carbon utilization efficiency

Welcome

Impact of substrate quality and soil carbon saturation on carbon utilization efficiency by

microbes

SSAC-691

Abhijit Sarkar Roll No.- 10532

Division of Soil Science and Agricultural Chemistry

Introduction

Carbon cycle

Carbon substrates and its quality

Soil carbon saturation

Carbon use efficiency of soil microbes

Conclusions

Future thrust

Contents

Lal et al., 2004

Carbon cycle: the controller of all nutrient cycles in soil

Photosynthesis

Organic compounds

burning Limestone or

coke or coal

Consumed by heterotrophs

Residues of

heterotrophs

Atmospheric CO2

Soil carbon saturation is defined as a soil’s unique limit to C

stabilization as a function of C input levels (at steady state ) based on

the cumulative behaviour of four C pools i.e. chemically-, physically-,

biochemically- protected, and non-protected pools.

Carbon input

Ca

rbo

n c

on

ten

t

Carbon input

Saturation level

Protection level

Protective capacity

Non-protected

Bio-chemically

protected

Micro-aggregate

protected

Silt+ clay protected

Six et al., 2002

What is soil carbon saturation?

What is utilization efficiency of carbon?

Carbon utilization efficiency (CUE), is defined as

the efficiency with which carbon taken up by the

microbial community is converted into microbial

biomass (Winzler and Baumberger, 1938; Clifton,

1946).

Growth yield efficiency: The proportion of

substrate that is assimilated into the biomass

alone, except which no microbial growth occurs

(Thiet et al., 2006).

Ecological efficiency : The efficiency with

which energy is transferred from one trophic

level to the next.

Soil microbes: how they utilize carbon?

Bacteria Fungi Actinomycetes

Protozoa

Mites

Mites attack immediately on dead leaves on

soil surface

Perforate and fragment the dead plant bodies

Facilitate microbial entry and offer a large

surface area for microbial action

Fungi proliferate hyphae and form mycelia on

decomposing organic matter

Protozoa feed on soil bacteria

Soil fauna ingest bacteria with food

Earthworm ingests the decomposing biomass

and soil and excrete granular soil aggregates

Factors controlling litter decomposition and substrate utilization by microbes in soil

Factors that regulate the substrate decomposition and its utilization by

soil microbes

Inherent factor: Soil carbon saturation

Geographic factor:

Latitude (LAT),

Altitude (ALT),

Climatic factor:

Mean annual temperature (MAT),

Mean annual precipitation (MAP),

Substrate quality factor:

Contents of N, P, K, Ca and Mg,

C:N ratio, and

Lignin:N ratio, Zhang et al., 2008

Typical values for organic matter input in some long-term agro-ecosystem experiments

Knabner, 2002

Commonly used soil

organic matter inputs

Plant litter

Crop residues (roots of

cereals and roots of

legumes, cover crops,

aquatic photosynthetic

biomass)

Farm yard manure (FYM)

Green manure

Animal manure

Composts

Type of vegetation Root-to-shoot ratio

Desert grassland 0.3-6

Steppe/ prairie 6

Temperate grassland 3.7

Montane grassland 6

Short grass steppe 13

Tropical grassland 0.5-2; 0.7

Forests (in average) 2

Temperate forest 2.5; 0.20

Boreal coniferous forest 4.0; 0.32

Tropical deciduous forest 0.34

Tropical evergreen forest 0.19

Mediterranean forest 0.25

Tundra 6.6

Root-to-shoot ratio (primary production) as an indicator for above- and below-ground contribution of plant litter in different vegetation types

Knabner, 2002

15

17 101

31 15 17 49

6

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

mea

n k

val

ue

vegetation type

15

5 154

7 55 3

7

24

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

mea

n k

val

ue

litter type

Variation of k value with different vegetation type and litter types at global scale

Litter decomposition in the

rain forest floors was >7-fold

faster than that in the tundra

floors

Litter with high concentration of

phenolics (tannin and lignin) and

low concentration of N were

generally decomposed slowly

Zhang et al., 2008

Onset of decomposition of different carbohydrates (cellulose

and hemi-cellulose) and lignin, showing the different rates of decomposition in the initial and the late phase

Berg and McClaugherty, 2007

Zhang et al., 2008

Impact of nutrients content (N, P, K, Ca and Mg), Lignin content, C:N and lignin:N ratio on litter decomposition

------ Positive correlation;

Zhang et al., 2008 ------ Positive correlation; ----- Negative correlation

Contd…..

Frey et al., 2013

Temperature response of microbial efficiency (%) in forest soil amended with substrate varying in lability

2 year warming 18 year warming

Substrate : Phenol; Heating temperature 5OC

Harvard Forest Long Term

Ecological Research (LTER),

Petersham, Massachusetts, USA

low

D

ive

rsit

y

hig

h

mild Stress strong

Hypothetical models of heavy metal inducing stress on microbial diversity and utilization of C from native sources

Giller et al., 1998

competitive

exclusion extinction

1

2

Plant residues

Structural C

High lignin, low N

2-4 years

C:N = 100-200

Metabolic C

Low lignin, high N

0.1-0.5 year

C:N = 10-25

Active SOM 1-2 years

C:N = 15-30

Slow SOM 15-100 years C:N = 10-25

Passive SOM 500-5000 years

C:N = 7- 10

CO2 CO2

CO2

CO2

CO2

CO2

A conceptual model of various pools of soil

organic matter (SOM) differing by their susceptibility to

microbial metabolism

Paustian et al., 1992

Ct (Losses through first- order decomposition kinetics of the SOC

pool )

I (input)

k (decomposition constant)

The linear (no saturation) model

The amount of C entering (I) a C pool is independent of the pool size (Ct)

Decomposition rates (k) are directly proportional to size of pool

The simple first order decay model proposed by Jenny in 1941.

Assumptions:

(1)

(2)

(3)

SOC content (Ct*) is directly proportional to C input level (I*). Paustian, 1994

Soil C saturation model

Ct

(Losses through first- order decomposition kinetics of the SOC

pool )

I (input)

k (decomposition constant)

(𝟏 −𝑪𝒕

𝑪𝒎

) (𝑪𝒕

𝑪𝒎

)

𝐂𝐦

The simple first-order decay model

(1)

The C saturation model has a whole soil

saturation limit (Cm) due to inherent

physicochemical limitations.

Over time, the soil C saturation concept may

be expressed as a simple modification to the

C input term in equation 1:

(4)

where,

Cm is the amount of C that can be

stabilized by the soil

𝐶𝑡

𝐶𝑚

is the fraction of the maximum

stabilized C pool of soil that lost

through first order decomposition

kinetics

Hassink and Whitmore, 1997

Assumption:

the amount of C that can be stabilized is limited

once saturation is reached any additional C

input will be not be stabilized but will be lost

from the system.

Saturattion deficit (𝑠𝑑) = ( 1 −𝐶𝑡

𝐶𝑚

)

where,

𝐶𝑡

𝐶𝑚

is the fraction of the maximum

stabilized C pool of soil that lost

through first order decomposition

kinetics

(4)

Hassink and Whitmore, 1997

The asymptotic relationship

between C input levels and

SOC content at steady-state

is a key attribute to the C

saturation model

Contd…

The two-pool mixed model

C2 (stable carbon pool)

I (input)

k2

𝟏 − 𝒂 𝒌𝟏(𝟏 −𝑪𝟐

𝑪𝟐𝒎

)

𝒂𝒌𝟏

𝑪𝟐𝒎

C1 (Labile residue C

pool)

𝑑𝐶𝑡

𝑑𝑡=

𝑑𝐶1

𝑑𝑡+

𝑑𝐶2

𝑑𝑡

𝑑𝐶1

𝑑𝑡= 𝐼 − 𝑎𝑘1𝐶1 − 1 − 𝑎 1 −

𝐶2

𝐶2𝑚

𝑘1𝐶1

𝑑𝐶𝑡

𝑑𝑡= 1 − 𝑎 1 −

𝐶2

𝐶2𝑚

𝑘1𝐶1 − 𝑘2𝐶2

The mixed model can be expressed by

where

and

where,

k1 and k2 is decomposition constant

a is partitioning coefficient for losses from C1, and

transfer of decomposition products to the more stable

pool C2

As C2 approaches saturation, the amount of

decomposed products from C1 transferred into

C2 decreases and ceased at saturation point.

At this point SOC accumulation in C1 proceeds

according to the linear model Stewart et al., 2007

Stewart et al., 2007

Development of soil organic C storage with

increasing C input

First order reaction

Zero order reaction

Stewart et al., 2007

Long-term agro ecosystem sites selected for use in comparative model analysis

Physically

protected

soil C

Bio-chemically

protected soil C

unprotected

soil C unprotected

soil C

Micro-aggregate –associated

Soil C

Silt and clay–associated

Soil C

Non-hydrolysable

Soil C

Six et al., 2002

Aggregate turnover Adsorption / Desorption

Condensation/ complexation

Litter quality

CO2

CO2

CO2

Time of

continuous

cultivation

Initial soil indicators

SOC

(g kg-1)

CECpotential

(mmolc kg-1)

CECeffective

(mmolc kg-1)

pHwater

(1:2.5)

Base saturation

(%)

0 105.0 ± 0.0 No data 331 ± 0.0 6.2 ± 0.1 No data

5 59.9 ± 2.3 320 ± 9 235 ± 13 6.3 ± 0.1 99.6 ± 0

20 32.6 ± 1.6 170 ± 3 47 ± 5 5.2 ± 0.1 70.2 ± 0

35 21.7 ± 1.4 187 ± 18 91 ± 5 5.5 ± 0.0 96.3 ± 0

105 21.1 ± 1.1 262 ± 3 116 ± 0.1 5.9 ± 0.0 98.1 ± 0

Kimetu et al., 2009

Initial soil characteristics for evaluation C saturation over different cultivation periods

Carbon mineralization: an evaluation of C saturation over different cultivation period

Cumulative C mineralization per unit soil organic C in un-amended soil with varying cultivation

histories over a more than one year incubation period (n = 3 and error bars are LSD at P < 0.05).

Forest soil as control

Soil site – Western Kenya

Soil order- Ultisol

Silt + clay – 74%

Sand – 26%

Crop – Maize (Zea mays L.)

Equation fitted

𝒚 = 𝒂 [𝟏 − 𝐞𝐱𝐩(−𝒃𝒙)] + 𝒄 [ 𝟏 − 𝐞𝐱𝐩(−𝒅𝒙)] where, a = size of labile pool

c = size of recalcitrant pool

b = mineralization rate for labile

pool

d = mineralization rate for

recalcitrant pool

x = mineralization time in days

Kimetu et al., 2009

Carbon mineralization: an evaluation of C saturation over different cultivation period

Cumulative C mineralization in soils with differing cultivation histories with (+OM) and without

(-OM) added OM over a more than one year incubation period (n = 3 and error bars are LSD at

P < 0.05).

Forest soil as control

Soil site – Western Kenya

Soil order- Ultisol

Silt + clay – 74%

Sand – 26%

Crop – Maize (Zea mays L.)

Equation fitted

𝒚 = 𝒂 [𝟏 − 𝐞𝐱𝐩(−𝒃𝒙)] + 𝒄 [ 𝟏 − 𝐞𝐱𝐩(−𝒅𝒙)]

where, a = size of labile pool

c = size of recalcitrant pool

b = mineralization rate for labile

pool

d = mineralization rate for

recalcitrant pool

x = mineralization time in days

Kimetu et al., 2009

Time (years)

Without OM addition Without OM addition With OM addition

(mg CO2 – C g-1 C) (mg CO2 – C g-1 soil) (mg CO2 – C g-1 soil)

0 73.5c 7.8a 15.8a

5 82.8bc 4.9b 10.3b

20 100.5ab 3.8c 7.8c

35 117.9a 3.0cd 8.1c

105 104.4ab 2.4d 7.9c

Total CO2-C mineralization over 374 days as a function of time under cultivation and OM additions

Kimetu et al., 2009

During microbial growth, CUE is equivalent to the microbial yield co-

efficient (Y, g Cmin g-1 Cs), i.e. biomass-C increment per amount of

substrate-C used

What is microbial yield coefficient?

𝑌 = − ∆𝐶𝑚𝑖𝑛

∆𝐶𝑠𝑢𝑏

During microbial growth, CUE is equivalent to the microbial yield co-

efficient (Y, g Cmin g-1 Cs), i.e. biomass-C increment per amount of

substrate-C used.

∆𝑪𝒎𝒊𝒏

∆𝑪𝒔𝒖𝒃

Increase in microbial biomass – C

Consumption of substrate - C

For estimation of CUE for growing microbial biomass, we used microbial

yield coefficient (Y).

In spite of wide variability of the experimental Y estimations in the range

of 0.1 to 0.8.

Blagodatskaya et al., 2014

Measurement of carbon utilization efficiency


CUE estimation under steady-state conditions:

CUE estimation during shift from dormancy to active stage:

Estimation of the CUE of microbial metabolism by specific respiration.

[Specific respiration is CO2 produced per time and microbial biomass unit.]

Hypothesis:

Both in presence and absence of an available substrate, microbial

communities in rhizosphere soil will have higher specific respiration rates

than those of non-rhizosphere soil.

CO2 /DNA ratio for comparison of the CUE by transition from dormant to active

stage for microbial communities with contrasting growth strategies.

Experimentally, the growth strategies can be evaluated by the maximal specific

growth rate under unlimited conditions that is greater for r-strategies

(allocthocnous) than for k-strategies (autocthonous).

Three complementary indices were applied as indicators of the

efficiency of microbial metabolism in the rhizosphere and non-

rhizosphere soil:

1. the CO2 / DNA ratio or specific respiration rate

2. The ∆CO2 / ∆ DNA ratio for growing biomass

3. CUE during microbial growth on glucose

a

b

d

c

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

half rate of N full rate of N

V b

asal,

ug

CO

2 g

-1 h

-1

rhizosphere soil non-rhizosphere soil

Basal respiration



b

a

c

b

0

10

20

30

40

50

60

70

80

90


DN

A,

ug

g-1


a

b

b b

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07


ug

CO

2-C

ug

DN

A-1

h-1


Specific respiration

DNA content



Microbial Efficiency- Matrix Stabilization (MEMS) framework to integrate plant litter decomposition and SOM stabilization

Above ground and below ground plant litters undergo microbial

decomposition, determines the quantity and chemical nature of

decomposition products.

More dissolved organic matter , carbohydrates, peptides formed from high

quality litter (e.g., fine roots and herbaceous).

Low-quality litters (e.g., needle and wood ) loses most of the C as CO2.

Proportionally more stable soil organic matter (SOM) accumulates in soils

with high matrix stabilization e.g.,

high expandable and non expandable phyllosilicates;

high Fe-, Al, Mn-oxides in acidic soils

polyvalent cations (Ca2+) in alkaline and calcareous soils and

high allophane content Cotrufo et al., 2013

Effects of plant litter quality on CO2 efflux and soil organic matter stabilization in the Microbial Efficiency- Matrix Stabilization (MEMS) framework

Cotrufo et al., 2013

Manzoni et al., 2012

How carbon –utilization efficiency (CUE) affects soil carbon sequestration ?

Blagodatsky et al., 2010

Simulation models: sequential utilization and parallel utilization of substrates in contrast with substrate availability

Sequential utilization model

Parallel utilization model

Cumulative priming effect (PE) = 12CO2ammended – CO2

0

Priming Effect (%) = 12CO2ammended – CO2

0 / CO20 x 100%

Real PE% = SOC derived 12CO2ammended – SOC derived CO2

0 / SOC derived CO20

x 100%

PE% = maintenan 12CO2ammended – maintenan CO2

0 / maintenan CO20 x 100%

d (maintenan CO2)/dt = m ACT. MB

=amount of SOC or DOC utilized by microbes

Simulation models: sequential utilization and parallel utilization of substrates in contrast with substrate availability

Blagodatsky et al., 2010

Natural variation in microbial carbon-use efficiency (CUE) among major ecosystems and in culture studies


Range of

terrestrial

biogeochemical

models

Correlation between soil microbial substrate properties, microbial community and substrate utilization in crop and forage production

systems

Rajan et al., 2012

Overview of methods used to estimate carbon-use efficiency (CUE) in aquatic and terrestrial systems


Impact of geographic factors on rate organic residues decomposition (k) at global scale

b

Variation of k values with latitude (a) and global pattern of litter decomposition

rates (k values) across LAT(b). Data at the top of column were numbers of k

values. Number of samples were 293.

The estimated k values tended to

decrease with latitude

Higher variability in k values was

shown in the low latitude

(between 10°N and 10°S)

compared with that in the high

latitude,

In general, k values at the

equator were the highest and

decreased with latitude toward

both the south and north poles.

However, the averaged k value in

the 10°– 20°N regions was much

lower than those in the adjacent

regions.

Zhang et al., 2008

Impact of climatic factors on rate organic residues decomposition (k) at global scale

Variation of k value with mean annual temperature (MAT)

(a) and mean annual precipitation (MAP) (b)

The k values increase with both

mean annual temperature (MAT)

and mean annual precipitation

(MAP).

Higher variability in k values was

found in areas where MAP was

between 1000 and 2000 mm.

At the global scale, to the extent

MAT was evidently more important

than MAP in regulating litter

decomposition.

Zhang et al., 2008

Conclusions

Above ground and below ground plant litters, determines

the quantity and chemical nature (quality) of decomposition

products and stabilized soil organic matter.

With increasing temperature, C:N ratios, lignin content,

lignin: N ratios and latitude decrease in carbon utilization

efficiency (CUE).

Carbon-utilization efficiency (CUE) is a good measurement

of microbial effectiveness in soil.

With increasing N, Ca, Mg, K contents increase the carbon-utilization efficiency (CUE).

Future thrust…

Bacteria and fungi are the dominant biota responsible

for decomposition, yet we know very little about their

respective contributions or how community dynamics

may be affected by litter quality.

Soil carbon saturation, still now an ambiguous concept

in itself for tropical and sub-tropical countries.

The carbon utilization efficiency (CUE) of soil fauna and

other macro organisms are still to determine.

carbon utilization efficiency

Science

soil bacteria soil fauna

soil factors

soil microbes inherent

soil surface perforate

division of soil science

granular soil aggregates

substrate utilization

ecological efficiency