Carbon utilization efficiency

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
Carboncontent
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
meankvalue
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
meankvalue
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

lowDiversityhigh
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
𝑪 𝒎
) (
𝑪𝒕
𝑪 𝒎
)
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
𝐶 𝑚
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
𝐶 𝑚
)
where,
𝐶𝑡
𝐶 𝑚
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 𝒌 𝟏(𝟏 −
𝑪 𝟐
𝑪 𝟐𝒎
)
𝒂𝒌 𝟏
𝑪 𝟐𝒎
C1
(Labile residue C
pool)
𝑑𝐶𝑡
𝑑𝑡
=
𝑑𝐶1
𝑑𝑡
+
𝑑𝐶2
𝑑𝑡
𝑑𝐶1
𝑑𝑡
= 𝐼 − 𝑎𝑘1 𝐶1 − 1 − 𝑎 1 −
𝐶2
𝐶2𝑚
𝑘1 𝐶1
𝑑𝐶𝑡
𝑑𝑡
= 1 − 𝑎 1 −
𝐶2
𝐶2𝑚
𝑘1 𝐶1 − 𝑘2 𝐶2
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 modelStewart 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 Cunprotected
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
Vbasal,ugCO2g-1h-1
rhizosphere soil non-rhizosphere soil
Basal respiration

b
a
c
b
0
10
20
30
40
50
60
70
80
90
DNA,ugg-1
a
b
b
b
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
ugCO2-CugDNA-1h-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) = 12CO2
ammended – CO2
0
Priming Effect (%) = 12CO2
ammended – CO2
0 / CO2
0 x 100%
Real PE% = SOC derived 12CO2
ammended – SOC derived CO2
0 / SOC derived CO2
0
x 100%
PE% = maintenan 12CO2
ammended – maintenan CO2
0 / maintenan CO2
0 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

More Related Content

What's hot (20)

Viewers also liked (6)

Similar to Carbon utilization efficiency (20)

Recently uploaded (20)

Carbon utilization efficiency