global rice diversity survey - plant physiology...1 leaf photosynthetic parameters related to...

1

Leaf photosynthetic parameters related to biomass accumulation in a 1

global rice diversity survey 2

Mingnan Qu a,#1

, Guangyong Zheng a,#

, Saber Hamdani a, Jemaa Essemine

a, Qingfeng 3

Song a, Hongru Wang

b, Chengcai Chu

b, Xavier Sirault

c and Xin-Guang Zhu

a,* 4

5

a Institute for Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 6

200032 China 7

b Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 8

Beijing 100101 China 9

c Australian Plant Phenomics Facility – The High Resolution Plant Phenomics Centre, 10

Corner Clunies Ross Street and Barry Drive, Canberra, ACT 2601, Australia. 11

12

13

*Corresponding author. Email: [email protected] 14

#These authors contribute equally to this work. 15

16

Abstract 17

Mining natural variations is a major approach to identify new options to improve crop 18

light use efficiency. So far, successes in identifying photosynthetic parameters 19

positively related to crop biomass accumulation through this approach are scarce 20

possibly due to the earlier emphasis on properties related to leaf instead of canopy 21

photosynthetic efficiency. This study aims to uncover rice natural variations to 22

identify leaf physiological parameters that are highly correlated with biomass 23

accumulation, a surrogate of canopy photosynthesis. To do this, we systematically 24

investigated 14 photosynthetic parameters (PTs) and 4 morphological traits (MTs) in 25

X.Z. conceived the experiment; M.Q., G.Z. and S.H. performed the experiments; M.Q.,

G.Z., S.H., H, J.E., Q.S., H.W., C.C., X.S. analyzed the data, M.Q and X.Z. wrote the

paper.

Funding: This work was supported by CAS Strategic Research Project [grant number

XDA08020301], Shanghai Municipal Natural Science Foundation [grant number

17YF1421800] and [grant number 14ZR1446700] and Bill & Melinda Gates

Foundation [grant number OPP1014417].

Plant Physiology Preview. Published on July 27, 2017, as DOI:10.1104/pp.17.00332

Copyright 2017 by the American Society of Plant Biologists

www.plantphysiol.orgon March 30, 2020 - Published by Downloaded from Copyright © 2017 American Society of Plant Biologists. All rights reserved.

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http://www.plantphysiol.org

2

a rice population, which consists of 204 USDA-curated minicore accessions collected 26

globally and 11 elite Chinese rice cultivars in both Beijing (BJ) and Shanghai (SH). 27

To identify key components responsible for variance of biomass accumulation, we 28

applied a stepwise feature selection approach based on linear regression models. 29

Though there are large variations in photosynthetic parameters measured in different 30

environments, we observed that photosynthetic rate under low light (Alow) was highly 31

related to biomass accumulation and also exhibited high genomic inheritability in 32

both environments, suggesting its great potential to be used as a target for the future 33

rice breeding programs. Large variations in Alow among modern rice cultivars further 34

suggest great potential of using this parameter in contemporary rice breeding for 35

improvement of biomass and hence yield potential. 36

37

Keywords: SNP-based heritability, biomass, photosynthetic traits under low light, 38

linear regression model. 39

40

Abbreviations: A: photosynthetic rates under high light; Alow: photosynthetic rates 41

under low light; Biomass: above-ground biomass; Ci: internal CO2 under high light; 42

Cilow: internal CO2 under low light; Fv/Fm: maximum PSII efficiency; gs: stomatal 43

conductance under high light; gslow: stomatal conductance under low light; GEs: 44

growth environments; h2

SNP: SNP-based heritability; Ls: stomatal limitation under 45

high light; MTs: morphological traits; PTs: Photosynthetic traits:; LfThck: leaf 46

thickness; Lslow: stomatal limitation under low light; PltHt: plant height; SPAD: SPAD 47

values; Tiller: Tiller number; WUE: water use efficiency under high light; Wlow: water 48

use efficiency under low light. 49

50

51



3

52

Introduction 53

Improving photosynthetic efficiency is regarded as a major target to improve crop 54

biomass production and yield potential (reviewed by Zhu et al., 2010; Long et al., 55

2006). The canopy photosynthetic efficiency, which is determined by leaf area index, 56

canopy architecture, and leaf photosynthetic properties, plays an important role in 57

determining the biomass accumulation (Long et al., 2006; Zhu et al., 2012). 58

Historically, improvement of canopy architecture, i.e. creating cultivars with 59

semi-drawf architecture, more erect leaves, and higher leaf area index, has played an 60

important role in traditional crop breeding (Hedden 2003; Peng et al., 2008), in 61

contrast, the improvement of leaf photosynthetic properties has played a minor or no 62

role during this process. Broadly, there are two major approaches to improve 63

photosynthetic efficiency, i.e. by genetically engineering photosynthetic efficiency if 64

an engineering target is well defined and by conventional crop breeding, i.e. 65

identifying those lines with superior photosynthetic efficiency and then crossing this 66

superior photosynthetic property into desired target cultivars (Long et al., 2006; Gepts, 67

2002). In either case, the major challenge now is to define effective photosynthetic 68

traits which can lead to enhanced biomass production. We have earlier demonstrated 69

that a system approach can be used to identify new targets to improve photosynthesis 70

by combining systems modeling and an evolutionary algorithm (Zhu et al., 2008). The 71

identified targets to improve photosynthesis have been transgenically tested both 72

inside the lab and also in the field (Rosenthal et al., 2011; Simkin et al., 2015). 73

Similarly, increase the speed of recovery from photoprotection has been demonstrated 74

to be a major approach to increase canopy photosynthesis and crop yield potential 75

(Zhu et al., 2004), which has been recently validated in model crop species tobacco in 76

the field (Kromdijk et al., 2016). This success of enhancing biomass production 77

through manipulation of photosynthesis clearly demonstrates that there is huge 78

potential to improve photosynthetic efficiency for greater biomass and yield 79

production. 80

81

82

83

84

85



4

Besides using a systems approach, another potentially rewarding approach to identify 86

parameters related to biomass production is through mining natural variations (Flood 87

et al., 2011; Lawson et al., 2012). The systems approach, to certain degree, increases 88

the potential range of physiological parameters that can be explored than observed in 89

existing cultivars. However, the success of this approach relies on availability of 90

highly sophisticated and accurate systems models for the process under study. The 91

advantage of mining natural variation is that we can collect biomass data and many 92

physiological parameters for a large number of germplasms simultaneously, which 93

facilitates the identification of parameters before availability of highly mechanistic 94

models for the involved processes. 95

96

So far, however, large scale systematic studies of natural variations of photosynthetic 97

parameters in major crops are scarce. Driever et al., (2014) reported natural variations 98

of photosynthetic parameters in 64 elite wheat cultivars and found that though there 99

are significant variation in photosynthetic capacity, biomass and yield, no correlation 100

exists between grain yield and photosynthetic capacity. They suggested that during 101

the breeding process, some traits might have been unintentionally selected out and 102

hence photosynthetic efficiency should be a major target to utilize during the future 103

wheat breeding (Driever et al., 2014; Carmo-Silva et al., 2017). Similar experiments 104

have been conducted in rice which reached similar conclusions, i.e. leaf 105

photosynthetic rate measured under saturating light levels do not show positive 106

correlation with biomass accumulation (Jahn et al., 2011). On the first sight, this is 107

rather contradictory to current theory of photosynthesis. However, if we consider the 108

canopy that the overall crop light use efficiency, where biomass accumulation can be 109

used as a surrogate, is determined by the total canopy photosynthesis, instead of leaf 110

photosynthesis. Indeed, our earlier modeling work showed that light-limited 111

photosynthesis can contribute up to 70% of the total canopy photosynthetic CO2 112

uptake rates, even at a moderate leaf area index of 4.8 (Song et al., 2013). The 113

proportion of light-limited photosynthesis will be even high under either high leaf 114

area index or under future elevated CO2 conditions (Zhu et al., 2012; Song et al., 115

2013). Large scale surveys of rice grain yield, harvest index and biomass 116

accumulation for rice cultivars released since 1966 have shown clearly that the grain 117

yield of cultivars released after 1980 were highly correlated to biomass accumulation, 118

suggesting improved canopy photosynthesis during the recent rice breeding (Peng et 119



5

al., 2001; Hubbart et al., 2007). The potential factors contributing to the canopy 120

photosynthesis in rice remain unknown. 121

122

In this study, we aim to identity leaf photosynthetic parameters that are highly 123

correlated biomass accumulation, a surrogate of canopy photosynthesis. To do this, 124

we surveyed a large number of leaf photosynthetic parameters and crop architectural 125

parameters at two different locations in China, i.e. Shanghai and Beijing. In this study, 126

to enable comprehensive survey of parameters relevant to canopy photosynthesis, we 127

measured photosynthetic parameters not only under high light but also under limiting 128

light conditions, with the intension to examine whether photosynthetic rates under 129

low light are positively correlated with biomass accumulation. Finally, to minimize 130

the potential complexity of source sink interaction during the grain filling stage, we 131

used biomass accumulation before flowering to avoid the complexity of source sink 132

interaction (Chang et al., 2017). To maximize the genetic diversity utilized in this 133

study, we used both a 215 global rice diversity population consisting of 204 minicore 134

accessions and 11 elite Chinese rice cultivars. Our result revealed that photosynthetic 135

rate under low light (Alow) is highly correlated with biomass accumulation in this 136

diverse rice germplasm population under both Beijing and Shanghai environments. 137

Genetic analysis further shows that Alow is under strong genetic control and hence are 138

amenable for breeding or genetic manipulations. The large variations of Alow in 139

modern rice variations and high genetic inheritance suggest that Alow can be used as a 140

promising target in rice marker assisted breeding. 141

142

143

Results 144

145

Variability of the parameters in the global rice diversity panel 146

As shown from Table 1, natural variations for both 14 photosynthetic traits (PTs) and 4 147

morphological traits (MTs) in Beijing (BJ) and Shanghai (SH) conditions showed 148

different levels of heterogeneity. The percentage genetic variation (PGV) is used to 149

represent the levels of natural variation of traits. The PGV is calculated by the 150

differences between extreme values over mean value in the population (see M&M for 151

more details). The values of PGV in BJ ranged from 3.6 to 197.6 for PTs, and ranged 152



6

from 77.2 to 227.9 for MTs; while the values of PGV in SH ranged from 21.5 to 280.1 153

for PTs and ranged from 85.9 to 170.0 for MTs. The trait with minimum natural 154

variation is Fv/Fm and its PGV under BJ/SH environmental conditions is only 12%. For 155

gas exchange related parameters under full light, the PGV values across the two 156

conditions decreased as follows: stomatal conductance under normal light (gs) > 157

stomatal limitation (Ls) > water use efficiency (WUE) > photosynthetic CO2 uptake 158

rate (A) > internal CO2 concentration under normal light (Ci), while the PGV values of 159

these parameters under low light decreased as follows: water use efficiency under low 160

light (Wlow) > stomatal limitation under low light (Lslow) > photosynthetic CO2 uptake 161

rate under low light (Alow) > stomatal conductance under low light (gslow) > internal CO2 162

concentration under low light (Cilow) (Table 1). The PGV of both dark respiration (Adark) 163

and stomatal conductance under dark (gsdark) were at least 120%, which was two times 164

higher than the PGV of the SPAD value. For morphological traits (Table 1), PGV 165

values showed drastic differences between experiments in BJ/SH environmental 166

conditions. The ranking of PGVs for biomass, tiller number, and leaf thickness 167

gradually decreased under BJ/SH environmental conditions. Most of the MTs showed 168

higher variations in PGV values under the BJ environment than those under SH, except 169

for the PGV of plant height (Table 1). 170

171

Estimation of SNP-based heritability (h2

SNP) on a functional trait provides the 172

information about whether this particular trait is under strong genetic control and hence 173

can be used as a potential parameter during crop breeding. SNP-based heritabilities 174

(h2

SNP) of PTs were in range of < 0.001 to 0.72 in BJ/SH environmental conditions 175

(Table 1). Among these PTs, only 4 PTs exhibited significant h2

SNP under BJ/SH 176

environmental conditions, including WUE, Alow, Adark, and SPAD (Table 1). For the MTs, 177

biomass accumulation and tiller number showed high h2

SNP under BJ/SH 178

environmental conditions (Table 1). 179

180

We further employed two-way ANOVA to analyze genotype × environment interaction 181

with regards to PTs and MTs. Results show that all PTs and MTs were significantly 182

different between BJ and SH environments. On one hand, we found strong 183

environmental effects on most of the collected PTs and MTs (Table 2); in contrast, 12 184

out of 18 collected traits were significantly affected by genotype factor, except Ls, gslow, 185

Cilow, Lslow, gsdark and leaf thickness (Table 2). Only Cilow was not significantly affected 186



7

by environment and genotype interactions (Table 2). 187

188

Correlation between biomass and other biological parameters 189

Pearson correlation coefficient was determined to evaluate the relatedness of biomass 190

with different PTs and MTs under BJ/SH environmental conditions (Fig. 1; Table S1; 191

Fig. S2; Fig. S3). As shown in Fig. 1 and Table S1, strong correlations were observed 192

between PTs under both normal light and low light conditions when datasets 193

measured under BJ/SH environmental conditions were combined. Fig. 2 shows the 194

correlation between MTs and PTs in BJ/SH environmental conditions (Fig. 2; Table 195

S2). Results reveal that A, gs, Ci, Alow,Wlow, Cilow, Fv/Fm, and SPAD exerted positive 196

correlation with plant height, tiller number and biomass. On the other hand, WUE, Ls, 197

Lslow, and Adark exerted negative correlation with plant height, tiller number, leaf 198

thickness, and biomass. As expected, there is huge variation in the measured PTs 199

between BJ and SH environmental conditions, suggesting the strong environment 200

impacts on most of the PTs (Fig 3), as shown earlier by the strong environment effect 201

on these parameters (Table 2). Certain photosynthetic parameters, including A, gs, 202

Alow and SPAD, showed high correlation index (R2) between both BJ and SH sites 203

(Fig. 3). 204

205

Linear regression model and stepwise feature selection 206

To identify the key parameters that dominate biomass variation under BJ/SH 207

environmental conditions, we employed a linear regression model (LRM) with a 208

stepwise optimization method based on the Akaike information criterion (AIC).We 209

first evaluated the prediction accuracy of the derived LRMs under BJ, SH and 210

combined datasets (Fig. 4). Our approach used a training dataset consisting of 90% of 211

the original data and a test dataset of the remaining 10% of the data (see Materials and 212

Methods for details). As shown in Fig. 4, the models predicted the values of biomass 213

under BJ, SH and combined environments (P < 0.001) with R2 between the predicted 214

and measured biomass ranging from 0.32 to 0.76 in training dataset (Fig. 4, A, C and 215

E). Furthermore, the model predicted the test dataset with R2 ranging from 0.37 to 216

0.72 across the three models (Fig. 4, B, D and F). These results suggest that the 217

derived LRMs can predict the biomass accumulation with a high level of confidence. 218

219

The PTs identified in these three LRMs were used for further analysis. The PTs 220



8

identified to highly correlated with biomass accumulation from different datasets were 221

shown in Fig. 5 and Table S3. The fitting equations under BJ/SH environmental 222

conditions were listed as followings: The obtained equations under BJ/SH 223

environmental conditions are: Biomass(BJ) = 0.096 gs + 0.115 Alow+ 0.311 × Plant 224

height + 0.355 × Tiller number; Biomass(SH) = 0.053 Ls - 0.081 WUE + 0.152 Alow + 225

0.077 Lslow + 0.127 × Leaf thickness + 0.341 × Plant height + 0.671 × Tiller number; 226

Biomass (Combine) = 0.072 gs + 0.169 Ci - 0.089 Ls + 0.107 Alow + 0.192 Wlow - 0.145 227

Lslow + 0.139 SPAD + 0.448 × Plant height + 0.556 × Tiller number. Two PTs (gs and 228

Alow) were identified in models for BJ; 4 PTs (WUE, Ls, Lslow and Alow) were 229

identified for SH and 7 PTs (gs, Wlow, SPAD, Ci, Ls, Lslow, and Alow) were identified 230

for the combined environments. These large variations in the identified PTs 231

responsible for biomass accumulation in different locations reflect the strong 232

environment impacts on many photosynthetic parameters. Surprisingly, even under 233

such great impacts of environments on photosynthetic parameters, Alow was 234

consistently identified to be closely associated with biomass accumulation in BJ/SH 235

environmental conditions (Fig. 5). gs was also shown to be a major variable associated 236

with biomass accumulation in both the BJ and the combined datasets. The Ls and Lslow 237

were identified in both SH and combined datasets (Fig. 5). Based on these obtained 238

LRMs, the PTs expected to be increased to improve biomass accumulation are gs, Alow, 239

Wlow and SPAD, while those need to be decreased are WUE, Ls, and Lslow (Table S3). 240

241

Ranking of Elite Cultivars within minicore Collection 242

To further evaluate the scope to manipulate Alow for improved biomass production, we 243

examined the distribution of Alow among the minicore panel and the distribution of Alow 244

in 11 current elite rice lines (Fig. 6). Under the BJ environment, Alow exhibits a normal 245

distribution in the minicore population (Fig. 6A) and also there is a huge variation of 246

Alow among the 11 elite rice lines (Fig. 6B). The distribution pattern and ranking of Alow 247

in SH environment are shown in Fig S1. We further evaluated the potential 248

improvements of Alow by calculating the percentage difference (PD) between Alow of 249

the elite lines and the highest Alow observed in the minicore population. There are 250

potentially 76.77% and 85.49% improvement in DHX-Z and ZH11 respectively if 251

their corresponding Alow can reach the maximal Alow in minicore, i.e. that for P4140. 252

253

Discussion 254



9

Natural variation in photosynthetic traits (PTs) is a largely unexploited resource which 255

can be used to identify new targets to breed or engineer higher photosynthetic 256

efficiency (Flood et al., 2011; Driever et al., 2014). Comparing the relatively 257

long-term perspective of engineering photosynthesis for greater yield (Long et al., 258

2015), mining natural variations of photosynthesis using natural population can lead to 259

reasonably short-term (<5 years) crop improvements (Parry et al., 2011). In this study, 260

we explored natural variations in photosynthetic parameters in rice that might are 261

related to biomass accumulation, a surrogate of canopy photosynthesis. Using linear 262

regression models (LRMs) constructed under different environments, we identified 263

photosynthetic rates under low light (Alow) as a major photosynthetic parameter with 264

high correlation with biomass accumulation under two drastically different 265

environments. Here we briefly discuss the major findings of this study and their 266

implications for rice breeding. 267

268

Natural variations and heritability of all photosynthetic parameters 269

Since 1960s, researchers started working on improving photosynthesis through 270

introgression, e.g. in soybean (Ojima, 1974). However, the progress was rather limited 271

because on one hand it remained unclear what photosynthetic traits (PTs) should be the 272

targets, and on the other hand, there were no effective molecular marks related to PTs 273

defined well enough to be used in breeding programs (Flood et al., 2011). The aim of 274

this study is to identify highly heritable PTs relevant to biomass production under 275

different environments. Since screening PTs is labor-intensive and time-consuming, 276

instead of using the global rice core collection of 1794 accessions, we used a minicore 277

diversity panel consisting of 204 global rice accessions, which is sufficiently diverse to 278

effectively represent the original core collection (Agrama et al., 2009) and also is 279

manageable especially for detailed photosynthesis phenotyping. 280

281

As expected, our data suggest that there are substantial variations among 282

photosynthetic parameters under BJ/SH environmental conditions (Table 1), 283

suggesting there is genetic diversity in PTs in rice that can be potentially exploited. 284

Furthermore, our heritability analysis shows that h2

SNP in many PTs, including SPAD, 285

stomatal limitation (Ls) and WUE under BJ/SH environmental conditions, were 286

around 0.6~0.7, which are closed to some earlier reports(Schuster et al., 1992; 287



10

McKown et al., 2014; Geber and Dawson, 1997; Table 1), suggesting that these 288

parameters are under strong genetic control in different species. It is worth 289

emphasizing that, in this study, our estimate of heritability h2

SNP utilizes not only 290

casual genes as in the traditional variance method (reviewed by Zaitlen and Kraft, 291

2012), but also considers other single nucleotide polymorphism markers (h2

SNP) 292

(Yang et al., 2011). The observed high levels of heterogeneity and relatively high 293

h2

SNP for many PTs suggest that these traits can be used as potential candidates in 294

marker assisted breeding for rice (Ackerly et al., 2000). 295

296

Alow is a photosynthetic trait which is highly correlated with biomass accumulation 297

under different environments 298

In this study, a stepwise feature selection approach was applied to the data collected 299

under either BJ or SH environments. With this method, we identified two PTs, i.e., gs 300

and Alow, in both the BJ and its combined datasets (Fig. 5). Both gs and Alow exhibited 301

high correlation with biomass (Fig. 2; Fig. S2; Fig. S3). The values of both 302

parameters show strong correlation between BJ and SH environments (Fig. 3). gs 303

showed a high h2

SNP and substantial natural variation among rice cultivars under 304

BJ/SH environmental conditions (Table 1, 2), suggesting that gs is a good parameter to 305

be used in rice breeding. In fact, gs screening based on thermal imaging (Takai et al., 306

2010b) has already been used in some breeding programs, e.g. the wheat yield potential 307

breeding in the physiology breeding program of CYMMIT (Rajaram et al., 1994). It is 308

worth noting that, in addition to gs itself being potentially important parameter for 309

breeding, faster response of gs to fluctuating light can be an adaptive trait for rice 310

under severe drought conditions (Qu et al., 2016). 311

312

Remarkably, the Alow instead of A under normal light was identified to be a 313

photosynthetic parameter highly correlated with biomass accumulation under BJ, SH 314

and combined datasets (Fig. 5; Table S3). Alow is also under strong genetic control, as 315

shown by its high h2

SNP (Table 1). Therefore Alow is a promising target for future rice 316

breeding improvements. This finding is remarkable since although it has been long 317

recognized that canopy-, instead of leaf-, photosynthesis is a major determinant of 318

biomass accumulation, so far, direct experimental evidence supporting the importance 319

of photosynthetic efficiency under low light is lacking. The strong correlation between 320



11

Alow and biomass accumulation reported here strongly support the notation that 321

photosynthetic CO2 uptake of the lower layer leaves, which usually experiences low 322

light levels, contribute substantially to the overall canopy photosynthesis and hence 323

biomass production. This finding increases the repertoire of parameters known so far 324

that can potentially improve canopy photosynthesis, which includes faster speed of 325

recovery from photoprotective status (Zhu et al., 2004), rapid recovery of stomata 326

conductance under fluctuating light (Lawson and Blatt, 2014; Qu et al., 2016), and 327

Rubisco with optimized kinetic properties (Zhu et al., 2004). Large scale genetic 328

screening of these different parameters and gene identification in this global minicore 329

are underway in our laboratory. 330

331

Potential value of the identified traits in current rice breeding 332

Natural distribution of Alow across the minicore panel yielded a normal distribution 333

(Fig. 6 B); furthermore, there are substantial variation of Alow in the modern elite rice 334

cultivars (Table 3; Fig. 6), suggesting large space to improve Alow to enhanced 335

biomass production in contemporary elite rice cultivars. By comparing the value of 336

Alow in the modern elite cultivars with the extreme values observed in the minicore 337

diversity panel under the BJ environment, we identified candidate donors that can be 338

used as genetic resources for Alow. For example, 76.77% and 85.49% improvement of 339

Alow can be achieved in DHX-Z and ZH11 (elite cultivar), respectively (Table 4), if 340

the causal genes (or QTLs) controlling Alow in P4140 can be transferred into these two 341

cultivars. 342

343

Conclusion 344

345

By mining natural variations of photosynthesis related traits in a natural rice diversity 346

panel, here we found that, among many photosynthetic parameters, photosynthetic 347

rates under low light (Alow) is highly correlated with biomass accumulation under 348

different environments. Furthermore, Alow shows high level of variability among 349

contemporary elite rice lines and it has a high inheritability. All these suggest that 350

Alow is a promising target for the future rice breeding programs. 351

352

Materials and Methods 353

354



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Plant material 355

356

The accessions from the USDA collected minicore rice diversity panel are from 76 357

countries covering 15 geographic regions, which consists of six groups, indica (35.4%), 358

aus (18.7%), tropical japonica (18.2%), temperate japonica (15.2%), aromatic (3.0%) 359

and their admixtures (9.6%) (Agrama et al., 2010; Li et al., 2010). The population 360

accounts for 12.1% of the global rice core accessions and displayed 100% coverage in 361

genetic variation (Agrama et al., 2010). In current study, we used 204 out of 217 362

accessions in the minicore population since the remaining 13 accessions have 363

extremely long growing seasons. In addition, we used 11 Chinese elite rice cultivars 364

(WCC1, WCC2, DHX-Z, HE19, KY131, XS134, ZH11, MH63, KALS, 9311, WY-4) 365

(Hamdani et al., 2015). 366

367

Measurements of leaf gas exchange 368

The 204 minicore panel and 11 elite rice lines were transplanted under two 369

environments, i.e. Beijing (BJ) (116.3943oE, 39.9820

oN) in May 2013 and Shanghai 370

(SH) (121.4530oE, 31.0428

oN) in May 2015. Averaged atmosphere temperature under 371

BJ/SH environmental conditions, during the growth periods from transplanting to 372

booting stage until large-scale measurements started, which spanned around 60 days, 373

were around 24.8 ± 3.1 and 25.5 ± 4.4 oC, respectively. Experiments were conducted 374

in pots and detailed experimental procedures were described in Hamdani et al., (2015). 375

Briefly, plants were sowed in 12L pots filled with commercial peat soil (Pindstrup 376

Substrate no. 4, Pindstrup Horticulture Ltd, Shanghai, China). For each accession, six 377

plants were planted in two pots with three plants per pot. Two pots for the same 378

accession were arranged close together to ensure formation of canopy. Pots from 379

different accessions were separated to avoid shading from a different accession. 380

During the growth period, plants were exposed to natural sunlight and were irrigated 381

daily. Fertilizers were applied twice per month. Experiments of leaf gas exchange 382

were conducted at 60 days after emergence (DAE). For each accession, we used four 383

replicates during the measurements of gas exchange related parameters. All the 384

photosynthesis measurements were finished within 10 days. To minimize the potential 385

errors introduced by potential growth stage differences, we measured photosynthetic 386

parameters from accession 1 through 215 sequentially for the first and third replicates, 387

and then from accession 215 to 1 sequentially for the second and fourth replicates. 388



13

Plants were acclimated in a controlled room with a temperature around 27 oC and the 389

PPFD around 600 mol m-2

s-1

for at least 60 minutes before gas exchange 390

measurements. During the measurements, two levels of photosynthetic photon flux 391

density, i.e. 1200 μmol mol-1

s-1

(normal light) and 100 μmol mol-1

s-1

(low light), 392

were used. Four portable infrared gas exchange system (Li-6400XT, Li-COR Inc., 393

Lincoln, NE, USA) were used simultaneously. An automatic program was applied to 394

measure gas exchange traits under two light levels, traits under normal light include 395

photosynthetic rates (A), stomatal conductance (gs), internal CO2 (Ci), water use 396

efficiency (WUE) and stomatal limitation (Ls); traits under low light include Alow, 397

Gslow, Cilow, Wlow and Lslow (see abbreviation section for more details). The process of 398

such program is as following: leaf was first maintained under a PPFD of 1200 mol 399

m-2

s-1

for at least 5 minutes or until gs reached a steady state, then PPFD was changed 400

to 100 mol m-2

s-1

for 25 minutes allowing gs to approach steady state as described 401

from Qu et al., (2016). During the measurements, the leaf temperature was maintained 402

at 25oC and relative humidity was maintained at ~ 75%, the reference CO2 403

concentration was set as 400 μmol mol-1

and we used the top fully expanded leaves 404

for this measurements. Data were automatically recorded and the average values 405

within the last 1 minute before light switch were used for data analyzing. 406

407

Measurements of dark respiration and maximal quantum yield 408 409

Experiments for dark respiration were conducted at 60 DAE. Respiration rates were 410

determined as net rates of CO2 efflux in darkness during night after 8:00 pm according 411

to Bunce (2007). Leaf temperatures were set to 25oC, reference CO2 concentration was 412

set to be 400 μmol mol-1

, and light level was set to be 0 μmol mol-1

s-1

. 413

414

Multi-Function Plant Efficiency Analyzer (M-PEA) chlorophyll fluorometer 415

(Hansatech, Kings Lynn, Norfolk, UK) was used to measure Fv/Fm, the maximal 416

quantum yield of photosystem II, following Hamdani et al., (2015). Fm represents the 417

maximum chlorophyll fluorescence, Fo is the minimum chlorophyll fluorescence and 418

Fv = Fm-Fo (Oxborough and Baker, 1997; Huang et al., 2016; Essemine et al., 2017). 419

420

Measurements of SPAD and leaf thickness 421

Experiments for SPAD and leaf thickness were conducted at 60 DAE. To estimate leaf 422



14

total chlorophyll content and leaf thickness, SPAD 502 Plus Chlorophyll meter 423

(Spectrum Technologies, Inc.) (Takai et al., 2010a) and Micrometer screw (Mitutoyo 424

Co.) were used, respectively. For each leaf, the chlorophyll content was estimated as 425

the mean of 5 chlorophyll content measurements at different positions in the middle 426

section of the leaf. Four replicates from four different plants were determined for both 427

leaf chlorophyll concentration and leaf thickness. 428

429

Measurements of plant morphological traits 430

The above-ground biomass accumulation (biomass), plant height (PltHt), and tiller 431

number (Tiller) were determined at 60 DAE according to Qu et al., (2016). At least 432

four replicates were measured for each parameter above. Samples for biomass 433

determinations were kept at 120 oC for 1 hour and then under 70

oC for at least 24 hours 434

in a baking oven until constant weight is reached before weights of biomass or leaf 435

segments were measured. 436

437

Regression model between biomass and morphological and photosynthetic traits 438

We used a linear regression model (LRM) to capture correlation of biomass with PTs 439

and MTs. The model is defined as follows: 440

y=β1x1+β2x2+…βvxv+ε 441

Where y is a vector representing biomass values of each rice accession, x is a vector of 442

independent variables, β is weighted coefficients corresponding to x, and ε is an error 443

vector. The model was constructed with a stepwise manner, which can identify highly 444

relevant parameters and remove low relevant parameters based on the Akaike 445

information criterion (AIC) according to Jin et al., (2014). In practice, a training 446

dataset including 90% items of the whole dataset was randomly extracted from the 447

original dataset and the remaining 10% data were used as a test dataset (Kawamura et 448

al., 2010; Iwasaki et al., 2013). The training dataset was first defined to build the 449

regression model, and then an independent validation was conducted on the test 450

dataset to check performance of the model. 451

452

Estimation of SNP-based heritability 453

The GCTA software (Version 1.25.2) (Yang et al., 2011) was employed to estimate 454

SNP-based heritability (h2

SNP) of 23 functional traits using 2.3M filtered SNPs of the 455

minicore population(Wang et al., 2016). GCTA implements the method in two steps, 456



15

i.e., generating a high-dimensional genetic relatedness matrix (GRM) between 457

individuals and then estimating the variance explained by all SNPs by a restricted 458

maximum likelihood (REML) analysis of the phenotypes with GRM (Yang et al., 459

2011). The significance of h2

SNP is assessed by likelihood ratio test (LRT), which is 460

the ratio of likelihood under the alternative hypothesis (H1: h2SNP≠0) to that under the 461

null (H0: h2SNP=0). The LRT and its corresponding p value were reported in the 462

GCTA output file. 463

464

Data analysis 465

In order to quantitatively evaluate the genetic variation of biological traits in the 466

combined population, percentage genetic variation (PGV) was calculated as 467

[(Xmax-Xmin) /X×100 (%), where Xmax, Xmin andX stands for maximum, minimum and 468

mean value in the population, respectively (Gu et al., 2014). The Pearson correlation 469

coefficient was calculated using R package (Corrplot) (3.2.1 version). 470

471

Supplemental data: 472

Supplemental Table S1. Correlation of photosynthetic traits and morphological traits 473

in global minicore panel and elite rice cultivars across the experiments of Beijing (BJ) 474

and Shanghai (SH) sites. 475

Supplemental Table S2. Correlation of photosynthetic traits with morphological 476

traits under Beijing (BJ) and Shanghai (SH) experiments. 477

Supplemental Table S3. Feature selection across Beijing, Shanghai and combined 478

sites. 479

Supplemental Figure S1. Trait distribution of elite cultivars and the minicore 480

accessions under the SH environment. 481

Supplemental Figure S2. Correlation of photosynthetic traits with biomass under the 482

Beijing (BJ) environment. 483


Shanghai (SH) environment. 485

486

487

488

489



16

490

Supplemental Table S1. Correlation of photosynthetic traits and morphological traits 491

in global minicore panel and elite rice cultivars across the experiments of Beijing 492

(BJ) and Shanghai (SH) sites. For trait abbreviations refer to Table 1. The colors in 493

cells ranging from red to green represent the correlation coefficient from negative 494

to positive correlation. 495

Supplemental Table S2. Correlation of photosynthetic traits with morphological 496

traits under Beijing (BJ) and Shanghai (SH) experiments. The values represent 497

Pearson correlation coefficient under BJ/SH experiments. Asterisks “*”, “**” were 498

depicted as P-value<0.05, 0.01, respectively. Refer to Table 1 for detailed 499

abbreviation of each trait. 500

Supplemental Table S3. Feature selection across Beijing, Shanghai and combined 501

sites. Traits abbreviations were as mentioned in detail in title of Table 1. SD and 502

Psignify standard deviation and P-value, respectively. 503

Supplemental Figure S1. Trait distribution of elite cultivars and the minicore 504

accessions under the SH environment. A: distribution of photosynthetic rates under 505

low light (Alow) of elite cultivars within the minicore accessions. B: Histogram 506

representing the distribution of Alow in the minicore diversity panel. The enclosed 507

numbers represent: WCC1①, WCC2②, DHX-Z③, HE19④, KY131⑤, XS134⑥, 508

ZH11⑦, MH63⑧, KALS⑨, 9311⑩, and WY-4⑪

. 509


Beijing (BJ) environment. The values of photosynthetic traits and biomass were 511

normalized ranging from 0 to 1. The values of Pearson correlation coefficient (R) 512

were calculated. Asterisks “*”, “**” represent P value < 0.05, and 0.01, 513

respectively. The abbreviated PT at right-bottom corner of each panel is as 514

described in details in Figure 1. 515


Shanghai (SH) environment. The values of photosynthetic traits and biomass were 517

normalized ranging from 0 to 1. The values of Pearson correlation coefficient (R) 518

were calculated. Asterisks “*”, “**” represent P value < 0.05, and 0.01, 519

respectively. The abbreviated PT at right-bottom corner of each panel is as 520

described in details in Figure 1. 521

522



17

Acknowledgments 523

This work thanks the anonymous reviewers for constructive comments which helped us 524

improve our revision. This work was supported by CAS Strategic Research Project 525

[grant number XDA08020301], Shanghai Municipal Natural Science Foundation 526

[grant number 17YF1421800] and [grant number 14ZR1446700] and Bill & Melinda 527

Gates Foundation [grant number OPP1014417]. 528

529

Figure Legends 530

Figure 1. Correlation of photosynthetic traits (PTs) and morphological traits (MTs) in 531

the global minicore panel and elite rice lines. Data were combined from Beijing (BJ) 532

and Shanghai (SH) experiments. The abbreviations of PTs are: A: photosynthetic 533

rates under high light; Alow: photosynthetic rate under low light; Biomass: 534

above-ground biomass; Ci: internal CO2 under high light; Cilow: internal CO2 under 535

low light; Fv/Fm: maximum PSII efficiency; gs: stomatal conductance under high 536

light; gslow: stomatal conductance under low light; Ls: stomatal limitation under high 537

light; Lslow: stomatal limitation under low light; SPAD: SPAD values; WUE: water 538

use efficiency under high light; Wlow: water use efficiency under low light; PltHt: 539

plant height; Tiller: tiller number; Thick: leaf thickness; Biomass: biomass 540

accumulation. 541

Figure 2. Graphic representation of correlations of different PTs with MTs in the 542

global minicore panel and elite rice lines under Beijing (BJ) and Shanghai (SH) 543

environments. Shaded area at the center represents negative correlation. Trait 544

abbreviations are given in Table 1. 545

Figure 3. Self-correlation of each photosynthetic trait in the global minicore panel 546

and elite rice lines grown in Beijing (BJ) and Shanghai (SH) environments. The 547

values of Pearson correlation coefficient (R) were calculated. Asterisks “*”, “**” 548

represent P value < 0.05, and 0.01, respectively. The abbreviated PT at right-bottom 549

corner of each panel is as reported in details in Figure 1. 550

Figure 4. Model construction and cross-validation in the global minicore panel and 551

elite rice lines under Beijing (A and B), Shanghai (C and D) and the combined (E 552

and F) datasets. The training dataset consists of 90% of the whole dataset and the 553

remaining 10% items were used as a test dataset (see Material and Method for 554

details). Predicted values of biomass versus observed values of biomass were used 555



18

during the cross-validation. Determination index (R2) reflects the accuracy of 556

regression between predicted and observed values. 557

Figure 5. Features selections analysis on photosynthetic traits (PTs) using Linear 558

Regression Models (LRMs). Key PTs identified by models were represented 559

constructed under Beijing (BJ), Shanghai (SH) and their combined environments 560

(Combine). The equations under BJ, SH and Combined environments are listed as 561

followings: Biomass(BJ) = 0.096 gs + 0.115 Alow+ 0.311 × Plant height + 0.355 × 562

Tiller number; Biomass(SH) = 0.053 Ls - 0.081 WUE + 0.152 Alow + 0.077 Lslow + 563

0.127 × Leaf thickness + 0.341 × Plant height + 0.671 × Tiller number; Biomass 564

(Combine) = 0.072 gs + 0.169 Ci - 0.089 Ls + 0.107 Alow + 0.192 Wlow - 0.145 Lslow + 565

0.139 SPAD + 0.448 × Plant height + 0.556 × Tiller number. 566

Figure 6. Trait distribution of elite cultivars and the minicore accessions under the 567

Beijing (BJ) environment. A: Histogram representing the distribution of 568

photosynthetic rates under low light (Alow) in the minicore diversity panel. B: 569

Phenotypic distribution of Alow of elite cultivars within the minicore accessions. 570

The enclosed numbers represent: WCC1①, WCC2②, DHX-Z③, HE19④, KY131⑤, 571

XS134⑥, ZH11⑦, MH63⑧, KALS⑨, 9311⑩, and WY-4⑪

. 572

573



19

Tables 574

Table 1. Natural variation and SNP-based heritability of PTs in the global minicore 575 panel and elite rice lines grown in Beijing (BJ) and Shanghai (SH) environments. 576 PGV was calculated as described in Materials and Methods. Symbols "*, **, ***" 577 represent P< 0.05, 0.01 and 0.001, respectively. The abbreviations of PTs are: A: 578 photosynthetic rates under high light; Alow: photosynthetic rates under low light; 579 Biomass: above-ground biomass; Ci: internal CO2 under high light; Cilow: internal 580 CO2 under low light; Fv/Fm: maximum PSII efficiency; gs: stomatal conductance 581 under high light; gslow: stomatal conductance under low light; Ls: stomatal limitation 582 under high light; Lslow: stomatal limitation under low light; SPAD: SPAD values; 583 WUE: water use efficiency under high light; Wlow: water use efficiency under low 584 light. h

2SNP represents SNP-based heritability. The two grey selected rows mean that 585

the data is available only for BJ site. n: represents the accessions number 586 587

Traits Sites N Range Mean ± SD PGV h2

SNP±S E

A (μmol m-2

s-1

) BJ 214 13.65~28.19 21.02±3.00 69.17 0.13±0.07

SH 186 12.44~39.76 24.35±5.02 112.20 0.15±0.03

gs (mmol m-2

s-1

) BJ 214 0.18~0.99 0.41±0.12 197.56 0.34±0.14*

SH 186 0.14~1.16 0.56±0.18 182.14 0.25±0.08*

WUE (mmol mol-1

) BJ 214 28.77~67.51 44.51±0.66 87.04 0.73±0.20**

SH 187 20.50~77.71 43.44±10.03 131.70 0.61±0.11**

Ci (μmol mol-1

) BJ 215 204.4~316.4 276.53±17.75 40.50 0.71±0.20**

SH 187 245.91~347.75 308.66±21.58 32.99 <0.001

Ls BJ 215 0.18~0.47 0.28±0.05 103.57 0.72±0.20**

SH 188 0.15~0.51 0.26±007 138.46 0.48±0.21**

Alow(μmol m-2

s-1

) BJ 214 2.56~6.62 4.87±0.65 83.37 0.37±0.12*

SH 187 2.26~6.42 3.76±0.67 110.64 0.36±0.12*

Gslow (mmol m-2

s-1

) BJ 214 0.17~0.26 0.12±0.04 75.00 0.08±0.13

Cilow (μmol mol-1

) BJ 214 242.10~342.60 299.76±17.19 33.53 0.29±0.13*

SH 188 265.12~380.37 353.63±15.56 32.59 <0.001

Wlow (mmol mol-1

) BJ 214 20.91~73.25 39.18±0.83 133.59 <0.001

SH 187 8.62~72.07 22.63±9.55 280.38 <0.001

Lslow BJ 214 0.27~0.79 0.61±0.83 85.25 0.08±0.03

SH 188 0.13~0.95 0.44±0.17 186.36 0.15±0.04

Adark(μmol mol-2

s-1

) BJ 214 -0.32~-0.87 -0.47±0.09 117.02 0.40±0.16*

SH 183 -0.21~-1.25 -0.53±0.22 233.96 0.26±0.06*

Gsdark (mol mol-1

) BJ 214 0.01~0.05 0.02±0.01 200.00 0.08±0.12

Fv/Fm BJ 214 0.82~0.85 0.84±0.01 3.57 0.34±0.13*

SH 182 0.66~0.83 0.79±0.03 21.52 0.12±0.03

SPAD BJ 213 27.13~65.2 37.28±4.54 102.12 0.60±0.16*

SH 180 23.53~47.70 36.47±4.77 66.27 0.53±0.17*



20

Biomass (g) BJ 214 4.60~63.40 25.8±9.36 227.9 0.35±0.22*

SH 184 6.69~64.35 22.74±1.04 97.40 0.25±0.03*

PltHt(cm) BJ 214 58.33~138.0 103.26±14.65 77.2 0.07±0.02

SH 188 55.0~130.75 91.11±13.78 170.04 <0.001

Tiller number BJ 214 5.67~30.67 12.93±4.36 193.3 0.25±0.12*

SH 188 6.25~25.67 23.27±2.99 85.93 0.20±0.05*

LfThck (μm) BJ 214 105.67~420.0 210.85±49.86 149.1 <0.001

SH 183 175.07~355.01 209.43±32.30 95.22 <0.001 588 589 590 591 592 593

Table 2. Analysis of variance (F-values and significance) on effects of environments, 594 genotype and environment × genotype interaction on each photosynthetic trait. 595 Stomatal conductance measured under low light (gslow) and at night (gsdark) were not 596 determined under SH experimental condition. ND: no determination. Symbols "*, **, 597 ***" were depicted as P < 0.05, 0.01 and 0.001, respectively. See Table 1 for the 598 detailed abbreviations of the PTs. 599

600

Traits Genotype Environment Interactions

A 5.494*** 165.50*** 8.931***

gs 5.607*** 358.80*** 11.100***

WUE 6.795*** 16.40*** 10.360***

Ci 3.350*** 376.50*** 2.488***

Ls 0.296 135.80*** 4.681***

Alow 3.617*** 708.80*** 6.958***

gslow 1.155 ND ND

Wlow 1.197* 333.40*** 17.340***

Cilow 1.907 109.10*** 1.127

Lslow 0.397 114.72*** 15.110***

Adark 4.338*** 55.84*** 21.120***

gsdark 0.980 ND ND

SPAD 7.68*** 10.18*** 5.438***

Fv/Fm 3.695*** 118.20*** 0.328***

Biomass 6.549*** 45.40*** 16.860***

PltHt 2.554*** 176.80*** 5.650***

Tiller number 1.696*** 2022.00*** 17.910***

LfThck 0.463 986.00*** 15.440*** 601



21

602 603



22

Table 3. Comparison of photosynthetic rates under low light (Alow) between elite 604 cultivars with those of the minicore accession showing highest Alow. Percentage 605 difference (PD) was expressed as: (Alow of extreme accession - Alow of elite 606 cultivar)/(Alow of extreme accession) × 100%. P4140 is an accession in the minicore 607 population which showed the highest values in Alow in the BJ environment. 608 609

610 Elite accessions Mean±SD PD

WCC1 5.39±0.45 22.88

WCC2 5.82±0.83 13.65

DHX-Z 3.74±1.03 76.77

HE19 5.93±0.29 11.68

KY131 5.66±0.88 17.02

XS134 4.87±0.51 35.91

ZH11 3.57±0.60 85.49

MH63 4.66±0.15 41.97

KALS 4.98±0.56 32.97

9311 5.12±0.51 29.21

WY-4 4.57±0.72 44.83

Target accession P4140

611 612 613 614 615



23

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763

764



Figure 1. Correlation of photosynthetic traits (PTs) and morphological traits (MTs) in

the global minicore panel and elite rice lines. Data were combined from Beijing (BJ)

and Shanghai (SH) experiments. The abbreviations of PTs are: A: photosynthetic rates

under high light; Alow: photosynthetic rate under low light; Biomass: above-ground

biomass; Ci: internal CO2 under high light; Cilow: internal CO2 under low light; Fv/Fm:

maximum PSII efficiency; gs: stomatal conductance under high light; gslow: stomatal

conductance under low light; Ls: stomatal limitation under high light; Lslow: stomatal

limitation under low light; SPAD: SPAD values; WUE: water use efficiency under

high light; Wlow: water use efficiency under low light; PltHt: plant height; Tiller: tiller

number; Thick: leaf thickness; Biomass: biomass accumulation.



Figure 2. Graphic representation of correlations of different PTs with MTs in the global

minicore panel and elite rice lines under Beijing (BJ) and Shanghai (SH)

environments. Shaded area at the center represents negative correlation. Trait

abbreviations are given in Table 1.



Figure 3. Self-correlation of each photosynthetic trait in the global minicore panel

and elite rice lines grown in Beijing (BJ) and Shanghai (SH) environments. The

values of Pearson correlation coefficient (R) were calculated. Asterisks “*”, “**”

represent P value < 0.05, and 0.01, respectively. The abbreviated PT at right-bottom

corner of each panel is as reported in details in Figure 1.



Figure 4. Model construction and cross-validation in the global minicore panel and

elite rice lines under Beijing (A and B), Shanghai (C and D) and the combined (E

and F) datasets. The training dataset consists of 90% of the whole dataset and the

remaining 10% items were used as a test dataset (see Material and Method for

details). Predicted values of biomass versus observed values of biomass were used

during the cross-validation. Determination index (R2) reflects the accuracy of

regression between predicted and observed values.



Figure 5. Features selections analysis on photosynthetic traits (PTs) using Linear

Regression Models (LRMs). Key PTs identified by models were represented

constructed under Beijing (BJ), Shanghai (SH) and their combined environments

(Combine). The equations under BJ, SH and Combined environments are listed as

followings: Biomass(BJ) = 0.096 gs + 0.115 Alow+ 0.311 × Plant height + 0.355 ×

Tiller number; Biomass(SH) = 0.053 Ls - 0.081 WUE + 0.152 Alow + 0.077 Lslow +

0.127 × Leaf thickness + 0.341 × Plant height + 0.671 × Tiller number; Biomass

(Combine) = 0.072 gs + 0.169 Ci - 0.089 Ls + 0.107 Alow + 0.192 Wlow - 0.145 Lslow +

0.139 SPAD + 0.448 × Plant height + 0.556 × Tiller number.



Figure 6. Trait distribution of elite cultivars and the minicore accessions under the

Beijing (BJ) environment. A: Histogram representing the distribution of

photosynthetic rates under low light (Alow) in the minicore diversity panel. B:

Phenotypic distribution of Alow of elite cultivars within the minicore accessions. The

enclosed numbers represent: WCC1①, WCC2②, DHX-Z③, HE19④, KY131⑤, XS134⑥, ZH11⑦, MH63⑧, KALS⑨, 9311⑩, and WY-4⑪.



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http://www.ncbi.nlm.nih.gov/pubmed?term=Long SP%2C Marshall%2DColon A%2C Zhu XG %282015%29 Meeting the global food demand of the future by engineering crop photosynthesis and yield potential%2E Cell 161%3A 56%2D66&dopt=abstract

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http://www.ncbi.nlm.nih.gov/pubmed?term=McKown AD%2C Guy RD%2C Quamme L%2C Kl�p?te J%2C La Mantia J%2C Constabel CP%2C El%2DKassaby YA%2C Hamelin RC%2C Zifkin M%2C Azam MS %282014%29 Association genetics%2C geography and ecophysiology link stomatal patterning in Populus trichocarpa with carbon gain and disease resistance trade%2Doffs%2E Mol Ecol 23%3A 5771%2D5790&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=McKown AD, Guy RD, Quamme L, Kl�p?te J, La Mantia J, Constabel CP, El-Kassaby YA, Hamelin RC, Zifkin M, Azam MS (2014) Association genetics, geography and ecophysiology link stomatal patterning in Populus trichocarpa with carbon gain and disease resistance trade-offs. Mol Ecol 23: 5771-5790

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http://www.ncbi.nlm.nih.gov/pubmed?term=Ojima M %281974%29 Improvement of Photosynthetic Capacity in Soybean Variety%2E Japan Agric Res Q 8%3A 6%2D12&dopt=abstract

http://search.crossref.org/?page=1&rows=2&q=Ojima M (1974) Improvement of Photosynthetic Capacity in Soybean Variety. Japan Agric Res Q 8: 6-12

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http://search.crossref.org/?page=1&rows=2&q=Oxborough K, Baker NR (1997) Resolving chlorophyll a fluorescence images of photosynthetic efficiency into photochemical and non-photochemical components - Calculation of qP and Fv'/Fm' without measuring Fo'. Photosynth Res 54: 135-142

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Takai T, Yano M, Yamamoto T (2010b) Canopy temperature on clear and cloudy days can be used to estimate varietal differences instomatal conductance in rice. F Crop Res 115: 165-170


Wang H, Xu X, Vieira FG, Xiao Y, Li Z, Wang J, Nielsen R, Chu C (2016) The power of inbreeding: NGS based GWAS of rice revealsconvergent evolution during rice domestication. Mol Plant. doi: 10.1016/j.molp.2016.04.018


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