optimum at

1 mM N treatment, while chlorophyll and organ N concentration were optimum

around 2 mM. Growth traits exhibited either their minimum or maximum at 0.5

or 1 mM possibly suggesting these as the optimal N levels. However, NP hardly

showed any change between 0.06 to 0.5 mM, and decreased under excess N.

Considering the shift in the optima of growth parameters towards 2 mM

through time and development, and no significant difference in plant dry mass

at 1 and 2 mM, 2 mM was concluded as the optimum N treatment to be used in

subsequent experiments to avoid N limitation for growing rice plants. The

results point to the importance of applying the optimum amount of N fertilizer

during early growth to sustain number of tillers, which have the potential to

become panicles in future. RGR was higher at moderate range of N levels, and

lower in plants grown on very low and high N supply during the steady state of

N supply. When compared at common plant sizes, data fell on one trait – plant

mass relationship suggesting N did not directly affect those growth components

when accounting for plant dry mass. This finding highlights the importance of

48

accounting for plant size when comparing growth traits. Finally, increased NP

amidst the decline in PNC assisted rice to maintain RGR at moderate N levels

(not under N deficiency) and starch accumulation at low N treatment after

cessation indicated that rice growth was more affected by N cessation rather

photosynthesis.

49

Chapter 3 – Genotypic variation in carbon and nitrogen economy of rice at whole plant level

3.1 Summary

In this study I investigated the extent of genotypic variation in relative growth

rate (RGR) and its underlying components among 10 genotypes of rice grown

on high and low N supply. Plants were grown for 48 days in a hydroponic

system under high (2 mM) and low (0.06 mM) N supply. Plants were harvested

at 5-6 days intervals over 6 weeks. Nitrogen productivity (NP) and net

assimilation rate (NAR) were found to be the key components that accounted

for variation in RGR at both high and low N conditions, rather than allocation of

N or biomass among above and below-ground organs. Genotypes varied in their

NP under both high and low N conditions. Importantly, the impact of low N

supply on NP differed markedly among the 10 genotypes, with three of those

genotypes maintaining relatively higher rates of growth, NP and NAR under low

N compared with the other seven genotypes. Although a majority of the traits

underpinning RGR changed as plants increased in size, comparison of trait

values at common plant sizes revealed N-supply induced changes in the values

of most traits.

3.2 Introduction

Lodging resistant, high-yielding rice varieties bred during the green revolution

(Khush, 1999) contributed to a dramatic increase in world cereal production

over the past 50 years. The increase in yields has been vitally important in

feeding the growing human population, both now and in the future. In most rice

paddies, yield increases depend heavily on inputs of nitrogen (N) fertilizer, the

key nutrient for plant growth and development (Singh et al., 1998). However, N

fertilizer has become a costly input in paddy farming, causing environmental

deterioration and economic losses. Thus, exploring genotypes that could take

up, assimilate and remobilize available N efficiently under limited N supply

would be immensely beneficial (Tester and Langridge, 2010, Singh et al., 1998,

50

Kant et al., 2011, Hirel et al., 2007, Raun and Johnson, 1999) and help minimize

the economic costs and the environmental foot-print of rice production

throughout the world. Genotypic variation for nitrogen use efficiency (NUE) is

known to exist in rice (Fageria and Barbosa Filho, 2001, Fageria et al., 2010,

Mae, 2011, Singh et al., 1998, Mahajan et al., 2012, Koutroubas and Ntanos,

2003, Inthapanya et al., 2000, Fukai et al., 1999). Yet, most studies were

performed using plants grown under optimum N supply. Further, the genetic

and physiological basis for variability of grain yield per unit N applied has not

been sufficiently explored (Hirel et al., 2007, Mae et al., 2005) particularly under

low N availability (Garnett et al., 2015). This may reflect variability in NUE not

being considered a priority when selecting varieties for breeding, particularly

where genetic selection was performed under high N to avoid N being a variable

(Garnett et al., 2015). Given this, identifying varieties that differ in efficiency of

N use – both under high and low N supply - and elucidating the physiological

basis for variability in N use efficiency under low N supply is crucial to

producing genotypes with enhanced NUE for future agriculture.

Exploring the underlying factors controlling growth during the

vegetative stage of development is important as NUE of rice over its whole life

cycle is partially determined by the efficiency with which N is used to support

vegetative growth in the lead up to the reproductive phase. For example, N

taken up from the soil and stored in healthy leaves and culms is remobilized to

grains during grain filling stage (Masclaux-Daubresse et al., 2010, Foulkes et al.,

2009). Moreover, 70-90% of the N in panicles was accumulated during

vegetative growth (Mae, 1997). Factors underpinning genotypic variation in

NUE are difficult to assess under field conditions, due to variations in soil

conditions (e.g. spatial variability in nutrient availability) potentially

contributing to NUE variations (Xu et al., 2012, Chan-Navarrete et al., 2014).

Hence, early selection for NUE is ideally assessed using hydroponic systems

(Van Loo et al., 1992). In a recent study on NUE in rice Namai et al. (2009)

classified 31 varieties into five groups based on their responses to N supply

(e.g. dry mass accumulation and efficiency of dry mass production per unit of

absorbed N) during the vegetative stage; that study showed genotypic variation

in the selected traits yet, did not clarify which mechanisms led that variation.

51

Similarly, in a field experiment, Fageria and Barbosa Filho (2001) categorized

eight lowland rice varieties into four groups based on NUE and grain yield

under conditions of low N supply; they identified three varieties (Rio Formoso,

CNA 7550, and CNA 7556) that exhibited above average yield and high NUE.

Thus, there is evidence of genotypic variation in NUE in rice grown under low N

supply. What is less clear, however, is what physiological mechanisms underpin

this variation, particularly during early vegetative growth.

To gain insights into how low N affects the efficiency of N use in rice, and

to assess how contrasting genotypes of rice differ in their ability to maintain

growth under low N, one can analyse plant growth from a whole plant carbon

(C) economy perspective. Here, the focus is placed on two factors: net

assimilation rate (NAR, g m-2 d-1; increase in plant mass per unit leaf area and

time) and the leaf area ratio (LAR, m2 kg-1; amount of leaf area per unit total

plant mass). Variations in NAR and LAR are often linked to differences in

relative growth rate (RGR, g g-1 d-1; increase in plant mass per unit of existing

plant mass and time) (Lambers et al., 1990). Eco-physiological studies on mostly

non-crop species have reported that variations in LAR (Poorter and Remkes,

1990, Poorter, 1989, Atkin et al., 1996a) – underpinned by variations in specific

leaf area (SLA; ratio of leaf area to leaf mass) – (Atkin et al., 1998b, Garnier,

1992) are more strongly correlated with variations in RGR than NAR. By

contrast, others reported opposite trends (Shipley, 2006, Shipley, 2002). For

rice, genotypic variation in RGR and its components were observed by Amin et

al. (2002) in a study with eight varieties, while Ehara et al. (1990) suggested in a

study comparing 35 varieties that variation in NAR is the predominant factor

contributing to variation in RGR of rice. Whether the same is true for plants

grown under both high and low N supply is not known.

Another approach to assessing what factors underpin variation in RGR,

particularly of plants grown under high and low N supply, is to analyse growth

from a N economy perspective. Using this approach, RGR is the product of

nitrogen productivity (NP, g gN-1 d-1; increase in mass per unit N per unit time)

and the plant N concentration (PNC gN g-1; plant N concentration per unit plant

mass). NP provides a measure of the efficiency of N use during vegetative

52

growth (Ingestad, 1979, Hirose, 1988, Garnier et al., 1995, Atkin et al., 1996b,

Lambers, 2008), being dependent on rates of whole-plant net C gain per unit

plant N and N partitioning among different organs. Often, variations in NP are

linked to variations in photosynthetic N use efficiency (PNUE, the rate of

photosynthesis per unit leaf N). From N economy perspective, a close

correlation between RGR and NP was observed by Atkin et al. (1998b), while

both NP and PNC were important to explain low growth rate in alpine species

(Atkin et al., 1996b) and faster growth of arctic species Oxyria digyna under

nitrate nutrition (Atkin and Cummins, 1994). While there is some evidence that

NP increases in plants grown on low N supply (Negrini, 2016), to my knowledge

no literature exits on the extent of genotypic variation of NP in rice, or how NP

of multiple rice genotypes is affected by N supply.

When assessing the change of a given trait that underpins changes in

RGR in response to N supply, attention needs to be given to the issue of

ontogeny, and plant size in particular. This is due to the fact that components of

the RGR are known to change during development as plant size increases

(Poorter and Pothmann, 1992, Tanaka and Yamaguchi, 1968). RGR and its

components often undergo ontogenetic drift (Evans, 1972), with ontogeny

needing to be accounted for when assessing environment-mediated changes in

growth rates. Moreover, in comparisons of multiple genotypes, ontogenetic drift

needs to be considered. For example, Bush and Evans (1988) found no

difference in RGR when comparing at a common dry mass during a study with

spring wheat, even though RGR differences were apparent at set time points.

Both RGR and NAR tend to decline as plants increase in size (Bourdot et al.,

1984, Mencuccini et al., 2005), with other traits such as SLA (Dijkstra and

Lambers, 1989, Atkin et al., 1998b) and PNC (Ekbladh et al., 2007) exhibiting

similar trends. Accounting for ontogeny is particularly important when asking

the question of whether factors such as N deficiency have a direct effect or not

on traits that underpin RGR. Often, studies assessing the effect of nutrient

supply on plant growth are limited to a single harvest (as was the case in

Chapter 2), where plants of differing sizes are compared (Rees et al., 2010).

Comparing growth traits at a common size using data from multiple harvests

53

rather one (Coleman et al., 1994) help to minimize confounding effects of plant

size when searching for N-induced differences in RGR and its components.

Given the role that more N efficient genotypes could play in minimizing N

inputs in paddy farming, there is an urgent need to screen rice varieties for

differences in ability to maintain vegetative growth under conditions of low N

supply. In this chapter, I screen N responses of 10 genotypes of rice to explore

which genotypes grow faster and use N efficiently for biomass production when

grown under limited N supply conditions. The study addressed the following

questions: (1) do the selected rice genotypes differ in potential growth rates

and NP when grown on optimum N supply as well as limited N supply; (2) do

the selected genotypes exhibit differential growth responses to low N - if so,

which genotypes perform better under low N; and, (3) is there a direct effect of

N supply on traits underpinning variation in RGR, or are differences in trait

values at any one time point a consequence of differences in rates of

development in low and high N grown plants? To address these questions,

plants were grown on two N levels identified in Chapter 2 as providing near

optimal N supply (2 mM N) and N-deficient conditions (0.06 mM N).

3.3 Materials and methods

3.3.1 Plant growth

Ten rice varieties were used in this experiment: Akihikari, Azucena, Bg 34-8,

Dular, IR 64, Koshihikari, Milyang 23, Nipponbare, Opus, Takanari based on past

literature for grain yield or NUE. Takanari and Opus are high yielding varieties

(pers. comm., Peter Snell, Dept. Primary Industries, Yanco, NSW) and BG34-8 is

a N-inefficient variety (Singh et al., 1998). The other seven genotypes were

selected based on a study by Namai et al. (2009) where the varieties were

separated into five groups depending on NUE. The experiment was conducted

during the period of January – March 2014 (i.e. late summer and early autumn

in Canberra). Seeds of all 10 varieties were kept in an oven at 42°C for seven

days to overcome seed dormancy (especially in Dular and Takanari). Seeds of

the Dular variety were manually de-husked prior germination to eliminate husk

imposed dormancy. Seeds of all varieties were separately placed in labelled

54

petri-dishes double layered with moistened filter papers and allowed to

germinate under dark conditions in an incubator at 30 °C for five days.

Following germination, seedlings were exposed to natural light by placing petri

dishes near a window at 20-24 °C for two days till they expanded their first set

of leaves. Seedlings were then transferred to moistened vermiculite and allowed

to acclimate under glasshouse conditions (day/night cycle of 28/22 °C,

evaporative cooling and gas heating, using Lexan cladding which excludes UV

radiation) for five days. Average midday irradiance at leaf level was 687 µmol

photons m-2 s-1 while midday irradiance outside the glasshouse was 1600 µmol

photons m-2 s-1. Individual seedlings at the three leaf stage were then placed on

nets attached to the inside of PVC tubes having a diameter of 3.7 cm and a height

of 13 cm. The tubes with seedlings were placed on a light-proof lid with holes

(3.7 cm diameter) and lids were used to hold 20 plants in place above the light-

proof containers of 22 L capacity filled with 16 L of nutrient solution with two

different N levels - low N (0.06 mM) and high N (2.0 mM) (both NH4+ and NO3-

mixed N-nutrition) - using the medium of (Hubbart et al., 2007), as shown

below. De-ionized water was used to make up a solution containing 0.03 mM

(LN) and 1.0 mM (HN) NH4NO3, 0.6 mM NaH2PO4.2H2O, 0.5 mM K2SO4, 0.8 mM

MgSO4, 0.2 mM CaCl2.6H2O, 0.07 mM Fe-EDTA, 9 M MnCl2.4H2O, 0.1 M

(NH4)6Mo7O24.4H2O, 37 M H3BO3, 0.3 M CuSO4.5H2O, 0.138 M NH4VO3, 0.75

M ZnSO4.7H2O, and 0.2 g l-1 potassium silicate solution (VWR Chemicals, No.

296546S). Figure 3.1 shows an overview of plant culture under glasshouse

conditions. The pH of each tanks solution was monitored and adjusted daily to

5.8-6.0 using 1M H2SO4 or 1M NaOH. The solution was continuously aerated and

replaced weekly. Initially plants were placed in half-strength nutrient solution

for one week then transferred to full-strength solution and left there for another

six days before beginning measurements.

3.3.2 Plant harvesting

A detailed functional growth analysis was performed for 48 days after

transplanting using six harvests separated by 5-6 day period, with six plants

(replicates) being harvested per genotype per N treatment at each harvest.

Altogether, 36 plants per each treatment combination at each harvest; plants

were separated into leaf blades, leaf sheaths (leaf sheath will be termed as

55

‘stem’ throughout the rest of the chapter) and roots. The fresh mass of each

organ (Mettler-Toledo Ltd., Port Melbourne, Victoria, Australia) and the leaf

area (leaf area meter, LI-3000C, Li-Cor Inc., Lincoln, NE, USA) were recorded.

Dry mass values were determined on oven dried material (70 °C for at least 72

hours).

3.3.3 Calculation of growth parameters

To assess the growth performance of rice plants under optimum (2 mM) and

deficient (0.06 mM) N supply, the following parameters were determined:

relative growth rate (RGR; mg g-1 d-1), leaf area ratio (LAR; m2 kg-1plant), net

Figure 3.1 An overview of plant culture under glasshouse conditions (A)

the hydroponic system used during present study to grow 10 genotypes

of rice under 0.06 and 2 mM (B) rice plants growing under 0.06 mM (left)

and 2 mM (right).

56

assimilation rate (NAR; g m-2 d-1), specific leaf area (SLA; m2 kg-1leaf), leaf mass

ratio (LMR; gleaf g-1plant), stem mass ratio (SMR; gstem g-1plant), root mass ratio

(RMR; groot g-1plant), plant N concentration (PNC; mg g-1) and nitrogen

productivity (NP; g gN-1 d-1).

From C economy perspective, RGR is related to NAR, LAR, SLA and LMR

according to:

RGR = NAR x LAR (Eqn. 3.1)

LAR = SLA x LMR (Eqn. 3.2)

NAR = RGR

LAR (Eqn. 3.3)

From N economy perspective, RGR is the product of NP and PNC according to:

RGR = NP x PNC (Eqn. 3.4)

with PNC being calculated according to:

PNC = (LNC x leaf DM + SNC x stem DM+ RNC x root DM)

plant DM (Eqn. 3.5)

where LNC, SNC and RNC are the leaf N concentration (mgN gLeaf-1), stem N

concentration (mgN gStem-1) and root N concentration (mgN gRoot-1), respectively.

Leaf, stem, root, and plant DM’s denote leaf, stem, root and plant dry mass,

respectively. To calculate NP, Eqn 3.4 is re-arranged to:

NP = RGR

PNC (Eqn. 3.6)

The following were used to assess how N is partitioned between different

organs:

LNR = Leaf N content

Plant N content (Eqn. 3.7)

SNR = Stem N content

Plant N content (Eqn. 3.8)

RNR = Root N content

Plant N content (Eqn. 3.9)

57

where LNR, SNR and RNR denote leaf nitrogen ratio (LNR, gLeaf N gPlant N-1), stem

nitrogen ratio (SNR, gStem N gPlant N-1) and root nitrogen ratio (RNR, gRoot N gPlant N-

1).

3.3.3.1 Calculation of relative growth rate (RGR)

The RGR of each genotype - N treatment combination was calculated in two

ways. In the first approach, the mean RGR value over the 14 - 48 days period

after transplanting (DAT) was calculated using the slope (b) of a 1st order linear

regression fitted through the natural logarithm of total plant dry mass plotted

against time, according to:

ln dry mass = a + b . time (Eqn. 3.10)

The mean RGR was used to calculate NAR and NP for the entire experiment

period using equations 3.3 and 3.6 respectively. For the second approach,

instantaneous RGR values at defined time points were calculated by first fitting

a 2nd order polynomial to the natural logarithm of total plant dry mass plotted

against time (Poorter and Lewis, 1986), with RGR then being taken as slope at

each time point the derivative of the 2nd order function:

RGR (at any given time point) = b + 2c . time (Eqn. 3.11)

Instantaneous RGR values calculated for each harvest time point (equation 3.11)

were then used to calculate NAR and NP values for each harvest time point

using equations 3.3 and 3.6 respectively.

3.3.3.2 Plasticity of growth parameters

To quantify whether individual traits differed in their degree of plasticity, trait

values of plants grown under low and high N supply were calculated according

to:

LN: HN% = Mean value of a given growth trait calculated for a given genotype at 0.06 mM

Mean value of a given growth trait calculated for the same genotype at 2 mMx 100

(Eqn. 3.12)

58

3.3.4 Chemical analysis

The total N concentration in leaves, roots and stems were separately extracted

using Kjeldahl method (Allen et al., 1974) and determined using LaChat

Quikchem 8500 series 2 flow injection analysis system (Lachat Instruments,

Milwaukee, WI, USA). Four replicates were considered per genotype per N

treatment when performing Kjeldahl digestion. Replicates of leaf samples were

pooled (similarly for stems and roots) at the first harvest and only leaves and

stems samples were pooled at the second harvest due to smaller plant sizes and

insufficient amount of material available at replicate level for Kjeldahl digestion.

3.3.5 Statistics

All data were tested for normality and homogeneity of variance. The differences

among time, N treatments, genotypes and the interactions between time and N

treatments, time and genotypes and genotypes and N treatments were tested

following three-way ANOVA procedure (general linear model) using SPSS

(version 21, SPSS, Chicago, IL, USA). Mean comparisons were performed using

Tukey’s post-hoc tests. Hierarchical multiple regression analysis was performed

to assess time-, N supply- and genotypic-dependent changes in growth

parameters while controlling for plant size. The hierarchical model controls for

the variation associated with the independent variable in the first model, while

predicting the dependant variable with the independent variable in the second

model. The independent variables were entered in four steps - in model 1, ln

plant dry mass (ln DM) was entered and held constant. In model 2, time was

entered. In model 3, N treatment was entered. In model 4, genotype was

entered. Therefore, predictors in model 1, 2, 3 and 4 are ln DM, ln DM and time,

ln DM, time and N treatment, ln DM, time, N treatment and genotype

respectively. The change in r2 in model 2 indicates the amount of unique

variance accounted for by the independent variables in the second step. The

change in r2 in model 3 indicates the amount of unique variance accounted for

by the independent variables in the third step. Similarly, the change in r2 in

model 4 indicates the amount of unique variance accounted for by the

independent variables in the fourth step. The relative contribution of each

underlying component to RGR was tested with path analysis using the software

IBM® SPSS® AmosTM 22.

59

3.4 Results

3.4.1 Is there evidence of genotype- nitrogen interaction (G x N) for

RGR and its underlying components?

Plant biomass increased over time (Fig. 3.2A), with the increase in dry mass

through time being significantly greater in 2 mM than 0.06 mM grown plants, as

shown by the significant time-nitrogen (T x N) interaction in Table 3.1. While

biomass production increased through time in all genotypes, there were

significant differences in biomass accumulation among the 10 genotypes.

Importantly, there was a significant genotype-nitrogen (G x N) interaction

indicating that genotypes differed in their response to low N supply. The

absence of a time x genotype x nitrogen (T x G x N) interaction indicates that the

above G x N effect was essentially constant across time. Taken together these

data demonstrate that the genotypes differ in their ability to maintain biomass

accumulation when grown under low N supply.

Underpinning the N-mediated variations in biomass accumulation were

the differences in RGR among genotypes (Fig. 3.2B). RGR of all genotypes at 2

mM declined over time. By contrast, when grown on 0.06 mM N, RGR increased

through time in the majority of genotypes, except Nipponbare and Koshihikari

where they continued to decline over time. As a result, N-supply mediated

decreases in RGR were more prominent in smaller plants than in older plants,

with the effect N on RGR being relatively minor at 48 DAT. Despite this, low N

grown plants remained smaller after 48 DAT (Fig. 3.2A).

60

Figure 3.2 Effect of N supply on (A) plant dry mass across time; (B) relative

growth rate (RGR, mg g-1 d-1) across time; (C) RGR as a function of plant

dry mass in log10 scale. Closed and open symbols represent 2 and 0.06

mM N supply respectively. Values for plant dry mass represent the mean

of 6 replicates (±SE). RGR for each time point was calculated by using the

derivative of the second order polynomial regression fitted to the ln plant

dry mass over time.

61

Importantly, there were marked differences among genotypes in their

ability to maintain growth under low N. For example, when grown on low N

supply, three genotypes (Takanari, IR 64 and Milyang 23) were able to maintain

40% of their high-N growth, while others were as low as 27% at 0.06 mM N

supply relative to 2 mM (Fig. 3.3A, Table 3.2).

Table 3.1 Results of a three-way analysis of variance (ANOVA) for growth

parameters related to carbon economy considering time (T), genotype (G)

and N treatment (N) as factors with the three-way interaction term is

shown as T x G x N. Degrees of freedom (df), F - values and significance

are presented. *p < 0.05, **p < 0.01, ***p < 0.001. n=720.

Effect

df

ln DM

LAR

SLA

LMR

SMR

RMR

T 5 692.32*** 204.56*** 237.49*** 30.81*** 60.39*** 38.17***

G 9 43.87*** 25.05*** 20.04*** 16.55***

95.95*** 49.92***

N

1 5892.53*** 507.66*** 3.06 1044.96*** 347.00*** 3152.02***

T x G

T x N

G x N

T x G x N

Error

45

5

9

45

564

0.74

233.16***

4.84***

0.91

1.62**

26.70***

2.46**

1.30

1.37

37.53***

4.80***

1.51*

1.75**

64.23***

3.33***

1.22

1.96***

38.25***

5.12***

0.79

2.64***

9.20***

4.35***

1.59*

62

Figure 3.3 Percentage of mean RGR and its underlying parameters under

0.06 mM (LN) N supply compared to 2 mM (HN) i.e. LN:HN% for (A)

relative growth rate (RGR); (B) net assimilation rate (NAR); (C) leaf area

ratio (LAR); (D) nitrogen productivity (NP) and (E) plant nitrogen

concentration (PNC). The mean RGR at each N level calculated by fitting a

first order linear regression to the ln transformed plant dry mass across six

harvests. NAR at each N level calculated by dividing above mean RGR by

LAR averaged values across six harvests as RGR is the product of NAR and

LAR. NP at each N level calculated by dividing above calculated mean RGR

by PNC averaged across six harvests as RGR is the product of NP and PNC.

Genotypes for NAR, LAR, NP and PNC are arranged following the order for

mean RGR from highest to the lowest.

63

Table 3.2 RGR and underlying growth components for 10 genotypes of rice at 2 (HN) and 0.06 (LN) mM N supply average across six harvests. Mean RGR at each

N level calculated by fitting a first order linear regression to the ln transformed plant dry mass across six harvests. NAR at each N level calculated by dividing

above mean RGR by LAR averaged values across six harvests as RGR is the product of NAR and LAR. NP at each N level calculated by dividing above calculated

mean RGR by PNC averaged across six harvests as RGR is the product of NP and PNC. Genotypes for growth components are arranged following the order

LN:HN% for mean RGR from highest to the lowest as shown in Fig. 3.3. (n=6±SE for LAR, SLA, LMR, SMR and RMR and n=4±SE for PNC, LNR, SNR and RNR)

Growth parameter N treatment Takanari IR 64 Milyang 23 Opus Dular Bg 34-8 Koshihikari Akihikari Azucena Nipponbare Average

RGR

(mg g-1

d-1

)

LN 53.1 50.0 43.3 41.3 39.2 36.9 35.3 33.2 35.0 29.7 39.7

HN 122.5 117.1 107.4 122.5 123.9 120.9 116.5 117.0 123.6 114.5 118.6

NAR

(g m-2

d-1

)

LN 4.36 4.33 3.41 3.96 2.86 3.90 3.08 3.15 3.52 2.41 3.50

HN 8.39 7.63 6.60 9.53 7.87 8.90 8.40 8.37 8.57 7.66 8.19

LAR

(m2 kg

-1)

LN 12.2±1.2 11.6±1.3 12.7±1.0 10.4±0.7 13.7±1.3 9.5±1.1 11.5±0.8 10.5±0.9 9.9±1.1 12.3±1.0 11.4

HN 14.6±1.3 15.4±1.2 16.3±1.1 12.9±1.3 15.7±1.1 13.6±1.4 13.9±1.2 14.0±1.4 14.4±1.3 14.9±1.1 14.6

SLA

(m2 kg

-1)

LN 35.8±3.4 35.2±4.6 38.9±3.8 32.1±3.2 39.6±5.6 29.5±4.2 37.7±4.2 36.2±4.2 31.1±4.5 41.2±5.9 35.7

HN 34.1±2.2 40.0±1.9 39.1±1.8 32.7±2.7 39.2±2.7 33.7±2.6 35.0±3.1 37.2±3.4 33.9±2.3 37.5±2.4 36.2

LMR

(g g-1

)

LN 0.33±0.02 0.33±0.01 0.33±0.01 0.33±0.01 0.36±0.02 0.33±0.02 0.32±0.02 0.30±0.01 0.35±0.02 0.31±0.02 0.33

HN 0.43±0.01 0.38±0.01 0.41±0.01 0.40±0.01 0.41±0.01 0.40±0.01 0.40±0.01 0.37±0.01 0.42±0.01 0.40±0.01 0.40

SMR

(g g-1

)

LN 0.31±0.01 0.32±0.01 0.31±0.01 0.34±0.01 0.24±0.01 0.31±0.01 0.35±0.01 0.36±0.01 0.28±0.00 0.35±0.01 0.32

HN 0.36±0.01 0.37±0.02 0.33±0.01 0.38±0.01 0.29±0.01 0.36±0.01 0.38±0.01 0.39±0.02 0.34±0.01 0.37±0.01 0.36

RMR

(g g-1

)

LN 0.36±0.00 0.35±0.01 0.36±0.01 0.32±0.01 0.41±0.01 0.36±0.01 0.33±0.01 0.34±0.01 0.37±0.01 0.34±0.01 0.35

HN 0.22±0.00 0.24±0.01 0.25±0.01 0.23±0.01 0.30±0.00 0.24±0.01 0.23±0.01 0.23±0.01 0.25±0.00 0.24±0.01 0.24

PNC

(mg g-1

)

LN 17.1±1.5 19.2±1.5 16.8±1.1 16.0±1.0 17.5±1.4 16.7±1.2 16.4±1.2 15.4±0.9 16.8±1.0 16.1±1.1 16.8

HN 38.9±2.3 38.6±2.1 39.5±2.0 36.5±2.7 37.5±2.2 36.9±2.4 36.9±2.4 35.3±1.8 38.7±1.9 36.1±1.7 37.5

NP

(g gN-1

d-1

)

LN 3.10 2.60 2.58 2.57 2.24 2.21 2.16 2.16 2.08 1.84 2.35

HN 3.15 3.03 2.72 3.35 3.31 3.28 3.16 3.31 3.19 3.17 3.17

LNR

(g g-1

)

LN 0.49±0.02 0.48±0.02 0.48±0.02 0.47±0.02 0.49±0.03 0.45±0.02 0.44±0.02 0.44±0.02 0.48±0.02 0.44±0.02 0.47

HN 0.58±0.01 0.54±0.01 0.56±0.01 0.57±0.01 0.56±0.01 0.56±0.01 0.56±0.02 0.53±0.01 0.57±0.01 0.55±0.01 0.56

SNR

(g g-1

)

LN 0.21±0.01 0.23±0.01 0.20±0.01 0.25±0.01 0.19±0.01 0.24±0.01 0.27±0.01 0.25±0.02 0.20±0.01 0.26±0.01 0.23

HN 0.26±0.01 0.27±0.01 0.24±0.00 0.26±0.01 0.23±0.01 0.26±0.01 0.27±0.01 0.29±0.01 0.24±0.00 0.28±0.02 0.26

RNR

(g g-1

)

LN 0.30±0.02 0.29±0.01 0.31±0.01 0.29±0.02 0.31±0.02 0.30±0.01 0.29±0.02 0.31±0.01 0.32±0.02 0.29±0.01 0.30

HN 0.16±0.01 0.19±0.02 0.20±0.02 0.17±0.01 0.21±0.01 0.18±0.02 0.17±0.01 0.18±0.02 0.18±0.01 0.18±0.02 0.18

64

To explore what underlying factors were responsible for the observed

time- N- and genotype-mediated differences in biomass accumulation, I now

assess variations in the underlying components of growth. LAR values

decreased as plants aged (Fig. 3.4A), with the decrease over time differing

among N treatments and genotypes (Table 3.1).

Figure 3.4 Effect of N supply on growth parameters across time (A and C)

and plant dry mass (B and D). (A) leaf area ratio (LAR, m2 kg-1) across time;

(B) LAR as a function of plant dry mass in log10 scale (C) net assimilation

rate (NAR, g m-2 d-1) across time; (D) NAR as a function of plant dry mass

in log10 scale. Closed and open symbols represent 2 and 0.06 mM N

supply respectively. Colour codes for genotypes are as below; Akihikari

(black), Azucena (red), Bg 34-8 (light green), Dular (yellow), IR 64 (blue),

Koshihikari (pink), Milyang 23 (cyan), Nipponbare (ash), Opus (brown),

Takanari (dark green). Values for LAR represent the mean of 6 replicates

(±SE). NAR for each time point was calculated by dividing instantaneous

RGR (calculated by using the derivative of the second order polynomial

regression fitted to the ln plant dry mass over time) by LAR at the

corresponding time point.

65

For example, in plants grown at 2 mM N, LAR in BG 34-8 (p < 0.01) and

Koshihikari (p < 0.01) declined faster with increasing age than Milyang 23. The

lowest LAR was exhibited by Opus at 2 mM, being significantly lower than

Milyang 23 (p < 0.001), Dular (p < 0.01) and IR 64 (p < 0.05). Low N treatment

led to an overall decline in LAR, with the response to low N varying among

genotypes; LAR declined by about 13% in Dular under 0.06 mM relative to 2

mM while that decline was 31% in Azucena (Fig. 3.3, Table 3.2). Importantly, the

genotypes that exhibited higher RGR under low N supply compared to 2 mM

(Takanari, IR 64 and Milyang 23) were not those that exhibited relatively high

LAR values under low N treatment. Hence, the ability to maintain growth at 0.06

mM seems unlikely to be linked to differences in biomass allocation per se, but

rather differences in the efficiency of photosynthetic C gain and respiratory C

use; that is, maintenance of NAR under low N supply is likely to be important.

Genotypic differences in NAR were observed under both N treatments,

with the response to N differing among genotypes (Fig 3.4C). For example, while

BG 34-8 exhibited relatively high NAR values at 2 mM (compared to other

genotypes), its NAR values at 0.06 mM were low (again, relative to other

genotypes). Importantly, compared to other genotypes, NAR of Takanari and

Opus were relatively high at both N treatments. NAR at 0.06 mM was about 70%

lower in Nipponbare relative to the value at 2 mM, while it was about 45% in IR

64 (Fig. 3.3). On average, the percentage reduction of NAR at 0.06 mM relative

to 2 mM was lower in IR 64, Takanari and Milyang 23 compared to the other

genotypes. Finally, while NAR was lower in 0.06 mM N grown plants (relative to

2 mM N grown plants), NAR increased through time under low N treatment,

whereas it was relatively constant under high N (Fig. 3.4C); as a result, the

relative difference in NAR values between 0.06 and 2 mM N grown plants was

less after 48 DAT compared to earlier growth. Taken together, these

observations suggest that the decrease in RGR at 2 mM over time was largely

due to the decrease in LAR, not NAR and that the increase in RGR in majority of

genotypes at 0.06 mM through time was largely associated with an increase in

NAR rather than LAR. Given that LAR decreased with time and was lower under

0.06 mM and response to N differed among genotypes, it is of interest to explore

what underlying traits (e.g. SLA, LMR) were responsible for those patterns.

66

There was significant difference in average SLA among genotypes.

Milyang 23 and IR 64 exhibited higher (Fig. 3.5B) SLA values (compared to

other genotypes) at both N treatments. SLA decreased with time (Fig. 3.5A,

Table 3.1), with the decrease in SLA with time being consistent across

genotypes, yet not among N treatments. SLA was initially higher in smaller

plants then declined with time under 0.06 mM as indicated by the T x N

interaction (Table 3.1). While there was no overall N effect on SLA when

averaged across all genotypes, the finding of a significant G x N interaction

suggests that 0.06 mM N treatment did affect SLA in some genotypes, but not

others.

The other component determining LAR is LMR, with averaged LMR

values differing significantly among the 10 genotypes. LMR values declined

with time at 2 mM (Fig. 3.5C). Although there was a decline in LMR during early

growth at 0.06 mM, that difference was absent when plants were larger,

reflecting the increase in LMR over time. Thus, changes in LMR through time

differed among N treatments (Table 3.1). Further, the change in LMR through

time differed among the 10 genotypes. The presence of a G x N interaction term

suggests that the change in LMR in response to N differed among genotypes, yet

that G x N interaction remained consistent over time. For example, Takanari

showed the highest LMR at 2 mM, being significantly higher than Akihikari (p <

0.001), Bg 34-8 (p < 0.05), IR 64 (p < 0.001), Koshihikari (p < 0.01), Nipponbare

(p < 0.01) and Opus (p < 0.01). By contrast, Dular had the greatest LMR at 0.06

mM compared to Akihikari (p < 0.001), BG 34-8 (p < 0.05), IR 64 (p < 0.01),

Koshihikari (p < 0.001), Milyang 23 (p < 0.001), Nipponbare (p < 0.001) and

Takanari (p < 0.05). Interestingly, the genotypes that exhibited the highest LMR

were not the ones which had high RGR at 0.06 mM, further indicating the

differences among genotypes in how LMR responded to low N supply.

67

Figure 3.5 Effect of N supply on growth parameters across time (A, C, E, G)

and plant dry mass (B, D, F, H). (A) specific leaf area (SLA, m2 kg-1) across time;

(B) SLA as a function of plant dry mass in log10 scale; (C) leaf mass ratio (LMR,

gg-1) across time; (D) LMR as a function of plant dry mass in log10 scale; (E)

stem mass ratio (SMR, gg-1) across time; (F) SMR as a function of plant dry

mass in log10 scale; (E) root mass ratio (RMR, gg-1) across time; (F) RMR as a

function of plant dry mass in log10 scale. Closed and open symbols represent

2 and 0.06 mM N supply respectively. Colour codes for genotypes are as

below; Akihikari (black), Azucena (red), Bg 34-8 (light green), Dular (yellow), IR

64 (blue), Koshihikari (pink), Milyang 23 (cyan), Nipponbare (ash), Opus

(brown), Takanari (dark green). Values are the mean of 6 replicates (±SE).

68

Associated with the above mentioned N-deficiency mediated decrease in

LMR was an increase in biomass allocation to roots (Fig. 3.5G). RMR values

where higher in 0.06 mM grown plants, both in small and larger plants; this

contrasted with the lack of N-mediated differences in LMR in larger plants. For

0.06 mM N grown plants, RMR declined over time in some genotypes, such as

Dular, Azucena, Nipponbare and Opus. By contrast, RMR hardly changed with

time in a majority of genotypes grown at 2 mM N (Table 3.1). RMR values

differed significantly among the 10 genotypes, with Dular exhibiting the highest

RMR under both N treatments. Opus had the lowest RMR at 0.06 mM N

treatment. The 10 genotypes differed in their RMR response to N supply, as

indicated by the significant G x N interaction term. The presence of a three-way

interaction among time, genotype and N treatment for RMR confirmed that the

above G x N interaction changed with time.

For 2 mM grown plants, decreases in LMR through time were associated

with increases in stem investment (i.e. increased SMR; Fig. 3.5E), whereas SMR

was largely constant under 0.06 mM. Hence, the response of SMR through time

differed between the two N treatments. Genotypes differed in the extent to

which biomass was allocated to stems over time (Table 3.1). The averaged SMR

significantly differed among genotypes; Dular exhibited the lowest (p < 0.001)

SMR while Akihikari showed the highest (p < 0.001) under both N treatments.

Genotypes allocated biomass differently in their stems in response to N

treatments, with these differences being consistent across time. For Dular

grown at 0.06 mM N, LMR values were maintained (relative to 2 mM N grown

Dular) at the expense of stems; this response differed from that of other

genotypes were the most common trade-off was one of leaf vs. root investment.

As a result, Dular was able to achieve the highest LAR of all 10 genotypes were

grown at 0.06 mM N (Fig. 3.3C). However, Dular was not able to achieve a

greater RGR due to lower NAR (Fig. 3.3A and C). Collectively, the above

observations highlight the importance of trade-offs in biomass allocation among

above- and below-ground organs when considering genotypic, N treatment, and

time mediated variations in LAR.

69

Having observed the factors accounting for variation in RGR from C

economy perspective, I now explore what might explain variation in RGR from N

economy perspective. There were marked differences in NP among the 10

genotypes, with average NP ranging from 2.5 to 3 g gN-1 d-1 (Fig. 3.6A, Table 3.2).

On average, NP was lower in plants grown on 0.06 mM N than plants grown on 2

mM N (Table 3.2). The absolute numbers of NP are informative; however, they

do not reveal how a particular genotype is performing under low N treatment

relative to its performance under high N treatment. Thus, the ratio between NP

(0.06 mM N) and (2.0 mM N) is considered as a better predictor of plant

performance at low N supply, compared to NP (0.06 mM N) alone. Importantly,

the 10 genotypes differed in their response to N. For example, while NP at 0.06

mM was about 35% lower in Akihikari relative to the value at 2 mM, the

percentage reduction of NP at 0.06 mM relative to 2 mM were 14, 5 and 2% in

IR 64, Milyang 23 and Takanari respectively, showing a homeostasis in NP (Fig.

3.3D and Table 3.2). NP increased with time in plants grown on 0.06 mM in

some genotypes (i.e. Milyang 23, Takanari, Opus and IR 64) whereas, the

opposite occurred in others (i.e. BG 34-8, Koshihikari and Nipponbare).

Collectively, these results highlight the presence of genotypic variation in NP to

low N supply, both in terms of magnitude of response and the direction of

changes that occurred through time.

Low N supply led to a lower PNC (Fig. 3.6C) in all genotypes with no

evidence of genotypic variation in this response. There was a decline in PNC

with time for plants grown at 2 mM, whereas PNC was relatively constant

through time in 0.06 mM grown plants. Thus, the change in PNC across time

differed between the two N treatments (Table 3.3). Changes in PNC across time

were largely similar among genotypes. Interestingly, compared to 2 mM N

grown plants, of all the genotypes, IR 64 exhibited the highest relative PNC

when grown 0.06 mM N (Fig. 3.3E). Genotypic differences in PNC were

relatively minor at 2 mM and not strongly related to variations in RGR.

Genotypes allocated proportionally more N to roots (RNR) and less to leaves

(LNR) at 0.06 mM N (Table 3.2). N ratios significantly varied among genotypes,

across time and N treatments (Table 3.3). The changes in N ratios across time

differed between the two N treatments.

70

Figure 3.6 Effect of N supply on (A) nitrogen productivity (NP, gDM gN-1 d-1)

across time; (B) NP as a function of plant dry mass in log10 scale; (C) plant

nitrogen concentration (PNC, mg g-1) across time; (D) PNC as a function of

plant dry mass in log10 scale. Closed and open symbols represent 2 and 0.06

mM N supply respectively. Colour codes for genotypes are as below;

Akihikari (black), Azucena (red), Bg 34-8 (light green), Dular (yellow), IR 64

(blue), Koshihikari (pink), Milyang 23 (cyan), Nipponbare (ash), Opus (brown),

Takanari (dark green). Values for PNC represent the mean of 6 replicates

(±SE). NP for each time point was calculated by dividing instantaneous RGR

for each time point (calculated by using the derivative of the second order

polynomial regression fitted to the ln plant dry mass over time) by PNC at

the corresponding time point.

71

Together, the N-economy observations suggest that the variation

observed in RGR (Fig. 3.2B) was largely associated with variation in NP rather

PNC under both N treatments. The decline observed in RGR across time at 2 mM

N was due to the decline in PNC across time as NP did not vary through time. On

the other hand, the increase in RGR across time at 0.06 mM was due to the

increase in NP over time as PNC did not vary much across time.

3.4.2 Do the findings of section 3.4.1 hold when assessing at a

common mass?

Given, that the growth traits described above varied with time, it is worth

considering whether those patterns were held when accounting changes in

plant dry mass. The growth parameters that have been previously plotted

against time were re-plotted against plant dry mass to assess whether the

observed trends were held when accounting for ontogenetic drift. If low N

Table 3.3 Results of a three-way analysis of variance (ANOVA) for growth

parameters related to nitrogen economy considering time (T), genotype (G)

and N treatment (N) as factors with the three-way interaction term is shown

as T x G x N. Growth parameters belong to first and second harvests were not

considered in this analysis due to pooling of samples when determining N

concentration by Kjeldahl analysis. Degrees of freedom (df), F - values and

significance are presented. *p < 0.05, **p < 0.01, ***p < 0.001. n=320.

Effect

df

PNC

LNR

SNR

RNR

T 3 22.03*** 14.27*** 5.59*** 5.49***

G 9 3.13*** 3.16*** 6.58*** 2.11*

N 1 2091.84*** 377.24*** 5.00* 623.34***

T x G 27 0.68 0.47 0.50 0.95

T x N 3 31.02*** 23.08*** 11.93*** 7.81***

G x N 9 1.04 2.29* 1.17 1.05

T x G x N 27 0.51 0.76 1.03 1.09

Error 238

72

supply had a direct effect on each growth parameter, then trait values should

differ between 0.06 and 2 mM N grown plants when compared at a common

plant size. Moreover, it may be that in cases where there was no apparent effect

of N supply on traits when not accounting for plant size, differences may emerge

at common plant sizes. An example of the latter is SLA, where the results of a

three-way ANOVA (Table 3.1) suggested no main effect of N supply, even though

SLA values appeared to be lower in 0.06 mM grown plants than in their 2 mM

counterparts, when compared at common plant sizes (Fig. 3.5B). Thus, when

accounting for plant size, N supply resulted in lower SLA values across the 10

genotypes. For other traits where there was an apparent N dependence when

assessed against time, the effect of N treatment was either maintained or

strengthened when accounting for plant size (e.g. LAR, NAR, RMR and NP). The

overall conclusion from inspection of Figures 3.4-3.6, therefore, is that patterns

observed on a time basis are largely held or strengthened when ontogeny is

accounted for.

To further assess the role of plant size, nutrient supply and genotype in

influencing trait values, hierarchical multiple regressions were conducted

(Table 3.4). The independent variables were entered in four steps - in model 1,

ln plant dry mass (ln DM) was entered and held constant. In model 2, time was

entered. In model 3, N treatment was entered. Similarly, in model 4, genotype

was entered. Therefore, predictors in model 1, 2, 3 and 4 were plant mass; plant

mass and time; plant mass, time and N treatment; and, plant mass, time, N

treatment and genotype respectively. Addition of plant mass alone (in model 1)

predicted variations in LAR; however, the explanatory power of the model was

relatively low. By contrast, addition of time (in model 2) and N treatment (in

model 3) resulted statistically significant increase in model fits, the unique

variance (r2) of 0.41 and 0.20, respectively, confirming that both time and N

treatment had strong influences on LAR.

73

Table 3.4 Results of Hierarchical multiple regression analysis to assess time, N

supply and genotypic dependent changes in growth parameters while controlling

for plant size

Trait Variable Model 1 Model 2 Model 3 Model 4

B β B β B β B β

LAR Constant 12.68*** 19.41*** 22.83*** 22.55***

ln plant dry mass -0.22** -0.10** 0.80*** 0.36*** -1.18*** -0.53*** -1.14*** -0.51***

Time -1.64*** -0.79*** -0.57*** -0.28*** -0.59*** -0.29***

N treatment -6.03*** -0.85*** -5.95*** -0.84***

Genotype 0.05 0.04

r2 0.010 0.426 0.627 0.629

F 6.95** 258.12*** 390.24*** 294.10***

∆r2 0.010 0.416 0.202 0.001

∆F 6.95** 504.28*** 376.18*** 2.75

SLA Constant 33.23*** 47.15*** 49.91*** 49.52***

ln plant dry mass -1.65*** -0.34*** 0.46** 0.09** -1.17*** -0.24*** -1.12*** -0.23***

Time -3.40*** -0.75*** -2.51*** -0.56*** -2.53*** -0.56***

N treatment -4.96*** -0.32*** -4.85*** -0.31***

Genotype 0.07 0.03

r2 0.115 0.497 0.526 0.527

F 88.42*** 337.37*** 251.94*** 189.13***

∆r2 0.115 0.383 0.029 0.001

∆F 88.42*** 519.23*** 41.25*** 0.86

LMR Constant 0.38*** 0.42*** 0.48*** 0.47***

ln plant dry mass 0.01*** 0.38*** 0.02*** 0.57*** -0.02*** -0.44*** -0.01*** -0.42***

Time -0.01*** -0.33*** 0.01*** 0.26*** 0.01*** 0.25***

N treatment -0.10*** -0.97*** -0.10*** -0.96***

Genotype 0.001 0.05

r2 0.144 0.215 0.482 0.484

F 117.53*** 95.83*** 216.31*** 163.38***

∆r2 0.144 0.071 0.267 0.002

∆F 117.53*** 63.21*** 359.08*** 2.86

SMR Constant 0.35*** 0.36*** 0.37*** 0.354***

ln plant dry mass 0.02*** 0.45*** 0.02*** 0.48*** 0.01*** 0.34*** 0.01*** 0.38***

Time -0.002 -0.06 0.001 0.024 0.000 -0.003

N treatment -0.02* -0.14* -0.01 -0.11

Genotype 0.002*** 0.12***

r2 0.200 0.202 0.208 0.221

F 177.52*** 90.02*** 62.02*** 50.21***

∆r2 0.200 0.002 0.006 0.013

∆F 177.52*** 2.21 5.02* 11.92***

RMR Constant 0.27*** 0.22*** 0.15*** 0.17***

ln plant dry mass -0.03*** -0.66*** -0.04*** -0.84*** 0.004* 0.10* 0.002 0.05

Time 0.01*** 0.30*** -0.01*** -0.25*** -0.009*** -0.22***

N treatment 0.12*** 0.90*** 0.12*** 0.87***

Genotype -0.003*** -0.11***

r2 0.441 0.499 0.725 0.736

F 557.40*** 352.00*** 618.65*** 491.29***

∆r2 0.441 0.058 0.225 0.012

∆F 557.40*** 82.41*** 577.30*** 30.79***

PNC Constant 27.82*** 36.86*** 60.94*** 61.19***

ln plant dry mass 4.25*** 0.69*** 5.10*** 0.82*** -2.43*** -0.39*** -2.46*** -0.40***

Time -3.49*** -0.40*** 0.18 0.02 0.19 0.02

N treatment -24.26*** -1.25*** -24.33*** -1.25***

Genotype -0.03 -0.01

r2 0.471 0.610 0.852 0.852

F 280.98*** 246.70*** 602.14*** 450.51***

∆r2 0.471 0.140 0.242 0.000

∆F 280.98*** 112.92*** 512.24*** 0.205

74

This confirms that the trends previously observed were direct effects of

time and N supply rather ontogeny (Fig. 3.4B). Addition of genotype (in model

4) did not significantly improve the r2 (Table 3.4). The differences observed

among genotypes for LAR (Table 3.1) were due to differences in plant sizes. For

SLA, including plant mass alone resulted in a model with an r2 of 0.115

suggesting that SLA changed with plant size. Addition of time significantly

improved the model fit by accounting for a further 38.3% of the variance. The

effect of N when correcting for ontogeny was not large, yet statistically

significant contributing a further 2.9% to the variance. Thus, N supply had an

effect on SLA when controlling for plant mass, albeit with the effect being

relatively minor (Fig. 3.5B, Table 3.4); importantly, accounting for genotype had

no effect on model fits for SLA (Table 3.4).

Did time, N supply and genotypic differences alter trends in biomass

allocation among organs when normalized to a common mass? N contributed

strongly (26.7%) to the unique variance in LMR as shown by Figure 3.5D while,

plant mass, time and genotypic differences accounted 14.4, 7.1 and 0.2%

respectively (Table 3.4). The fact that the model significantly improved

following inclusion of plant mass alone indicates LMR changed with plant size.

Time also influenced LMR to some extent, while effects due to differences in

genotypes were small. Hence, the genotypic variation observed for LMR (Table

3.1) could be due to discrepancies in plant sizes at each harvest. For SMR, the

contribution by plant mass, time, N and genotypes for the unique variance were

20, 0.2, 0.6 and 1.3% respectively. Similar to LMR, SMR being predicted alone by

Note: The independent variables were entered in four steps - in model 1, ln plant dry mass

(ln DM) was entered and held constant. In model 2, time was entered. In model 3, N

treatment was entered. In model 4, genotype was entered. Therefore, predictors in model 1,

2, 3 and 4 are ln DM, ln DM and time, ln DM, time and N treatment, ln DM, time, N

treatment and genotype respectively. The change in r2 in model 2 indicates the amount of

unique variance accounted for by the independent variables in the second step. The change

in r2 in model 3 indicates the amount of unique variance accounted for by the independent

variables in the third step. Similarly, the change in r2 in model 4 indicates the amount of

unique variance accounted for by the independent variables in the fourth step. B indicates

the unstandardized coefficient and β indicates the standardized coefficient. *p < 0.05, **p <

0.01, ***p < 0.001. n=720 for growth traits except PNC, where n=320 for PNC.

75

plant mass suggests that SMR varied with plant size. However, neither N nor

time significantly contributed to the unique variance of SMR. Hence, the time

and N effects on SMR proposed by the three-way ANOVA (Table 3.1) could be

due to differences in plant sizes at each harvest. Inspection of Figure 3.5F

supports the conclusions that variations in SMR are largely independent of time

and N supply. Unlike LMR there was a small, yet significant genotypic effects on

this trait. Plant mass alone contributed to 44.1% of unique variance in RMR;

however the effect of plant mass on RMR was not significant when considered

along with other parameters. Time, N supply and genotype significantly changed

the unique variance for RMR by 5.8, 22.5, and 1.2% respectively. The effect of N

supply on RMR was relatively large as shown in Figure 3.5H. Taken together, the

above results point to LMR and RMR being actively influenced by time and N

supply, with SMR and RMR also being influenced by genotype, when accounting

for ontogeny.

From N economy perspective, plant mass, time and N treatment

accounted for 47.1, 14 and 24.2% of PNC variance. Overall, N had a strong

influence on PNC, which changed with plant mass. Genotype had no influence on

PNC in accord with Fig 3.6D.

3.4.3 To what extent do the factors underlying variation in RGR

differ under low and high N?

A path analysis was performed to gain insights into the extent to which each

underlying component relates to genotypic variations in RGR, from both C and N

economy perspectives (Fig. 3.7). RGR was the response variable. NAR and LAR

were explanatory variables from C economy perspective, while NP and PNC

were explanatory variables from N economy perspective. Each explanatory

variable and the response variable used in the path analysis represent the

means of 10 genotypes. Path analysis was performed separately for each N

treatment. Error terms were negligible; hence, they were not shown in the path

diagrams. Single-arrowed lines along with path-coefficients indicate the direct

influence of each explanatory variable on the response variable. The path

coefficients measure how a change of one unit standard deviation of one

variable affects another variable (expressed on same units) independent of

76

other variables (Poorter and Remkes, 1990, Poorter, 1989). Double-arrowed

lines accompanied by correlation coefficients indicate the association between

explanatory variables (Chan-Navarrete et al., 2014).

First, I explored to what extent C economy traits contribute to variation

in RGR across the 10 genotypes. Considering all genotypes together at 2 mM, the

Figure 3.7 Path diagrams showing the relationship between RGR and its

underlying components of 10 genotypes of rice (averaged across six

harvests) from both C and N economy perspective under two N levels. Mean

RGR at each N level calculated by fitting a first order linear regression to the

ln transformed plant dry mass across six harvests. NAR at each N level

calculated by dividing above mean RGR by LAR averaged values across six

harvests as RGR is the product of NAR and LAR. NP at each N level

calculated by dividing above calculated mean RGR by PNC averaged across

six harvests as RGR is the product of NP and PNC. The variation in RGR

explained by each underlying parameter is indicated by the path co-efficient

as shown closer to the uni-directional arrow. Significance of relationship is

shown as *p < 0.05, **p < 0.01, ***p < 0.001. The correlation co-efficients

that represent the relationship among parameters are shown closer to the

bi-directional arrows. See results (section 3.4.3) for further explanation.

77

effect of NAR on RGR was 1.61 (p < 0.01) while, the effect of LAR was minor

(0.98, non-significant) (Fig. 3.7). A significant negative correlation of -0.94 (p <

0.05) was found between NAR and LAR. NAR alone (without any covariance

with other variables) was positively correlated with variations in RGR under

high N supply. However, due to the strong negative correlation between NAR

and LAR, a decrease in LAR suggests an increase in NAR. Due to the strong

relationship between NAR and RGR and lack of statistically significant

relationship between LAR and RGR, the effects of NAR dominate; thus RGR

increases when increasing NAR. When considering 0.06 mM grown plants, the

magnitudes of path-coefficients were smaller compared to 2 mM. NAR (0.96, p <

0.001) and LAR (0.46, p < 0.01) both positively influenced RGR at 0.06 mM. NAR

negatively influenced LAR (-0.34); however, that effect was negligible. LAR

alone (without any covariance with other variables) was positively correlated

with RGR under low N. Although a decrease in LAR indicates an increase in NAR,

the negative correlation between LAR and NAR was rather weak in the 0.06 mM

grown plants. Due to the close relationship between NAR and RGR, the effects of

NAR again dominate over low LAR in those plants grown on low N supply;

consequently, RGR will increases as NAR increases under low N.

Having observed the significant positive contribution of LAR to variation

in RGR at 0.06 mM, it is of interest to see how underlying components of LAR

(i.e. SLA and LMR) along with NAR contribute to variation in RGR at 0.06 mM

(Fig. 3.8).

78

Including all genotypes collectively, all three factors NAR (1.04, p <

0.001), LMR (0.40, p < 0.001) and SLA (0.47, p < 0.001) have a positive effect on

RGR. However, the effect of NAR on RGR was stronger than LMR and SLA. There

were negative NAR-SLA and LMR-SLA correlations, as well as a positive NAR-

LMR correlation. However, those were rather weak and statistically non-

significant. NAR, LMR and SLA together explained 90.3% variation in RGR.

Collectively, these results point to the importance of NAR for variations in RGR,

Figure 3.8 Path diagram showing the relationship between RGR and its

underlying components (NAR, LMR and SLA) of 10 genotypes of rice

(averaged across six harvests) from C economy perspective at 0.06 mM N

level. Mean RGR at each N level calculated by fitting a first order linear

regression to the ln transformed plant dry mass across six harvests. NAR

at each N level calculated by dividing above mean RGR by LAR averaged

values across six harvests as RGR is the product of NAR and LAR. The

variation in RGR explained by each underlying parameter is indicated by

the path co-efficient as shown closer to the uni-directional arrow.

Significance of relationship is shown as *p < 0.05, **p < 0.01, ***p <

0.001. The correlation co-efficients that represent the relationship among

parameters are shown closer to the bi-directional arrows. See results

(section 3.4.3) for further explanation.

79

and the way in which NAR is then linked to variations in leaf structure and/or

biomass allocation to above and below ground organs.

Now I examine to what extent N economy traits contribute to variation in

RGR (Fig. 3.7). Taking all genotypes together, both NP (1.41, p < 0.001) and PNC

(0.85, p < 0.001) contributed positively to variation in RGR for 2 mM N grown

plants. There was a negative correlation between NP and PNC for plants grown

under high N supply, albeit with the relationship not being statistically

significant. Not surprisingly, PNC alone (without any covariance with other

variables) was positively correlated with RGR under high N. Due to the close

relationship between NP and RGR, the effects of NP dominate over low PNC in

determining variation in RGR in high-N grown plants. Consequently, RGR

increases as NP increases under high N. Though the magnitude of path

coefficients were lower in 0.06 mM N grown plants, both NP (1.81, p < 0.001)

and PNC (0.35, p < 0.001) positively contributed to variation in RGR. In contrast

to plants grown under 2 mM N, there was a positive (but not significant)

correlation between NP and PNC in 0.06 mM N plants. Thus, while NP and PNC

contribute to genotypic variations for plants grown on high and low N supply,

variations in NP of greater importance.

3.5 Discussion

3.5.1 Did the factors that account for variations in RGR among the 10

genotypes vary with N supply?

During the present study, path analysis along with other data (Fig. 3.3 to 3.6)

provided evidence on the extent to which each underlying component

contributes to genotypic variation in RGR, both under high and low N supply.

Across all genotypes, NAR (from a C economy perspective) and NP (N economy

perspective) were the key traits driving genotypic variation in RGR irrespective

of N supply. Several past studies also reported that NAR (Shipley, 2006, Shipley,

2002, Amin et al., 2002, Ehara et al., 1990) and NP (Atkin et al., 1998b) best

explain variation in RGR across species. The observed negative correlation

between NAR and LAR under high N was also consistent with past studies, and

likely due to following reasons: firstly, thicker leaves with many palisade cell

layers and more investment of N per unit leaf area in photosynthetic machinery,

80

thus leading to high NAR values in association with a lowering of SLA and LAR

(Konings, 1989, Lambers et al., 1990, Poorter and Remkes, 1990). NAR and NP

are related traits, being influenced by the rate of net C gain (and thus mass

accumulation), either expressed on an area or N basis, respectively. The factors

that influence NP, such as the efficiency of N use during vegetative growth

(particularly photosynthetic N use efficiency (PNUE) and/or reduced C losses by

respiration per unit plant N, and plant C content) (Lambers et al., 1990) will

strongly influence NAR. PNUE is, in turn, strongly influenced by factors such as

patterns of investment of N in non-photosynthetic and photosynthetic

components (see Chapter 4). Thus, genotypic variation of NP, both under low

and high N supply conditions, are likely to be crucial in determining growth

rates of rice.

3.5.2 Genotypic variation in ability to grow on low N supply

Several researchers have attempted to identify genotypic variation in NUE of

rice under high N and low N conditions, using field-based data sets (Chen et al.,

2013). Genotypic differences in N uptake (Wada et al., 1986, Ichii and Tsumura,

1989), photosynthesis (Makino, 2005), radiation interception (Lubis et al.,

2013), sink formation and efficient translocation of C into spikelets (Mae, 2011,

Ntanos and Koutroubas, 2002) might have led variation among genotypes for

NUE and biomass production. However, in past studies, growth traits have

rarely been examined during vegetative stage, with none having quantified NP

of rice. NP is a useful trait in studies seeking to gain insights into the efficiency

of biomass production during early growth, which may lead a greater NUE in

the subsequent grain filling stage. In my study, there was 23% of genotypic

variation in NP across the 10 genotypes under well-fertilized conditions. By

contrast, there was about 70% variation in NP among genotypes under low N

supply (Table 3.2), suggesting that genotypes did not respond equally to low N

supply, and that the efficiency of N use is of greater importance in determining

genotypic variation in RGR under low N supply. Importantly, three of the

genotypes - Takanari, IR 64 and Milyang 23 - maintained RGR better than the

other genotypes when grown on low N supply, largely via maintaining NP; in

those genotypes that did not maintain growth rates as well under N deficient

conditions, NP was substantially reduced. Having established that there was a

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differential response of genotypes to low N and three genotypes maintained

their growth well, it is of interest to explore what underlying components might

enable their performance during early vegetative growth.

3.5.3 What underlying components account for differential

responses to low N supply?

Past studies have reported a peak function (i.e. a bell-shaped curve) for NP

values when plants are grown over a wide range of N levels (Ingestad, 1977, van

der Werf et al., 1993b). As N supply decreases away from optimal levels to

moderate N deficiency, NP initially increases, reaching a peak at moderate N

supply; NP values often decline together with decreasing N supply from

moderate N levels towards severe N deficiency (van der Werf et al., 1993b).

Such patterns have been seen across a range of species. For instance, Macduff et

al. (2002) observed a decline in NP of Lolium perenne under high N addition

rates which suggests those data are remained in the first region explained above

(consistent with NP increasing when N supply decreases). However, few studies

have assessed the effect of severe N deficiency on NP. The present study with

rice provides evidence of severe N deficiency (i.e. at 0.06 mM N supply) being

associated with reduced NP (20-40% reduction compared to high N grown

plants) in most genotypes. By contrast, NP was less affected by severe N stress

in three genotypes. Given NP declined in a majority of the 10 genotypes under

low N, what mechanisms might have assisted those three genotypes to maintain

near homeostasis of NP?

Leaf and whole-plant NP are strong influenced by PNUE (Garnier et al.,

1995). Carbon dioxide (CO2) assimilation rate is known to increase along with

increasing leaf N concentration (Evans, 1983, Makino and Osmond, 1991).

Plants often allocate less of their leaf N to photosynthetic machinery if grown

under high N availability (Pons et al., 1994b). The amount of N allocated to

leaves during the present study was about 16% lower under low N compared to

high N. Thus, the above mentioned three genotypes might have allocated

relatively more N to photosynthetic apparatus under low N and thereby

increased PNUE. PNUE typically declines as plants increase in size (Kitajima et

al., 1997) in part due nutrient limitation (Escudero and Mediavilla, 2003) and

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mutual-shading. Hence, better light interception by smaller canopy might have

favoured PNUE under low N. Yet, the three genotypes that performed well

under low N supply were not smaller than the other genotypes – thus, smaller

stature is unlikely to account for their improved NP values. While I cannot

exclude the possibility that canopy structure may have differed among the

genotypes (perhaps allowing for more efficient light use in IR64, Milyang 23 and

Takanari) it seems likely that greater allocation of N to photosynthesis and/or

reduced respiratory rates per unit plant N may have played a role. Increased

allocation to photosynthesis is likely to be associated with reduced N

investment in other components (e.g. cell walls, secondary compounds).

The decline in NP under low N in the majority of the 10 genotypes might

have been associated with a greater investment of C below-ground which might

have further resulted in increased respiratory C losses in roots. Van der Werf et

al. (1992c) reported an increase in C losses associated with maintenance of root

biomass and ion uptake under limited N conditions, while Poorter et al. (1995)

observed a decline in shoot and root respiration to a similar extent under low N.

However, the proportion of daily fixed C respired in roots [which is known to be

10-30% under general conditions (Van der Werf et al., 1989)] increase

substantially (19-50%) under low N (Van der Werf et al., 1992b, Lambers et al.,

1996) mainly attributed to the higher specific energy costs for nitrate uptake

(Van der Werf et al., 1994, Lambers et al., 1998). Both ammonium and nitrate

ions are actively taken up by plants under low N (Glass et al., 2002). N uptake

under low N could be energy intensive due to the activity of energy demanding

high affinity transport system and lower influx : efflux ratio (Lambers and

Poorter, 1992). Genotypic variation observed in past studies in relation to the

efficiency of N uptake (Glass, 2003) could be due to variation in root

morphology as well as different rates of efflux. There are genotypic and

interspecific variations of rates of N efflux (Glass, 2003). Scheurwater et al.

(1999) found high respiratory costs for net nitrate transport was partly

ascribed to high efflux of nitrates in slow growing species compared to fast

growing counterparts. Thus, less N efflux along with less ATP cost and turnover

could potentially lead to lower respiratory maintenance costs and reduced C

losses below ground in those three rice genotypes over others.

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Less C gain by the smaller canopy, while higher C losses via root

respiration associated with the larger root biomass (Poorter et al., 1995)

together with less efficiency of N use under severe N deficiency might have led

to reduced growth under low N in the majority of genotypes during present

study. However, increased C gain along with reduced C losses might have

facilitated homeostasis of NP and thereby aided maintenance of RGR in those

three genotypes irrespective of the decline in PNC under low N. Several studies

have investigated PNUE and N partitioning to photosynthesis in rice (Makino et

al., 2003, Makino and Osmond, 1991); this chapter adds to these past studies,

showing how N supply affects efficiency of N use at the whole plant level, during

early vegetative growth.

3.5.4 The effect of ontogeny

In the present study, most traits changed as plants increased in size, as

previously discussed (Evans, 1972, Coleman et al., 1994, Poorter et al., 2012,

Poorter and Pothmann, 1992). Yet, having maintained their patterns when

compared at a common time and plant mass suggests that there was direct

influence of N treatment on key traits per se. However, differences were found

when interpreting traits quantitatively among two types of comparisons. For

instance, time dependent changes became hard to distinguish when comparing

at common plant mass, while genotypic differences became more distinct at 2

mM N treatment. The effect of N became more prominent for some traits

particularly at 0.06 mM (e.g. LAR, SLA, NAR, RMR and NP) when comparing

plants at a common plant mass. The qualitative interpretations at a common age

were largely held when accounting for plant mass, while the magnitude of

responses varied [i.e. either reduced or become evident (Coleman et al., 1994)]

when comparing at a common mass (Poorter and Pothmann, 1992, Negrini,

2016). Hence, for most traits, the conclusions reached from single harvests are

largely maintained when ontogeny is accounted for. The only significant

exception was SLA, where an absence of N effects seen in time-based

comparisons was not held when plants were compared at common sizes. Thus,

N supply does affect rice leaf structure, a finding that single time point sampling

would have failed to identify (Marañón and Grubb, 1993).

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3.6 Conclusions

In conclusion, faster growth of rice plants at both high (2 mM) and low (0.06

mM) N levels were linked to differences in the efficiency of C and N use (as

indicated by NAR and NP respectively) rather than resource allocation among

different organs at the whole plant level. Importantly, NP markedly differed

among rice genotypes at each N level and that variation was much greater under

low N relative to high N. In fact, rice genotypes Takanari, IR 64 and Milyang 23

performed well compared to other genotypes exhibiting faster growth, NP and

NAR under low N relative to high N. While some growth traits changed with

ontogeny, the observed phenotypic changes in growth traits were direct effects

of N per se rather differences in plant sizes.

3.7 Future directions

Performing a complete dose response curve for above three genotypes

Takanari, IR 64 and Milyang 23 would help to elucidate any genotypic

differences in the shape of the peak function for NP. Further work is also needed

to draw any conclusions about mechanisms that might have led higher NP in

rice (e.g. enhanced photosynthetic N use efficiency, reduced respiratory costs at

organ and whole plant level particularly under low N). Moreover, it is important

to study how these three genotypes perform in the field while confirming the

phenotype at field level and understanding mechanisms driving NUE.

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Chapter 4 - Genotypic variation and the effect of N supply on leaf photosynthetic nitrogen use efficiency (PNUE) of rice

4.1 Summary

In this study I investigated the effect of nitrogen (N) supply on leaf level

photosynthetic N use efficiency (PNUE) [as indicated by carboxylation capacity

and net assimilation rate (N basis)] of ten rice genotypes. Light-saturated

photosynthesis (A) was measured at two atmospheric CO2 concentrations: 400

ppm [A400, where A can be Ribulose-1,5-bisphosphate carboxylase/oxygenase

(Rubisco) limited] and 1500 ppm [A1500, where A is limited by Ribulose-1,5-

bisphosphate (RuBP) regeneration]. Plants were measured between 35-48 days

after transplanting in the experiment previously described in Chapter three.

Reduced demand for carbon dioxide (CO2) by plants receiving a low N supply

was matched with reduced CO2 supply by partially closing stomata in rice

genotypes. As three genotypes (Takanari, IR 64, Milyang 23) maintained whole

plant growth (i.e. relative growth rate, RGR) and nitrogen productivity (NP) at

low N (Chapter three), in this chapter I explored whether those genotypes also

exhibited enhanced leaf level PNUE, perhaps due to maintenance of

carboxylation capacity together with partitioning of more N to photosynthesis

(Rubisco and electron transport components) under low N conditions. While

there was tendency for higher PNUE in the three selected genotypes, no

statistically significant differences in PNUE were found among the 10 genotypes

and N levels, either as main or interactive effects. Yet, both carboxylation

capacity and net assimilation rate (N basis) strongly correlated with whole plant

NP under low N providing some evidence that these parameters might explain

whole plant NP at low N. The ratio of dark respiration to carboxylation capacity

remained largely constant across N treatments and genotypes. An additional

component of the chapter was an assessment of methods for quantifying

Rubisco abundance (e.g. Western blots vs [14C]CPBP Rubisco content assays).

The Western blot method for determining Rubisco content is less labour

intensive than the [14C]CPBP assay, but there have been limited direct

comparisons between the two methods. I assessed the ability of Western

86

blotting procedure to accurately estimate the amount of Rubisco of a given

sample when performed with standards calibrated with [14C]CPBP assay. There

was a significant positive correlation between the amount of Rubisco estimated

via Western blotting approach vs. the amount of Rubisco quantified via

[14C]CPBP Rubisco content assay. Thus, performing Western blotting procedure

with standards pre-determined with [14C]CPBP Rubisco content assay can be

considered as a rapid method of estimating Rubisco from multiple samples.

Further, investigations are needed to confirm these results with rice and other

species.

4.2 Introduction

Chapter two provided evidence for N-deficiency resulting in changes in whole-

plant growth, with decreased N supply also being linked to increases in the

efficiency of N use (i.e. N productivity (NP) increased). Subsequently, the results

of Chapter three showed that the responses of growth and NP to low N varied

among 10 selected rice genotypes, with three genotypes (Takanari, IR 64,

Milyang 23) maintaining growth and NP under N-limited conditions. Past work

has shown that variations in NP are linked to differences in whole plant

photosynthetic nitrogen use efficiency (PNUE), respiration per unit N basis, N

allocation among different organs and/or whole plant carbon (C) concentration

(Lambers et al., 1990, Garnier et al., 1995, Atkin et al., 1996b), with leaf-level

PNUE often being the most important component. In this chapter, I investigate

the extent to which variations in whole-plant NP in the selected lines of rice are

linked to variations in leaf-level PNUE and associated patterns of N allocation.

Photosynthesis, the key determinant of net C uptake in plants,

contributes to the biomass accumulation and yield in rice (Takai et al., 2013) by

converting radiation energy to biochemical energy using water (H2O) and C

dioxide (CO2) in the presence of chlorophyll. According to the biochemical

model of C3 photosynthesis (Farquhar et al., 1980), under ambient atmospheric

CO2 conditions, the rate of CO2 assimilation is limited by CO2 supply, the enzyme

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) which catalyses

the reaction of CO2 fixation depending on the catalytic rate, and the amount of

active Rubisco in a leaf (Zhu et al., 2010). Under CO2 saturating conditions, CO2

87

assimilation is limited by the regeneration of ribulose-1,5-bisphosphate (RuBP),

which is in turn dependent on formation of ATP and NADPH by photosynthetic

electron transport (Evans, 2013, Yamori et al., 2011). Rubisco carboxylation

capacity (Vcmax) is known as the rate limiting factor of C3 photosynthesis under

current atmospheric conditions (Makino, 2005). In many species, there is a

close relationship between Rubisco (Evans, 1989, Makino et al., 1994, Makino et

al., 1997) and N per unit leaf area (Na), reflecting the fact that 15-30% of leaf N

is invested in Rubisco (Makino, 2003). Importantly, however, the relationship

between rates of photosynthesis and organic N content [i.e. photosynthetic N

use efficiency (PNUE)] of a leaf is not constant (Poorter and Evans, 1998).

Several past studies reported interspecific variation in PNUE at the leaf

level (Pons et al., 1994a, Poorter and Evans, 1998, Takashima et al., 2004,

Hikosaka, 2004, Hikosaka, 2010, Westbeek et al., 1999) which is often indicated

by rate of net photosynthesis (A) per unit N (i.e. AN) or Rubisco carboxylation

rate per unit N (Vcmax, N). Reduced PNUE is often accompanied by increased

allocation of leaf N to non-photosynthetic components at the expense of

photosynthesis (Poorter and Evans, 1998, Westbeek et al., 1999, Hikosaka and

Shigeno, 2009, Takashima et al., 2004, Warren and Adams, 2001). Non-

photosynthetic components include: N investment in nucleic acids and

mitochondria (Evans and Seemann, 1989, Makino and Osmond, 1991); N

partitioning to cell walls (Onoda et al., 2004), sclerenchyma, epidermal cells,

and secondary metabolites (Warren et al., 2000a); and N investment in other

compounds such as proline, storage proteins and protective compounds (e.g.

polyamines, anti-fungal and anti-herbivore compounds). Investment in these

components comes at the expense of photosynthetic components, potentially

reducing PNUE (Lambers and Poorter, 1992, Poorter and Evans, 1998). In

addition, differences in CO2 assimilation rate per unit photosynthetic N – which

will influence PNUE - can occur, driven by factors such as: variation in Rubisco

specific activity (carboxylation activity per unit of enzyme); activation state of

Rubisco; light-limitations; differences in the fraction of light intercepted by

leaves; feedback inhibition of photosynthesis; differences in respiration in the

light; and, variation in intercellular (Ci) (Warren et al., 2003) and chloroplastic

(Cc) CO2 concentration (Lambers and Poorter, 1992, Onoda et al., 2004, Poorter

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and Evans, 1998, Lambers, 2008, Pons and Westbeek, 2004, Hikosaka, 2004).

While Hikosaka (2004) concluded that variation in PNUE within a species to be

smaller compared with interspecific variation, the possibility remains that the

findings of Chapter 3 (i.e. variation in NP among the 10 genotypes of rice) could

be linked to intra-specific variation in PNUE at the leaf level. To my knowledge

no study has previously investigated existence of genotypic variation in PNUE in

an individual species, including rice.

Increases in PNUE can enable plants to exhibit better physiological

performance under sub-optimal conditions (Flood et al., 2011). Given that, what

is known about how sub-optimal N supply influences PNUE? Whole-plant PNUE

is known to increase at low N (Pons et al., 1994a, Negrini, 2016). However, there

is contradictory evidence for the response of leaf-level PNUE to low N supply.

Warren et al. (2003) found that Vcmax per unit leaf N (Vcmax,N) of Pinus sylvestris

remained constant irrespective of N supply, with Hikosaka (2010) and Pons and

Westbeek (2004) reporting similar findings. By contrast, another study found

that leaf-level PNUE linearly decreased with increasing leaf N per unit leaf area

(Na) (Cheng and Fuchigami, 2000), underpinned by reduced use of

photosynthetic capacity, N investment in non-photosynthetic components

and/or low specific activity of Rubisco (Pons et al., 1994a). A negative

correlation was also observed between leaf mass per unit area (Ma) and PNUE

(Pons and Westbeek, 2004, Hikosaka, 2010); other studies have also reported

that low PNUE is associated with high Ma (or low SLA) (Poorter and Evans,

1998, Reich et al., 1998a, Hikosaka, 2004)and high leaf Na (Lambers and

Poorter, 1992). Thus, PNUE tends to be lower in thick-leaved plants that contain

high concentrations of total N per unit leaf area. Both PNUE (Harrison et al.,

2009) and fraction of leaf N invested in Rubisco (nR) (Ellsworth et al., 2004) are

known to decrease with increasing Ma. Moreover, species with high Ma partition

less N to thylakoids and Rubisco (Poorter and Evans, 1998), thus decrease

PNUE. High Ma further reduces leaf conductance (Flexas et al., 2008), with

resultant variations in intercellular Ci correlating with PNUE (Warren and

Adams, 2004). This raises the question of how N-induced changes in Ma might

influence Ci and leaf PNUE in rice, an area that is currently under-studied.

89

Similarly, the impact of low N supply on N investment patterns in rice is, to my

knowledge, unknown.

Further insights can be obtained by considering N partitioning within the

photosynthetic apparatus. Here, we need to consider N allocation to: pigment-

protein complexes (nP) that occur within photosystems I (PSI) and II (PSII) and

the light harvesting complex (LHC) (Evans, 1989); electron transport (nE); and,

nR (Evans and Seemann, 1989, Hikosaka, 2004, Pons et al., 1994a). Under sub-

optimal conditions, N is often partitioned among these components to optimize

photosynthesis (Lambers and Poorter, 1992, Hikosaka et al., 2006). For

instance, a greater fraction of N is allocated to the LHC under shade (Evans and

Terashima, 1987, Makino et al., 1997). Similarly, we can expect greater

allocation of N to Rubisco under N deficiency. N investment in thylakoid

proteins has been reported to decline with decreasing N supply (Lambers and

Poorter, 1992). Other studies observed that the fraction of leaf N allocated in

Rubisco increases with increasing N supply (Evans, 1983, Sage et al., 1987,

Seemann et al., 1987, Evans and Terashima, 1988, Terashima and Evans, 1988)

while others suggest that above fraction remains constant (Makino et al., 1988,

Makino and Osmond, 1991, Brown et al., 1996, Warren et al., 2000b). Such

discrepancies could be ascribed to differences in leaf age of each individual

experiment, as Rubisco degrades rapidly with senescence (Makino et al., 1992).

Several past studies attempted to understand N partitioning to Rubisco

in rice. For example, in a study with rice variety ‘Sasanishiki’, the fraction of N

allocated in Rubisco remained constant in the 27-28% range, irrespective of N

treatment (Makino et al., 1984). By contrast, a later study by the same group

Makino et al. (1992) reported N allocation to Rubisco increased with increasing

area-based concentrations of leaf N in ‘Sasanishiki’ but remained constant in a

selected wheat variety (‘Asakaze’). Clearly, the impact of low N treatment on N

allocation to Rubisco is variable, highlighting the need for more studies to

elucidate patterns in N partitioning to Rubisco and other photosynthetic

components in a range of rice varieties. To date, most studies investigating

kinetic properties of Rubisco and associated gas exchange characteristics have

examined a single genotype of rice, and/or compared a single rice line with

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other species such as maize (Makino et al., 2003, Schmitt and Edwards, 1981)

and wheat (Sudo et al., 2003, Makino et al., 1988, Schmitt and Edwards, 1981).

In addition to changes in the pattern of N investment, variations in PNUE

could also reflect changes in the activation state and/or kinetic properties of

Rubisco (Harrison et al., 2009). This is because Rubisco needs to be activated by

Rubisco activase to facilitate its catalytic reactions (Cheng and Fuchigami, 2000,

Portis, 1990). N deficiency is known to influence Rubisco activation (Portis,

1990), with Rubisco activation state being lower in high N grown leaves

(Mächler et al., 1988, Yamori et al., 2011), leading to a lower PNUE (Cheng and

Fuchigami, 2000). Similarly, Li et al. (2009) observed that in rice, Rubisco was

less active in high N grown plants compared with plants grown at low and

intermediate N supply. In such cases, inactive Rubisco may represent N storage

component under high N (Li et al., 2009). Further, Mächler et al. (1988)

provided evidence for enhanced photosynthetic capacity in wheat under N

deficiency, underpinned by increased Rubisco activity despite lower Rubisco

content. In such cases, higher activity of Rubisco could result from increased

maximum catalytic turnover of the carboxylase (kcat) function of Rubisco.

Estimates of kcat are obtained by quantifying the slope of maximum Rubisco

carboxylation rates against the number of Rubisco sites (µmols m-2). A greater

kcat indicates a faster turnover rate of Rubisco. To date, no study has

investigated the effect of low N treatment on kcat of rice, relative to plants grown

under high N treatment.

The objective of this chapter was to quantify the extent to which leaf-

level PNUE of 10 selected rice genotypes varies in response to N availability

during early vegetative growth. The study addressed the following specific

objectives: (1) does N supply influence PNUE of rice at the leaf level, and if so,

does the effect of N supply on PNUE differ among the selected rice genotypes?

Here, attention will be given to understanding whether the three rice genotypes

(Takanari, IR 64 and Milyang 23) that maintained superior growth and NP

under low N supply (see Chapter 3) exhibit enhanced leaf-level PNUE,

particularly at low N; and, (2) which factors (e.g. increased Ci, greater N

allocation to photosynthetic components, faster Rubisco) account for variation

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in PNUE of low N grown plants? As part of the methodology of this study, I

assessed the accuracy of Western blotting procedure to estimate Rubisco

abundance via comparison with results of a [14C]CPBP Rubisco content assay.

Using the results of this approach, I quantified the amount of Rubisco of high

and low N grown plants using the Western blot procedure, with kcat values also

being estimated.

4.3 Materials and methods

4.3.1 Plant growth

This chapter presents leaf-level gas exchange parameters from the experiment

described in Chapter 3 where ten rice genotypes were grown under two N

treatments (0.06 and 2 mM). Leaf gas exchange measurements were taken

during last three harvests (i.e. 35-48 days after transplanting) of that

experiment (Fig. 4.1).

Figure 4.1 Measuring leaf gas exchange characteristics of 10

genotypes of rice grown under two N levels (2 and 0.06 mM) using

Licor6400 gas exchange system.

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4.3.2 Measurements

4.3.2.1 Gas exchange measurements

Leaf level gas exchange measurements were made on most recently fully

expanded leaves using Licor6400 gas exchange systems between 9 am to 3 pm

on each sampling day. Light-saturated photosynthesis (A) was measured at a

photosynthetic photon flux density of 1800 μmol m-2 s-1 and a relative humidity

of 60-70% with the leaf chamber block temperature set to 28°C. Measurements

of A were taken at two atmospheric CO2 concentrations [i.e. at 400 ppm (A400,

where A is Rubisco limited) and at 1500 ppm (A1500, where A is limited by RuBP

regeneration)]. Thereafter, leaf dark respiration (RD) was measured at an

atmospheric CO2 concentration of 400 ppm CO2 following a period of 30 minutes

in darkness. The flow rate in the leaf chamber was set to 500 µmol s-1 when

measuring A400 and A1500 and lowered to 300 µmol s-1 for RD. Before each

measurement both the sample and the reference gas lines were matched and

data were recorded once received a stable signal for CO2.

4.3.2.1.1 Estimation of photosynthetic capacity

To gain insights in to how N deficiency influenced photosynthetic metabolism of

rice, maximal area-based rates of Rubisco carboxylation (Vcmax, a) and light-

saturated electron transport (Jmax, a) were calculated following the model of

photosynthesis established by Farquhar, von Caemmerer and Berry (Farquhar

et al., 1980), assuming that A was Rubisco-limited at 400 ppm and RuBP

regeneration limited at 1500 ppm, according to:

Vcmax, a= A400, a+RD, a (Ci+Kc (1+ O Ko

⁄ ))

(Ci- Γ*) (Eqn. 4.1)

Jmax, a= A1500, a+RD, a (4Ci+8Γ* )

(Ci- Γ*) (Eqn. 4.2)

where A400, a and A1500, a are the area-based rates of light-saturated

photosynthetic rate at CO2 concentrations of 400 ppm and 1500 ppm measured

at 28°C, respectively; RD, a is the rate of leaf dark respiration measured at 28°C

on an area basis; Ci is the CO2 partial pressure at intercellular space; Kc and Ko

93

are the Michaelis–Menten constants of Tobacco Rubisco for CO2 (404 µbar at

25°C) and O2 (248 mbar at 25°C), respectively; O is the partial pressure of

oxygen at Rubisco; Γ* is the CO2 compensation point in the absence of

respiration [36.9 µbar at 25°C; von Caemmerer et al. (1994)]. Values of Kc, Ko

and Γ* were adjusted to the growth temperature using the temperature-

dependences of each parameter reported in (Von Caemmerer, 2000). The

estimates for Vcmax, a and Jmax, a were then normalized to 25°C using the

Arrhenius function (Farquhar et al., 1980) as below:

Vcmax, a25 =𝑉𝑐𝑚𝑎𝑥,𝑎

𝑒[

(𝑇−298)𝐸𝑎298𝑟(273+𝑇)

] (Eqn 4.3)

Jmax, a25 = 𝐽𝑚𝑎𝑥,𝑎/ [exp [ (𝑇−298) 𝐸

298 𝑅𝑇] [

1+exp(298 S−H

298 𝑅)

1+exp(ST−H

𝑅𝑇)

]] (Eqn 4.4)

where Vcmax, a25 and Jcmax, a25 are the calculated maximal rates of carboxylation

and electron transport at 25°C on a leaf area basis; r is the universal gas

constant (8.314 J K-1 mol-1); T is leaf temperature (K); Ea is the activation energy

(58.52 kJ mol-1 for Vcmax, a and 37 kJ mol-1 for Jmax, a); S is the entropy factor (710

kJ mol-1) and H is the rate of decrease of the function above optimum (220 kJ

mol-1). Rates of leaf RD at 25°C on area basis i.e. RD, a25 were then calculated

according to:

RD, a25 = 𝑅𝐷𝑄10[(𝑇−25)

10] (Eqn. 4.5)

where 𝑄10 is proportional increase in RD, a per 10°C rise in temperature [

assuming a 𝑄10 value of 2.23 for leaf RD (Atkin et al., 2005)].

4.3.2.2 Determining leaf structure and chemistry

Following gas exchange measurements, leaf area and fresh mass values were

recorded as described in Chapter 3; note: sampled leaves were the same leaves

used for gas exchange measurements or a leaf from a parallel tiller at a similar

development stage. Thereafter, a subset of sampled tissues were snap frozen in

liquid nitrogen and stored at -80°C up until use for subsequent chlorophyll and

94

Rubisco assays in the laboratory. Other subsets of sampled tissues were placed

in an oven at 70°C for at least 72 hours and then weighed.

4.3.2.2.1 Leaf mass per unit area (Ma) and total leaf N concentration (Na)

Leaf mass per unit leaf area (Ma) was calculated for each dried sample. Total leaf

N concentration on a leaf area basis (Na) of dried leaves was determined using

Kjeldahl acid digest method (Allen et al., 1974) as detailed in Chapter 3.

4.3.2.2.2 Leaf Chlorophyll concentration

Chlorophyll pigments were extracted from known areas (~2 cm2) of frozen leaf

pieces using N,N-Dimethylformamide (DMF) as previously described (Porra et

al., 1989, Jacobsen, 2012). The absorbance of each sample extract was measured

at 750, 663.8 and 646.8 nm using an Agilent Cary 60 spectrophotometer

(Agilent Technologies Australia (M) Pty Ltd, Victoria 3170, Australia, 2014).

Chlorophyll a and b concentrations were calculated using formulae given below;

A663.8 = A663.8-A750

A646.8 = A646.8-A750

Chlorophyll a (nmol/ml) = 13.43 A663.8 - 3.47 A646.8

Chlorophyll b (nmol/ml) = 22.90 A646.8 - 5.38 A663.8

Chlorophyll a+b (nmol/ml) = 19.43 A646.8 + 8.05 A663.8

4.3.2.2.3 Rubisco quantification

The [14C]-2’-carboxypentitol-1, 5-bisphosphate ([14C]CPBP) Rubisco content

assay is a reliable method of quantifying the amount of Rubisco sites in a given

sample (Butz and Sharkey, 1989, Ruuska et al., 1998). Yet, there are limitations

associated with the above assay; for example, the method is relatively time

intensive when large number of samples need to be quantified, with the method

needing to be done by personnel who aware of radiation safety procedures. The

method also generates radioactive waste. As part of the methodological aspect

of this chapter, I explored the possibility of quantifying the amount of Rubisco

by comparing results from the [14C]CPBP Rubisco content assay with those of a

Western blotting procedure, with the expectation that the latter approach could

speed up Rubisco quantification.

95

Standards previously quantified using the [14C]CPBP Rubisco content

assay (and therefore with known numbers of Rubisco sites) were used as a

reference in the Western blotting procedure to quantify the amount of Rubisco

sites in several unknown rice samples (Fig. 4.2):

Step 1 - the amount of Rubisco sites in four samples (namely HN1, HN2 from 2

mM and LN1 and LN2 from 0.06 mM) were quantified using [14C]CPBP Rubisco

content assay (see section 4.3.2.2.3.1) thus, the amount of Rubisco sites in each

sample was known.

Step 2 - the standards (S1-S5) were made only from HN1 (see section

4.3.2.2.3.2) to be used as the reference in Western blotting approach (thus, HN1

was a sample as well as the standard in Western blotting) and the amounts of

Rubisco sites loaded per lane for each standard was known (as HN1 was

previously tested with [14C]CPBP Rubisco content assay).

Step 3 - the ability of Western blotting procedure (see section 4.3.2.2.3.3) to

accurately quantify the amount of Rubisco sites loaded per lane from four

known samples was assessed. The amount of Rubisco sites loaded per lane for

samples (HN1, HN2, LN1 and LN2) were quantified from Western blotting using

the standard curve (i.e. the amount of Rubisco sites loaded per lane (ng) from

standards (S1-S5) versus the density of the band, Fig. 4.3A and B).

Step 4 - the Rubisco sites observed per lane for each sample (quantified via

Western blotting approach) was plotted against the actual Rubisco sites loaded

per lane for each sample (quantified via [14C]CPBP Rubisco content assay

approach) to test the correlation between both (Fig. 4.9A) (see section

4.3.2.2.3.4).

Step 5 –the amount of Rubisco sites loaded per lane in unknown samples was

quantified via Western blotting procedure using HN1 as the standard (see

section 4.3.2.2.3.5). Based on the standard curve (i.e. the amount of Rubisco

sites loaded per lane from standards (S1-S5) versus the density of the band) the

amount of Rubisco sites per m2 in each unknown sample was determined. Then

catalytic turnover rate of carboxylase (kcat) was determined by the slope of the

plot, Rubisco carboxylation on area basis normalized to 25°C (Vcmax, a25) versus

Rubisco sites per m2 (Fig. 4.9B) to get insights about the accuracy of this

combined method as well as the turnover rate of Rubisco at low and high N.

96

Vcmax, a25 was determined on the same or a parallel leaf of each genotype under

same treatment and developmental stage as described above under section

4.3.2.1.

Figure 4.2 A schematic diagram to illustrate the steps 1- 5 followed for the rapid

estimation of Rubisco via Western blotting using standards calibrated with [14C]CPBP.

kcat

Slope

Rubisco sites in HN1, HN2, LN1 and LN2 calculated from the standard curve based on band intensity (see Fig. 4.3 A and B).

Step 2 – Standards

(S1-S5) for Western

blotting made by

diluting HN1

Step 3 - Western

blotting with HN1,

HN2, LN1 and LN2 as

unknown samples.

Four technical

replicates per

biological replicate

Rice genotype ‘Takanari’ HN1 and HN2 - two biological replicates from 2 mM LN1 and LN2 - two biological replicates from 0.06 mM 1 cm

2 from HN1.

0.5 cm2 from HN2, LN1 and LN2.

Rubisco sites in standards (S1-S5) are known

Step 1 - [14C]CPBP

binding assay

Rubisco sites for HN1, HN2, LN1 and LN2 are known

Protein extraction using Tenbroeck homogenizer

Rubisco quantified from Western blotting

Rubisco quantified from [

14C]CPBP

binding assay

Step 4 – Test the correlation

between Rubisco quantified

from [14C]CPBP binding assay

vs. Western blotting (see Fig.

4.9 A).

Three biological replicates from Rice genotypes ‘Azucena, IR 64, Takanari’ grown at 2 mM and 0.06 mM

Protein extraction 0.5 cm

2 leaf piece

using Tissuelyzer

Step 5 – Rapid-estimation of Rubisco from unknown samples (see Fig. 4.9 B).

Western blotting with standards (S1-S5) previously made by diluting HN1 during step 2 Three technical replicates per biological replicate

Vcmax based on gas exchange measurements (see section 4.3.2.1.1)

Vcmax (µmol m

-2 s

-1)

Rubisco sites (µmol m-2

)

97

0

0.5

1

1.5

2

2.5

0 2000 4000 6000Ru

bis

co a

ctiv

e s

ites

load

ed

per

lan

e (

ng

)

Band density (Intensity * mm2)

B)

55 kDa

LSU

13.75 kDa

SSU

A)

Samples of HN1, HN2, LN1 and LN2

S1 S2 S3 S4 S5

HN1 standards

A B C D E F G H

Figure 4.3 (A) Western blot analysis for rice Rubisco extracted from frozen leaf

samples of rice genotype Takanari grown under 2 mM and 0.06 mM N supply.

Two biological replicates were selected from each N level (namely HN1, HN2

from 2 mM and LN1 and LN2 from 0.06 mM). Standard curve (S1-S5) was

prepared by a dilution series of rice Rubisco from HN1. The last eight bands

labelled by letters A-H represent samples from HN1, half dilution of HN1, HN2,

half dilution of HN2, LN1, half dilution of LN1, LN2, half dilution of LN2

respectively. The bands on top and bottom indicate the large (LSU) and small

(SSU) sub-units of Rubisco; (B) The standard curve i.e. the amount of Rubisco

sites loaded per lane (ng) from standards (S1-S5) versus the density of the

corresponding band. Standards and samples are shown by black and blue solid

circles respectively. The equation for the standards curve, y = 7.15E-08x2+6.80E-

05x. Samples A, C and E were outside the calibration range and were excluded

from the analysis.

98

4.3.2.2.3.1 Step 1 -Quantifying number of sites per m2 by [14C]CPBP Rubisco

content assay

The content of Rubisco sites (µmoles per m2) was quantified by Soumi Bala

(Research School of Biology, Australian National University, Canberra),

following the protocol previously described (Whitney and Andrews, 2001,

Galmés et al., 2013) for [14C]CPBP Rubisco content assay. Two biological

replicates of rice variety Takanari were selected from each N level (namely HN1,

HN2 from 2 mM and LN1 and LN2 from 0.06 mM). A piece of leaf (0.5 cm2 for all

except HN1, where 1 cm2 was used from HN1 as this will be the standard in

addition for being one of the samples for subsequent western blotting) was

ground with a TenBroeck glass homogenizer placed on wet ice. For each sample,

extraction buffer (1 ml per 0.5 cm2 of leaf area) was added (50 mM Bicine -

NaOH, 1 mM EDTA, 1% (w/v) PVPP, 10 mM MgCl2, 10 mM NaHCO3, 10 mM DTT,

0.01% Triton, pH 7.8) plus 10 µl of plant protease inhibitor cocktail (Sigma-

Aldrich Co, Castle Hill, NSW, Australia). The extract was transferred to an

Eppendorf tube and centrifuged (13,000 × g; 5 min; 4°C). The resulting

supernatant was transferred to a new Eppendorf tube. 50 µl of supernatant

from each sample was transferred to labelled pony vials with three technical

replicates for HN1 and one technical replicate for HN2, LN1 and LN2. The pony

vials containing samples were kept at room temperature for 30 minutes (to

activate Rubisco). Then 1 µl of [14C]CPBP was added to each sample and left at

room temperature for 5 minutes prior to placing the vials on ice awaiting

chromatography. Gel filtration chromatography was used to separate unbound

[14C]CPBP from [14C]CPBP bound to Rubisco (Whitney and Andrews, 2001,

Galmés et al., 2013). The 14C content was measured by scintillation counting as

described (Whitney and Andrews, 2001). Total protein concentration in the

extract supernatants was immediately measured with a dye binding assay

[Coomassie Plus (Bradford) Assay Kit]. A volume equivalent to one third of the

remaining extract was added to the remaining protein extract from 4 x SDS

containing 10% (v/v) beta-mercaptoethanol. Aliquots (13 µl) were made from

each sample extract, directly snap frozen in liquid nitrogen (N2) and stored at -

80 °C up until use with subsequent Western blotting.

99

4.3.2.2.3.2 Step 2 -Making bulk standards (S1-S5)

A bulk standard of HN1 was made by diluting an aliquot of HN1 using a diluent

consisting of above protein extraction buffer and 4 x SDS containing 10% (v/v)

beta-mercaptoethanol at 3:1 ratio. Standards (S1-S5) were made by mixing

above bulk standard of HN1 with diluent at a volume ratio of 500:500, 333:667,

250:750, 250:750, and 100:900 respectively. Aliquots (13 µl) were made from

each bulk standards, immediately snap-frozen in liquid N2 and stored at -80 °C.

4.3.2.2.3.3 Step 3 - Western blotting procedure

The volume need to be pipetted from each 13 µl aliquot (described in section

4.3.2.2.3.1) was calculated by considering the protein concentration in each

original sample protein extract, the amount of protein need to be loaded per

lane where the diluent (4 x SDS containing 10% (v/v) β-mercaptoethanol) was

added to make bulk sample from each up to 100 µl. Diluted samples along with

standards (S1-S5) were heated to 80 °C for 10 minutes and centrifuged (14,000

× g; 5 min; room temperature). A volume of 10 µl was loaded per lane from a

sample or standard in all gels. Proteins were separated on 4-12% NuPAGE Bis-

Tris gels (Invitrogen - Life Technologies, Carlsbad, CA, USA) according to the

manufacturer’s instructions, using the MOPS-based buffer system, and

transferred to Immobilon-P PVDF membranes (Merck Millipore, Kilsyth, Vic.,

Australia) using an XCell II Blot module (Invitrogen). Membranes were blocked

with 5% skim milk powder in Tris-buffered saline containing 0.5% Tween-20

(TBS-T) and Rubisco was detected by an antibody raised in rabbits against

tobacco Rubisco (used at 1:5000) prepared by Spencer Whitney (Research

School of Biology, Australian National University, Canberra). Secondary

antibody (goat-anti-rabbit-alkaline phosphatase conjugate, Agrisera) was

diluted 1:5000. Blots were visualized using Attophos AP fluorescent substrate

system (Promega, Madison, WI, USA) and imaged using a Versa-Doc (Bio-Rad,

Hercules, CA, USA) imaging system. Blots were analysed using Quantity One

software (Bio-Rad). The amount of sites (ng) observed in each sample lane

(HN1, HN2, LN1 and LN2) were quantified using a quadratic function fitted to

the slope of the plot of the amount of sites loaded from each standard (S1-S5)

versus corresponding band densities obtained for each standard within the

standard curve (Fig. 4.3A and B).

100

4.3.2.2.3.4 Step 4 - The correlation between observed (estimated via Western

blotting approach) versus actual (quantified via [14C]CPBP Rubisco

content assay approach) Rubisco sites loaded per lane

Next, the amount of Rubisco sites observed per lane (estimated via Western

blotting approach) was plotted against the amount of Rubisco sites loaded per

lane (quantified via [14C]CPBP Rubisco content assay approach) to confirm the

ability of Western blotting procedure to accurately estimate the amount of

Rubisco sites loaded per lane for a given sample (Fig. 4.9A).

4.3.2.2.3.5 Step 5 - Quantifying the amount of Rubisco sites loaded per lane in

unknown samples via Western blotting procedure

HN1 standards were used to quantify Rubisco sites in unknown samples

extracted from rice genotypes Azucena, IR 64 and Takanari from both 2 and

0.06 mM of N supply. There were three biological replicates and three technical

replicates per genotype and N combination. Proteins were extracted from each

0.5 cm2 piece of leaf (from all 18 samples separately) using a Tissuelyser

(instead of using the Tenbroeck glass homogenizer) as previously described in

section 4.3.2.2.3.1. Samples were ground using a 5-mm stainless steel bead in

each Eppendorf tube using a TissueLyser II (QIAGEN, Hilden, Germany) at 20 Hz

for 2 min. The amount of sites (ng) observed in each sample lane for these

unknown samples were quantified using a quadratic function fitted to the slope

of the plot of the amount of sites loaded from each standard (S1-S5) versus

corresponding band densities obtained for each standard, within the standard

curve. Thereafter, the amount of Rubisco sites per m2 in each unknown sample

was determined considering the amount of leaf area (m2) loaded per lane in the

calculation while assuming 55 kDa of molecular mass per Rubisco site. The

catalytic turnover of carboxylase (kcat) was determined by the slope of the plot,

Rubisco carboxylation rate on area basis normalized to 25°C (Vcmax, a25) versus

Rubisco sites per m2 (Fig. 4.9B) to get insights about the accuracy of this

combined method as well as the turnover rate of Rubisco at low and high N.

4.3.2.3 Estimation of N allocation in photosynthetic metabolism

Leaf chlorophyll content on area basis, Vcmax, a25 and Jmax, a25 were used to

estimate the N partitioned in three key components of photosynthesis (i.e.

pigment-protein complexes, electron transport and Rubisco). An assumption of

101

40 mol N mol-1 of chlorophyll (Evans and Seemann, 1989) was made when

calculating N allocation to pigment-protein complexes (nP). N allocation to

Rubisco (nR) was estimated from Vcmax, a25 (Harrison et al., 2009), assuming a kcat

of 3.5 mol CO2 (mol Rubisco sites)−1 s−1 at 25 °C for rice (von Caemmerer et al.,

1994). Further, I assumed that Rubisco was fully activated and mesophyll

conductance was infinite. Assumptions of 160 mol electrons (mol cytochrome

f)-1 s-1 and 8.85 mol N (mmol cytochrome f)-1 (Evans and Seemann, 1989) were

made when estimating N partitioning to electron transport components (nE)

from Jmax, a. The proportion of total leaf N allocated to each photosynthetic

component was calculated by dividing the N investment in each component by

the N content per unit leaf area. The sum of above three fractions was

considered as the total fraction of N allocated in photosynthetic apparatus (nA).

The remaining fraction (i.e. 1- nA) was considered as the fraction of N invested

in non-photosynthetic components.

4.3.3. Statistics

Data were analyzed using SPSS (version 21, Chicago, IL, USA) and tested for

normality and homogeneity of variance. Data transformations were carried out

to ensure normality and homogeneity of variance when necessary. A two way

ANOVA procedure (general linear model) was performed considering N

treatment and genotype as factors for leaf chemical, structural and gas exchange

parameters related to photosynthesis except nA, which was calculated by adding

mean values of nP, nR and nE. In situations where there was no significant N x G

interaction term, an independent sample t-test was conducted to compare traits

averaged across genotypes among two N treatments. Linear regression was

used to describe the best-fit relationship among pairs of variables.

4.4 Results

4.4.1 Effect of N supply and genotypic differences on leaf chemistry,

structure, gas exchange parameters and leaf PNUE

Table 4.1 shows values of a range of traits related to leaf chemistry, structure

and physiology of plants grown on low and high N supply, for all 10 rice

genotypes. For several traits (e.g. Ma, Na, Chlorophyll content, gs, area- and

mass-based rates of A400, Vcmax, a25, Jmax, a25) there was a significant main effect of

102

N supply, whereas for other traits (e.g. N-based rates of A400), N had no overall

effect (Table 4.2). The two way ANOVA (Table 4.2) also confirmed that there

was no significant N x G interaction term for most parameters except leaf mass

per unit area (Ma , p < 0.001) – thus, other than for Ma, the effect of N supply did

not statistically differ among the genotypes, when considering all 10 genotypes

collectively. Given this, independent sample t-tests were carried out to explore

differences between the two N treatments when averaged across all 10

genotypes. Both leaf N per unit area (Na) and chlorophyll (a+b) content

significantly (p < 0.001) reduced (by about 40 and 45% respectively) under low

N treatment (Tables 4.1 and 4.3) when considering values averaged across all

genotypes. There were genotypic differences (p < 0.001) for leaf Na (Table 4.2).

Leaf chlorophyll (a+b) content strongly correlated with leaf Na [Leaf chlorophyll

(a+b) = 8.27 + 281.527 * Na, r2 = 0.394, p < 0.001] at high N (Fig. 4.4), but not

significantly at low N.

Figure 4.4 Leaf chlorophyll (a+b) content is plotted against leaf

nitrogen per unit area (Na). Closed and open symbols represent high

and low N supply respectively. Values are means (n=3; ± SE).

Relationship between leaf Na and chlorophyll (a+b) content at 2 mM

is indicated by the solid line [Leaf chlorophyll (a+b) = 8.3 + 281.5 *

Na, r2=0.394, p < 0.001] while the non-significant relationship at 0.06

mM is indicated by the broken line.

103

Genotype

N level

mM

Leaf Na

g m-2

Leaf Ma

g m-2

Chlorophyll

a+b

µmol m-2

gs

mol m-2

s-1

Ci

ppm

Ci/ Ca

A400, a

µmol m-2

s-1

A400, m

nmol g-1

s-1

A400, N

µmol gN-1

s-1

Vcmax, a25

µmol m-2

s-1

Jmax, a25

µmol m-2

s-1

Jmax, a25

/ Vcmax ,a25

Vcmax, N25

µmol gN-1

s-1

RD, a25

/ Vcmax, a25

Takanari 2 1.60±0.02 34.2±1.1 496.0±11.7 0.75±0.10 304.8±8.5 0.79±0.02 24.7±2.4 633.5±29.8 13.0±0.4 82.3±4.5 129.5±15.5 1.68±0.19 47.3±2.4 0.008±0.001

0.06 0.88±0.08 35.3±2.9 352.1±26.4 0.63±0.17 302.9±19.1 0.80±0.02 19.9±2.0 569.0±44.9 18.0±0.8 76.0±5.0 120.2±9.1 1.58±0.05 77.9±8.5 0.012±0.002

IR-64 2 1.42±0.07 26.0±0.5 394.9±50.4 0.21±0.04 266.5±15.6 0.67±0.04 17.5±3.2 663.7±130.6 12.7±3.4 76.3±13.7 123.9±5.5 1.93±0.37 53.4±14.0 0.015±0.003

0.06 1.06±0.08 39.8±1.5 226.9±42.9 0.32±0.08 263.5±21.3 0.62±0.08 19.0±2.3 543.0±93.6 15.4±2.5 84.7±13.3 119.3±6.8 1.55±0.20 55.2.±8.8 0.011±0.002

Milyang 23 2 1.43±0.04 28.4±0.6 539.4±81.1 0.26±0.07 280.3±15.7 0.70±0.05 15.1±3.8 539.0±140.1 12.1±2.8 62.0±15.9 137.5±4.4 1.79±0.21 49.8±11.2 0.010±0.003

0.06 0.77±0.08 30.4±1.3 271.5±58.8 0.22±0.04 281.7±7.3 0.70±0.02 16.0±2.6 519.0±68.1 16.1±1.5 63.4±9.0 109.0±13.6 1.67±0.03 66.6±6.6 0.011±0.001

Opus 2 1.69±0.05 35.8±0.4 578.3±47.0 0.33±0.06 281.5±15.6 0.70±0.04 17.5±2.6 487.7±72.9 10.3±2.0 72.6±11.7 141.3±10.5 1.81±0.18 43.1±8.7 0.015±0.004

0.06 0.97±0.04 38.8±3.4 259.0±16.8 0.27±0.06 311.5±4.9 0.76±0.02 13.3±1.7 349.4±39.2 11.2±1.0 50.0±4.5 98.0±7.9 1.84±0.13 47.7±5.3 0.016±0.004

Dular 2 1.45±0.05 28.8±1.1 440.0±73.0 0.26±0.04 256.9±9.3 0.64±0.02 17.3±1.8 560.7±43.4 11.3±0.5 75.1±6.5 129.1±6.4 1.75±0.09 50.4±2.0 0.012±0.002

0.06 0.86±0.05 32.8±1.5 194.8±22.0 0.21±0.04 292.8±14.0 0.73±0.03 13.7±2.4 342.9±34.6 12.2±0.9 54.6±8.0 92.4±8.1 1.71±0.04 50.1±1.7 0.016±0.001

Bg 34-8 2 1.67±0.14 35.5±0.9 424.3±7.3 0.79±0.02 307.5±7.8 0.77±0.02 24.8±3.5 785.4±42.2 17.1±1.3 94.8±11.3 149.2±13.2 1.48±0.01 55.2±8.4 0.008±0.001

0.06 1.08±0.09 43.9±2.7 221.5±7.0 0.46±0.10 310.0±8.5 0.77±0.02 17.1±2.1 392.9±49.5 13.1±0.8 63.8±8.0 112.8±11.5 1.80±0.09 48.4±1.8 0.014±0.001

Koshihikari 2 1.71±0.08 35.4±1.5 471.8±36.9 0.39±0.13 303.3±18.1 0.76±0.05 14.7±3.6 405.6±103.1 9.0±2.4 56.9±13.0 120.3±3.0 1.91±0.32 34.8±8.8 0.015±0.003

0.06 0.80±0.05 33.6±2.7 264.0±30.5 0.21±0.05 297.3±9.3 0.74±0.02 10.1±1.5 309.6±44.5 10.0±1.7 40.5±5.5 68.7±0.0 1.97±0.11 41.1±6.7 0.024±0.004

Akihikari 2 1.61±0.10 32.3±1.2 407.9±26.0 0.47±0.08 275.7±10.7 0.69±0.03 24.3±1.0 750.5±58.7 15.3±1.2 98.5±7.5 151.4±9.7 1.56±0.08 61.2±3.9 0.008±0.001

0.06 0.84±0.01 38.4±0.6 264.4±3.4 0.33±0.10 295.3±10.8 0.74±0.03 13.9±3.0 369.9±75.8 11.3±2.1 52.8±9.8 88.0±0.0 1.63±0.08 45.4±5.9 0.014±0.003

Azucena 2 1.80±0.13 33.2±0.9 506.8±90.5 0.35±0.04 271.4±2.8 0.68±0.01 20.3±1.0 592.8±42.0 10.6±0.2 84.4±4.3 128.7±9.5 1.52±0.06 44.3±2.1 0.010±0.001

0.06 1.18±0.13 46.1±1.6 289.6±36.1 0.35±0.12 265.5±21.7 0.66±0.05 16.6±3.4 367.2±80.9 11.2±3.6 68.9±9.2 113.8±12.9 1.59±0.02 50.7±10.4 0.015±0.002

Nipponbare 2 1.37±0.11 30.2±0.6 442.7±31.0 0.30±0.05 271.5±5.1 0.71±0.02 22.0±3.7 738.3±133.5 17.7±4.2 88.1±13.0 123.3±26.0 1.21±0.20 69.2±15.7 0.010±0.001

0.06 0.83±0.07 36.8±1.2 222.6±47.4 0.31±0.11 303.1±11.3 0.76±0.03 13.1±2.4 384.9±67.2 11.8±1.9 54.2±10.2 98.4±0.0 1.95±0.14 46.9±10.2 0.012±0.002

Mean of all 10 G 2 1.57±0.05 32.0±1.1 470.2±18.8 0.41±0.06 281.9±5.5 0.71±0.01 19.8±1.2 615.7±38.7 12.9±0.9 79.1±4.2 133.4±3.5 1.66±0.07 50.9±3.1 0.011±0.001

0.06 0.93±0.04 37.6±1.6 256.6±14.0 0.33±0.04 292.4±5.4 0.73±0.02 15.3±0.9 414.8±29.3 13.0±0.8 60.9±4.2 102.1±5.1 1.73±0.05 53.0±3.5 0.014±0.001

Mean of three G 2 1.48±0.06 29.5±2.5 476.8±42.8 0.41±0.17 283.9±11.2 0.72±0.04 19.1±2.9 612.1±37.6 12.6±0.3 73.5±6.0 130.3±4.0 1.80±0.07 50.2±1.8 0.011±0.002

(Takanari, IR 64 and Milyang 23) 0.06 0.90±0.08 35.2±2.7 283.5±36.7 0.39±0.12 282.7±11.4 0.71±0.05 18.3±1.2 543.7±14.4 16.5±0.8 74.7±6.2 116.1±3.6 1.60±0.03 66.5±6.5 0.011±0.001

Mean of other seven G 2 1.61±0.06 33.0±1.0 467.4±22.2 0.41±0.07 281.1±6.9 0.71±0.02 20.1±1.4 617.3±54.8 13.1±1.3 81.5±5.4 134.8±4.7 1.61±0.09 51.2±4.4 0.011±0.001

0.06 0.94±0.06 38.6±1.9 245.1±12.4 0.30±0.03 296.5±5.8 0.74±0.01 14.0±0.9 359.6±10.7 11.5±0.4 55.0±3.5 96.0±5.8 1.79±0.06 47.2±1.2 0.016±0.001

Abbreviation: leaf Na = leaf N per unit area, leaf Ma = leaf mass per unit leaf area, gs= stomatal conductance for CO2 diffusion in the leaf measured at 400 µmol mol-1

atmospheric [CO2]

per unit area, Ci=intercellular CO2 partial pressure measured at 400 µmol mol-1

atmospheric [CO2] per unit area, Ci/ Ca = the ratio between intercellular and atmospheric (400 ppm)

partial pressures, A400, a = light-saturated net photosynthesis measured at 400 µmol mol-1

atmospheric [CO2] per unit area, A400, m = light-saturated net photosynthesis measured at 400

µmol mol-1

atmospheric [CO2] per unit mass, A400, N = light-saturated net photosynthesis measured at 400 µmol mol-1

atmospheric [CO2] per unit leaf N, Vcmax, a25

= maximum

carboxylation velocity of Rubisco per unit area normalised to 25°C, Jmax, a25

= maximum rate of electron transport per unit area normalised to 25°C, Jmax, a25

/ Vcmax, a25

= ratio of maximum

rate of electron transport over maximum carboxylation velocity of Rubisco, both normalised to 25°C, Vcmax, N25

= ratio of maximum carboxylation velocity of Rubisco normalised to 25°C

per unit leaf N, RD, a25

/ Vcmax, a25

= ratio of leaf dark respiration per unit area normalised to 25°C (RD, a25

) to Vcmax, a25

. Values are mean (n=3, 4, 5 or 6) ± SE. A two-way ANOVA was carried

out to test any interaction term between N levels and genotypes (see table 4.2). Genotypic differences for A400, a, A400, m, A400, N and Vcmax, a25

, Jmax, a25

, Jmax, a25

/ Vcmax, a25

are further

illustrated in Figures 4.5 and 4.6 respectively.

Table 4.1 Leaf chemical, structural and gas exchange characteristics of 10 genotypes of rice under high and low N supply

104

Although not statistically significant, there was about 20% reduction in

stomatal conductance (gs) under low N compared to high N grown plants

(Tables 4.1 and 4.3). Significant (p < 0.001) differences were also found among

genotypes for gs (Table 4.2). Despite these differences in gs, neither Ci nor the

ratio between Ci and atmospheric partial pressures (Ci/Ca) was influenced by N

supply (Table 4.3), suggesting that declines in gs were matched by a decline in

photosynthetic capacity.

Table 4.2 Results of two-way ANOVA for variables presented in Tables 4.1 and

4.4. F-statistics, degrees of freedom (df) and significance of each factor and

their interactions are presented.

N levels Genotypes N levels*Genotypes p value for

N levels *

Genotypes

interaction

df 1 9 9

Leaf Na (g m-2

) 259.479*** 4.005*** 1.372 0.220

Leaf Ma (g m-2

) 52.081*** 7.974*** 4.190*** 0.000

Chlorophyll a+b (µmol m-2

) 115.991*** 2.026 0.767 0.647

gs (mol m-2

s-1

) 4.208* 5.833*** 0.847 0.575

Ci (ppm) 2.776 2.363* 0.723 0.686

Ci/ Ca 2.812 2.901** 0.582 0.809

A400, a (µmol m-2

s-1

) 14.342*** 2.273* 0.997 0.448

A400, m (nmol g-1

s-1

) 31.782*** 2.236* 1.460 0.175

A400, N (µmol gN-1

s-1

) 0.017 1.620 1.302 0.254

Vcmax, a25

(µmol m-2

s-1

)

16.142*** 2.104* 1.383 0.207

Jmax, a25

(µmol m-2

s-1

)

38.879*** 1.459 1.424 0.189

Jmax, a25

/ Vcmax, a25

0.799 1.081 1.583 0.133

Vcmax, N25

(µmol gN-1

s-1

)

2.073 1.466 0.563 0.822

RD, a25

/ Vcmax, a25

2.552 1.897 1.236 0.284

nP 10.797** 1.361 1.210 0.316

nR 1.716 1.515 0.497 0.871

nE 5.761* 1.521 1.208 0.305

Abbreviation: leaf Na = leaf N per unit area, leaf Ma = leaf mass per unit leaf area, gs= stomatal

conductance for CO2 diffusion in the leaf measured at 400 µmol mol-1

atmospheric [CO2] per unit area,

Ci=intercellular CO2 partial pressure measured at 400 µmol mol-1

atmospheric [CO2] per unit area, Ci/ Ca

= the ratio between intercellular and atmospheric (400 ppm) partial pressures, A400, a = light-saturated net

photosynthesis measured at 400 µmol mol-1

atmospheric [CO2] per unit area, A400, m = light-saturated net

photosynthesis measured at 400 µmol mol-1

atmospheric [CO2] per unit mass, A400, N = light-saturated net

photosynthesis measured at 400 µmol mol-1

atmospheric [CO2] per unit leaf N, Vcmax, a25

= maximum

carboxylation velocity of Rubisco per unit area normalised to 25°C, Jmax, a25

= maximum rate of electron

transport per unit area normalised to 25°C, Jmax, a25

/ Vcmax, a25

= ratio of maximum rate of electron

transport over maximum carboxylation velocity of Rubisco, both normalised to 25°C, Vcmax, N25

= ratio of

maximum carboxylation velocity of Rubisco normalised to 25°C per unit leaf N, RD, a25

/ Vcmax, a25

= ratio of

leaf dark respiration per unit area normalised to 25°C (RD, a25

) to Vcmax, a25

, nP = fraction of leaf N in

pigment-protein complexes, nR = fraction of leaf N in Rubisco and nE = fraction of leaf N in electron

transport. *p < 0.05, **p < 0.01, ***p < 0.001. Statistics were not performed for nA as that was calculated

using mean values of nP, nR and nE. If above N x G interaction was non-significant, an independent

samples t-test was carried out to determine any significant difference among N levels.

105

There were genotypic variations (Table 4.2) in Ci (p < 0.05) and Ci/Ca (p <

0.01). When considering individual genotypes or averaged across all 10

genotypes, A400, a and A400, m declined at low N (Fig. 4.5A, B and Tables 4.1 and

4.3). Genotypic differences (Table 4.2) were also found for A400, a, (p < 0.05) and

A400, m, (p < 0.05). Averaged across all 10 genotypes, A400, N was unaffected by N

supply (Fig. 4.5C, Tables 4.1 and 4.3). No correlation was found between A400, N

and leaf Na (Fig. 4.7E). Taken together, these observations suggest that, when

Figure 4.5 Bar graphs showing genotypic variation in light-saturated net

photosynthesis measured at 400 µmol mol-1 atmospheric [CO2] (A) per

unit area (A400, a); (B) per unit mass (A400, m); (C) per unit N (A400, N) under

high and low N supply (n=3-6; ± SE). The values are given in Table 4.1.

106

averaged across all 10 genotypes, there were relatively consistent responses to

low N supply.

Table 4.3 An independent samples t-test was carried out to determine if there

were significant differences for chemical, structural and gas exchange

parameters among N levels at each genotype group [i.e. all 10G, the average of

three genotypes (3G) that maintained growth and NP at low N (Chapter three)

and other seven genotypes (7G)] and among 3G and 7G at each N treatment.

Parameter

All 10 G 3G Other 7 G 2 mM 0.06 mM

differences

among 2 and

0.06 mM

differences

among 2 and

0.06 mM

differences

among 2 and

0.06 mM

differences

among 3G

and other 7G

differences

among 3G

and other 7G

Leaf Na

(g m-2

)

*** ** *** n.s. n.s.

Chlorophyll a+b

(µmol m-2

)

*** * *** n.s. n.s.

gs

(mol m-2

s-1

)

n.s. n.s. n.s. n.s. n.s.

Ci

(ppm)

n.s. n.s. n.s. n.s. n.s.

Ci/ Ca

n.s. n.s. n.s. n.s. n.s.

A400, a

(µmol m-2

s-1

)

** n.s. ** n.s. *

A400, m

(nmol g-1

s-1

)

*** n.s. ** n.s. ***

A400, N

(µmol gN-1

s-1

)

n.s. ** n.s. n.s. ***

Vcmax, a25

(µmol m-2

s-1

)

** n.s. *** n.s. *

Jmax, a25

(µmol m-2

s-1

)

*** n.s. (0.057) *** n.s. n.s. (0.066)

Jmax, a25

/ Vcmax, a25

n.s. n.s. (0.069) n.s. n.s. n.s. (0.078)

Vcmax, N25

(µmol gN-1

s-1

)

n.s. n.s. (0.073) n.s. n.s. **

RD, a25

/ Vcmax, a25

n.s. n.s. * n.s. n.s. (0.055)

nP * n.s. * n.s. n.s.

nR n.s. n.s. (0.091) n.s. n.s. **

nE n.s. * n.s. n.s. **

nA n.s. n.s. n.s. n.s. *

107

Abbreviation: leaf Na = leaf N per unit area, leaf Ma = leaf mass per unit leaf area, gs= stomatal conductance for

CO2 diffusion in the leaf measured at 400 µmol mol-1

atmospheric [CO2] per unit area, Ci=intercellular CO2

partial pressure measured at 400 µmol mol-1

atmospheric [CO2] per unit area, Ci/ Ca = the ratio between

intercellular and atmospheric (400 ppm) partial pressures, A400, a = light-saturated net photosynthesis measured

at 400 µmol mol-1

atmospheric [CO2] per unit area, A400, m = light-saturated net photosynthesis measured at

400 µmol mol-1

atmospheric [CO2] per unit mass, A400, N = light-saturated net photosynthesis measured at 400

µmol mol-1

atmospheric [CO2] per unit leaf N, Vcmax, a25

= maximum carboxylation velocity of Rubisco per unit

area normalised to 25°C, Jmax, a25

= maximum rate of electron transport per unit area normalised to 25°C, Jmax,

a25

/ Vcmax, a25

= ratio of maximum rate of electron transport over maximum carboxylation velocity of Rubisco,

both normalised to 25°C, Vcmax, N25

= ratio of maximum carboxylation velocity of Rubisco normalised to 25°C

per unit leaf N, RD, a25

/ Vcmax, a25

= ratio of leaf dark respiration per unit area normalised to 25°C (RD, a25

) to Vcmax,

a25

, nP = fraction of leaf N in pigment-protein complexes, nR = fraction of leaf N in Rubisco, nE = fraction of leaf

N in electron transport and nA = total fraction of leaf N invested in photosynthetic metabolism. *p < 0.05, **p

< 0.01, ***p < 0.001, n.s. - non-significant. p value is shown within brackets in situations where the significance

is marginal.

108

Given the above findings, how do biochemical activities underpinning A

change in response to low N and across genotypes? There was genotypic

variation for maximum carboxylation velocity of Rubisco per unit area

normalised to 25°C (Vcmax, a25, p < 0.05); however, no significant differences were

found for the maximum rate of electron transport per unit area normalised to

Table 4.4 Average values for the fractions of N partitioned to photosynthesis

in 10 genotypes of rice under high and low N supply

Genotype

N level

(mM)

nA

nP

nR

nE

Takanari 2 0.36 0.15 ± 0.01 0.15 ± 0.01 0.05 ± 0.00

0.06 0.50 0.16 ± 0.02 0.24 ± 0.03 0.09 ± 0.01

IR-64 2 0.39 0.15 ± 0.03 0.17 ± 0.04 0.07 ± 0.00

0.06 0.35 0.10 ± 0.02 0.17 ± 0.03 0.08 ± 0.01

Milyang 23 2 0.42 0.19 ± 0.02 0.16 ± 0.04 0.07 ± 0.00

0.06 0.45 0.15 ± 0.03 0.21 ± 0.02 0.09 ± 0.01

Opus 2 0.37 0.18 ± 0.01 0.14 ± 0.03 0.06 ± 0.00

0.06 0.34 0.12 ± 0.00 0.15 ± 0.02 0.07 ± 0.00

Dular 2 0.37 0.14 ± 0.01 0.16 ± 0.01 0.07 ± 0.00

0.06 0.35 0.12 ± 0.01 0.16 ± 0.01 0.07 ± 0.01

Bg 34-8 2 0.40 0.16 ± 0.00 0.17 ± 0.03 0.07 ± 0.01

0.06 0.35 0.13 ± 0.01 0.15 ± 0.01 0.07 ± 0.01

Koshihikari 2 0.33 0.16 ± 0.00 0.11 ± 0.03 0.06 ± 0.00

0.06 0.32 0.13 ± 0.02 0.13 ± 0.02 0.06 ± 0.01

Akihikari 2 0.39 0.12 ± 0.02 0.19 ± 0.01 0.08 ± 0.01

0.06 0.35 0.14 ± 0.01 0.14 ± 0.02 0.07 ± 0.01

Azucena 2 0.35 0.16 ± 0.02 0.14 ± 0.01 0.05 ± 0.00

0.06 0.39 0.16 ± 0.02 0.16 ± 0.03 0.07 ± 0.01

Nipponbare 2 0.47 0.17 ± 0.01 0.22 ± 0.05 0.08 ± 0.02

0.06 0.36 0.13 ± 0.03 0.15 ± 0.03 0.08 ± 0.01

Mean of all 10 G 2 0.38 ± 0.01 0.16 ± 0.01 0.16 ± 0.01 0.07 ± 0.00

0.06 0.38 ± 0.02 0.13 ± 0.01 0.17 ± 0.01 0.07 ± 0.00

Mean of three G 2 0.39 ± 0.02 0.16 ± 0.01 0.16 ± 0.01 0.07 ± 0.01

(Takanari, IR 64 and Milyang 23) 0.06 0.43 ± 0.04 0.13 ± 0.02 0.21 ± 0.02 0.09 ± 0.00

Mean of other seven G 2 0.38 ± 0.02 0.16 ± 0.01 0.16 ± 0.01 0.07 ± 0.00

0.06 0.35 ± 0.01 0.13 ± 0.01 0.15 ± 0.00 0.07 ± 0.00

Abbreviation: nA = total fraction of leaf N invested in photosynthetic metabolism, nP = fraction of leaf N

in pigment-protein complexes, nR = fraction of leaf N in Rubisco, nE = fraction of leaf N in electron

transport. Values are mean (n=3, 4, 5 or 6) ± SE. A two-way ANOVA was carried out to test any

interaction term between N levels and genotypes (see table 4.2).

109

25°C (Jmax, a25) (Fig. 4.6 A and B, Table 4.2). Averaged across all 10 genotypes,

Jmax, a25 and Vcmax, a25 were significantly (p < 0.001 and p < 0.01 respectively)

reduced by N deficiency (Table 4.3). Jmax, a25 and Vcmax, a25 strongly correlated

with leaf Na where relationships were: Jmax, a25 = 40.1 + 56.2 * Na (r2 = 0.513, p <

0.001) and Vcmax, a25 = 24.1 + 32.1 * Na (r2 = 0.207, p < 0.001) (Fig. 4.7A and B).

Importantly, these relationships were held only when data of both N treatments

were pooled together. The lack of correlation between Vcmax, a25 and leaf Na

within a given N treatment suggests that the efficiency of N use in carboxylation

varies among genotypes within a given N treatment. Despite this, a two-way

ANOVA found no significant differences among genotypes or N levels in the

maximum carboxylation velocity of Rubisco per unit N normalised to 25°C

(Vcmax, N25) (Tables 4.1, 4.2 and 4.3, Fig. 4.6D, 4.7D and 4.8). The ratio of Jmax,

a25/Vcmax, a25 remained largely constant across genotypes and N treatments (Fig.

4.6C, Tables 4.1, 4.2 and 4.3) and no correlation was found with leaf Na (Fig. 4.7

C). The ratio of leaf dark respiration per unit area normalised to 25°C (RD, a25) to

Vcmax, a25 (i.e. RD, a25/ Vcmax, a25) remained largely constant across genotypes and N

treatments (Tables 4.1, 4.2 and 4.3).

Taken together, the above results suggest stomata partially closed in

response to low N when average across all genotypes. Yet, constancy of Ci/Ca

was maintained by matching ~20% reduction in gs with proportionally a similar

reduction in Vcmax, a25 (Tables 4.1 and 4.3). This indicates a synchrony between

reduced CO2 supply and the demand i.e. the reduced photosynthetic capacity

under low N. Although the percentage change was not identical, there was a

concomitant reduction in Vcmax, a25 and Jmax, a25 along with leaf Na. Accordingly;

these results indicate that A and its underlying components were down-

regulated to a similar extent at low N.

110

Figure 4.6 Bar graphs presenting (A) maximum rate of electron transport

per unit area normalised to 25°C ( Jmax, a25); (B) maximum carboxylation

velocity of Rubisco per unit area normalised to 25°C (Vcmax, a25); (C) Jmax,

a25:Vcmax, a

25 ratio (both normalised to 25°C) and (D) maximum carboxylation

velocity of Rubisco per unit leaf N normalised to 25°C (Vcmax, N25) for 10

genotypes of rice under high and low N supply (n=3-6; ± SE). The values are

given in Table 4.1.

111

Figure 4.7 Relationships between N per unit leaf area (Na) and (A) maximum

rate of electron transport normalised to 25°C on area basis ( Jmax, a25) ; (B)

maximum carboxylation velocity of Rubisco on area basis normalised to 25°C

(Vcmax, a25) ; (C) Jmax, a

25:Vcmax, a25 ratio (both normalised to 25°C); (D) maximum

carboxylation velocity of Rubisco on leaf N basis normalised to 25°C (Vcmax, N25)

and (E) light-saturated net photosynthesis measured at 400 µmol mol-1

atmospheric [CO2] on N basis (A400, N). Closed and open symbols represent high

and low N supply respectively. Colour codes for genotypes are as given in Figure

4.4 (n=3-6; ± SE). Relationships between leaf Na and Jmax, a25 [Jmax, a

25 = 40.095 +

56.191 * Na, r2=0.513, p < 0.001] and leaf Na and Vcmax, a

25 [Vcmax, a25 = 24.081 +

32.101 * Na, r2=0.207, p < 0.001] are indicated by solid lines.

112

The absence of any significant N supply-mediated changes in Vcmax, N25

was a surprise, as the reduction in Vcmax, a25 (~20%) was markedly less than the

decrease in leaf Na (~40%) at low N (Fig. 4.7B, Tables 4.2 and 4.3). Thus, while

N-mediated changes in Vcmax, N25 were not significant, the possibility remains

that N supply did affect N allocation. For example, plants might reallocate N to

maintain photosynthetic capacity at low N treatment. This could have been

achieved by investing relatively more N in key components of photosynthesis

(e.g. Rubisco). Given that, I explored whether the fraction of N in each

photosynthetic component [i.e. Rubisco (nR), electron transport (nE) and

pigment-protein complexes (nP)] vary across N treatments and genotypes.

According to a two-way ANOVA, there was no G x N interaction or statistically

significant genotypic differences for any of above fractions (Table 4.2). N

allocation patterns of 10 genotypes on average did not significantly change

across N treatments (Table 4.3), except the reduction (p < 0.01) in the fraction

of N invested in pigment protein complexes (Table 4.3). There was also no

difference among high and low N grown plants in the total fraction of leaf N

allocated to photosynthesis (nA; Fig. 4.8B, Table 4.4) across all 10 genotypes.

Collectively, these results suggest there was no significant difference among 10

genotypes for their patterns of N partitioning to different components of

photosynthesis except the reduction of the fraction of N invested in pigment

protein complexes at low N.

Finally, the data from the present study was in agreement with the global

relationship identified based on Glopnet database (Hikosaka, 2004, Wright et al.,

2004) for photosynthetic rate per unit leaf N against Ma as indicated by the

solid line in Figure 4.8C. Importantly, N supply did not significantly change the

relationship between Vcmax, N25 and Ma.

113

Figure 4.8 The relationships between leaf mass per unit leaf area, Ma and (A) leaf N

per unit area, Na . The solid lines in Figure 4.9A indicate the relationship between leaf

Ma and leaf Na (0.06 mM) [Leaf Na = 0.444 + 0.013 * Ma, r2=0.251, p < 0.001] at low N

and [Leaf Na = 0.120 + 0.045 * Ma, r2=0.539, p < 0.001] at high N; (B) total fraction of

leaf N invested in photosynthetic metabolism, nA (C) maximum carboxylation velocity

of Rubisco on leaf N basis normalised to 25°C (Vcmax, N25). The solid line in Figure 4.9C

represents the global relationship between photosynthetic rate per unit leaf N and Ma

(Hikosaka, 2004, Wright et al., 2004), where the relationship for Vcmax, N25 at any given

Ma calculated based on the equations given in Harrison et al. (2009). Colour codes for

genotypes are as given in Figure 4.4.

114

4.4.2 Rapid estimation of Rubisco via Western blotting using

standards pre-determined with [14C]CPBP Rubisco content

assay

As part of the methodology, I explored the possibility of quantifying Rubisco by

using Polyacrylamide gel electrophoresis (PAGE) and Western blotting together

with Rubisco standards calibrated with [14C]CPBP. This approach could be more

rapid which would then enable more estimates of Rubisco than would be

possible using the [14C]CPBP assay. The ability of Western blotting procedure to

accurately estimate the amount of Rubisco of a given sample was confirmed by

the strong significant correlation (Rubisco via WB = 1.11 * Rubisco via CPBP,

r2=0.89, p < 0.001) between the amount of Rubisco estimated via Western

blotting approach vs. the amount of Rubisco quantified via [14C]CPBP Rubisco

content assay (Fig. 4.9A). The Western blot approach was then used to quantify

Rubisco in unknown samples of Azucena, IR 64 and Takanari from both high

and low N treatments. Plotting Vcmax, a25 taken from gas exchange measurements

for each sample against the estimated Rubisco sites per m2 reveals the catalytic

turnover of carboxylase (kcat) from the slope (Fig. 4.9B). The line is drawn to

illustrate a value of kcat of 3.5 mol CO2 (mol Rubisco sites)−1 s−1. If we first

consider the Takanari data obtained by grinding the leaf with a Tenbroeck glass

homogenizer, kcat values of 2.55 and 3.97 mol CO2 (mol Rubisco sites)−1 s−1 at 25

°C were estimated for high and low N respectively. Since Vcmax, a25 values were

similar regardless of N treatment; this implies that Rubisco activation state may

have been reduced in the HN treatment. Secondly, Rubisco contents determined

on extracts made using the Tissuelyser were consistently lower than those

extracted with the Tenbroeck homogenizer.

115

Figure 4.9 (A) The amount of Rubisco sites observed per lane estimated

from Western blotting (WB) approach versus actual amount of Rubisco

sites loaded per lane quantified from [14C]CPBP binding assay (CPBP)

[Rubisco via WB = 1.11 * Rubisco via CPBP, r2=0.89, p < 0.001], each data

point represents a sample (see section 4.3.2.2.3.4); (B) Rubisco

carboxylation rate per unit leaf area normalized to 25°C (Vcmax, a25) versus

Rubisco sites per m2. Closed and open symbols represent high and low N

supply respectively. (n=3; ± SE).

116

4.5 Discussion

The present study investigated the extent to which leaf-level photosynthetic N

use efficiency (PNUE) of 10 selected rice genotypes varies in response to N

availability during early vegetative growth. Both leaf N per unit area (Na) and

chlorophyll (a+b) content reduced by about 40 and 45% respectively under low

N treatment when considering values averaged across all genotypes.

Photosynthesis (A) and its underlying components were down-regulated to a

similar extent at low N while maintaining a relatively constant Jmax, a25/Vcmax, a25

ratio. Across all 10 genotypes, there was no genotypic-N interaction term or

genotypic variation for PNUE. Further, genotypic differences were not found for

patterns of N partitioning to different components of photosynthesis except the

reduction of the fraction of N invested in pigment protein complexes at low N.

The relationship between Vcmax,N25 and leaf mass per unit area (Ma) was not

changed by the N supply. The ratio of leaf dark respiration per unit area

normalised to 25°C (RD, a25) to Vcmax, a25 (RD, a25/ Vcmax, a25) remained largely

constant across genotypes and N treatments. The lack of N-mediated shifts in N

allocation and PNUE (expressed either as N-based rates of net photosynthesis or

Rubisco activity), was unexpected, given the results of Chapter 3. In subsequent

sections, I discuss what is known about the effects of N supply on

photosynthesis and N allocation, and what factors might contribute to the lack

of significant N-mediated phenotypes in the current chapter.

4.5.1 N mediated changes in chemical and gas exchange properties

Leaf nitrogen is known as a major factor influencing leaf photosynthetic

capacity where area-based rates of A correlated with leaf Na in rice (Yoshida and

Coronel, 1976, Ohsumi et al., 2007). Further, about 75% of leaf N was invested

in the chloroplast (Stocking and Ongun, 1962). Both rice (Makino et al., 1994)

and wheat (Evans, 1983) are known to exhibit curvilinear responses of area-

based rates of A versus Na indicating that PNUE declines as leaf Na increases.

When plants were supplied with lower N concentrations, both leaf Na and

chlorophyll (a+b) content were reduced in all genotypes, indicating a reduced

protein complement at low N consistent with past studies (Evans and

Terashima, 1987, Ghannoum et al., 2005). Conversely, greater N supply

117

increases leaf N content (Ookawa et al., 2004, Hirasawa et al., 2010b, Yamori et

al., 2011). Further, leaf chlorophyll (a+b) content correlates with leaf Na (Evans,

1989, Ripullone et al., 2003, Pons and Westbeek, 2004), only at high N

treatment in the present study. While chlorophyll does not contain much N (4

mol N mol-1 chlorophyll), it cannot exist in the leaf unless it is complexed into a

protein (Evans, 1989). Pigment-protein complexes accounted for 15% of leaf N

in my study, indicating that such complexes represent one of the larger pools of

N in a leaf. There were genotypic differences for leaf Na as previously reported

by Hirasawa et al. (2010b), with IR 64, BG 34-8 and Azucena in the present

study being able to maintain a greater leaf Na at low N (Table 4.1) compared

with others. Thus, overall, both leaf Na and chlorophyll (a+b) content reduced

under low N treatment when considering values averaged across all 10

genotypes.

The magnitude of stomatal opening and their density influence CO2

diffusion to the interior of the leaf and this can further limit photosynthesis.

Leaves produced under the low N treatment had reduced stomatal conductance

(gs) compared to leaves grown under high N supply. Several past studies

(Yoshida and Coronel, 1976, Sage and Pearcy, 1987, Hirasawa et al., 2010b)

reported a strong positive linear relationship between leaf Na and gs owing to

the linear relationship between photosynthesis and leaf Na and gs and

photosynthesis (Sage and Pearcy, 1987, Wong et al., 1979, Yoshida and Coronel,

1976). Consistent with Makino (2011), stomatal conductance varied among the

10 selected rice genotypes used in my study. For instance, Takanari was able to

maintain higher gs than Koshihikari, in agreement with past studies (Takai et al.,

2013, Hirasawa et al., 2010b, Ohsumi et al., 2007). In the present study, Ci/Ca

remained mostly constant (~ 0.6-0.7) across genotypes and N supply, as

previously been reported (Zhu et al., 2010). A relatively constant Ci/Ca of

present study was achieved by reducing the demand for CO2 [i.e. RuBP

carboxylation (Vcmax, a25)] to match the ~20% reduction in CO2 supply when

stomata are partially closed under low N conditions. The genotypic differences

found for Vcmax, a25 during present study were, however, in contrast to Makino et

al. (1987) who found no variation in kinetic properties of Rubisco among rice

varieties. Along with Vcmax, a25, the maximum rate of RuBP regeneration (Jmax, a25)

118

decreased when decreasing leaf Na (or vice versa) in agreement with Yamori et

al. (2011). Both Vcmax, a25 and Jmax, a25 strongly correlated with leaf Na across N

treatments indicating the role of N as a key constituent of chloroplastic stromal

enzymes and thylakoid proteins (Evans and Terashima, 1988, Terashima and

Evans, 1988, Evans, 1989). Collectively, a reduction in area- and mass-based A

was observed under N deficiency.

The ratio of Jmax, a25 to Vcmax, a25 remained largely constant across all

genotypes and N treatments during present study. Several studies (Thompson

et al., 1992, Wullschleger, 1993, Crous et al., 2008, Warren et al., 2003) have

provided evidence that the balance between above two components is

maintained across a wide range of species and growth conditions (e.g. nitrogen

& light). By contrast, others (Mächler et al., 1988, Yamori et al., 2011, Evans and

Terashima, 1987) suggest that photosynthesis is limited by RuBP carboxylation

rather RuBP regeneration under N deficiency. One key factor responsible for a

change in Jmax, a25 to Vcmax, a25 ratio, where it occurs, is N partitioning to enzymes

involved in those two components (Yamori et al., 2011), where optimum

partitioning of N is crucial (Evans, 2013) to maintain the efficiency of

photosynthesis. When averaged across 10 genotypes, N partitioning patterns to

photosynthesis and within photosynthetic apparatus did not change with N

supply, except a reduction in N partitioning to pigment protein complexes at

low N (Tables 4.3 and 4.4). Thus, these results further support a constant ratio

of Jmax, a25 to Vcmax, a25.

In the present study, I made the assumption that mesophyll conductance

was not limiting. However, past work by Chen et al. (2014) has reported some

degree of limitation by mesophyll conductance. This would mean that the CO2

concentration in the chloroplast (Cc) is not actually equal to intracellular CO2

concentration (Ci) but, remains somewhat less. Based on a study across a broad

range of tropical and temperate species, Bahar (2016) found that as long as we

use the correct constants for each and the mesophyll conductance is not

severely limiting there is no difference to the Vcmax estimate. Thus, there are two

sets of kinetic constants. Firstly, there are Kc and Ko values (Von Caemmerer,

2000) which often recommended when making the assumptions Ci= Cc and

infinite conductance. Secondly, if one makes the assumption that mesophyll

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conductance is limiting then there will be an estimate of that mesophyll

conductance thus, required to use different Kc and Ko values accordingly. Thus, it

is unlikely, the estimates of Vcmax are going to change dramatically unless the

mesophyll conductance was extremely low and becomes a severely limiting

factor. Thus, the main criterion is using appropriate kinetic constants when

following different modeling procedures.

Past studies on large scale climate and crop models often consider a

balance between carbon (C) loss and gain, and assume this ratio (i.e. RD, a25/

Vcmax, a25) to be constant irrespective of plant age, growth rate, species and

environmental variations (Gifford, 1994, Gifford, 2003). However, this ratio is

not always constant. For instance, Atkin et al. (2015) shows plants growing in

cold places have a much higher ratio of C loss per unit C gain than plants grown

in warmer places. In a study with wheat, the above balance remained largely

constant irrespective of atmospheric CO2 concentration and temperature while

slightly increased under N deficiency (Gifford, 1995). In my study, I have

explored the extent to which this ratio varies with N supply and across 10 rice

genotypes which differ in their growth rates and NP. There was no evidence for

statistically significant differences among genotypes, across N treatments or

genotype-N interaction (Tables 4.1, 4.3 and 4.4). Thus, the present results agree

with assuming a constant ratio for the balance between C gain and loss when

modelling rice in relation to N supply.

4.5.2 N mediated changes in leaf PNUE and N partitioning to

photosynthesis and within the photosynthetic apparatus

Averaged across 10 genotypes there was no genotype-N interaction term or

genotypic variation for photosynthetic N use efficiency (PNUE) in agreement

with Hikosaka (2004) who concluded that variation in PNUE within a species to

be smaller compared with interspecific variation. These results suggest that the

variation in leaf-level PNUE is not what is contributing to the genotypic

variation observed for NP at whole-plant level. This can be due to relatively

small differences that exist among genotypes which are hardly distinguished by

statistical methods. That said, if one compares the three genotypes (3G, i.e.

Takanari, IR 64 and Milyang 23) that exhibited enhanced NP and faster growth

at low N (Chapter three) with the seven genotypes (7G) that did not exhibit

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enhanced NP, one does see differences in PNUE and associated traits. For

example, when comparing 3G with 7G, 3G maintained higher Vcmax, a25 at low N

compared with 7G, while Vcmax, N25 was greater in 3G than 7G (p < 0.01) (Tables

4.1 and 4.3). Further, these 3G appeared to have a greater (p < 0.001) A on N

basis (A400, N) compared with their counterparts at low N (Fig. 4.5C, Tables 4.1

and 4.3). Whilst there was no difference in N partitioning pattern among those

3G with 7G at high N, these 3G allocated relatively a greater fraction of N in

photosynthesis (i.e. Rubisco and electron transport components) at the expense

on non-photosynthetic components (Fig. 4.10, Tables 4.3 and 4.4). By contrast,

the other 7G allocated 3% more N to non-photosynthetic components at the

expense of N in pigment protein complexes and Rubisco. Thus, the greater

fraction of N invested in Rubisco and electron transport components might

explain the enhanced leaf PNUE (as indicated by A400, N and Vcmax, N25) in 3G,

possibly leading to greater NP at low N.

Both A400, N [NP at low N = 0.909 + 0.111 * A400, N, r2=0.599, p < 0.005] and

Vcmax, N25 [NP at low N = 0.937 + 0.027 * Vcmax, N, r2=0.638, p < 0.003] strongly

correlated with whole plant NP at low N (Chapter 3 and Fig. 4.11) providing

some indication that these parameters at the leaf level might explain variations

in NP at whole plant level. Clearly, more studies are needed with multiple

genotypes and more replicates to explore this further, particularly given that

when analysing all 10 genotypes collectively, there was no statistical evidence of

genotypic or N mediated differences. Further work is also needed to see if

variation in PNUE at the whole-shoot level [which depends in part on radiation

use efficiency (RUE) at canopy level] differs among the genotypes, particularly

under low N supply. Here, factors such as canopy architecture, canopy height,

light extinction co-efficient (K) that indicates the leaf spread underpinned by

leaf angle and curvature of the leaf blade (Peng, 2000) need to be assessed at

the whole plant level.

Relative partitioning of N to Rubisco was 16% on average under high N

treatment of present study while past studies reported 27% of N in Rubisco in

rice compared with wheat (20%) and Maize (8.5%) (Evans, 1989, Makino et al.,

2003). This discrepancy could be due to the fact that carboxylation capacity was

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estimated using Michaelis–Menten kinetic constants for carboxylation (KC) and

oxygenation (KO) of tobacco. The fraction of N in Rubisco was reported to be

27% in rice when Rubisco content reaches its maximum (Makino et al., 1984).

Thus, a lower fraction of N in Rubisco in my study might indicate that at the

early vegetative stage, maximum Rubisco content was not been achieved yet.

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Figure 4.10 Pie charts show the percentage of leaf N in pigment-protein complexes, nP; percentage of leaf N in electron

transport components, nE; percentage of leaf N in Rubisco; nR, for each N treatment (2 and 0.06 mM) when averaged (A)

the three genotypes (Takanari, IR 64 and Milyang 23) that maintained growth and nitrogen productivity (see Chapter 3);

(B) the other seven genotypes (Opus, Dular, BG 34-8, Koshihikari, Akihikari, Azucena and Nipponbare). nP estimated from

chlorophyll content, nE estimated from maximum electron transport rate normalised to 25°C i.e. Jmax,a25 and nR was

estimated from maximum carboxylation velocity of Rubisco normalised to 25°C i.e. Vcmax,a25. The percentage of leaf N in all

other components (e.g. cell walls, secondary compounds for defence etc.) is represented by ‘nother’.

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4.5.3 Rapid estimation of Rubisco via Western blotting using

standards pre-determined with [14C]CPBP Rubisco content

assay

The strong correlation between Rubisco quantified with [14C]CPBP and Western

blotting confirmed the ability of the Western blotting procedure to accurately

estimate Rubisco. Estimation of Rubisco using the Western blotting procedure

would be beneficial because it is quicker, thereby allowing more samples to be

quantified for Rubisco. Unfortunately, Rubisco was poorly extracted when the

tissue-lyser was used to prepare the extract and there was insufficient time to

repeat and extend this approach. Thus, this method needs to be validated for

rice and other crop and non-crop species. The maximum catalytic turnover of

carboxylase (kcat, mol CO2 mol Rubisco sites-1 s-1) was estimated by the slope of

the plot (Fig. 4.9B) based on Western blots performed for the rice genotype

‘Takanari’ grown at high and low N supply. A higher kcat means a faster Rubisco

where lower numbers of Rubisco sites are required to achieve a given

Figure 4.11 Nitrogen productivity (NP) at 0.06 mM (LN) is plotted

against (A) net assimilation rate (N basis) i.e. A400, N at LN and (B)

carboxylation capacity (N basis) i.e. Vcmax, N25 at LN. The solid lines

indicate the relationships between NP and A400, N at LN [NP at LN =

0.909 + 0.111 * A400, N, r2=0.599, p < 0.005] and NP and Vcmax, N25

at LN

[NP at LN = 0.937 + 0.027 * Vcmax, N25, r2=0.638, p < 0.003]. n=4 for A400,

N and Vcmax, N25. See Chapter 3 for calculation of NP.

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carboxylase catalytic capacity thus, this can lower costs associated with Rubisco

(Evans, 2013). A greater kcat was found for low N-grown plants [.e. 3.97 mol CO2

(mol Rubisco sites)−1 s−1 at 25 °C] compared with high N grown counterparts

[i.e. 2.55 mol CO2 (mol Rubisco sites)−1 s−1 at 25 °C]. Rubisco can act as a N

storage compound (Warren et al., 2003, Cheng and Fuchigami, 2000, Warren et

al., 2000b) in addition to its role as a catalytic enzyme. This smaller kcat at high N

could indicate an increased fraction of inactive Rubisco (Cheng and Fuchigami,

2000, Li et al., 2009). Alternatively, inadequate extraction of Rubisco (Warren et

al., 2000a, Harrison et al., 2009) from silicon-rich (Ma, 2004) rice leaves could

result in overestimation of kcat at low N. The severity of low N treatment in the

present study might have made Rubisco extraction more difficult and less

sensitive to the methods used to quantify Rubisco. Any change in mesophyll

conductance or diffusional constraints for CO2 could also influence the

estimated maximum carboxylation rate and kcat. Different kcat values are

available in literature for rice depending on different approaches used by

researchers when estimating Rubisco while it is known as 30-40% lower

compare with other species (Makino, 2003, Makino, 2005). For instance, 2.69 at

28 °C (Sage, 2002) and 1.69 for rice, 2.50 for wheat at 25 °C (Makino et al.,

1988), 2.87 (in vitro) and 3.53 (in vivo) for Tobacco at 25 °C and kcat varied

between 2-6 mol CO2 (mol Rubisco sites)−1 s−1 across species (Harrison et al.,

2009). Thus, the values observed during present study for rice at low and high N

conditions appears reasonable.

4.6 Conclusions

When averaged across genotypes, reduced demand for intercellular CO2 was

matched with CO2 supply by partially closing stomata at low N. Across all 10

genotypes, there was no genotypic-N interaction term or genotypic variation for

PNUE. Further, genotypic differences were not found for patterns of N

partitioning to different components of photosynthesis except the reduction of

the fraction of N invested in pigment protein complexes at low N. However,

there were strong correlations between whole plant NP and PNUE (as indicated

by A400, N and Vcmax, N25) at low N providing some evidence that variation in leaf

level photosynthetic N use efficiency at low N could be contributing to a greater

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whole-plant NP at low N. Further, a separate comparison of three genotypes

(Takanari, IR 64 and Milyang 23) that exhibited high NP at low N (Chapter

three) with the other seven genotypes suggests that there might be enhanced

PNUE [as indicated by carboxylation capacity and net assimilation rate (N

basis)] in those three genotypes at low N. These 3 genotypes exhibited

maintenance of carboxylation capacity at low N along with partitioning more N

to photosynthesis (Rubisco and electron transport components), while the

other seven genotypes exhibited lower, on average, carboxylation capacity and

allocated proportionally more N to non-photosynthetic components at low N.

Clearly, further work with more replicates is needed to elucidate any genotypic

variation in PNUE contributing to NP in these genotypes at low N conditions.

The ratio of dark respiration to carboxylation capacity remained largely

constant across N treatments and genotypes. Finally, the ability of Western

blotting procedure to accurately estimate the amount of Rubisco of a given

sample was confirmed by the strong significant correlation found between the

amount of Rubisco estimated via Western blotting approach vs. the amount of

Rubisco quantified via [14C]CPBP Rubisco content assay. Thus, performing

Western blotting procedure with standards pre-determined with [14C]CPBP

Rubisco content assay can be considered as a rapid method of estimating

Rubisco from multiple samples. A lower catalytic turnover rate of carboxylase

(kcat) observed at high N i.e. 2.55 mol CO2 (mol Rubisco sites)−1 s−1 at 25 °C

compared with low N i.e. 3.97 mol CO2 (mol Rubisco sites)−1 s−1 at 25 °C could

indicate a lower activation state if Rubisco in leaves from plants supplied with

high N.

4.7 Future directions

Clearly, additional work with more replicates is needed to elucidate any

genotypic variation for PNUE of rice genotypes at both leaf and shoot level and

its ability to explain variation in NP at low N conditions. Moreover, it is

important to understand the effect of radiation use efficiency of these genotypes

and its contribution to PNUE at whole plant level and its linkage with NP

particularly at low N conditions. Due to time constraints light-saturated

photosynthesis (A) was measured at two atmospheric CO2 concentrations i.e. at

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400 ppm (A400, assuming A is Rubisco limited) and at 1500 ppm (A1500, assuming

A is RuBP regeneration limited). In a future experiment it would be

recommended to perform A-Ci curves to confirm these results for the three

genotypes that performed well at low N. Further work is also needed to

understand activation state of Rubisco, canopy architecture, differences in light

interception patterns of three key performers. It would be useful to assess

mesophyll conductance and leaf anatomy to understand diffusional constraints

for CO2 within leaves as kcat can vary depending on maximum carboxylation rate

which can be influenced by diffusional constraints particularly at high N supply.

The rapid method of Rubisco estimation from unknown samples using Western

blotting procedure with standards pre-determined with [14C]CPBP Rubisco

content assay need to be repeated as Rubisco was not fully extracted during

present study particularly from low N grown silicon-rich rice leaves due to the

use of tissuelyzer instead of Tenbroeck homogenizer. Further, it would be useful

to validate the above method for different other crop and non-crop species.

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Chapter 5 – Effect of N supply on respiratory characteristics of rice

5.1 Summary

The present chapter sought to assess the impact of nitrogen (N) supply and

genotypic differences on respiratory (R) fluxes of rice at the tissue, organ and

whole-plant levels. Leaf R was measured in the dark (RD) and also in the light

(RL; using the Kok method). Photosynthesis (A) at the leaf level, and leaf N and

sugar and starch profiles were also quantified. Measurements were made using

plants used in Chapters 2 and 3. The impact of N supply on R fluxes of

individual organs, the proportional contribution of R in individual organs to

daily whole-plant R, and R-N scaling relationships for each organ were assessed.

The chapter also explores the impact of N supply on light inhibition of leaf R and

the proportion of daily fixed carbon dioxide (CO2) released by R. Respiration in

the shoot contributed 70% to daily whole plant respiration compared with the

root, with this contribution remaining largely insensitive to N supply. This was

further confirmed by a near common slope observed for R-N scaling

relationships of leaves and roots, with respiratory fluxes per unit N being higher

in roots. Cessation of N supply had a greater inhibitory effect on root R than leaf

RD. A two-fold variation was observed for rates of leaf RD and RL across

genotypes within a given N level, with rates of leaf R per unit N being higher in

low N grown plants. Light inhibited leaf R, with RL being reduced by low N.

Neither RL nor light inhibition of leaf R correlated with leaf N on area basis (Na).

Variation in light inhibition of leaf R was largely accounted for by differences in

rates of leaf RL (rather than RD), with variation in the latter correlating with

variations in Rubisco activity (either carboxylation or oxygenation) in the light.

N supply had no impact on the fraction of daily fixed CO2 released by R at the

whole-plant level during early growth; however, in low-N grown plants, the

above fraction increased during later growth as a consequence of reduced

whole-plant A. Ratios of R:A at the leaf level were lower in the light compared to

dark, but both remained constant across N supply. Genotypic differences were

found for this ratio at leaf level in the dark.

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5.2 Introduction

Understanding nitrogen (N) mediated changes in photosynthesis (A) and plant

respiration (R) is crucial to improve productivity in rice, as these physiological

processes drive net assimilation rate (NAR) and nitrogen productivity (NP),

with increases in NAR and NP being linked to faster growth (as seen in Chapter

three). In contrast to the abundance of studies exploring the effect of N supply

on A (Makino et al., 1984, Makino et al., 2003, Tanaka and Makino, 2009,

Makino, 2011, Sudo et al., 2014), little attention has been given to

understanding N-mediated changes in R, with the literature gap being

particularly acute in rice. R supports growth and maintenance of plant tissues

by converting photosynthate to usable forms of energy while providing carbon

(C) skeletons for biosynthesis. R is also a determinant of the C balance of

individual plants, with 20-80% of daily fixed C being respired, with leaves being

responsible for about 35% of whole plant carbon dioxide (CO2) release (Van der

Werf et al., 1994, Atkin and Lambers, 1998). Rates of R are influenced by the

demand for energy and C-skeletons by metabolic processes (Farrar et al., 2000,

Comas and Eissenstat, 2004). In whole-plant models, R is typically partitioned in

to three functional components: growth, maintenance and ion uptake (Van der

Werf et al., 1992b), with the relative contribution of each component depending

on development stage, tissue type and environment (Amthor and Baldocchi,

2001, Lambers, 2005). Thus, one might expect fluxes of R to differ between

roots and leaves, reflecting differences in energy demand processes within the

two tissue types.

One factor that might contribute to marked differences in root and leaf R

is the extent to which R is needed to support nitrate reduction and assimilation

(Scheurwater et al., 2002, Luo et al., 2013). In both tissues, C skeletons are

removed from the tricarboxylic acid (TCA) cycle to support NH4+ assimilation

via the glutamine synthase (GS) – glutamate 2-oxoglutarate aminotransferase

(GOGAT) cycle. Thus, high rates of N assimilation will affect R in root and

leaves. However, as leaves are often the main site for nitrate reduction, much of

the energy and reducing power needs of nitrate reduction and assimilation are

met by A, thus, reducing energy demands via shoot R (Lambers, 1983, Johnson,

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1990, Thornley and Cannell, 2000). By contrast, R must provide most of the

energy requirements of root-based nitrate reduction. Further differences will

result from energy costs associated with sucrose synthesis (Kromer, 1995) and

phloem loading (Bouma et al., 1995) in leaves but not in roots. Similarly,

contrasting chemistries contribute to differences in maintenance R

requirements of roots and leaves; here, differences in energy demands for

protein and lipid turn over, cellular ion gradient maintenance and damage-

repair are likely (Amthor, 2000, Thornley, 2000, Cannell and Thornley, 2000).

While proteins such as Rubisco are only present in green tissues (influencing

energy demand), only roots experience costs associated with ion uptake from

soils (Van der Werf et al., 1994, Lambers et al., 1998, Mengel and Viro, 1978). In

addition to such variations in energy demand resulting in differences in fluxes of

R in leaves and roots, the impact of low N supply on R is also likely to differ

between the two tissue types. Little is known, however about how N availability

influences specific rates of R in leaves and roots, particularly in crop species

such as rice.

Under limited N supply, root growth is often accelerated relative to that

of shoots (Boot et al., 1992, Van der Werf et al., 1992b); this shift in biomass

allocation to roots enhances foraging ability (Hermans et al., 2006, Chardon et

al., 2010, Luo et al., 2013), with the additional growth of roots being associated

with a dilution of tissue N concentration in roots. N deficiency is known to

induce transcription of several ammonium (NH4+) and NO3- transporters

(Hermans et al., 2006, Kant et al., 2011, Luo et al., 2013), while net influxes of

NH4+ and NO3- are lowered and root (nitrate reductase) NR activity is reduced

(Luo et al., 2013, Schlüter et al., 2013). Proteins that are products of amino acid

metabolism are also affected, with overall protein content (Liang et al., 2013),

associated protein turnover rates and maintenance of solute gradients

(Scheurwater et al., 2000, Scheurwater et al., 1998, Van der Werf et al., 1992a)

being reduced in N-deficient roots. The associated reduced energy demand

could increase adenylate restriction of respiratory metabolism (Dry and

Wiskich, 1982), leading to a lower root R. This suggests an overall reduction in

mass-based rates of root R under N deficiency. However, increases in relative

biomass investment in roots could potentially lead to a greater overall CO2

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release by root R under low N, depending on the extent to which specific rates of

root R are lowered by N deficiency. Past studies provide evidence for increased

root R to support ion uptake and maintenance of biomass under low N (Van der

Werf et al., 1992b) while others suggest the opposite (Poorter et al., 1995,

Lambers, 1979). Here, a crucial factor influencing root R will be the impact of N

deficiency on specific costs of nitrate uptake, with some studies suggesting that

ions are actively transported across root cell membranes at low N (Atwell et al.,

1999) and the energy demand of high affinity transporters (operating at low N

supply) are greater than that of low affinity transporters (Glass et al., 1990,

Siddiqi et al., 1990, Lambers and Poorter, 1992). For rice, it is unclear how root

R might change in response to N supply.

N deficiency often reduces photosynthesis (Chapin III, 1991, Luo et al.,

2013). As a result, limitations in N supply could limit the amount of available

photosynthate, lowering leaf R (Atkin et al., 2013). On the other hand,

carbohydrates often accumulate in leaf cells under N deficiency (Chapin III,

1991, Zhao et al., 2005, Noguchi and Terashima, 2006, Hermans et al., 2006,

Lemaire et al., 2008, Luo et al., 2013), partly due to less C demand for amino

acid biosynthesis (Schlüter et al., 2012, 2013) and retarded leaf growth (Chapin

III, 1991, Zhao et al., 2005, Lemaire et al., 2008, Liang et al., 2013); sink

limitations could further exert a negative feedback on A (Hermans et al., 2006).

Overall, demand for respiratory energy in leaves could be lowered under N

deficiency due to: less N assimilation; reduced protein concentrations

[particularly Rubisco (Beadle and Long, 1985, Chapin III, 1991)] and associated

protein turnover (De Vries, 1975); low rates of A (Chapin III, 1991, Luo et al.,

2013); and, reduced sucrose synthesis (Kromer, 1995) during the day and

phloem loading (Bouma et al., 1995). On the other hand, reduced ATP demand

for biosynthesis along with the presence of excess reducing equivalents could

activate the alternative pathway of R under N deficiency (Hoefnagel et al., 1998,

Noguchi and Terashima, 2006), potentially requiring increases in mitochondrial

oxygen (O2) uptake. Moreover, leaf R can be accelerated due to energy

demanding processes involved in leaf senescence and N remobilization

(Pilbeam, 1992, Kant et al., 2011). Hence, it remains unclear how low N supply

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might influence leaf R of rice, particularly when scaling up fluxes to the whole-

shoot, and whole-plant levels.

In crop and climate models, tissue N concentrations are often used to

predict rates of leaf and root R (Cox et al., 2000, Sitch et al., 2003), based on

assumed R-N scaling relationships (Reich et al., 2008). Indeed, R scales with N in

leaves (Ryan, 1995, Reich et al., 1998c, Tjoelker et al., 1999, Mitchell et al., 1999,

Noguchi and Terashima, 2006), roots (Reich et al., 1998c, Atkinson et al., 2007,

Tjoelker et al., 2005) and at the whole-plant level (Reich et al., 2006, Reich et al.,

2008). According to Reich et al. (2003), R-N scaling relationships in leaves are

independent of N supply, with rates of R moving up and down a common R-N

relationship as N supply increase/decrease. Similarly, when analysed at the

whole-plant level, a common R-N scaling relationship can be maintained

irrespective of growth conditions (e.g. N availability, temperature, light and

atmospheric composition) (Reich et al., 2006). Atkinson et al. (2007) also found

that R-N relationships of roots were maintained when high N grown plants were

shifted to a zero-N treatment for several days. However, while Wright et al.

(2001) observed that the slope of the R-N relationship is maintained in plants

growing at sites that differ in N availability and rainfall, differences in the

elevation of relationships (i.e. y-axis intercept) were observed (with rates of leaf

R being higher at a given N in plants growing at N-deficient or arid sites).

Similarly, Atkin et al. (2008) found that while growth temperature did not alter

the slope of the R-N relationship in leaves, rates of leaf R at a given N were

higher in cold grown plants than their warm grown counterparts. Bahn et al.

(2006) observed that R-N correlation changes with geographical location, with

the authors suggesting that this might be linked to a dependency of the above

correlation on N availability. Importantly, most of above studies were based on

comparison of multiple species adapted to different environments, with less

data being available (particularly in crop species such as rice) on whether R-N

relationships of leaves, roots and whole plant are maintained (or not) within a

single species when plants are grown on a wide range of N levels. Thus, to fully

understand how leaf and root R of rice responds to differences in N supply,

further work is needed comparing the response of the two tissue types, both for

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plants grown on different steady-state N levels and individual plants that

experience progressive depletion of N.

When considering the effect of N availability on leaf R, thought needs to

be given to the fact that leaf R persists in the light (RL, non-photorespiratory CO2

evolution in the light) as well as in the dark (RD). RL plays an important role

providing ATP for NH4+ assimilation and sucrose synthesis and C skeletons (e.g.

2-oxoglutarate and oxaloacetate) for NH4+ assimilation and amino acid

biosynthesis (Atkin et al., 2000a, Tcherkez et al., 2008). Further, RL may reduce

the photoinhibitory damage to the photosynthetic electron transport system by

oxidizing excess photosynthetic reducing equivalents and assists repairing D1

protein of photosystem II by providing mitochondrial ATP (Hoefnagel et al.,

1998). Importantly, RL is usually lower than RD, with the degree of light

inhibition varying from 0-100% reflecting the differential response of RD and RL

to environmental variations (Crous et al., 2012). Light inhibition of leaf R often

depends on the cellular energy status (Hurry et al., 2005), removal of C-

skeletons for amino acid biosynthesis by truncating the TCA cycle in the light

(Igamberdiev et al., 2001, Tcherkez et al., 2005) and photorespiration (Atkin et

al., 2013). Thus, if N assimilation (that occurs in the light) slows down under N

deficiency, we may expect less inhibition of leaf R due to the less demand for C

skeletons. Then again, low Rubisco investment under low N (Beadle and Long,

1985, Chapin III, 1991) would reduce the associated rates of photorespiration

and as a consequence the degree of light inhibition would be lower. Thus far,

inhibition of leaf R was observed under conditions that favour photorespiration

(Atkin et al., 1998a, Atkin et al., 1998c, Hurry et al., 2005, Zaragoza‐Castells et

al., 2007) due to photorespiration-dependent reductions in pyruvate

dehydrogenase (PDH) activity (Randall et al., 1990). By contrast, Tcherkez et al.

(2008) found that the degree of light inhibition lowered when exposed to low

CO2 for short periods (i.e. high photorespiration) creating increased demand for

TCA cycle intermediates involved in the recovery of photorespiratory pathway

intermediates in the peroxisome. Recently, several studies have provided

evidence supporting the hypothesis that increased rates of photorespiration

being linked to higher rates of RL (Ayub et al., 2011, Crous et al., 2012, Griffin

and Turnbull, 2013). Yet, uncertainty remains about the direction of this link

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between photorespiration and the degree of light inhibition of leaf R. Further,

little is known about how N deficiency affects light inhibition; N supply could

influence Rubisco investment [where 24-33% of N is in the form of Rubisco

(Makino, 2003)], which may then modulate photorespiration leading variations

in the degree of inhibition of leaf R. To my knowledge, only a few studies have

reported the effect of N supply or leaf N concentration on RL (Shapiro et al.,

2004, Heskel et al., 2012, Atkin et al., 2013, Ayub et al., 2014) and none were on

cereals. Some of them observed a positive correlation between RL/RD with leaf N

concentration (Heskel et al., 2012, Atkin et al., 2013, Ayub et al., 2014) while

Shapiro et al. (2004) observed no change. The conflicting nature of these

observations highlights the need for further work on RL and the degree of light

inhibition of leaf R with regard to N supply.

The objective of this chapter was to understand how root and leaf

respiratory characteristics of rice vary in response to N availability during early

vegetative stage. Plants grown in Chapter 2 (i.e. single genotype exposed to

several N levels) and Chapter 3 (i.e. 10 genotypes grown at two N levels) were

used. The study addressed the following specific objectives: (1) to what extent

does R in leaves and roots differ in their response to variations in N supply; (2)

does N supply alter the relative contribution of each organ to daily whole plant

R, and/or proportion of daily fixed CO2 released by R at organ and whole plant

level; (3) how robust are R-N scaling relationships when N availability is altered,

both via steady-state differences in N supply and after cessation of N supply, and

when comparing multiple genotypes of rice; and, (4) how does N availability

influence the degree of light inhibition of leaf R.

5.3 Materials and methods

5.3.1 Plant growth

This chapter presents gas exchange data (i.e. respiratory fluxes in leaves, shoot

and the root of rice genotype/s) from two experiments previously described in

Chapters 2 and 3. During the dose-response experiment described in Chapter 2,

all physiological measurements (respiratory fluxes in leaves and the root) were

performed on day 53 and 63 after transplanting. For the 10 genotypes grown in

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Chapter 3, leaf gas exchange measurements were taken over the last three

harvests (i.e. from 35-48 days after transplanting); moreover, root and shoot

respiratory measurements were taken on the second and fifth harvests (i.e. 21

and 42 days after transplanting, respectively).

5.3.2 Measurements

5.3.2.1 Leaf gas exchange measurements

In both of the above experiments, leaf gas exchange parameters were obtained

from light response curves of net CO2 exchange (Anet) which were measured on

most recently fully expanded leaves using a Li-Cor 6400 XT portable

photosynthesis system (LI-COR Inc., Lincoln, NE, USA) with a CO2 controller and

a 6 cm2 chamber with red-blue light source (6400-02B). The block temperature

was set to 28 °C (similar to the growth temperature) and light-response

measurements were taken at ambient atmospheric CO2 (i.e. 400 ppm);

consequently, it was not necessary to correct for CO2 diffusion through the

chamber gasket (Atkin et al., 2013). Relative humidity inside the chamber was

maintained between 60-70%. Light-saturated photosynthesis (A1500) was

measured at 1500 µmol m-2 s-1 photosynthetic photon flux density (PPFD) after

leaves had been exposed to saturating irradiance in the cuvette for 10 minutes.

Thereafter, the irradiance response of Anet was measured, beginning at 500

µmol m-2 s-1 , followed by 200, 100, 100, 80, 60, 55, 50, 45, 40, 35, 30, 25, 20, 15,

10, 5 and ending at 0 µmol m-2 s-1 (i.e. darkness). An equilibration period of 5

minutes was allowed at each irradiance level before gas exchange was recorded.

Anet in darkness (i.e. leaf RD) was measured followed by 10 mins of darkness.

Flow rate through the chamber was maintained at 500 µmol s-1 for

measurements at high irradiances (i.e. 1500 to 100 PPFD) and 300 µmol s-1 for

the lower irradiances (i.e. 100 PPFD and below). Light response measurements

were typically begun three hours after sun rise and were completed by mid-

afternoon.

The Kok (1948) method was employed to estimate leaf respiration in the

light (RL), assuming that the response of Anet to light is linear near low

irradiance and the deviation from linearity occurs close to the light

compensation point (Weerasinghe et al., 2014). RL was estimated from the y-

135

axis intercept of a first order linear regression fitted to the region between 20

and 60 µmol m-2 s-1 of Anet -irradiance plots (data were often curvilinear at

irradiances above 70 µmol m-2 s-1 PPFD; Fig. 5.1). The Kok r2 values obtained in

two experiments [i.e. seven N treatments (see Chapter 2) and 10 genotypes (see

Chapter 3)] are given in Tables 5.1 and 5.2.

One limitation of estimating RL using the Kok method is the fact that

internal CO2 concentration (Ci) gradually increases along with the decrease in

measuring irradiance and that increase in Ci suppresses photorespiration while

increasing carboxylation. As a consequence, Anet increases relatively in the linear

region (Villar et al., 1994) resulting in the slope of the linear region plotted

through observed data being less than it would be if Ci remained constant (Villar

et al., 1994, Kirschbaum and Farquhar, 1987). Thus, RL can be underestimated

by the Kok method. Accordingly, RL was estimated after correcting for changes

in Ci (Kirschbaum and Farquhar, 1987), whereby rates of RL were adjusted (by

iteration) to ensure that the intercept of plots of photosynthetic electron

transport (J) against irradiance are minimized, assuming a Γ* of 40 ppm at 25°C.

J was calculated according to Farquhar & von Caemmerer (1982):

J =

[(4(𝐴net+𝑅L)) (Ci+2Γ*)]

(Ci− Γ*) (Eqn. 5.1)

where Γ* is the CO2 compensation point in the absence of RL (von Caemmerer

and Farquhar, 1981). Rates of oxygenation and carboxylation by Rubisco (Vo

and Vc, respectively) at any given irradiance were calculated according to

Farquhar & von Caemmerer (1982):

Vc = 1

3[(

𝐽

4) + 2(Anet + 𝑅L)] (Eqn. 5.2)

and:

Vo = 2

3[(

𝐽

4) − (Anet + 𝑅L)] (Eqn. 5.3)

136

Figure 5.1 The ‘Kok’ effect is illustrated in the plot of net CO2 exchange rate

(Anet, µmol CO2 m-2 s-1) versus irradiance (µmol photons m-2 s-1). Solid circles

indicate measured rates of Anet over the 0-100 µmol photons m-2 s-1 range,

including the rate of leaf respiration in darkness (RD = 1.10 µmol CO2 m-2 s-1).

The break from linearity at irradiances below 20 µmol photons m-2 s-1

(dashed line) is shown, with a linear regression fitted (r2 = 0.99 for this

replicate) to values between 20-60 µmol photons m-2 s-1 to estimate

apparent rates of leaf R in the light (RL ‘apparent’, ◊ = 0.39 µmol CO2 m-2 s-1)

at the y-axis intercept. Actual rate of RL (□ = 0.38 µmol CO2 m-2 s-1) are also

shown, which was calculated by considering the changes occur in internal

CO2 concentration (Ci) when irradiance get declined (Kirschbaum and

Farquhar, 1987). Above 60 µmol photons m-2 s-1, increases in Anet with

irradiance were not linear (dotted line extension of linear regression from 20-

60 µmol photons m-2 s-1 range data). Data are from a single replicate of the

genotype ‘Azucena’ grown under steady state (2 mM) of N supply during the

experiment with 10 genotypes of rice.

137

5.3.2.2 Root respiration

To compare respiratory fluxes in leaves/shoots with that of roots, root R

measurements were also undertaken during both experiments previously

described in Chapter 2 and Chapter 3. Plants were transported from the

glasshouse to the laboratory in vessels containing the nutrient solution in which

they had been grown. For larger root systems (e.g. from older and/or high-N

grown plants), roots were longitudinally divided into two equal portions using a

scalpel, and one half of the root system (detached from the stem) used for

respiration measurements. For smaller root systems, whole root systems were

used. O2 uptake rate was polarographically measured using Clark-type O2

electrodes (Dual digital model 20; Rank brothers, Cambridge, UK) by placing

them in a 50 ml air tight cuvette filled with 20 ml of air-saturated nutrient

solution (from the corresponding N treatment) buffered with 10 mM

morpholine ethane sulphonic acid (MES) (pH 5.8) at 28 °C. Roots were placed in

cuvettes and left for 10 minutes to equilibrate. Respiration rate was measured

over the subsequent 5 minutes. The measuring temperature of electrodes was

kept constant using Thermomix 1442D ethylene glycol bath and Frigomix-U

with Storktronic heating element (B. Braun, Australia) where Ethylene glycol

was continuously circulated via tubes and the jacket of cuvettes.

5.3.2.3 Shoot respiration

For the experiment comparing 10 genotypes (see Chapter 3), shoot respiration

was measured in parallel with root respiratory measurements by placing the

detached leaves and the stem of each plant inside a Walz 14 x 10 cm leaf

chamber (3010-GWK1 and LED-Panel RGBW-L084, Walz, Germany) connected

to a Li-Cor 6400 XT gas exchange system (Li-COR Inc., Lincoln, NE, USA). Air

within the chamber was mixed using a fan attached to the chamber while

constantly being circulated via the chamber at 500 µmol s-1 of flow rate. Block

temperature was set at 28 °C as of growth temperature and measurements were

taken at ambient CO2 i.e. 400 ppm. RD was measured followed by a 10 min dark

equilibration period.

Shoot R was also estimated using measured rates of leaf RL and RD

measured for the same individual following the equation 5.4 (excluding

138

respiration in stems) to compare the estimated shoot R versus measured shoot

R.

5.3.2.4. Carbon balance calculations

5.3.2.4.1. Calculation of daily respiration by leaves and roots as a percentage of

combined daily leaf and root respiration

For plants grown in Chapter 2, shoot respiration was not measured due to

logistical constraints during dose response experiment. Thus, only leaf

respiration (i.e. excluding R taking place in the stem) was considered in the

calculation below. Respiratory fluxes in leaves and roots were scaled up to the

whole-plant level using biomass allocation data. Daily leaf R was calculated

considering the inhibitory effects of light on respiration during the daytime as

previously described by Atkin et al. (2007).

Daily respiration by leaves = (LMR x 12

24 x leaf RL) + (LMR x

12

24 x leaf RD )

(Eqn. 5.4)

where 12/24 is the relative length of both day and night periods, leaf RL and leaf

RD are respiration during day and night time respectively.

Daily respiration by roots = RMR x 24

24 x root R (Eqn. 5.5)

The combined daily respiration of leaves and roots was calculated by adding

above equations 5.4 and 5.5. Daily respiration by leaves as a percentage of

combined daily leaf and roots respiration was calculated by dividing equation

5.4 by the total of equations 5.4 and 5.5. Similarly, daily respiration by roots as a

percentage of combined daily leaf and roots respiration was calculated by

dividing equation 5.5 by the total of equations 5.4 and 5.5. For these

calculations, a respiratory quotient (RQ) of 1 was assumed for roots based on

Bloom (1992), Youngdahl et al. (1982) and Duan et al. ( 2007).

139

5.3.2.4.2. Calculation of daily whole plant respiration as a fraction of daily whole

shoot photosynthesis

For the 10 genotypes grown in Chapter 3, both shoot and root respiratory

measurements were made, as described in Sections 5.3.2.2 and 5.3.2.3

respectively. Daily whole plant respiration was calculated by adding above

measured shoot and root respiration rates assuming a respiratory quotient (RQ)

of 1. The rate of daily whole-shoot photosynthesis on leaf area basis (whole-

plant A) was calculated by using the equation provided for NAR (Atkin et al.,

1996a) as below:

NAR = Whole plant 𝐴− [

𝑆ℎ𝑜𝑜𝑡 𝑅 ∗ (LMR+SMR)

(LMR∗SLA)]−[

(𝑅𝑜𝑜𝑡 𝑅 ∗ RMR)

(SLA∗LMR)]

𝐶𝐶 (Eqn. 5.6)

where shoot R and root R are the rates of shoot and root R per unit dry mass and

day, respectively. LMR (leaf mass ratio), SMR (stem mass ratio) and RMR (root

mass ratio) are the proportions of whole plant biomass allocated to leaves,

stems and roots respectively. SLA (specific leaf area) is the amount of leaf area

per unit leaf mass, whereas CC represents the plant C concentration. Dried leaf,

stem and root samples (from the sixth harvest of the experiment described in

Chapter 3) were ground to a fine powder and approximately 4 mg of samples

were transferred into tin capsules (IVA Analysentechnik, Meerbusch, Germany).

C was measured using a Fison’s Isochrom Continuous Flow Isotope Ratio Mass

Spectrometer (CF-IRMS) following combustion in a Carlo Erba CE1110 CHN-S

analyser (CE Instruments, Milan, Italy) (Table 5.4). CC calculated for second and

fifth harvests based on CC measured for the sixth harvest (see Table 5.4)

assuming the percentage C in leaves, roots and the stem of each genotype at a

given N treatment did not change with time. Daily whole plant respiration as a

fraction of daily whole shoot photosynthesis was calculated by dividing daily

whole plant respiration (leaf area basis) by daily whole-shoot photosynthesis

(leaf area basis).

5.3.3 Statistics

All data were tested for normality and homogeneity of variance and analysed

using SPSS (version 21, SPSS, Chicago, IL, USA). During the dose-response

140

experiment (see Chapter 2), a two-way ANOVA procedure (general linear

model) was conducted considering N treatment and cessation as factors for leaf

and root respiration on dry mass basis (leaf and root RD, m) and N basis (leaf and

root RD,N). An analysis of covariance (ANCOVA) was conducted with organ N

concentration (Nm) as the covariate and time as the grouping/independent

variable. ANCOVA was performed for root and leaf RD,m. Existing correlations

among gas exchange, structural and chemical parameters were identified using

Pearson correlation. Bivariate relationships were analysed with linear

regression. Stepwise regression procedure was performed to identify the

factors contributing to the variation observed in respiration in the light (RL) and

light inhibition of dark respiration (RL/RD). For the experiment with 10

genotypes (see Chapter 3), a two-way ANOVA followed by Tukey’s post-hoc test

was conducted for leaf gas exchange, structural and chemical parameters

considering genotype and N treatment as factors. A three-way ANOVA was

performed for the balance between shoot R and plant R, considering time,

genotype and N treatment as factors. Data were log10 transformed to meet

normality and homogeneity of variance where necessary.

5.4 Results

5.4.1 Effect of nitrogen supply on dark respiration of leaves and

roots of a single genotype

To investigate whether respiration fluxes in the dark (RD) of leaves and roots

differ in their response to N supply, rates on dry mass and N basis at each N

level (Fig. 5.2) were calculated for the single genotype (Nipponbare) grown on

seven N levels, as described in Chapter 2. Here, I first compare the overall effect

of the seven N treatments across both 53 and 63 DAT plants, and thereafter

consider what effect cessation of N supply had on respiratory fluxes. Roots

exhibited higher respiratory fluxes compared to leaves, especially under high N

conditions (Fig. 5.2). Table 5.1 shows that, when combining both the 53 and 63

DAT data together, the seven N treatments had a significant effect on mass- and

N-based rates of leaf and root respiration. Similarly, there was a main effect of

cessation of N supply on mass- and N-based rates of leaf and root respiration

(with rates being reduced following 10-days of cessation of N supply), with the

141

absence of any significant interaction terms indicating that responses to the

seven N treatments were similar both before and after cessation of N supply.

Overall, respiratory fluxes in roots appeared relatively more sensitive to any

change in N supply than leaves, particularly following cessation of N supply (Fig.

5.2).

Past studies comparing multiple species have suggested that at a given

tissue N concentration, rates of respiration are higher in roots than in leaves

(Reich et al., 2008), and that root R-N relationships are similar for plants grown

on either steady-state differences in N supply or plants subjected to cessation of

Figure 5.2 Leaf (green) and root (brown) respiration expressed on (A) organ

dry mass basis (leaf RD, m and root RD, m) and (B) N basis (leaf RD, N and root

RD, N) are plotted against N supply at two time points. The harvest

conducted 53 days after transplanting (DAT) is defined as ‘Before’ (i.e.

following growth on steady-state N levels). All plants were then subjected

to ‘zero’ N for 10 days, after which plants were harvested 63 DAT – this

harvest is defined as ‘After’. The ‘x’ axis is on log10 scale. (n = 3; ± SE). Two-

way ANOVA results for N treatment x cessation are shown in Table 5.1.

142

N supply for short periods (Atkinson et al., 2007). To assess whether these

observations hold for both leaf and root R-N relationships in rice, plots were

made of respiratory rates against tissue N concentrations for the two tissue

types and sampling days (53 and 63 DAT). Figure 5.3 shows that there were

fundamentally different relationships for respiration in roots and in leaves.

Consistent with Reich et al. (2008), roots had higher respiratory rates at

any given N concentration compared to leaves except at 0.06 and 0.12 mM N

treatments after the cessation. Further, ANCOVA (Table 5.2) revealed that when

accounting for tissue N concentration, root RD, m was significantly different

between plants grown on steady-state N supply versus plants exposed to 10-

days of N cessation (i.e. there were different R-N slopes) whereas there was no

difference between steady-state and N cessation plants in rates of leaf RD, m

when tissue N concentrations were accounted for in the ANCOVA slopes. Thus,

similar slopes exist for leaf RD, m-N relationships for 53 and 63 DAT plants

Table 5.1 Results of a two-way analysis of variance (ANOVA) with factors

nitrogen treatment and cessation of N supply for chemical, structural and

physiological parameters. F-values and their significance are presented where

degrees of freedom is shown within parenthesis. *p < 0.05, **p < 0.01, ***p <

0.001.

Dependent variable N-treatment Cessation N-treatment x Cessation

Leaf RD, m 3.1* (6) 22.1*** (1) 1.1 (6)

Root RD, m 8.2*** (6) 74.1*** (1) 0.39 (6)

Leaf RD, N 2.2* (6) 5.8* (1) 1.0 (6)

Root RD, N 3.7** (6) 25.4*** (1) 0.88 (6)

Leaf RD, a 3.1* (6) 12.1*** (1) 0.90 (6)

Leaf RL, a 0.37 (6) 0.97 (1) 0.66 (6)

Na 21.8*** (6) 25.7*** (1) 3.5* (6)

Ma 3.2* (6) 46.2*** (1) 4.5** (6)

Abbreviation: Leaf RD, m = mass based leaf respiration in the dark, Root RD, m = mass based

root respiration in the dark, Leaf RD, N = N based leaf respiration in the dark, Root RD N = N

based root respiration in the dark, Leaf RD, a = area based leaf respiration in the dark, Leaf RL, a

= area based leaf respiration in the light, Na = area based N concentration, Ma = leaf mass

per unit area

143

whereas root RD, m-N relationships differ between plants grown on steady state

N and plants exposed to 10 days of N cessation (Fig. 5.3), with cessation having

a greater inhibitory effect on root RD, m than leaf RD, m. Results of linear

regressions further suggest a fairly common R-N slope for leaves and roots

during the steady state of N supply (Fig. 5.3).

Using data on biomass allocation (in Chapter two) and respiratory fluxes

in organs (this chapter), I calculated daily rates of leaf plus root respiration

(weighted for relative biomass allocation to each organ). This approach

provides an estimate of ‘whole-plant’ respiration for each N treatment, ignoring

Figure 5.3 Leaf (green) and root (brown) respiration on organ dry mass

basis are plotted against N concentration in organs at two time points.

The harvest conducted 53 days after transplanting (DAT) is defined as

‘Before’ (i.e. following growth on steady-state N levels). All plants were

then subjected to ‘zero’ N for 10 days, after which plants were harvested

63 DAT – this harvest is defined as ‘After’. Relationships (both x and y axes

are on log10 scale) during the steady state of N supply: for roots,

respiration = -0.03 + 1.06 * N (p < 0.01, r2 = 0.36), for leaves, respiration =

-1.24 + 1.54 * N (p < 0.001, r2 = 0.47). Relationship (both x and y axes are

on log10 scale) after cessation of N supply: for roots, respiration = -4.68 +

4.51 * N (p < 0.001, r2 = 0.41). (n = 3; ± SE). See table 5.2 for ANCOVA

results when testing differences in slopes for Leaf and root R among two

time points when accounting N concentration as the covariate.

144

contribution of stem material to whole-plant respiration and assuming that leaf

respiration of the most recently mature leaf is similar to that of other leaves.

Using this approach, I found that on 53 DAT, combined daily leaf and root

respiration was largely homeostatic across five of the seven steady-state N

supply levels (0.12-2 mM N), but lower and higher in 0.06 and 4.0 mM N treated

plants, respectively (Fig. 5.4). Thus, despite rates of root respiration declining

with decreasing N supply (Fig. 5.2), respiration of leaves plus roots combined

only decreased/increased at the extreme low and high levels of N supply.

When considering the proportional contribution by leaves and roots to

‘whole-plant’ respiration under steady state N supply, daily root R contributed

60-80% of overall respiratory flux, whereas leaf R contributed only 20-40%

(Fig. 5.5A). That pattern was largely held across N treatments for plants grown

on steady state N supply. However, this trend changed after 10-days cessation of

N supply (Fig. 5.5B); daily root R represented a smaller fraction (10-15%)

compared to daily leaf R (85-90%) for plants previously grown on 0.06 mM N

supply and the percentage contribution by daily leaf R gradually decreased for

plants that had been previously grown on higher steady-state N levels. Thus,

while steady-state N supply has little effect on the relative contribution of leaf

and root respiration to overall plant fluxes, those supply levels strongly

Table 5.2 Results of an analysis of covariance (ANCOVA) with organ N

concentration (Nm) as the covariate and time (T) as the

grouping/independent variable. There was no significant interaction

term thus, not violated the assumption of homogeneity of regression

slopes for dependent variables mass based leaf respiration in the dark

(Leaf RD, m) and mass based root respiration in the dark (Root RD, m)

permitting to perform ANCOVA. Significance is denoted as *p < 0.05,

**p < 0.01, ***p < 0.001. n.s. and d.f. indicate non-significance and

degrees of freedom respectively.

Dependent

variable

T Log10Nm T x Log10Nm Total

d.f.

Leaf RD, m n.s. *** n.s. 40

Root RD, m ** ** n.s. 44

145

influence the relative contribution of the two tissue types to whole plant

respiration following N cessation.

Figure 5.4 Combined daily leaf and root respiration expressed on plant

dry mass basis is plotted against N supply - log10 scale at two time

points. The harvest conducted 53 days after transplanting (DAT) is

defined as ‘Before’ (i.e. following growth on steady-state N levels). All

plants were then subjected to ‘zero’ N for 10 days, after which plants

were harvested 63 DAT – this harvest is defined as ‘After’. Daily leaf

respiration was calculated considering the effects of light on respiration

during the daytime. Combined daily leaf and root respiration was

calculated by adding daily leaf and root respiration expressed on plant

dry mass basis together. Stem respiration and stem N concentration was

not considered in this calculation. Statistics were not performed for this

parameter which was calculated at mean level due to several missing

data for leaf respiration in the light and the fact of not measuring the

same replicates for leaf and root R at all times during dose response

experiment described in Chapter 2.

146

Figure 5.5 Dose response bar graphs showing daily leaf and root R as a

percentage of combined daily leaf and root R (A) before cessation of N supply; (B)

following cessation of N supply and (C) values averaged across genotypes (as there

was no significant genotypic effect or G x N interaction as suggested by the three

way ANOVA, Table 5.6) for daily shoot and root R as a percentage of daily whole

plant R. Due to ontogenetic decline in respiratory fluxes with time and significant T

x N interaction the data at high N of harvest two (H2 HN), low N (H2 LN) of harvest

two and high N of harvest five (H5 HN), low N of harvest five (H5 LN) were shown

separately (n=3-4; ± SE). Statistics were not performed for above figures 5.5A and

B as calculations were done at mean level due to several missing data for leaf

respiration in the light and the fact of not measuring the same replicates for leaf

and root R at all times during dose response experiment described in Chapter 2.

147

5.4.2 Effect of nitrogen supply on dark respiration of leaves and

roots of 10 genotypes

I now explore whether patterns observed in Nipponbare held across all 10

genotypes grown on low (0.06 mM) and high (2 mM) steady-state N supply. In

contrast to the above findings for Nipponbare where leaf R was relatively

homeostasis across N levels (Fig. 5.2), across all 10 genotypes there was a

significant N effect on leaf respiration (p < 0.001) for both leaf RD, a and leaf RD, m

(Fig. 5.6, Table 5.5), with leaf RD, a differing significantly (p < 0.05) among the 10

genotypes (2-way ANOVA; Table 5.5). Growth on low N supply resulted in

significantly lower rates of leaf RD, a (p < 0.001) in Milyang 23, Opus, Koshihikari

and BG 34-8 compared with those genotypes grown on high N. Thus, while the

single genotype (i.e. Nipponbare) suggested homeostasis of leaf respiratory

fluxes in low and high N grown plants, that pattern was not held across all 10

genotypes. A three-way ANOVA confirmed that the average root respiration in

the dark on a leaf area basis (root RD, a) significantly reduced under low N

treatment (p < 0.001) and varied across genotypes (p < 0.05) and time (p <

0.001) (Tables 5.4 and 5.6). This parameter decreased through time yet, that

was consistent across genotypes and N treatments. Further, the genotypes did

not differ in their response to N treatments. Further, a two way ANOVA

performed at each harvest (Table 5.5) confirmed that no genotypic differences

exist for root RD, a when average across N treatments in contrast to the

differences observed among 10 genotypes for leaf RD, a.

148

Figure 5.6 Bar graphs showing genotypic variation in area based rates

of leaf respiration in the dark (RD, a). (n=3-6; ± SE except, BG 34-8 at 2

mM where n=2; ± SE). See table 5.5 for two-way ANOVA results.

149

Table 5.3 Leaf chemical, structural and physiological characteristics of rice

variety Nipponbare at seven N treatments during the dose response experiment

(Chapter 2)

Dependent variable Time 0.06 mM 0.12 mM 0.25 mM 0.5 mM 1 mM 2 mM 4 mM

Leaf RD, a before 0.26±0.08 0.51±0.13 0.68±0.13 0.73±0.06 0.43±0.09 0.54±0.15 0.73±0.20

(µmol m-2

s-1

) after 0.18±0.02 0.28±0.02 0.43±0.14 0.29±0.09 0.40±0.03 0.42±0.02 0.49±0.05

Leaf RL, a before 0.16±0.15 0.34±0.26 0.16±0.05 0.48±0.24 0.25±0.08 0.18±0.02 0.41±0.27

(µmol m-2

s-1

) after 0.69±0.42 0.23±0.09 0.38±0.29 0.37±0.25 0.26±0.13 0.34±0.15 0.56±0.53

RL/RD ratio before 0.37±0.26 0.20±0.16 0.24±0.06 0.69±0.38 0.34±0.11 0.41±0.08 0.66±0.43

after - 0.90±0.41 0.34 - 0.36±0.10 0.49±0.41 -

Na before 0.71±0.02 1.00±0.05 1.32±0.05 1.52±0.02 1.61±0.10 1.67±0.07 1.26±0.19

(g m-2

) after 0.79±0.10 0.94±0.06 0.95±0.03 1.08±0.01 1.23±0.13 1.33±0.12 1.25±0.10

Ma before 27.5±1.5 31.6±0.8 39.0±0.5 41.9±1.2 39.5±2.5 35.7±0.5 31.4±4.0

(g m-2

) after 45.2±5.0 42.7±0.2 42.6±0.3 41.6±1.0 42.7±3.4 40.6±0.6 40.6±1.3

Kok r2 before 0.96±0.01 0.96±0.01 0.98±0.01 0.93±0.04 0.95±0.02 0.97±0.01 0.89±0.03

after 0.78±0.03 0.97±0.01 0.90±0.04 0.95±0.01 0.93±0.04 0.94±0.00 0.93±0.05

Abbreviation: Leaf RD, a = area based leaf respiration in the dark, Leaf RL, a = area based leaf

respiration in the light, RL/RD ratio = ratio of respiration in the light to that in the dark, Na = area

based N concentration, Ma = leaf mass per unit area, Kok r2 = coefficients of determination (r

2)

for linear regressions fitted to light response curves over the 20-60 µmol photons m-2

s-1

PPFD

range (used to calculate RL using the Kok method). Data for RL/RD ratio under N treatments of

0.06, 0.5 and 4 mM at the time point ‘after’ are excluded due to excessive number of outliers for

those curves.

150

Table 5.4 Leaf and root chemical, structural and physiological characteristics of 10 genotypes under high (HN, 2 mM) and

low N (LN, 0.06 mM) supply

Organ Dependent

variable

N

treatment Takanari IR 64 Milyang 23 Opus Dular Bg 34-8 Koshihikari Akihikari Azucena Nipponbare

Leaf RD, m

(nmol gDM-1

s-1

)

LN 21.0±4.1 17.1±8.1 12.5±0.8 23.5±1.3 31.9±7.1 24.6±3.1 15.8±4.6 27.4±2.7 25.0±6.2 24.1±6.5

HN 25.9±5.2 25.4±2.5 33.5±9.5 34.3±0.1 22.7±3.6 39.6±8.5 34.1±3.0 35.6±1.7 28.2±7.9 40.8±3.7

RL, a

(µmol m-2

s-1

)

LN 0.31±0.09 0.51±0.18 0.31±0.10 0.42±0.03 0.44±0.05 0.32±0.12 0.45±0.05 0.65±0.12 0.36±0.10 0.38±0.11

HN 0.29±0.09 0.74±0.17 0.49 0.47±0.12 0.71±0.03 0.60±0.27 0.45±0.19 0.53±0.10 0.36±0.09 0.92

RL, m

(nmol gDM-1

s-1

)

LN 12.9±4.0 15.6±5.7 9.4±2.8 13.8±0.4 17.4±2.8 10.3±3.6 18.8±2.0 20.3±4.4 10.3±4.3 12.4±3.5

HN 9.4±2.5 27.8±8.3 18.2 16.9±6.0 19.7±3.2 20.6±7.5 14.4±6.0 18.3±2.8 12.8±3.2 31.8

RL/ RD (ratio) LN 0.52±0.13 1.07±0.18 0.73±0.19 0.59±0.04 0.82±0.03 0.40±0.14 0.86 0.75±0.15 0.41±0.06 0.50±0.11

HN 0.54±0.17 1.03±0.25 1.02±0.05 0.32±0.10 1.33±0.18 0.41±0.05 0.40±0.14 0.43±0.08 0.49±0.06 0.97±0.13

RD/ Ag (ratio)

LN 0.03±0.00 0.03±0.01 0.04±0.01 0.04±0.00 0.03±0.00 0.05±0.01 0.02±0.01 0.04±0.00 0.06±0.01 0.04±0.01

HN 0.04±0.00 0.03±0.00 0.04±0.01 0.04±0.00 0.03±0.00 0.04±0.00 0.04±0.00 0.04±0.00 0.05±0.00 0.04±0.00

RL/ Ag (ratio)

LN 0.02±0.00 0.02±0.00 0.03±0.01 0.03±0.00 0.02±0.00 0.02±0.01 0.03±0.00 0.03±0.01 0.02±0.00 0.02±0.01

HN 0.02±0.00 0.03±0.01 0.04±0.01 0.02±0.01 0.04±0.01 0.02±0.00 0.02±0.01 0.02±0.01 0.02±0.00 0.04±0.01

VO, 1500 LN 8.7±1.0 6.5±2.1 4.2±1.1 6.8±0.3 8.3±1.1 6.0±0.9 5.2±0.6 7.7±0.5 5.4±1.2 4.7±0.7

(µmol m-2

s-1

) HN 6.3±1.3 9.8±1.0 9.6±1.2 9.4±0.3 10.2±0.1 11.0±0.5 10.9±1.4 9.7±0.5 5.7±1.5 12.4±1.0

Vc, 1500 LN 30.5±3.3 22.7±7.2 12.7±2.5 22.6±2.0 27.8±3.5 19.9±3.4 17.9±2.5 26.3±1.5 18.6±3.5 16.0±2.5

(µmol m-2

s-1

) HN 20.6±4.4 29.8±2.1 31.2±3.0 30.5±1.2 30.0±0.6 37.1±4.0 33.2±3.4 29.8±1.4 18.5±4.7 34.4±1.4

Na LN 0.90±0.04 - 0.73 0.75±0.02 0.72±0.05 0.67±0.04 0.58±0.03 0.73±0.05 0.82±0.04 0.61

(g m-2

) HN 1.41±0.10 1.69±0.32 1.57 - 1.60±0.09 - 1.60 1.55±0.01 1.45±0.05 1.30

Nm LN 27.6±1.4 28.9±0.0 24.2±5.0 22.6±1.8 27.1±2.8 23.3±4.8 25.4±1.5 22.8±1.4 22.0±4.0 21.2±0.3

(mg g-1

) HN 49.9±1.3 50.7±2.9 47.7±4.8 49.0±0.0 49.7±1.1 41.0±0.0 51.7±2.1 51.5±1.8 51.8±2.6 47.2±3.7

Ma LN 31.9±0.3 32.7±0.7 32.8±0.5 32.4±1.8 28.2±2.8 31.4±3.2 24.1±1.3 31.4±1.0 38.4±2.1 29.8±2.5

(g m-2

) HN 29.9±2.1 30.7±5.6 29.8±1.7 30.7±4.3 30.5±1.6 36.3±6.7 31.5±1.4 29.9±0.3 28.1±0.9 26.8±2.4

CC at H2 LN 34.2±0.1 33.6±0.1 33.3±0.1 33.7±0.0 33.1±0.1 33.9±0.1 34.0±0.0 33.6±0.1 33.6±0.1 33.9±0.0

(mmol g-1

) HN 36.2±0.1 35.0±0.0 35.7±0.0 35.6±0.0 36.4±0.2 36.1±0.0 35.9±0.0 36.1±0.0 36.3±0.1 35.5±0.1

151

Abbreviation: Leaf RD, m = mass based leaf respiration in the dark, Leaf RL, a = area based leaf respiration in the light, Leaf RL, m = mass based leaf

respiration in the light, RL/RD ratio = ratio of respiration in the light to that in the dark, RD/ Ag ratio = ratio of respiration in the dark to light saturated

gross photosynthesis measured at irradiance 1500 µmol photons m-2

s-1

and 400 µmols mol-1

atmospheric CO2 (where, Ag = Asat +RL), RL/ Ag ratio =

ratio of respiration in the light to light saturated gross photosynthesis measured at irradiance 1500 µmol photons m-2

s-1

and 400 µmols mol-1

atmospheric CO2 (where, Ag = Asat +RL), Vo, 1500 = Rubisco oxygenation velocity at light saturated irradiance 1500 µmol photons m-2

s-1

, Vc, 1500 =

Rubisco carboxylation velocity at light saturated irradiance 1500 µmol photons m-2

s-1

, Na = area based N concentration, Nm = mass based N

concentration, Ma = leaf mass per unit area, CC = plant carbon concentration calculated for second (H2) and fifth (H5) harvests based on

measurements on the sixth harvest, Kok r2 = coefficients of determination (r

2) for linear regressions fitted to light response curves over the 20-60

µmol photons m-2

s-1

PPFD range (used to calculate RL using the Kok method), Root RD, m = mass based root respiration in the dark and Root RD, a =

area based root respiration in the dark.

CC at H5 LN 34.1±0.1 33.0±0.1 33.2+0.1 33.2±0.1 32.8±0.3 33.4±0.1 34.1±0.0 33.4±0.1 33.4±0.1 34.0±0.1

(mmol g-1

) HN 36.3±0.0 35.0±0.0 35.8±0.0 35.6±0.0 36.2±0.0 36.1±0.0 35.9±0.1 36.1±0.0 36.2±0.0 35.5±0.0

Kok r2 LN 0.97±0.01 0.94±0.04 0.90±0.06 0.97±0.01 0.95±0.02 0.91±0.03 0.97±0.01 0.91±0.01 0.97±0.01 0.94±0.02

HN 0.98±0.01 0.96±0.02 0.89±0.03 0.94±0.02 0.95±0.03 0.98±0.00 0.98±0.01 0.96±0.01 0.98±0.01 0.95±0.04

Root RD, m at H2 LN 51.7±5.0 43.4±2.4 50.9±3.9 55.8±7.7 55.1±8.9 47.4±6.5 56.7±8.1 45.5±5.2 45.2±4.2 68.3±5.0

(nmol gDM-1

s-1

) HN 96.8±13.2 89.6±12.0 75.8±4.3 106.2±12.0 73.6±5.6 93.6±7.4 104.6±14.7 96.9±10.2 98.8±10.2 97.8±5.9

RD, a at H2 LN 3.8±0.5 3.0±0.5 3.4±0.3 4.5±0.8 3.3±0.7 4.6±0.8 4.2±0.6 3.6±0.4 3.6±0.3 4.4±0.5

(µmol m-2

s-1

) HN 6.0±0.7 6.3±1.7 4.3±0.1 6.3±0.4 4.3±0.9 6.0±0.6 5.9±0.8 5.3±0.2 5.9±0.9 5.7±0.7

Nm at H5 LN 11.5±0.8 12.3±0.3 11.9±0.8 11.5±0.4 10.9±0.4 12.2±0.5 12.5±0.3 11.5±0.8 12.8±0.4 12.3±0.5

(g m-2

) HN 45.3±4.6 44.4±4.5 46.2±2.7 39.4±3.9 41.2±3.5 45.0±4.8 44.5±4.1 35.4±4.0 48.0±3.6 39.3±4.5

RD, m at H5 LN 31.7±3.5 16.9±2.6 22.4±4.2 27.4±4.0 33.2±12.0 13.5±1.7 22.1±3.1 27.1±8.5 18.7±1.8 19.0±7.1

(nmol gDM-1

s-1

) HN 43.8±4.6 46.3±4.3 39.6±3.6 40.6±4.3 29.5±1.0 61.5±9.7 37.4±2.6 59.0±11.2 50.5±8.3 42.8±9.5

RD, a at H5 LN 2.7±0.4 1.5±0.3 1.9±0.4 2.9±0.4 2.4±0.8 1.5±0.2 1.9±0.2 2.6±0.7 2.3±0.1 2.2±1.2

(µmol m-2

s-1

) HN 3.2±0.3 3.3±0.4 2.5±0.3 3.3±0.2 2.0±0.3 4.9±0.8 3.1±0.3 4.7±0.8 3.7±0.6 2.8±0.6

Nm at H5 LN 17.2±0.7 17.3±1.5 15.5±0.5 14.5±0.7 14.0±0.7 16.0±0.7 15.4±0.2 13.6±1.1 14.3±1.6 16.2±0.7

(g m-2

) HN 29.5±1.1 26.5±2.4 27.9±4.0 26.0±3.1 22.1±2.9 27.4±1.1 25.0±2.2 27.4±0.4 28.4±1.6 25.7±3.4

152

The availability of whole shoot and root data at the second (H2) and fifth

(H5) harvests provided the opportunity to further explore the relative

contributions of above and below ground organs to whole-plant fluxes, taking

into account not just respiration of mature leaves, but also leaves of different

developmental stages and stem respiration. According to a three-way ANOVA,

the fraction of whole-plant R taking place in shoots did not differ significantly

among the 10 genotypes (Table 5.6). Yet there were significant main effects of

time (p < 0.001) and N treatment (p < 0.001). Further, the effect of N treatment

changed via time. Thus, genotypes were averaged at each N treatment and

presented for each harvest separately (Fig. 5.5C). When averaged across the 10

genotypes, daily shoot R contributed more (70%) to daily whole-plant R

compared with root R (30%) at high N (HN) at the H2 and H5 harvests. At H2,

the contribution to whole-plant respiration by roots was 10% higher under low

N (LN) than high N (HN). Importantly, in contrast to the earlier findings using

data for leaf and root R alone, rates of shoot R were considerably higher when

Table 5.5 Results of a two-way analysis of variance (ANOVA) for

leaf and root gas exchange, chemical and structural parameters

considering N treatment (N) and genotype (G) as factors with the

two-way interaction term is shown as N x G. Degrees of freedom

(df), F - values and significance are presented. *p < 0.05, **p <

0.01, ***p < 0.001. n=33-85

Organ Parameter N G

N x G P for N x

G

Error

df

Total

df

df 1 9 9

Leaf RD, a 22.4*** 2.43* 1.78 0.092 59 79

RD, m 20.6*** 1.34 1.12 0.361 59 79

RL, a 5.93* 2.22* 1.30 0.260 54 74

RL, m 4.08* 1.69 1.47 0.185 54 74

RL/RD 0.16 5.68*** 2.38* 0.023 59 79

RL/Ag 0.32 1.59 1.73 0.100 64 84

RD/Ag 2.54 4.18*** 1.28 0.265 62 82

Vo, 1500 46.25*** 2.12* 4.66*** 0.0005 63 83

Vc, 1500 27.32*** 1.75 4.49*** 0.0005 63 83

Na 185.84*** 2.51* 1.93 0.076 39 59

Nm 384.58*** 1.20 0.85 0.573 38 58

Ma 0.54 1.05 1.68 0.115 58 78

Root RD, a at H2 123.30*** 1.42 1.09 0.382 60 80

RD, a at H5 23.87*** 1.61 1.95 0.061 60 80

Abbreviation: Leaf RD, a = area based leaf respiration in the dark, Leaf RD, m =

mass based leaf respiration in the dark, Leaf RL, a = area based leaf respiration in

the light, Leaf RL, m = mass based leaf respiration in the light, RL,/ RD ratio = ratio

of respiration in the light to that in the dark, RD/ Ag ratio = ratio of respiration in

the dark to light saturated gross photosynthesis measured at irradiance 1500

µmol photons m-2

s-1

and 400 µmols mol-1

atmospheric CO2 (where, Ag = Asat

+RL), RL/ Ag ratio = ratio of respiration in the light to light saturated gross

photosynthesis measured at irradiance 1500 µmol photons m-2

s-1

and 400

µmols mol-1

atmospheric CO2 (where, Ag = Asat +RL), Vo, 1500 = Rubisco

oxygenation velocity at light saturated irradiance 1500 µmol photons m-2

s-1

, Vc,

1500 = Rubisco carboxylation velocity at light saturated irradiance 1500 µmol

photons m-2

s-1

, Na = area based N concentration, Nm = mass based N

153

measured directly using the Walz chamber compared to when estimated from

measurements made on mature leaves using the Licor 6400 6 cm2 chamber (Fig.

5.7). Further, comparison of these directly measured whole shoot R fluxes

showed that shoots are the dominant organ contributing to whole plant

respiration. Yet, as was found for the earlier work on leaf and root R of

Nipponbare, N supply had little effect on the contribution of shoots and roots to

whole plant R in the 10 genotypes, at least when considering older plants at H5.

Table 5.6 Results of a three-way analysis of variance (ANOVA) for Root

RD, a and % RShoot/ RPlant considering Time (T), genotype (G) and N

treatment (N) as factors with the three-way interaction term is shown as

T x G x N. Degrees of freedom (df), F - values and significance are

presented. *p < 0.05, **p < 0.01, ***p < 0.001. Only second and fifth

harvests were considered for Root RD, a (n=160) whereas, data belongs

to all six harvests were considered for % RShoot/ RPlant (n=720). Similar

results were obtained for % RRoot/ RPlant as of % RS hoot/ RPlant.

Effect

df

Root RD, a

% RShoot/ RPlant

T 1 98.74*** 36.59***

G 9 2.09* 1.03

N

1 58.16*** 20.69***

T x G

9 0.81 1.32

T x N

1 2.66 36.65***

G x N

9 1.36 1.64

T x G x N

9 0.79 1.15

Error 120 117

154

Given the strength of R-N scaling relationships observed in the single

genotype (Nipponbare), when plants were grown on varying levels of N supply

(Fig. 5.3), I now assess R-N scaling relationships across the 10 genotypes grown

on 0.06 and 2.0 mM N. To achieve this, leaf respiration in the dark on both mass

(Fig. 5.8A) and area (Fig. 5.8B) bases were plotted against corresponding leaf N

concentrations. Two observations can be made. The first is that at any given N

level, rates of R at any given leaf N concentration varied two-fold across the 10

genotypes, suggesting that there are marked genotypic differences in the

efficiency of respiratory energy production and/or use across the selected rice

varieties. Secondly, averaged across all 10 genotypes, low N treatment had a

significant (mass basis: p < 0.05, r2 = 0.067; area basis: p < 0.01, r2 = 0.126, see

Table 5.7) but relatively minor inhibitory effect on rates of leaf R at any given

leaf N. As a result, respiratory N-use efficiency (i.e. rates of leaf R at given N)

were higher in low-N grown plants.

Figure 5.7 Relationship between estimated shoot R and measured shoot

R for the same replicate during 10 genotypes experiment described in

Chapter 3. Shoot R was measured as described in section 5.3.2.3 where

shoot R was estimated using equation 5.4 in section 5.3.2.4. The dashed

line indicates the 1:1 relationship. Statistics were not performed for

above parameters as data are presented at replicate level.

155

Root respiration expressed on a mass basis (Root RD, m) significantly

correlated with root N concentration on mass basis (Nm) (see Table 5.7) across

Figure 5.8 Relationships between (A) leaf respiration in the darkness (Leaf RD,

m) on mass basis versus leaf N per unit mass (Nm); (B) leaf respiration in the

dark (Leaf RD, a) on area basis versus leaf N per unit leaf area (Na); (C) leaf

respiration in the light (Leaf RL, m) on mass basis versus leaf N per unit mass

(Nm); (D) leaf respiration in the light (Leaf RL, a) on area basis versus leaf N per

unit leaf area (Na) and (E) root respiration in the darkness (Root RD, m) on mass

basis versus root N per unit mass (Nm). Data belong to second (H2) and fifth

(H5) harvests are shown in Figure 5.8E by circles and triangles respectively.

Closed and open symbols indicate high (2 mM) and low (0.06 mM) N supply

respectively. (n=3-6; ± SE for leaf traits except, BG 34-8 at 2 mM where n=2; ±

SE for RD, a and RD, m. and n=4; ± SE for root traits at both harvests). See table

5.7 for linear regression analyses results for figures 5.8A, B and E. Linear

regressions (dark lines) are fitted only for significant relationships.

156

10 genotypes at both second and fifth harvests in agreement with the single

genotype. However, when plants were older relationship observed at the second

harvest shifted down and became slightly steeper reflecting the changes in

energy demand through time.

5.4.3 How does N availability influence the degree of light inhibition

of leaf R?

Having quantified responses of leaf respiration in the darkness to variations in

leaf N supply, I now examine the extent to which N supply affected rates of leaf

R in the light (RL), both for the single genotype (Tables 5.1 and 5.3) and for the

10 genotypes grown on two N levels (Tables 5.4 and 5.5). For the single

genotype (Nipponbare) rates of leaf RL on an area basis (leaf RL, a) on 53 DAT

(i.e. on steady-state N supply) varied among the seven N levels, with no clear

pattern with respect to N level (Table 5.3). Further, no significant main effects of

N treatment, cessation or an interaction term were found for leaf RL, a (Table

5.1). For the 10 genotypes, N had a significant (p < 0.05) effect on leaf RL, a and

leaf RL on mass basis (leaf RL, m) (Table 5.4 and 5.5). There were significant

differences in leaf RL, a among 10 genotypes and the response to low N supply

was consistent across genotypes as indicated by the absence of nitrogen-

genotype (N x G) interaction for leaf RL, a (Table 5.5). By contrast, genotypes did

not differ in rates of RL expressed on mass basis (leaf RL, m). To further explore

genotypic and N mediated changes in leaf RL, I plotted mass and area-based

rates of RL against the corresponding leaf N concentrations (Fig. 5.8 C,D). As

was the case with RD, rates of leaf RL at any given leaf N varied markedly among

the 10 genotypes. Interestingly, while mean rates of leaf RL were generally

lower in low-N grown plants, regression analysis found no significant RL-N

relationship, either a mass or area basis. Thus, deficiencies in N supply have less

effect on respiratory rates in the light than rates in darkness.

For most genotypes leaf RL, a < leaf RD, a (Fig. 5.9), demonstrating that in

most cases, light inhibited leaf R. When leaf RL, a was expressed as a fraction of

leaf RD, a at high N, light inhibited respiration in some genotypes but not others

e.g. IR 64, Milyang 23 and Nipponbare (Table 5.4). A two-way ANOVA (Table

157

5.5) confirmed that there was a significant (p < 0.001) difference among

genotypes in the degree of light inhibition, and that while there was no main

effect of N supply on RL/RD, there was a significant N x G interaction term,

underpinned by the fact that in some genotypes (Takanari, IR 64 and BG 34-8) N

supply had little effect of light inhibition, while in others N deficiency decreased

(Opus, Koshihikari and Akihikari) or increased (Milyang 23, Dular, Azucena and

Nipponbare) the degree of light inhibition.

5.4.3.1 Predicting rates of leaf RL and the degree of light inhibition of leaf R

One of the objectives of this chapter was to explore which factors account for

the variability in RL and the degree of light inhibition of leaf R? For this purpose,

all area-based gas exchange data from both experiments [i.e. data from the

experiment with single genotype under seven N treatments (see Chapter 2) and

10 genotypes of rice at two N treatments (see Chapter 3)] were combined to a

single data set. Next, gas exchange parameters were plotted against chemical

and metabolic parameters prior any statistical investigations (Fig. 5.10).

Figure 5.9 Relationship between Leaf respiration in the light (RL, a) and

leaf respiration in the darkness (RD, a) both are expressed on area during

10 genotypes experiment described in Chapter 3. Closed and open

symbols indicate high (2 mM) and low (0.06 mM) N supply respectively.

The dashed line indicates the 1:1 relationship. See table 5.7 for linear

regression analysis results.

158

Figure 5.10 Combined data from both experiments previously described in Chapter 2 (shown in diamonds) and Chapter 3

(shown in circles). Leaf respiration in the light (RL, a, open symbols), leaf respiration in the darkness (RD, a, closed symbols) and

RL/RD, ratio of respiration in the light to that in the dark are plotted against leaf N per unit leaf area (Na), Rubisco oxygenation

velocity at light saturated irradiance 1500 µmol photons m-2 s-1 (Vo, 1500); Rubisco carboxylation velocity at light saturated

irradiance 1500 µmol photons m-2 s-1 (Vc, 1500). (n=3-6 for 10 genotypes experiment described in Chapter 3 where n=2-3 for

some N treatments at dose-response experiment described in Chapter 2; ±SE where n≥3). Linear regressions (dark lines) are

fitted only for significant relationships. RL/RD = 1 is indicated by the horizontal dotted line. See table 5.7 for linear regression

analyses results.

159

Both leaf RL and leaf RD exhibited positive correlations with velocity of

light-saturated Rubisco oxygenation (Vo, 1500) and velocity of light-saturated

Rubisco carboxylation (Vc, 1500). Pearson product-moment correlation (or

Spearman’s rank order correlation in situations where normality was violated)

was performed to identify any other existing correlations: significant positive

correlations were found between leaf RD, a vs. Na (p < 0.01, r = 0.280), leaf RL, a vs.

Vo, 1500 (p < 0.001, r = 0.447), leaf RD, a vs. Vo, 1500 (p < 0.001, r = 0.647), leaf RL, a vs.

Vc, 1500 (p < 0.001, r = 0.431), leaf RD, a vs. Vc, 1500 (p < 0.001, r = 0.653) and RL/RD

vs. Vo, 1500 (p < 0.05, r = 0.250).

Linear regression analyses (Table 5.7) were performed to identify

relationships among above variables further. When considering all data

collectively, variations in RL, a were strongly correlated with variations in RD, a (p

< 0.000, r2 = 0.178), with the slope of the RL, a vs. RD, a plot being 0.29 (i.e. the

average degree of light inhibition of leaf R was 71%). Variations in RL/RD were

strongly correlated with variations in RL, a (p < 0.000, r2 = 0.526), but not with

RD, a. Thus, variations in RL/RD are driven primarily by variations in RL, a rather

RD, a. When further investigating the factors underlying variation in RL, a, RD, a and

RL/RD, no correlations were found among RL, a vs. Na and RL/RD vs. Na while RD, a

was weakly correlated with Na (p < 0.01, r2 = 0.069). There were strong

correlations between RL, a vs. Vo, 1500 (p < 0.000, r2 = 0.192), RD, a vs. Vo, 1500 (p <

0.000, r2 = 0.413), RL, a vs. Vc, 1500 (p < 0.000, r2 = 0.178) and RD, a vs. Vc, 1500 (p <

0.000, r2 = 0.421). A weak correlation was found between RL/RD vs. Vo, 1500 (p <

0.05, r2 = 0.053). Taken together, these observations point to functional

relationships between RD, a and RL, a and the activity of Rubisco. Interestingly,

the lack of any relationship between light inhibition and leaf N, even though

inhibition is linked to Rubisco activity, suggests the fraction of leaf N invested in

Rubisco is variable (among genotypes and/or treatments) and/or the activation

state of Rubisco is variable. Makino (2003) reported that 24-33% of N in a rice

leaf is in the form of Rubisco, and as such, there is evidence that N allocation

may indeed differ.

160

Table 5.7 Results of linear regression analyses to explore relationships among

leaf and root respiratory, chemical and metabolic parameters.

Description y-

axis

x-axis r2 y-axis intercept Slope p Figure reference

Leaf RD, m Nm 0.067 18.556 0.247 0.031 5.8A

RD, a Na 0.126 0.487 0.262 0.005 5.8B

RD, a Na 0.069 0.388 0.221 0.006 5.10

RD, a Vo, 1500 0.413 0.245 0.073 0.000 5.10

RD, a Vc, 1500 0.421 0.217 0.024 0.000 5.10

RD, a RL/RD -0.002 0.803 -0.085 0.367 -

RL, a Na -0.009 0.435 -0.033 0.645 5.10

RL, a RD, a 0.151 0.261 0.250 0.000 5.9

RL, a RD, a 0.178 0.202 0.285 0.000 -

RL, a Vo, 1500 0.192 0.182 0.038 0.000 5.10

RL, a Vc, 1500 0.178 0.187 0.011 0.000 5.10

RL, a RL/RD 0.387 0.136 0.471 0.000 -

RL/RD Na -0.009 0.682 -0.055 0.596 5.10

RL/RD Vo, 1500 0.053 0.405 0.030 0.011 5.10

RL/RD Vc, 1500 0.028 0.445 0.007 0.051 5.10

RL/RD RL, a 0.526 0.173 0.909 0.000 -

RL/RD RD, a 0.005 0.680 -0.117 0.226 -

Roots at H2 RD, m Nm 0.793 37.436 1.287 0.000 5.8E

Roots at H5 RD, m Nm 0.681 -6.145 1.922 0.000 5.8E

Data for Figures 5.8 and 5.9 are from the experiment with 10 genotypes described in chapter

three. Data for figure 5.10 and the rest represent combined data from both experiments

previously described in Chapters 2 and 3. Significant values (p < 0.05) are shown in bold, while

values close to significance are shown in italic. RD, m, mass based respiration in the dark; RD, a,

area based respiration in the dark; RL, a, area based respiration in the light; Na, area based N

concentration; Vo, 1500, Rubisco oxygenation velocity at light saturated irradiance 1500 µmol

photons m-2

s-1

; Vc, 1500, Rubisco carboxylation velocity at light saturated irradiance 1500 µmol

photons m-2

s-1

; RL/RD, ratio of respiration in the light to that in the dark. H2 and H5 indicate

second and fifth harvests respectively.

161

Given the above findings, a stepwise regression analysis was carried out

to establish which combination of traits best accounted for variation in RL, a and

RL/RD. RL, a alone contributed to the 45.6% of the scatter in RL/RD, with RL/RD =

1.001 RL, a + 0.202 (p < 0.000). A two component model (by incorporating RD, a

to a model already consists with RL, a) explained 79.7% of variation in RL/RD

where RL/RD = 1.396 RL, a – 0.777 RD, a + 0.590 (p < 0.000). Adding other variables

(e.g. leaf Na, Ma, Vo, 1500, Vc, 1500 etc.) did not significantly improve the explanatory

power of the model. Next, the relationship between RL, a and leaf functional traits

was analysed with stepwise regression as described above and Vc, 1500 was able

to explain 12.3% of the variation in RL, a where RL, a = 0.010 Vc, 1500 + 0.215 (p <

0.000). However, replacing Vc, 1500 with Vo, 1500 produced a similar model [RL, a =

0.028 Vo, 1500 + 0.238 (p < 0.01)], which explained 10.3% of variation in RL, a.

Taken together, these observations suggest that the variations in RL/RD mainly

rely on the variation in RL, a and to a lesser extent by RD, a while the key factors

governing variation in RL, a are the prevailing rates of carboxylation and/or

oxygenation of Rubisco.

5.4.4 How does N supply influence the proportion of daily fixed CO2

released by R

To determine how N supply and genotypic differences influence the potential

balance between CO2 release by respiration (both in the darkness as well as in

the light) and light-saturated CO2 uptake, the ratio of leaf RD to gross

photosynthesis (Ag) measured at 400 ppm [CO2] was calculated [Ag = Asat +RL]

based on leaf level gas exchange measurements. RD/Ag averaged across

genotypes (0.04) did not change in response to N supply, but did differ

significantly (p < 0.001) among genotypes (Table 5.4 and 5.5). Neither N nor

genotype had a significant impact on the ratio of RL to Ag. Averaged across

genotypes and N levels, the RL/Ag ratio was 0.03 (i.e. RL/Ag < RD/Ag).

Availability of whole shoot and root R along with growth analysis data

presented in Chapter three provided an opportunity to analyse gas exchange

rates at the whole-plant level (Fig. 5.11, see section 5.3.2.4.2 for calculations).

Whole-plant rates of A and R at H5 were lower compared with H2. There were

marked genotypic differences in whole-plant A (at H2 and H5), R (at H2), and

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R/A (at H5). N deficiency reduced whole-plant R in a majority of genotypes

except IR 64, Milyang 23 and BG 34-8 at H2. Similarly, whole-plant A declined in

most genotypes except Milyang 23 and BG 34-8 at H2 and Opus and Koshihikari

at H5. Thus, there was no consistent pattern across the 10 genotypes in their

whole-plant gas exchange response to N supply. At H2, whole-plant R/A was

largely constant (near 0.4) across genotypes and N levels, except IR 64 and

Koshihikari (under low N) where R/A was greater in the low N than high N

plants. At H5, the general pattern was one of whole-plant R/A being greater in

low N grown plants in most genotypes, reflecting the lower rates of whole-plant

A under low N. Thus, while there is a generally coupling of respiration to

photosynthesis at whole plant level, this coupling is not maintained in older,

larger plants at H5.

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Figure 5.11 Bar graphs showing genotypic variation in daily whole plant

respiration (whole plant R), daily whole shoot photosynthesis (whole plant

A), and daily whole plant respiration as a fraction of daily whole shoot

photosynthesis (whole plant R/A) at second (H2) and fifth (H5) harvests of 10

genotypes experiment described in Chapter 3. See section 5.3.2.4.2 for

details on the calculation which was performed at replicate level for whole

plant R where n=3-4; ± SE, while whole plant A and whole plant R/A was

performed at mean level. Statistics were not performed on these parameters.

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5.5 Discussion

The present study provides insights into respiratory fluxes of rice at the tissue,

organ and whole-plant levels, in the dark and also in the light. Rates of root R

were higher (particularly at high N) and more susceptible to N deficiency than

leaf R. The combined daily leaf and root R was homeostatic across steady state

levels of N supply, with root R accounting for a greater percentage of whole-

plant respiration (60-80%) compared with leaf R (20-40%). However, the

influence of N supply was prominent after cessation, with the relative

contribution of root R to combined daily whole plant R being reduced (10-15%)

compared to leaf R (85-90%). Direct measurements of whole-shoot and root R

measurements revealed that shoot R is the dominant organ which accounts 70%

of whole plant R; N supply had a minor impact on this parameter. R-N scaling

relationships were maintained during steady state N supply, with similar slopes

being found for leaf and root R; however, roots formed fundamentally different

R-N scaling relationships following cessation of N supply. Consistent with Reich

et al. (2008), rates of root R at any given N was higher than leaf R. Leaf RD per

unit N varied by two fold across the 10 genotypes at a given N level, and higher

rates of RD per unit N was observed in low N grown plants. Similarly, leaf RL per

unit N varied across genotypes, albeit with no significant leaf RL-N relationship

being found. N deficiency had less impact on leaf RL compared with RD in overall.

Light inhibited leaf R in some genotypes, not in others and the response to N

varied among the 10 genotypes. Most variation in light inhibition was accounted

by variation in leaf RL which was driven by prevailing rates of Rubisco

carboxylation and/or oxygenation. R closely coupled with A at whole plant level,

yet not in older plants.

5.5.1. To what extent do leaves and roots differ in their respiratory

response to variations in N supply?

Respiratory fluxes in leaves (leaf R) and roots (root R) of rice differed in their

responses to steady state N availability. In roots, rates of R (mass basis) (Fig.

5.2A) markedly decreased with decreasing N supply in agreement with past

studies (Poorter et al., 1995, Clarkson, 1985, Van der Werf et al., 1992b), likely

reflecting a decline in rates of N assimilation (Volder et al., 2005), and lower N

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concentration (Fig. 2.2B) which in turn is likely associated with reduced protein

content and turnover activities (Clarkson, 1985, Scheurwater et al., 2000,

Scheurwater et al., 1998, Van der Werf et al., 1992a). Reduced energy demand

associated with maintenance of solute gradients may also have played a role,

particularly for roots shifted from steady-state N to cessation of N supply. The

observed relatively less steep response (Fig. 5.2 A and B) during the steady-

state N supply (compared to plants that experienced cessation of N supply for

10 days) indicates that specific costs of N uptake (Van der Werf et al., 1994,

Lambers et al., 1998, Van der Werf et al., 1992b, Lambers et al., 1996) may have

been higher in plants grown on 0.06 mM compared to those grown on high N,

reflecting the higher specific costs of actively taking up NO3- (Jackson et al.,

2008, Mengel and Viro, 1978) associated with lower influx : efflux ratios

(Lambers and Poorter, 1992, Poorter, 1991) and activation of high affinity

transport system (HATS) for NO3- by low concentration of glutamine under low

N (Glass et al., 2002, Glass et al., 2001) and costs of transport Scheurwater et al.

(1999). Thus, while root R decreased along with N supply, increased specific

costs of ion uptake likely lead to relatively high rates of root R in plants grown

on 0.06 mM.

To what extent did leaves differ from roots in their response to low N

supply? Rice genotype ‘Nipponbare’ largely maintained leaf R (Fig. 5.2 AB)

across N supply; however, that response was not held across all 10 genotypes.

Several past studies (Atkin and Day, 1990, Poorter et al., 1990, Tjoelker et al.,

2005) reported variations in leaf R among species. As is the case for roots, the

observed N-supply induced reduction of leaf R in some genotypes likely reflects

reduced costs associated with maintenance of leaf proteins (van der Werf et al.,

1993b), lower protein turnover activities (De Vries, 1975, Bouma et al., 1994)

and ion gradient maintenance (De Vries, 1975, Cannell and Thornley, 2000,

Thornley and Cannell, 2000). Reduced rates of phloem loading may also be a

factor, as phloem loading accounts ~30% of respiratory costs in the dark

(Bouma et al., 1995) and low-N grown plants are likely to have lower rates of

phloem sugar export. Rice prefers NH4+ over NO3- (Duan et al., 2007) when both

ions available in a mixture and if NH4+ assimilated in roots (Hoefnagel et al.,

1998, Tabuchi et al., 2007), this might further reduced energy demand

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associated with N assimilation in the leaf (Thornley, 2000, Scheurwater et al.,

2002). Interestingly, some genotypes exhibited increased rates of leaf R despite

declines in leaf Nm (Fig. 2.2A) with decreasing N supply. In such cases, it might

be that increased senescence and remobilization (Yoneyama and Sano, 1978,

Mae and Ohira, 1981, Makino, 2005, Kumagai et al., 2009, Pilbeam, 1992) under

N deficiency could have increased the demand for respiratory energy under low

N supply. If so, the net result of the above factors might be that rather than low

N supply might result in no change in respiratory fluxes.

When determining fluxes at whole plant level, estimates of combined

daily leaf and root R remained largely homeostatic across the range of steady

state N levels (Fig. 5.4). Change in root R influenced this parameter to a greater

extent relative to leaf R, as root R accounted 60-80% of this parameter (Fig.

5.5A). Increased biomass allocation to roots at low N can increase C costs below-

ground. However, that was offset by reduced rates of root R under low N

reflecting factors outlined above. Thus, biomass allocation (i.e. RMR) is not a

solid indicator of the respiration and associated activities that occur in roots.

Yet, it is important to incorporate biomass allocation data when modelling

respiratory fluxes at whole plant level by crop modellers or physiologists. After

cessation of N supply, there was a slight drop in respiration at whole plant level

towards limited N supply (Fig. 5.4) largely due to a reduction root R.

5.5.2. How N mediated changes in relative contribution of each organ

to daily whole-plant R

As mentioned above, estimates of daily leaf R contributed less (20-40%) to the

combined fluxes of daily leaf and root R, than did daily root R (60-80%) (Fig.

5.5A). By contrast, shoot R accounted more (70%) for daily whole-plant R (Fig.

5.5C) compared with root R (30%). This discrepancy could be due to few

reasons: (1) daily leaf R was estimated based on measurements of a recently

fully expanded leaf which largely represents the respiratory demand for

maintenance (i.e. protein turnover and ion gradient maintenance) rather

growth; (2) roots represent mostly the whole root system being a dynamic

mixture of young and old tissues, where root tips exhibit higher R than old

tissues; and, (3) shoot R includes the whole canopy, with stems (i.e. the region

where cell division occur in rice) being different to the fluxes of a mature leaf.

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Estimated shoot R (calculated excluding the stem) were lower (Fig. 5.7)

compared to measured shoot R, suggesting that the stem component accounts

for a substantial portion of shoot R. Further, the proportion of above-ground

biomass as the stem could be significant in a crop like rice. Thus, it is important

to consider stem R when studying fluxes at whole plant level. In a study with

lowland rain forest community, leaves, stems (including branches) and roots

accounted 51-56%, 32-38 % and 9-15 % of total respiration respectively

(Amthor and Baldocchi, 2001). The extent to which the root and shoot

contributed to total R depends on the root to shoot ratio, N content in the shoot

and the root, the amount of growth occurring in each organ, and activities they

are engaged in. Importantly, N had a minor influence on proportional

contribution of R in above and below-ground organs to daily whole plant R,

irrespective of whether how the above-ground component was calculated.

5.5.3. How robust are R-N scaling relationships?

The present study provided an opportunity to assess R-N scaling relationships

of rice from different perspectives, including as a function of N availability (i.e.

steady-state differences in N supply and after cessation of N supply), across

genotypes, among organs, in the dark as well as in the light. Consistent with past

evidence, there were correlations between leaf R and N (Ryan, 1995, Noguchi

and Terashima, 2006, Loveys et al., 2003, Tjoelker et al., 1999, Reich et al.,

1998c, Griffin et al., 2001) and root R-N (Reich et al., 1998c, Bahn et al., 2006,

Burton et al., 2002, Tjoelker et al., 2005, Pregizter et al., 1998). While R-N

scaling relationships were similar for leaves irrespective of how N was supplied

- in agreement with Reich et al. (2003) - N deficient leaves did not exhibit a

higher elevation (i.e. higher rates of R at a given N) compared with their high-N

grown counterparts as was previously observed by Wright et al. (2001). While

the finding that roots exhibited fundamentally a different R-N relationships after

cessation compared to plants grown on steady-state N differed from that of

Atkinson et al. (2007), my results did confirm the susceptibility of root R to N

availability. There were common slopes for leaf and root R during the steady

state of N supply indicating that the proportional change in respiratory rate

with declining N supply is similar for the two organs (Fig. 5.3).

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While steady-state N supply had similar proportional effects on leaf and

root R, the elevation (intercept) and the overall position were higher for root R

than leaf R, as found previously by Reich et al. (2008). The reasons for this are

as follows. Firstly, a smaller proportion of N is allocated in mitochondria (4-

7%) compared with chloroplast (75-80%) (Makino and Osmond, 1991).

Further, the proportional allocation of total leaf N to mitochondria and the ratio

of mitochondrial respiratory enzyme activities to leaf N content decreased when

increasing leaf N content. Secondly, ATP, C-skeletons and reducing equivalents

that are produced by the light reaction of photosynthesis can support metabolic

activities thus, could reduce demand for respiratory products in a leaf (Cannell

and Thornley, 2000). Thirdly, Rubisco which accounts for 24-33% of total N in a

rice leaf (Makino, 2003) is relatively stable and does not create a particularly

high demand for respiratory energy relative to the amount of N invested in it.

Fourthly, roots are metabolically more active than leaves, due to high protein

turnover, ion uptake transport and assimilation and maintenance of ion

gradients (Bouma, 2005, Van der Werf et al., 1992a, Bouma et al., 1994).

Further, a substantial fall in root R following cessation confirmed such

expensive activities occur in roots. In brief, R-N scaling relationships varied

depending on the organ and the impact of N availability on R was greater in

roots than in leaves. Hence, more attention may need to be given to roots rather

leaves when modelling C fluxes to predict performances of rice under low N

conditions.

Given different R-N relationships for leaves and roots with respect to

changing N availability, it is inappropriate to predict R of individual organs of a

given species based on a common relationship. Collectively, this behaviour

might indicate two separate logarithmic responses for leaf and root R that pass

via the origin and get saturated towards high N (~ above 30 and 50 mg g-1 for

roots and leaves respectively). In the saturated region leaves may store N

mostly in organic forms (e.g. Rubisco, cell walls and other structures within the

cell) while roots may store N as alanine in vacuoles particularly under anoxic

conditions (Reggiani et al., 2000) and ammonium ions in the vacuole as well as

in the cytoplasm (Wang et al., 1993) (as negligible amount of nitrates stored in

rice). Further work is needed to assess these possibilities. Finally, it is worth

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noting that the above scaling relationship patterns were held across the 10 rice

genotypes. Leaf R varied two-fold across and within a given N level, with low N

grown plants exhibiting higher rates of respiration at a given tissue N

concentration.

5.5.4 N mediated changes and genotypic differences in RL and the

degree of light inhibition of leaf R

R in the light (RL) was lower compared to R in the dark (RD) (Fig. 5.9) in the

majority of genotypes at high and low N. This might indicate withdrawal of C-

skeletons from the tri-carboxylic acid cycle (TCA) cycle to facilitate N

assimilation in the light exhibiting lower RL. In addition, increased use of TCA

cycle organic acids (e.g. citrate) in the light could also truncate the TCA cycle

lowering RL (Gauthier et al., 2010, Tcherkez et al., 2012, Atkin et al., 2013). If C

and N metabolism depend on excess ATP and reducing equivalents that are

produced by light reaction of photosynthesis (Cannell and Thornley, 2000)

rather than RL, this may further lower fluxes of RL. However, the extent to which

this persists is unclear as photosynthesis often reduced under N deficiency due

to negative feedback from accumulated carbohydrates (Chapin III, 1991,

Noguchi and Terashima, 2006, Hermans et al., 2006). Rates of RL represent CO2

release by the TCA cycle plus the oxidative pentose phosphate pathway (OPPP)

that continues in the light and the amount of CO2 release by latter is lower than

the former (Atkin et al., 2013). Thus, to what extent do above processes

continue in the light is important when interpreting RL relative to RD.

Which factors account for the variability in RL and the degree of light

inhibition of leaf R? A positive relationship was found between RL and Rubisco

oxygenation capacity (Vo, 1500) indicating increased demand for respiratory

products when photorespiration is in operation, consistent with past studies

(Tcherkez et al., 2008, Griffin and Turnbull, 2013, Crous et al., 2012, Ayub et al.,

2014). RL/RD also positively correlated with Vo, 1500 as previously observed

(Wang et al., 2001, Shapiro et al., 2004, Ayub et al., 2011, Crous et al., 2012,

Griffin and Turnbull, 2013) suggesting that the gradual suppression of

photorespiration increases the degree of light inhibition of leaf R (i.e. decrease

in the RL/RD ratio) over the range of 0.2-1.33 for RL/RD and 0.79-12.4 µmol m-2 s-

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1 for Vo, 1500 (Fig. 5.10) where corresponding values were 0.55-1 and 1.5-10 for

wheat (Griffin and Turnbull, 2013). According to Tcherkez et al. (2008) the

above observations reflects the demand for C skeletons for NH2 transfer during

the recovery of photorespiratory intermediates. The fact that a positive

predictive relationship was also found between RL and Vc, 1500 indicates Rubisco

carboxylation might further place demands on respiratory products (e.g. ATP)

in the light (e.g. sucrose synthesis). Collectively, variation in RL/RD was largely

explained by variations in RL which is in turn dependent on Rubisco activity.

The present results are in agreement with Griffin and Turnbull (2013) where

suppression of photorespiration under future high CO2 climate can reduce RL,

influencing the C use efficiency in rice.

To what extent did N supply influence RL and the degree of light

inhibition of leaf R? N mediated changes to the degree to which inhibition of

pyruvate dehydrogenase (PDH) activity persists and the cellular demand for

respiratory products [e.g. C skeletons required for photorespiratory NH2

transfer (Tcherkez et al., 2008) and N assimilation (Hurry et al., 2005, Hoefnagel

et al., 1998), demand for respiratory products imposed by the activity of

Rubisco e.g. ATP for sucrose synthesis (Kromer, 1995) and protein turnover (De

Vries, 1975)] determine variability in RL and RL/RD. RL reduced under low N in

most genotypes (Table 5.4 and 5.5) partly due to less Rubisco at low N (Beadle

and Long, 1985). Yet, RL did not correlate with leaf Na (Fig. 5.8 C and D, Fig. 5.10)

and I found no evidence for a distinct R-N correlation in the light in contrast to

fluxes in the dark. Shapiro et al. (2004) also found a statistically non-significant

correlation between RL and Na. Further, there was no main effect of N on RL/RD

(Table 5.5) and RL/RD did not correlated with Na either (Fig. 5.10 and Table 5.7)

during the present study in agreement with Shapiro et al. (2004). My results are

in contrast to that of several other studies (Atkin et al., 2013, Heskel et al., 2012,

Ayub et al., 2014) who reported a positive correlation between RL/RD and Na.

These contrasting findings may reflect variation in N allocation patterns and

activation state of Rubisco across genotypes and N treatments. Chapter four of

the thesis suggests genotypic differences for N allocation to Rubisco. Further

work is needed to explore differences in activation state among genotypes as

low N grown plants might exhibit higher activation state of Rubisco.

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5.5.5. The effect of N supply on the balance between respiration and

photosynthesis at organ and whole-plant level

The balance between R and photosynthesis (A) strongly influences the rate of

biomass accumulation of individual plants (Gifford, 2003, Millar et al., 2011). My

study shows that N supply did not impact on RD/Ag at the leaf level. There were,

however, differences among genotypes for this parameter (Table 5.5). As a

consequence of light inhibition of leaf R, RL/Ag was lower (0.03) than RD/Ag

(0.04) consistent with past studies (Crous et al., 2012, Weerasinghe et al., 2014).

Neither N supply nor genotypic differences influenced RL/Ag. Several past

studies have also reported constant ratios for RL/Ag (Ayub et al., 2011, Atkin et

al., 2000b, Atkin et al., 2013). When expressed at whole plant level, R/A was

largely constant, being about 0.4 across genotypes at early stages of growth.

This indicates the interdependence of R and A where R depend on A for

substrates and A depends on R for ATP, reducing equivalents and C-skeletons

for N assimilation and amino acid biosynthesis (Kromer, 1995, Hoefnagel et al.,

1998, Atkin et al., 2000a, Hurry et al., 2005, Noguchi and Yoshida, 2008). This

ratio slightly increased up to 0.5 under low N in majority of genotypes due to a

reduction in whole plant A of larger plants on the fifth harvest. According to

Amthor and Baldocchi (2001) the above ratio (integrated for the whole growing

season) for cereals (i.e. maize, rice and wheat) could vary between 0.3-0.6.

Taken together, N supply did not influence either RD/Ag or RL/Ag at leaf level.

RL/Ag was lower compared with RD/Ag at leaf level due to light inhibition of leaf

R. Thus, constant RL/Ag and RD/Ag ratios at leaf level can be used when

modelling C fluxes in rice in relation to N supply, yet attention needs to be given

for genotypic differences in RD/Ag at leaf level. Constancy of whole plant R/A

(0.4) irrespective of N supply can be useful in such modelling in rice during

early growth, but not in later growth especially at sub-optimal N supply. The

balance between R/A at whole plant level was about ten times higher than in

individual leaf. According to Atkin et al. (2007) this difference could be due to

three reasons: primarily, R in the stem and roots can increase the whole plant R

component compared to an individual leaf. Secondly, the light interception

across a canopy at whole plant level is non-uniform due to variations in leaf

angles and canopy architecture, thus A at whole plant level is poorly

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represented by a light saturated individual leaf used in gas exchange

measurements. Thirdly, presence of both young and old tissue at whole plant

level can result variations in gas exchange rates.

5.6 Conclusions

The present study has highlighted the fact that root R is more susceptible to low

N conditions compared with leaf R. Biomass allocation to roots per se was not a

solid indicator of the contribution of root R to whole plant R etc. While shoots

contributed 70% to daily whole plant R, the proportional contribution of each

organ to daily whole plant R was independent of N supply. R-N scaling

relationship was largely held for leaves and roots with a fairly common slope

during steady-state of N supply. R at a common N was higher for roots

compared with leaves indicating primary differences in physiological activities

among organs. A fundamentally different R-N scaling relationship was formed in

roots as a consequence of N cessation reflecting an alteration of energy demand

in roots. R-N scaling relationship of leaves was relatively robust to cessation

compared with roots. Leaf RD varied by two fold across genotypes within a given

N level and low N grown plants exhibited a greater respiratory N use efficiency.

Light inhibited leaf R in rice and leaf RL was reduced by low N. Yet, neither RL

nor light inhibition of leaf R correlated with leaf Na indicating potential variation

in N allocation patterns and activation state of Rubisco across N treatments and

genotypes. Genotypic differences were found for RL and light inhibition of leaf R.

Variation in light inhibition of leaf R was largely accounted for by leaf RL which

was in turn dependent on the activity of Rubisco (either carboxylation or

oxygenation) in the light. There was no impact of N supply on the fraction of

daily fixed CO2 released by R at the whole-plant level during early growth.

However, the above fraction at whole-plant level increased during later growth

as a consequence of reduced whole-plant A at low N. Respiration:

photosynthesis ratios at leaf level were slightly lower in the light compared to

dark, but both remained constant across N supply. Attention needs to be given

to genotypic differences when interpreting the above ratio at leaf level in the

dark.

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5.7 Future directions

Further work is needed to investigate differences among genotypes and across

N treatments for activation state of Rubisco which can help to elucidate the lack

of correlation between leaf N and light inhibition despite the contribution of RL

to light inhibition and its (RL) close relationship with Rubisco activity.

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Chapter 6 –Concluding remarks and future directions

6.1 Overview of the thesis

Nitrogen (N) is one of the key determinants of crop growth and yield, with N

deficiencies often resulting in retarded growth and reductions in harvestable

yields and quality. Crop varieties produced by the ‘Green Revolution’ (Khush,

1999, Lassaletta et al., 2014) have helped alleviate hunger and poverty

throughout the world. Yet, yields of such crops are heavily dependent on N

inputs (Mueller et al., 2012), with their N uptake efficiency being relatively low

(Glass, 2003). This encourages farmers to apply excessive amounts of N

fertilizer to soil which in turn create environmental issues (e.g. eutrophication,

soil acidification and greenhouse gas emissions) and economic losses. On the

other hand, crop production needs to be increased [including rice yields by 60-

70% (Takai et al., 2013)] to feed the rising human population which is predicted

to reach 9.7 billion by 2050 (UN, 2015); achieving higher yields would

potentially increase N fertilizer use [240 MMt by the year 2050 (Tilman, 1999)]

around the world. Thus, future crop production needs to be accomplished in a

sustainable manner with minimum N inputs while introducing genotypes that

are efficient in N use. Nitrogen productivity (NP) can be considered as a useful

indicator of the efficiency of N use for biomass production during vegetative

growth, which has the potential to lead to a better N use efficiency (NUE) at

reproductive stage. NP is well characterized in the field of plant eco-physiology;

by contrast, less is known about how NP varies among crop genotypes.

Therefore, in my thesis, I adopted an eco-physiological based approach to

evaluate genotypic variation in growth and NP of rice during early vegetative

growth.

To explore how whole-plant growth of rice and its underlying

components respond when exposed to a logarithmic series of available N, I first

established what N concentrations are needed to create N-deficient phenotypes

of a single genotype; this study is one of the few attempts to understand

components underpinning rice growth based on a detailed dose-response

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experiment (Chapter 2). N-deficient phenotypes were observed over the 0.06-

0.12 mM N range, while relative growth rate (RGR) was optimum at moderate

(0.5-2 mM) levels of N supply. Increased NP under severe N deficiency was not

sufficient to compensate for a greater decline in leaf and plant N concentrations;

consequently, net assimilation rate (NAR) and RGR both declined under low N

supply. The results of Chapter 2 can also be used to provide inferences for

variation in N supply under field conditions. For example, high N fertility in

paddy soils is important to a farmer to ensure rice yields; however, overly high

N availability during early growth can result in high N toxicity symptoms.

Applying the optimum amount of N fertilizer during early stages of growth

would be sufficient to boost leaf and tiller number, while avoiding deleterious

effects of high N supply on plant growth and reduce runoff of excess N into

rivers/lakes/oceans. Subsequent increases in N supply could then be applied as

plant demand increases; ensuring yields are not N limited. Therefore,

manipulation of the amount as well as timing when plants experience N

fertilizer could be important both in terms of preventing toxicity during early

growth or deficiency later.

In Chapter 3, I assessed genotypic variation of 10 genotypes of rice for

their capacity to grow under low N conditions by performing a functional

growth analysis during early vegetative growth. NP varied across genotypes to a

greater extent (~70%) at low N compared with high N (23%). Based on the

above approach, three rice genotypes (Takanari, IR 64 and Milyang 23) were

identified for relatively high RGR at low N compared with high N associated

with high NAR and NP. Thus, the key components driving faster growth in rice

were the efficiency of carbon (C) and N use, rather than resource allocation

among organs at the whole-plant level. Given that, next I investigated what

physiological mechanisms could explain the observed maintenance of growth

and NP at low N conditions. In Chapter 4, I assessed the extent to which leaf-

level photosynthetic N use efficiency (PNUE) could explain improved whole-

plant performance of rice genotypes at low N. There was tendency for higher

PNUE in the three selected genotypes at low N (as indicated by enhanced net

photosynthesis on N basis and Rubisco capacity per unit N, due to maintenance

of photosynthetic capacity at low N along with partitioning more N to

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photosynthesis particularly Rubisco and electron transport components);

however, no statistically significant differences in PNUE were found among the

10 genotypes and N levels, either as main or interactive effects. Yet, whole plant

NP at low N strongly correlated with leaf PNUE i.e. both net photosynthesis and

Rubisco capacity on N basis (Fig. 4.11). Thus, there is some evidence that

differences in PNUE at the leaf level might explain the observed variations in

whole-plant NP at low N. Given this, I suggested that further work is needed

with more replicates and multiple genotypes including Takanari, IR 64 and

Milyang 23 to confirm the contribution of leaf-level PNUE to whole-plant NP at

low N. Further, measurements of shoot photosynthesis along with N analysis are

necessary to calculate PNUE at shoot level and to investigate its contribution to

whole-plant NP. In Chapter 5, I investigated the extent to which respiration

could influence above performance at low N. The above mentioned three

genotypes that exhibited high whole-plant NP under low N supply did not

exhibit a consistent pattern for whole-plant respiration at low N; respiration

was relatively homeostatic across different levels of N supply, which contrasts

with the inhibitory effect of low N supply on rates of photosynthesis. Further, no

correlation was found between leaf respiration in the dark (N basis) and whole

plant NP at low N. In addition to the above findings, my thesis provided some

important insights on respiratory fluxes of rice in relation to N supply at tissue,

organ and whole-plant levels in the dark (RD) and also in the light (RL); this

study helps address a knowledge gap in our understanding of variation in rates

of respiration in rice compared to the abundance of studies on photosynthesis.

The results highlight the importance of incorporating biomass allocation data

with measurements of tissue-specific respiratory rates when modelling C fluxes

at whole-plant level. Respiration in the shoot contributed 70% to daily whole

plant respiration compared with the root, with this proportional contribution

not changing in response to N supply. This was further confirmed by a near

common slope observed for R-N scaling relationships of leaves and roots during

the steady state of N supply, with the results being in agreement with Reich et

al. (2008). The higher respiratory fluxes per unit N in roots, high susceptibility

of root respiration to low N, and alteration of R-N scaling relationship of roots

following N cessation, all reflected high energy costs associated with roots (e.g.

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N uptake and assimilation) compared with leaves. Thus, attention needs to be

given to roots when modelling C fluxes of rice at low N conditions.

Leaf RD varied by two fold across genotypes within a given N level and

low-N grown plants exhibited greater respiratory N use efficiency than their

high-N grown counterparts. To my knowledge, this is the only study that has

attempted to examine RL and light inhibition of leaf R in rice, particularly with

respect to N supply. Light inhibited leaf R and leaf RL was reduced by low N. Yet,

neither RL nor light inhibition of leaf R correlated with leaf N content. Genotypic

differences were found for RL and light inhibition of leaf R. Variation in light

inhibition of leaf R was largely accounted by leaf RL which was in turn

dependent on the activity of Rubisco (either carboxylation or oxygenation) in

the light. There was no impact of N supply on the fraction of daily fixed CO2

released by R at the whole-plant level during early growth, but increased at the

whole-plant level during later growth as a consequence of reduced whole-plant

A at low N. Respiration : photosynthesis ratios at leaf level were slightly lower in

the light compared to dark, but both remained constant across N supply.

Thus, the overall objective together with all four specific objectives

outlined earlier in my thesis was addressed in Chapters 2, 3, 4 and 5,

respectively.

6.2 Agronomic considerations

Having established that there was genotypic variation for NP in rice at each N

supply, and that Takanari, IR 64 and Milyang 23 performed well at low N

relative to high N conditions, I now explore to what extent the results of present

study (in a hydroponic system under glasshouse conditions) could be related to

the performance (Table 6.1) in the field. Unlike in a glasshouse, the performance

of genotypes under field conditions could differ depending on climatic (incident

radiation, temperature and day length) (Evans, 1976), soil and other factors at

each field location. For instance, Mae (2011) and Takai et al. (2006) reported

field-based NUE of 38.3 and 44.6 respectively for the rice variety Nipponbare.

While this sort of comparison is not definitive, my thesis research suggests the

genotypes that were efficient in N use in the field are also the ones that

179

maintained high RGR and NP during vegetative stage, as a majority of genotypes

maintained their rankings (except IR 64, Akihikari and Azucena) for RGR and NP

during vegetative growth in relation to the NUE in the field. There was 47%

variation among and the highest (Takanari) and the lowest (Nipponbare) for

NUE (excluding IR 64, Akihikari and Azucena). Sink capacity (i.e. the product of

1000 grain mass in g and total number of spikelets per unit land area of m2) per

unit plant N was 1.6 times greater in Takanari than in Nipponbare; thus,

Takanari could achieve a higher yield per unit plant N than Nipponbare (Mae,

2011). Further, Takanari, Milyang 23 and Nipponbare exhibited a crop growth

rate of 26.0, 23.2 and 19.9 g m-2 d-1, respectively, during tillering to primordia

initiation stage (Takai et al., 2006); this is consistent with rankings obtained for

RGR of the present study. Genotype rankings for RGR and NP from my

glasshouse study were similar to that of field data results (from literature) for

CGR and NUE, suggesting the findings of my study may have wider relevance for

field-grown rice.

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Genotypes were arranged following the order for RGR of present study from highest to the lowest. References were given only for grain yield,

applied N fertilizer and NUE. NUE (kg of grain yield/ kg of applied N fertilizer) was calculated by dividing the amount of grain yield (kg/ha) by the

amount of applied N fertilizer (kg/ha). Genotypic variation for NP was calculated as [(maximum NP– minimum NP)/ minimum NP]*100. Genotypic

variation for NUE was calculated as [(maximum NUE– minimum NUE)/ minimum NUE]*100.

Table 6.1 Background of the genotypes used in the present study

Genotype Type Parents Country of

origin

Year of

release

Duration Vegetative

growth or

days to

full

heading

1000

grain

mass

(g)

Grain

yield

(t/ ha)

Applied N

fertilizer

(kg/ ha)

NUE i.e.

grain yield

(kg)/ Fertilizer

N (kg)

References

Takanari indica x

japonica

Cross-bred from Milyang

25 and Milyang 42 where

both carrying the semi-

dwarf gene sd-1

Japan 1990 116 78 23.5 9.83 150 65.53 Takai et al. (2006)

IR 64 indica IR5657-33-2-1 and

IR2061-465-1-5-5

Philippines 1985 118

57 25.6 3.8 75 50.67 Haefele et al. (2008)

Milyang 23 indica x

japonica

Milyang 23 was bred using

semi-dwarf variety IR 24

Korean

Republic

1976 116 77

25.6 9.09 150 60.60 Takai et al. (2006)

Opus japonica Australia 143 22.5 8.90 150 59.33 Troldahl (2014)

Dular Indica

landrace

- India - 105 45

3.28 60 54.67 Pande and Singh (1970)

Bg 34-8 indica Sri Lanka 119

8.20 150 54.66 Singh et al. (1998)

Koshihikari japonica Cross-bred from Norin 22

and Norin 1

Japan 1956 143 21.9 7.30 150 48.66 Troldahl (2014)

Akihikari japonica Cross-bred from

Toyonishiki and Remei

Japan 1976

21.6 10.11 130 77.76 Mae (2011)

Azucena Japonica

landrace

- Philippines - 90

2.29 90 25.44 Atlin et al. (2006)

Nipponbare japonica Japan 1963 119 84

23.4 6.69 150 44.60 Takai et al. (2006)

181

Given the above, the NUE values observed in the field are likely to reflect

inherent differences among genotypes in how they minimize C loss, maximize C

gain, optimize N and biomass allocation during vegetative growth; knowledge

on such mechanisms is particularly useful under N limited conditions. Takanari

has often been recognized for its better performance compared with

reference/standard genotypes such as Koshihikari, Nipponbare etc. (Horie et al.,

2003, Hirasawa et al., 2010a, Mae, 2011, Ida et al., 2009), when assessing results

of field experiments. Whilst dry mass at heading was medium compared to

other genotypes (Ohnishi et al., 1999), Takanari exhibited high grain yield

(Ohsumi et al., 2007, Ohnishi et al., 1999, Takai et al., 2006, Nagata et al., 2001),

associated with high net assimilation rate, photosynthesis (Xu et al., 1997,

Taylaran et al., 2011) and harvest index of 50% (Ohnishi et al., 1999, Takai et al.,

2006) underpinned by better light interception (Xu et al., 1997), radiation use

efficiency (Katsura et al., 2007), stomatal conductance (Ohsumi et al., 2007,

Taylaran et al., 2011, Takai et al., 2013, Hirasawa et al., 2010a), leaf/ plant N

content (Ohsumi et al., 2007, Taylaran et al., 2011, Hirasawa et al., 2010a), N

partitioning within the plant (Taylaran et al., 2011), Rubisco content (Xu et al.,

1997) and carboxylation efficiency (Xu et al., 1997). Collectively, such

characteristics likely support faster growth and high NP of Takanari during

early growth leading to high grain yield and NUE.

IR 64 is also known for its relatively high crop growth rate and grain

yield (Khush, 1987, Peng et al., 2000). Tillering capacity was highest in IR 64

during present study (data not shown), which is considered as a favourable

characteristic to ensure canopy photosynthesis and panicle number ultimately

contributing to the yield (Shimono and Okada, 2013). Thus, high tillering

capacity along with plant N concentration (see Chapter 3) might drive to faster

growth and NP of IR 64. Yet, its superior performance was not consistent with

field-based evidence for NUE (Table 6.1). This discrepancy could be due to the

presence of unproductive tillers which may cause mutual shading (Ladha et al.,

1998), thus reducing light interception leading to less C gain and grain yield of

the crop. Further, the low spikelet number per m2 (Peng et al., 2000) might

explain low NUE observed for IR 64 in the field. Milyang 23 is also considered a

high yielding variety with high biomass, grain yield, spikelets per panicle,

182

spikelets per m-2, radiation use efficiency and was ranked next to Takanari

(Takai et al., 2006). The yield of Milyang 23 was 36% greater than Nipponbare

(Horie et al., 2003) and this could be due to a higher efficiency of grain filling in

Milyang 23 compared with Nipponbare (Horie et al., 1997). Thousand grain

mass was highest in Milyang 23 and IR 64 relative to other genotypes used in

my study. Early vigour of the Australian variety Opus was similar to that of

Koshihikari; yet, grain yield was above average for Opus while Koshihikari was

a below average yielding variety (Sivapalan et al., 2007). Similarly, Opus had a

higher rank for growth and NP compared with Koshihikari during my study.

Dular, an indica-type landrace is known for its high responsiveness to N and

greater shoot dry mass relative to plants grown without N (Namai et al., 2009).

BG 34-8 was identified as a N–inefficient genotype (Singh et al., 1998) which

exhibits low yields at low N and high responsiveness to N. Both Dular and BG

34-8 showed moderate RGR and NP during my study. Akihikari is reported as a

high yielding variety that maintain high plant N content, ratio of grain yield to

total dry matter and NUE (Mae, 2011). By contrast, RGR and NP of Akihikari

were lower compared with other genotypes when grown under glasshouse

conditions (Table 6.1). The lower growth and NP observed for Azucena were in

agreement with field-based evidence, as Azucena is a japonica type landrace

grown under upland conditions with high N responsiveness (Namai et al., 2009)

and low yields (2.29 t ha-1) (Atlin et al., 2006). The NUE (kg kgN-1) for sink

formation, dry matter and grain production were lowest for Nipponbare

compared with Takanari and another rice variety Saikai 198 (Mae, 2011) and

these studies were in agreement with findings of my glasshouse study where

Nipponbare exhibited the lowest RGR and NP. Thus, overall, there is a

consistency of performance for majority of genotypes used in my study when

compared with field-based literature, highlighting the likely genetic basis of

field performance of the selected rice lines.

6.3 Potential targets of interest to improve nitrogen

productivity in rice

NP can be considered as an important measure of the efficiency of using N for

biomass production during vegetative growth, which can be optimized by

183

altering physiological traits contributing to C and N economy of rice at leaf and

whole plant level. Several factors ensure efficient investment of N in

photosynthesis [e.g. leaf N content, leaf chlorophyll content, radiation

interception, partitioning a greater fraction of N to photosynthesis (Rubisco and

electron transport components) at the expense of non-photosynthetic

components. Moreover, thinner leaves with high specific leaf area, faster

Rubisco (i.e. a greater kcat), high carboxylation and RuBP regeneration capacity,

high stomatal and mesophyll conductance, high intercellular partial pressure of

CO2 relative to the ambient and light inhibition of leaf R] can further contribute

to high PNUE at the leaf level (Poorter and Evans, 1998) possibly leading to a

greater NP and faster growth at whole-plant level. During my study, there was

no statistically significant difference for leaf PNUE (as indicated by net

photosynthesis on N basis and carboxylation capacity per unit N) among 10

genotypes or differential response of genotypes to low N. However, leaf PNUE

strongly correlated with whole plant NP at low N. Hence, in agreement with past

studies (Poorter et al., 1990, van der Werf et al., 1993b, Garnier et al., 1995) leaf

PNUE might be a potential target to improve NP in rice. Further, the three

genotypes that maintained growth and NP at whole-plant level provided some

evidence that the enhanced PNUE at leaf level can potentially be associated with

maintenance of carboxylation capacity at low N (while other genotypes

decrease), with higher PNUE being linked to greater partitioning of leaf N to

photosynthesis (Rubisco and electron transport components). Clearly, further

work is needed to investigate whether leaf PNUE underpinned by above factors

could be a potential target to improve NP of rice in future.

Ideally, a crop with enhanced whole-plant NP would exhibit, enhanced

photosynthetic N use efficiency (PNUE) supported by a greater biomass (high

leaf mass ratio) and N allocation (high leaf N ratio) to leaves rather the stem

(low stem mass ratio and stem N ratio) and root (low root mass ratio and root

N ratio) components. Further, reduced C loss in the shoot (reduced respiration

per unit N in the shoot) and in the root system (reduced respiration per unit N

in the shoot associated with low specific costs of N uptake and assimilation) can

enhance NP. Due to time and logistical constraints, PNUE at whole-plant level

was not examined during my study. Thus, whole-shoot PNUE and factors

184

influencing that parameter (e.g. radiation use efficiency) could be potential

targets if one seeks to improve NP of rice at the whole-plant level as neither

resource (C and N) partitioning nor C loss via respiration at the leaf level could

alone explain variation in NP in rice in response to low N.

6.4 Significance of the study

My PhD research has shown the importance of using an eco-physiological

approach to evaluate rice growth during vegetative growth. As far as I am

aware, this is one of the few studies attempted to analyse rice growth based on a

detailed dose-response experiment and the only study to attempt to analyse rice

growth from a N economy perspective. The study has the potential to provide

important insights about physiological mechanisms underlying growth and NP

in rice. This approach, if combined with non-destructive phenomic tools, could

potentially be applied when screening multiple genotypes for traits

underpinning NP and growth during early stages of growth, which would then

lead to enhanced NUE in subsequent reproductive stages of development.

Although not statistically significant, there is some evidence that several

genotypes exhibited enhanced NP, with higher NP potentially being linked to

enhanced PNUE at the leaf-level, underpinned by maintenance of Rubisco

carboxylation capacity and partitioning of more N to photosynthesis (Rubisco

and electron transport components). In addition, my study provided some

important insights for respiratory fluxes in rice at the tissue, organ and whole-

plant levels, both in the dark as well and in the light. My thesis is the only study

to have investigated light inhibition of leaf R in rice and one of the few studies

attempted to understand light inhibition of leaf R in relation to N supply.

6.5 Future directions to improve nitrogen productivity in rice

One avenue for future work would be trying to explore the extent to which

genotypic variation observed for NP and growth (based on glasshouse

experiments) are held when evaluating those genotypes under field conditions

and taking into account ontogeny. Further, it would be worth investigating how

robust these findings are across multiple sites. It would be useful to conduct a

comprehensive field-based experiment throughout the growth cycle using

185

multiple genotypes to establish linkages between NP and NUE. The present

study had no opportunity to investigate the extent to which PNUE at leaf level

was influenced by the radiation use efficiency (RUE). Thus, it would be useful to

investigate whether genotypic variation exists for RUE, its contribution for leaf

PNUE and its ability to explain variation observed for NP at the whole-plant

level. At this stage, my results indicate that leaf PNUE might explain genotypic

variation observed for NP to some extent. Further, PNUE at the shoot level could

be a key factor driving variation in NP at whole-plant level. Measurements of

shoot photosynthesis together with N analysis of the shoot would facilitate

calculating this parameter. It would be useful to understand genotypic

variations exist for PNUE at shoot level and quantify linkages with variations

observed for NP and growth at the whole-plant level. Further exploring the

traits underpinning differences in RUE and light interception by the canopy (e.g.

canopy architecture, canopy height, leaf orientation and angle, light extinction

co-efficient (K) etc. (Peng, 2000)) would be important to understand the drivers

of variation in whole-plant NP, particularly under N deficient conditions.

Recently, several authors (Ashikari and Matsuoka, 2006, Miura et al.,

2011, Reynolds and Langridge, 2016) highlighted the importance of exploiting

natural genetic variation and analysing quantitative trait loci (QTLs) including

mapping, cloning and pyramiding QTLs for key agronomic and physiological

traits. The advances of molecular biology, mainly the availability of map-based

sequences of the rice genome based on rice variety Nipponbare (IRGSP, 2005),

would facilitate cloning of QTLs and pyramiding such QTLs for breeding. Once

the key traits underpinning NP of rice genotypes, particularly at low N

conditions, are elucidated, it would be useful to identify and validate QTLs,

markers and candidate genes that are responsible for NP gains. Further, trait

stacking/QTL-pyramiding approaches which are based on marker assisted

selection, can be used to accumulate QTLs of interest to a new variety (Ashikari

and Matsuoka, 2006) targeting low N environments. Linking genotypic variation

observed for physiological traits responsible for NP to biochemical based

markers may permit faster selection of genotypes in future breeding

programmes.

186

187

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213

Appendix

RGR and underlying components of 10 genotypes of rice grown under two N treatments (2 and 0.06 mM) over six harvests

Growth parameter Days after

transplanting

(DAT)

mM Takanari IR 64 Milyang 23 Opus Dular BG 34-8 Koshihikari Akihikari Azucena Nipponbare

Plant dry mass (g) 14 2 0.15±0.03 0.24±0.04 0.15±0.01 0.10±0.01 0.14±0.02 0.21±0.04 0.10±0.01 0.15±0.04 0.26±0.04 0.11±0.02

21 2 0.35±0.11 0.55±0.15 0.37±0.05 0.19±0.04 0.38±0.07 0.43±0.12 0.23±0.05 0.27±0.06 0.63±0.12 0.19±0.05

28 2 1.20±0.19 1.64±0.32 0.90±0.15 0.80±0.19 0.89±0.14 1.41±0.28 0.71±0.17 0.86±0.22 1.35±0.22 0.52±0.07

35 2 2.41±0.42 3.27±0.71 1.94±0.28 2.66±0.56 2.96±0.61 4.11±0.63 1.53±0.38 2.08±0.39 4.98±1.13 1.36±0.15

42 2 4.89±0.93 5.80±0.30 3.34±0.72 3.54±0.57 4.99±0.99 5.96±1.01 2.34±0.51 3.53±0.64 7.37±0.50 2.18±0.40

48 2 8.63±0.49 13.2±2.4 6.35±0.77 6.79±1.01 10.1±1.9 10.9±2.0 5.94±1.03 6.59±1.17 16.5±2.6 4.72±0.66

14 0.06 0.07±0.00 0.08±0.01 0.08±0.01 0.06±0.00 0.06±0.01 0.08±0.01 0.05±0.00 0.08±0.01 0.11±0.01 0.05±0.00

21 0.06 0.08±0.01 0.12±0.01 0.10±0.01 0.07±0.01 0.06±0.01 0.12±0.01 0.07±0.01 0.10±0.01 0.12±0.01 0.08±0.01

28 0.06 0.13±0.02 0.15±0.02 0.12±0.01 0.08±0.00 0.09±0.00 0.13±0.01 0.08±0.01 0.11±0.01 0.22±0.06 0.10±0.01

35 0.06 0.20±0.01 0.23±0.02 0.17±0.02 0.14±0.01 0.12±0.01 0.19±0.02 0.12±0.02 0.15±0.01 0.23±0.02 0.12±0.01

42 0.06 0.23±0.03 0.30±0.02 0.23±0.01 0.15±0.02 0.13±0.03 0.23±0.01 0.13±0.02 0.16±0.01 0.20±0.02 0.11±0.02

48 0.06 0.43±0.07 0.50±0.05 0.41±0.08 0.25±0.04 0.23±0.04 0.30±0.05 0.17±0.03 0.27±0.04 0.48±0.13 0.18±0.02

Relative growth rate

(RGR, mg g-1

d-1

)

14 2 153.6 136.6 126.2 133.8 148.5 163.5 133.1 136.8 133.4 126.1

21 2 141.0 128.2 117.8 128.2 137.3 146.7 126.1 128.4 130.6 121.9

28 2 128.4 119.8 109.4 122.6 126.1 129.9 119.1 120.0 127.8 117.7

35 2 115.8 111.4 101.0 117.0 114.9 113.1 112.1 111.6 125.0 113.5

42 2 103.2 103.0 92.6 111.4 103.7 96.3 105.1 103.2 122.2 109.3

48 2 92.4 95.8 85.4 106.6 94.1 81.9 99.1 96.0 119.8 105.7

14 0.06 42.5 33.8 14.5 20.5 19.4 42.2 51.4 15.3 24.6 39.0

21 0.06 46.7 39.4 25.7 28.9 27.8 39.4 45.8 22.3 28.8 36.2

28 0.06 50.9 45.0 36.9 37.3 36.2 36.6 40.2 29.3 33.0 33.4

35 0.06 55.1 50.6 48.1 45.7 44.6 33.8 34.6 36.3 37.2 30.6

42 0.06 59.3 56.2 59.3 54.1 53.0 31.0 29.0 43.3 41.4 27.8

48 0.06 62.9 61.0 68.9 61.3 60.2 28.6 24.2 49.3 45.0 25.4

Net assimilation rate

(NAR, g m-2

d-1

)

14 2 8.18 7.51 6.87 9.99 7.8 9.6 8.21 8.48 7.59 7.49

21 2 8.48 7.57 6.69 7.74 8.44 8.97 7.40 6.88 7.58 6.79

28 2 7.76 6.72 5.58 7.55 6.68 8.19 7.40 7.84 7.62 7.14

35 2 9.56 7.86 7.00 12.60 8.78 10.68 9.45 9.71 10.19 8.78

214

42 2 7.91 7.23 6.00 9.47 7.17 7.57 8.31 8.17 9.50 7.56

48 2 8.80 8.90 6.98 10.80 7.43 9.13 10.59 9.92 12.19 9.59

14 0.06 2.74 2.21 0.94 1.65 1.09 3.01 3.57 1.19 1.80 2.88

21 0.06 2.88 2.52 1.57 2.34 1.57 3.71 3.58 1.65 2.18 2.18

28 0.06 4.52 4.66 3.27 3.76 3.03 4.53 3.69 2.86 3.70 3.15

35 0.06 6.23 5.49 4.32 5.19 4.31 5.06 3.90 4.35 4.90 2.92

42 0.06 5.19 5.20 5.16 5.32 4.17 3.54 2.52 4.48 5.14 2.19

48 0.06 6.41 7.03 6.54 6.89 5.14 3.32 2.32 5.73 5.51 2.56

Leaf area ratio

(LAR, m2 kg

-1)

14 2 18.8±0.6 18.2±0.5 18.4±0.5 13.9±1.8 19.0±1.0 17.0±0.9 16.2±1.0 16.1±0.6 17.6±0.8 16.8±0.6

21 2 16.6±1.2 16.9±1.5 17.6±0.5 16.6±0.8 16.3±1.1 16.4±0.7 17.0±1.6 18.7±1.4 17.2±0.9 17.9±1.8

28 2 16.6±0.7 17.8±1.0 19.6±0.5 16.3±0.7 18.9±1.0 15.9±0.6 16.1±0.4 15.3±0.4 16.8±0.4 16.5±1.0

35 2 12.1±0.5 14.2±0.5 14.4±0.6 9.29±1.11 13.1±0.8 10.6±0.6 11.9±0.5 11.5±0.8 12.3±0.6 12.9±0.6

42 2 13.0±0.7 14.3±0.7 15.4±0.9 11.8±1.1 14.5±1.4 12.7±0.7 12.7±0.7 12.6±0.4 12.9±0.6 14.5±0.7

48 2 10.5±0.4 10.8±1.0 12.2±0.7 9.88±0.47 12.7±0.8 8.97±0.58 9.36±0.49 9.68±0.62 9.83±0.54 11.0±0.5

14 0.06 15.5±0.5 15.3±0.5 15.5±0.9 12.4±0.2 17.8±1.0 14.0±1.0 14.4±0.6 12.8±0.7 13.6±0.8 13.6±0.5

21 0.06 16.2±2.4 15.7±1.8 16.4±2.0 12.3±0.9 17.7±1.6 10.6±0.3 12.8±0.7 13.5±1.1 13.2±1.1 16.6±1.0

28 0.06 11.3±0.7 9.66±0.51 11.3±0.2 9.92±0.44 11.9±0.9 8.09±0.70 10.9±0.6 10.3±0.3 8.93±1.34 10.6±0.8

35 0.06 8.85±0.23 9.22±0.51 11.1±0.3 8.81±0.23 10.4±0.5 6.68±0.41 8.87±0.51 8.35±0.69 7.60±0.59 10.5±0.7

42 0.06 11.4±0.9 10.8±1.3 11.5±0.4 10.8±0.5 12.7±0.8 8.77±0.54 11.5±0.5 9.66±0.73 8.06±0.79 12.7±1.9

48 0.06 9.81±0.41 8.68±0.25 10.5±0.6 8.89±0.42 11.7±0.8 8.63±0.36 10.4±0.6 8.61±0.46 8.16±1.38 9.9±0.54

Specific leaf area

(SLA, m2 kg

-1)

14 2 41.4±1.2 43.5±0.9 43.4±0.8 38.4±1.8 44.6±2.1 40.8±1.3 41.5±2.1 42.7±1.6 40.8±2.0 42.6±2.1

21 2 38.5±2.8 43.9±2.3 41.8±1.7 39.8±2.3 46.6±8.1 39.4±1.6 45.0±4.8 50.2±3.9 38.6±1.5 45.2±3.9

28 2 36.2±0.9 44.0±1.4 43.1±1.1 37.1±1.1 43.9±1.0 37.1±0.6 37.3±0.9 38.5±0.5 36.7±1.7 39.8±1.1

35 2 30.1±1.0 37.7±1.3 35.7±1.0 25.6±2.5 32.3±1.7 28.2±0.8 29.9±1.0 31.3±1.5 29.9±1.1 33.2±0.7

42 2 30.8±0.9 38.5±1.4 37.8±1.0 29.6±1.7 35.2±2.0 31.3±1.3 30.6±1.4 32.8±0.9 31.4±0.7 35.0±1.0

48 2 27.4±0.6 32.6±1.6 33.1±0.7 25.8±2.7 32.5±1.0 25.2±1.2 25.6±1.0 27.7±0.9 26.0±0.8 29.7±0.7

14 0.06 42.0±1.3 45.1±1.4 44.4±1.7 39.9±1.2 49.3±1.5 42.0±1.6 44.3±1.6 42.0±2.2 42.5±2.4 44.1±2.3

21 0.06 48.9±5.5 53.5±5.8 55.7±5.3 43.2±3.7 62.9±6.0 43.0±3.9 55.6±3.5 53.9±4.8 46.8±5.3 69.0±8.2

28 0.06 36.3±2.3 29.4±1.6 35.6±0.9 32.3±1.6 33.9±2.5 24.6±1.8 34.5±1.9 35.7±1.4 30.0±0.9 36.5±2.5

35 0.06 28.1±0.7 27.9±1.7 34.1±0.5 25.0±0.7 28.2±0.8 20.5±1.1 28.4±1.6 28.8±2.4 22.0±1.3 33.3±3.3

42 0.06 30.5±2.7 29.0±3.1 32.9±1.3 28.4±2.5 30.7±2.0 23.3±1.0 31.4±2.2 29.3±1.7 20.7±1.5 35.5±4.6

48 0.06 28.9±1.5 26.4±1.0 31.1±1.3 24.0±1.1 32.3±1.9 23.4±1.0 32.4±3.0 27.4±1.8 24.4±1.3 29.1±2.1

Leaf mass ratio

(LMR, g g-1

)

14 2 0.45±0.01 0.42±0.01 0.42±0.01 0.39±0.04 0.43±0.00 0.42±0.01 0.39±0.01 0.38±0.01 0.43±0.01 0.40±0.01

21 2 0.43±0.01 0.39±0.03 0.42±0.01 0.42±0.02 0.42±0.01 0.42±0.01 0.38±0.02 0.37±0.01 0.45±0.01 0.40±0.02

28 2 0.46±0.01 0.40±0.01 0.46±0.01 0.44±0.02 0.43±0.01 0.43±0.01 0.43±0.01 0.40±0.01 0.46±0.02 0.41±0.01

35 2 0.40±0.01 0.38±0.00 0.40±0.01 0.40±0.01 0.41±0.01 0.37±0.01 0.40±0.01 0.37±0.01 0.41±0.01 0.39±0.01

215

42 2 0.42±0.01 0.37±0.01 0.41±0.01 0.39±0.02 0.41±0.02 0.41±0.01 0.41±0.01 0.39±0.01 0.41±0.01 0.42±0.01

48 2 0.38±0.01 0.33±0.02 0.37±0.01 0.38±0.01 0.39±0.01 0.36±0.01 0.37±0.01 0.35±0.01 0.38±0.01 0.37±0.01

14 0.06 0.37±0.01 0.34±0.01 0.35±0.01 0.31±0.00 0.36±0.02 0.33±0.02 0.33±0.01 0.31±0.01 0.32±0.01 0.31±0.01

21 0.06 0.28±0.02 0.29±0.01 0.30±0.02 0.30±0.04 0.28±0.02 0.26±0.02 0.25±0.02 0.25±0.01 0.29±0.01 0.25±0.02

28 0.06 0.31±0.00 0.33±0.01 0.32±0.01 0.31±0.01 0.35±0.01 0.33±0.02 0.32±0.01 0.29±0.01 0.34±0.04 0.29±0.01

35 0.06 0.32±0.01 0.33±0.01 0.33±0.01 0.35±0.01 0.37±0.01 0.33±0.01 0.31±0.01 0.29±0.02 0.34±0.01 0.32±0.01

42 0.06 0.38±0.01 0.38±0.03 0.35±0.00 0.36±0.02 0.41±0.00 0.38±0.01 0.37±0.02 0.33±0.01 0.39±0.02 0.35±0.00

48 0.06 0.34±0.01 0.33±0.01 0.34±0.01 0.37±0.01 0.36±0.01 0.37±0.01 0.33±0.02 0.32±0.01 0.39±0.06 0.34±0.01

Stem mass ratio

(SMR, g g-1

)

14 2 0.33±0.01 0.33±0.01 0.30±0.00 0.35±0.04 0.27±0.01 0.33±0.01 0.35±0.01 0.34±0.01 0.32±0.01 0.33±0.01

21 2 0.36±0.01 0.33±0.02 0.32±0.01 0.34±0.02 0.30±0.01 0.37±0.01 0.37±0.01 0.39±0.02 0.33±0.01 0.34±0.02

28 2 0.33±0.01 0.34±0.01 0.30±0.01 0.36±0.00 0.27±0.00 0.33±0.01 0.34±0.01 0.37±0.01 0.32±0.05 0.35±0.01

35 2 0.38±0.00 0.38±0.01 0.36±0.01 0.41±0.01 0.30±0.00 0.38±0.01 0.39±0.01 0.41±0.01 0.35±0.01 0.39±0.01

42 2 0.35±0.01 0.38±0.01 0.34±0.01 0.38±0.01 0.29±0.01 0.36±0.01 0.37±0.01 0.38±0.01 0.33±0.01 0.36±0.01

48 2 0.39±0.00 0.44±0.02 0.38±0.01 0.42±0.01 0.32±0.01 0.40±0.01 0.43±0.02 0.46±0.02 0.38±0.01 0.42±0.01

14 0.06 0.28±0.01 0.29±0.01 0.28±0.01 0.34±0.01 0.25±0.01 0.29±0.01 0.32±0.01 0.31±0.01 0.29±0.01 0.33±0.01

21 0.06 0.36±0.02 0.35±0.01 0.34±0.01 0.36±0.02 0.26±0.01 0.36±0.03 0.37±0.02 0.39±0.01 0.29±0.02 0.37±0.02

28 0.06 0.32±0.01 0.31±0.01 0.32±0.01 0.37±0.01 0.22±0.00 0.31±0.01 0.34±0.01 0.37±0.01 0.28±0.09 0.37±0.01

35 0.06 0.33±0.00 0.32±0.01 0.33±0.01 0.33±0.00 0.24±0.01 0.30±0.01 0.35±0.02 0.38±0.01 0.29±0.01 0.35±0.01

42 0.06 0.27±0.01 0.31±0.01 0.29±0.01 0.32±0.01 0.22±0.00 0.27±0.01 0.31±0.01 0.34±0.02 0.27±0.02 0.31±0.01

48 0.06 0.31±0.01 0.32±0.00 0.31±0.01 0.34±0.01 0.23±0.01 0.32±0.02 0.39±0.01 0.38±0.01 0.27±0.03 0.36±0.01

Root mass ratio

(RMR, g g-1

)

14 2 0.22±0.01 0.25±0.01 0.28±0.01 0.26±0.01 0.31±0.01 0.25±0.00 0.26±0.01 0.28±0.02 0.25±0.01 0.28±0.01

21 2 0.21±0.01 0.22±0.01 0.26±0.01 0.24±0.02 0.28±0.01 0.22±0.01 0.24±0.02 0.24±0.02 0.23±0.01 0.26±0.01

28 2 0.21±0.00 0.26±0.01 0.24±0.01 0.20±0.01 0.30±0.01 0.24±0.01 0.22±0.01 0.23±0.01 0.26±0.02 0.24±0.01

35 2 0.21±0.01 0.24±0.01 0.24±0.01 0.23±0.01 0.29±0.01 0.25±0.01 0.21±0.01 0.22±0.01 0.24±0.01 0.22±0.01

42 2 0.23±0.01 0.25±0.01 0.26±0.00 0.22±0.01 0.31±0.01 0.24±0.01 0.22±0.01 0.24±0.02 0.26±0.02 0.22±0.01

48 2 0.23±0.01 0.23±0.01 0.25±0.01 0.20±0.01 0.29±0.01 0.24±0.00 0.20±0.01 0.19±0.01 0.24±0.01 0.21±0.01

14 0.06 0.35±0.01 0.37±0.01 0.38±0.01 0.35±0.01 0.39±0.01 0.38±0.01 0.36±0.01 0.39±0.01 0.39±0.01 0.36±0.01

21 0.06 0.36±0.02 0.36±0.02 0.37±0.01 0.34±0.02 0.45±0.01 0.38±0.02 0.38±0.02 0.36±0.01 0.43±0.01 0.37±0.01

28 0.06 0.37±0.01 0.36±0.00 0.37±0.01 0.32±0.01 0.42±0.01 0.36±0.02 0.34±0.01 0.34±0.01 0.38±0.01 0.34±0.01

35 0.06 0.36±0.01 0.35±0.01 0.34±0.01 0.31±0.01 0.39±0.01 0.37±0.01 0.33±0.01 0.33±0.01 0.37±0.00 0.33±0.00

42 0.06 0.35±0.01 0.32±0.03 0.36±0.01 0.32±0.01 0.37±0.01 0.35±0.01 0.31±0.02 0.33±0.01 0.35±0.01 0.34±0.01

48 0.06 0.35±0.01 0.35±0.01 0.35±0.01 0.29±0.01 0.40±0.01 0.31±0.01 0.29±0.01 0.30±0.01 0.34±0.02 0.30±0.01

Plant nitrogen

concentration

(PNC, mg g-1

)

14 2 45.8 44.6 45.2 47.5 44.3 41.1 41.8 41.3 43.9 41.8

21 2 44.2 43.9 44.5 38.4 43.0 43.3 44.4 34.0 44.4 39.1

28 2 40.0±0.6 38.6±2.6 40.8±1.4 38.6±0.9 38.3±2.1 39.4±0.6 38.5±1.3 38.7±0.3 39.3±1.6 34.7±2.9

35 2 33.2±1.5 37.8±0.7 36.9±0.7 32.0±2.1 36.3±1.3 37.2±1.5 35.1±2.1 35.3±0.4 35.6±3.2 36.7±1.6

216

42 2 38.6±1.7 36.0±2.7 38.1±3.1 33.5±3.2 32.0±2.5 33.7±2.2 33.2±2.5 33.8±0.9 36.4±2.6 34.2±2.1

48 2 32.0±2.1 30.7±3.6 31.9±2.2 29.3±2.3 31.1±4.1 26.6±2.7 28.2±1.7 28.9±2.4 32.6±5.1 30.0±2.0

14 0.06 19.0 22.2 19.5 18.2 19.8 20.8 20.3 18.6 20.3 18.9

21 0.06 11.0 13.1 12.2 11.8 11.7 12.7 12.4 11.8 13.4 12.4

28 0.06 16.6±0.4 17.7±0.8 17.0±1.0 14.8±0.7 17.4±1.9 15.1±0.5 15.5±0.7 14.3±0.8 15.7±1.7 13.7±1.1

35 0.06 16.6±0.6 19.3±1.1 15.9±1.0 16.8±0.6 16.5±0.9 15.7±0.5 14.6±0.9 15.6±0.9 16.2±1.7 16.9±0.5

42 0.06 21.7±1.1 23.9±1.1 19.5±0.7 18.3±0.7 21.4±1.6 19.9±1.2 18.8±0.5 16.7±1.5 19.3±0.9 19.1±0.9

48 0.06 17.9±0.7 19.1±1.0 16.7±1.0 16.4±0.8 17.9±0.9 16.2±0.9 16.6±0.5 15.4±0.8 15.9±1.8 15.8±0.8

Nitrogen

productivity

(NP, g gN-1

d-1

)

14 2 3.36 3.06 2.79 2.816 3.35 3.98 3.19 3.31 3.04 3.02

21 2 3.19 2.92 2.65 3.337 3.19 3.39 2.84 3.77 2.94 3.11

28 2 3.21 3.10 2.69 3.176 3.30 3.29 3.09 3.10 3.25 3.39

35 2 3.48 2.94 2.74 3.661 3.16 3.04 3.19 3.17 3.51 3.09

42 2 2.67 2.86 2.43 3.325 3.25 2.85 3.16 3.05 3.36 3.20

48 2 2.91 3.12 2.68 3.644 3.03 3.08 3.51 3.33 3.68 3.53

14 0.06 2.24 1.53 0.74 1.12 0.98 2.03 2.53 0.82 1.21 2.07

21 0.06 4.25 3.01 2.11 2.45 2.37 3.10 3.70 1.89 2.15 2.93

28 0.06 3.07 2.55 2.17 2.52 2.08 2.43 2.59 2.04 2.11 2.45

35 0.06 3.31 2.62 3.03 2.72 2.70 2.16 2.37 2.33 2.30 1.81

42 0.06 2.73 2.35 3.03 2.96 2.48 1.56 1.54 2.60 2.15 1.46

48 0.06 3.52 3.19 4.12 3.73 3.36 1.76 1.45 3.21 2.83 1.61

Leaf nitrogen ratio

(LNR, g g-1

)

14 2 0.56 0.55 0.53 0.54 0.54 0.57 0.51 0.50 0.55 0.53

21 2 0.52 0.49 0.50 0.54 0.50 0.54 0.48 0.50 0.51 0.53

28 2 0.62±0.02 0.57±0.01 0.60±0.01 0.60±0.02 0.58±0.01 0.58±0.02 0.58±0.01 0.55±0.01 0.58±0.02 0.57±0.02

35 2 0.61±0.02 0.54±0.01 0.57±0.01 0.57±0.01 0.57±0.01 0.55±0.01 0.59±0.01 0.56±0.02 0.60±0.01 0.57±0.01

42 2 0.58±0.01 0.54±0.01 0.57±0.01 0.59±0.01 0.58±0.00 0.57±0.01 0.57±0.01 0.52±0.01 0.59±0.01 0.57±0.01

48 2 0.58±0.03 0.55±0.01 0.58±0.03 0.59±0.03 0.60±0.01 0.58±0.01 0.61±0.02 0.58±0.01 0.61±0.01 0.54±0.03

14 0.06 0.55 0.53 0.52 0.49 0.49 0.48 0.48 0.47 0.48 0.48

21 0.06 0.42 0.45 0.46 0.45 0.40 0.38 0.37 0.42 0.44 0.40

28 0.06 0.45±0.03 0.45±0.02 0.47±0.04 0.42±0.04 0.50±0.02 0.40±0.04 0.44±0.02 0.45±0.03 0.47±0.03 0.37±0.03

35 0.06 0.45±0.01 0.42±0.01 0.42±0.02 0.43±0.01 0.45±0.01 0.41±0.01 0.40±0.02 0.37±0.02 0.44±0.01 0.42±0.01

42 0.06 0.54±0.01 0.57±0.03 0.53±0.01 0.52±0.02 0.60±0.02 0.53±0.03 0.50±0.02 0.49±0.02 0.54±0.03 0.48±0.02

48 0.06 0.52±0.02 0.48±0.01 0.48±0.03 0.49±0.02 0.48±0.03 0.49±0.02 0.46±0.03 0.46±0.02 0.50±0.05 0.47±0.03

Stem nitrogen ratio

(SNR, g g-1

)

14 2 0.29 0.29 0.25 0.290 0.25 0.27 0.31 0.32 0.26 0.28

21 2 0.28 0.22 0.24 0.237 0.22 0.25 0.29 0.26 0.24 0.22

28 2 0.24±0.03 0.25±0.03 0.23±0.02 0.26±0.02 0.22±0.01 0.26±0.01 0.25±0.02 0.28±0.01 0.25±0.01 0.27±0.02

35 2 0.24±0.01 0.27±0.01 0.24±0.01 0.26±0.01 0.23±0.01 0.28±0.01 0.26±0.02 0.28±0.01 0.23±0.01 0.28±0.01

217

42 2 0.26±0.01 0.28±0.01 0.25±0.01 0.25±0.01 0.21±0.01 0.24±0.02 0.27±0.02 0.29±0.02 0.24±0.01 0.27±0.01

48 2 0.26±0.02 0.29±0.01 0.26±0.04 0.26±0.04 0.22±0.02 0.26±0.02 0.25±0.01 0.29±0.02 0.25±0.01 0.34±0.04

14 0.06 0.19 0.20 0.18 0.22 0.18 0.23 0.23 0.20 0.21 0.25

21 0.06 0.21 0.21 0.18 0.22 0.18 0.26 0.26 0.23 0.15 0.23

28 0.06 0.26±0.01 0.27±0.01 0.23±0.02 0.29±0.02 0.22±0.01 0.29±0.03 0.27±0.02 0.27±0.02 0.22±0.02 0.31±0.02

35 0.06 0.23±0.00 0.26±0.02 0.22±0.01 0.28±0.00 0.21±0.01 0.25±0.01 0.29±0.03 0.31±0.00 0.23±0.01 0.28±0.01

42 0.06 0.19±0.01 0.20±0.02 0.17±0.01 0.24±0.02 0.16±0.00 0.19±0.02 0.23±0.01 0.24±0.02 0.20±0.03 0.23±0.01

48 0.06 0.19±0.02 0.23±0.01 0.22±0.01 0.25±0.02 0.19±0.01 0.25±0.01 0.30±0.02 0.27±0.02 0.21±0.02 0.26±0.01

Root nitrogen ratio

(RNR, g g-1

)

14 2 0.15 0.16 0.22 0.17 0.22 0.16 0.18 0.18 0.19 0.19

21 2 0.20 0.27 0.26 0.20 0.23 0.26 0.21 0.25 0.20 0.29

28 2 0.15±0.01 0.18±0.01 0.17±0.02 0.14±0.00 0.20±0.01 0.16±0.02 0.16±0.01 0.17±0.01 0.18±0.01 0.17±0.01

35 2 0.16±0.01 0.19±0.02 0.19±0.01 0.17±0.01 0.20±0.01 0.17±0.01 0.15±0.01 0.16±0.01 0.17±0.01 0.16±0.02

42 2 0.16±0.00 0.18±0.01 0.18±0.01 0.17±0.00 0.21±0.01 0.19±0.01 0.16±0.00 0.19±0.02 0.18±0.01 0.16±0.02

48 2 0.16±0.00 0.16±0.01 0.16±0.01 0.15±0.02 0.18±0.00 0.16±0.01 0.15±0.01 0.13±0.01 0.14±0.01 0.12±0.01

14 0.06 0.26 0.27 0.30 0.28 0.32 0.29 0.29 0.33 0.31 0.27

21 0.06 0.39 0.33 0.33 0.37 0.36 0.35 0.35 0.33 0.39 0.29

28 0.06 0.29±0.02 0.28±0.01 0.30±0.02 0.29±0.02 0.28±0.01 0.30±0.03 0.29±0.00 0.32±0.03 0.31±0.02 0.32±0.02

35 0.06 0.31±0.01 0.32±0.02 0.35±0.01 0.29±0.01 0.34±0.01 0.34±0.01 0.31±0.01 0.32±0.02 0.34±0.02 0.30±0.01

42 0.06 0.28±0.02 0.27±0.01 0.29±0.01 0.25±0.02 0.24±0.01 0.27±0.01 0.27±0.02 0.27±0.01 0.26±0.03 0.30±0.02

48 0.06 0.29±0.03 0.29±0.01 0.30±0.02 0.26±0.02 0.33±0.02 0.26±0.02 0.24±0.03 0.27±0.01 0.29±0.03 0.27±0.02

Notes – Only means are given for derived parameters RGR, NAR and NP. RGR for each time point was calculated by using the derivative of the second order polynomial regression fitted to the ln plant

dry mass over time. NAR for each time point was calculated by dividing instantaneous RGR (calculated by using the derivative of the second order polynomial regression fitted to the ln plant dry mass

over time) by LAR at the corresponding time point. NP for each time point was calculated by dividing instantaneous RGR for each time point (calculated by using the derivative of the second order

polynomial regression fitted to the ln plant dry mass over time) by PNC at the corresponding time point. Replicates of leaf samples were pooled (similarly for stems and roots) at the first harvest and

only leaves and stems samples were pooled at the second harvest due to smaller plant sizes and insufficient amount of material available at replicate level for Kjeldahl digestion. Thus, only means are

given for PNC, LNR, SNR and RNR at first and second harvests at each N treatment. n = 6±SE for plant dry mass, LAR, SLA, LMR, SMR and RMR. n = 4±SE for PNC, LNR, SNR and RNR.