Modelling organic carbon turnover in salt-affected soils A thesis submitted to The University of Adelaide in fulfilment of the requirements for the degree of Doctor of Philosophy Soils School of Agriculture, Food and Wine The University of Adelaide March 2011 Raj Setia
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Modelling organic carbon turnover in salt-affected soils · 1.4 Mapping of salt-affected soils using remote sensing techniques 7 1.5 Organic matter decomposition 12 1.6 Soil organic
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Modelling organic carbon turnover in salt-affected soils
A thesis submitted to The University of Adelaide in fulfilment of the requirements for the degree of Doctor of Philosophy
Soils
School of Agriculture, Food and Wine
The University of Adelaide
March 2011
Raj Setia
ii
Dedicated to my parents and wife
iii
TABLE OF CONTENTS
ACKNOWLEDGEMENTS V
ABSTRACT VII
DECLARATION XI
LIST OF PUBLICATIONS XII
CHAPTER 1: REVIEW OF LITERATURE
CHAPTER 2 Manuscript 1 :
Severity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imagery
42
CHAPTER 3 Manuscript 2 : Is CO2 evolution in saline soils affected by an osmotic effect and calcium carbonate?
54
1.1 Introduction 1 1.2 Properties of salt-affected soils 3 1.3 Effect of salinity and sodicity on plant growth 6 1.4 Mapping of salt-affected soils using remote
sensing techniques 7
1.5 Organic matter decomposition 12 1.6 Soil organic carbon pools 14 1.7 Influence of salinity and sodicity on soil organic
carbon turnover 15
1.8 Modelling of soil organic carbon 17 1.9 Classification of soil organic carbon models 18
1.10 Evaluation, comparison and application of SOC models
20
1.11 Rothamsted carbon model 21 1.12 Modifications of RothC for subsoils, waterlogged
soils and Andosols 23
1.13 Measurable and modelled pools in RothC 23 1.14 Integration of RothC with spatial data in the
geographical information system 24
1.15 Conclusions and knowledge gaps 25 1.16 Structure of thesis 26
References 29
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CHAPTER 4 Manuscript 3 : Salinity effects on carbon mineralization in soils of varying texture
68
CHAPTER 5 Manuscript 4 : Relationships between carbon dioxide emission and soil properties in salt-affected landscapes
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CHAPTER 6 Manuscript 5: Introducing a decomposition rate modifier in the Rothamsted carbon model to predict soil organic carbon stocks in saline soils
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CHAPTER 7 Manuscript 6 : Simulation of salinity effects on soil organic carbon: past, present and future carbon stocks
I would never reach this successful accomplishment of the project without God’s grace
and help. Thanks to my parents, the engineers of my wisdom who have sacrificed their
today for my better future. My wife Deepika is my pillar of support, my source of
strength and inspiration. I can not express the gratitude I feel towards your
unwavering support.
I wish to express my gratitude to A/Prof. Petra Marschner, my principal supervisor, for
encouragement throughout the project which helped me developing a professional
approach towards scientific research and becoming a good scientist. Petra, you are an
encouraging supervisor and a nice person too; I really appreciate you for your keen
interest, unceasing encouragement and discussions which helped me to solve many of
the problems encountered during the course of my research.
I am also thankful to my other supervisors Dr. Jeff Baldock, Prof. David Chittleborough
and A/Prof. Megan Lewis. To Jeff, for valuable suggestions and discussions; to David
for his efficient communication and extended help; to Megan for guidance and support
for the remote sensing study.
I express my sincere appreciation for the contribution and help of my informal
supervisors Prof. Pete Smith and Dr. Jo Smith. To Pete for hosting me at University of
Aberdeen and helping me in carbon modelling; to Jo for introducing me the exciting
world of modelling, accepting my millions of emails and keeping me positive. Jo, your
contribution to this study is immense and without that, it would not have culminated
in this thesis.
I would like to thank Ms. Pia Gottschalk for hosting my visit to Berlin. The help and
time you gave me for the GIS run of India and Australia is greatly appreciated. You
taught me the unwritten details of RothC.
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I would also like to thank Drs P Rengasamy and Rob Murray for useful discussions
about salinity and sodicity which helped me in designing the experiments. This
research project would not have been possible without the support of Dr. P. K. Sharma
who granted me leave for a PhD at The University of Adelaide. He provided the
necessary facilities for collection of soil samples from the Indian site. I am thankful to
Dr. V. K. Verma for help during field work in India.
I am also thankful to Mr Sean Forrester for MIR analyses and predictions, Ms Athina
Massis for technical support, Mr Colin Rivers for help in the lab and field, Dr David
Summers, Mr Ramesh Raja Segaran and Mr Beng Umali for mapping, Dr Karen
Baumann for help in the lab, Mr Sudhir Yadav for technical help and Ms Suman Verma
for proof reading.
I am extremely grateful for the following awards: EIPRS scholarship, The University of
Adelaide Scholarship, Research abroad scholarship, Francis and Evelyn Clark Soil
Biology Scholarship, and the CRC-FFI grant which funded my visit to University of
Aberdeen and Free University, Berlin.
I would like to acknowledge and thank to Northern and Yorke Natural Resources
Management Board, CRC-FFI and Department of Climate Change for funding part of
my project.
I lovingly acknowledge my sisters and their kids and husbands for their unconditional
support. I would also like to thank Vaneet, Sumit, Poonam, Rishi and Neenu who made
me laugh and think of other things than my work.
Last but not the least, thanks are due to one and all those who happily helped me. All
these thanks are, however, only a fraction of what is due to the Almighty who blessed
me to express these words.
vii
ABSTRACT
Salinity and sodicity are major constraints for crop production in arid and semi-arid
regions of the world. Salt-affected soils cover 6.5% of the total land area of the world.
Since the global soil carbon (C) pool is greater than the atmospheric and biotic pool
combined, changes in soil organic matter content will affect atmospheric carbon
dioxide (CO2) concentration. Therefore it is important to understand soil organic
carbon (SOC) dynamics. Soil organic carbon models, which have been successfully
validated for non-saline soils, are important for estimation of past and future SOC
contents and for evaluating management effects on SOC. However, it was unclear if
they accurately predict CO2 emission/SOC stocks in salt-affected soils. In this work, an
integrated approach using remote sensing, incubation experiments, modelling and
geographical information system was used to simulate SOC dynamics in salt-affected
soils at field and regional scale in the past, present and the future.
Satellite imagery was used to map soil salinity and select soil sampling sites in
two climatically distinct regions which also differ in cause of salinity: Kadina, South
Australia and Muktsar district (Punjab), India. High resolution multispectral satellite
imagery (Quick bird, spatial resolution 0.6 m) was used to map salinity (~1:10000 scale)
in an agricultural area around Kadina, South Australia where salinity associated with
ground water or an impermeable subsoil is wide-spread. Resourcesat-I (spatial
resolution 23.5 m) was used for mapping salinity on a 1:50000 scale in Muktsar
(Punjab), India where salinity is induced by irrigation. Unsupervised classification of the
Quick bird imagery (September, 2008) covering the study area in South Australia
(hereafter called Australia) allowed differentiation of severity levels of salt-affected
soils, but these levels did not match those based on electrical conductivity (EC) and
sodium adsorption ratio (SAR) measurements of the soil samples, primarily because
the expression of salinity was strongly influenced by paddock-level variations in crop
type, growth and prior land management. Segmentation of the whole image into 450
paddocks and unsupervised classification using a paddock-by-paddock-approach
resulted in a more accurate discrimination of salinity with image derived salinity
classes correlated with EC but not with SAR. For the Indian site (hereafter called India),
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Resourcesat-I LISS-III data of April 2005, October 2005 and February 2006 was visually
interpreted for variation in spectral properties. The map of salt-affected soils was
generated after integration of ground and laboratory data with delineated land use
units from the satellite data. On the basis of land use and soil types, 120 (59 salt-
affected and 61 non-salt-affected) and 160 (70-salt-affected and 90-non-salt-affected)
soils were collected from 0-0.30 m depth from the Indian and Australian sites,
respectively.
Salt-affected soils occur in dry climates and often contain calcium carbonate
(CaCO3) particularly at pH > 7.5. Therefore, using CO2 emission as a measure of
microbial activity and SOC decomposition in these soils is problematic, but an
experiment involving addition of 2% wheat residues and varying the rate of calcium
carbonate added to a non-calcareous soil showed that CO2 emission from salt-affected
soils was not affected by CaCO3 addition in the presence of residues.
It has been suggested that the salt concentration in the soil solution (osmotic
potential) is a better parameter than the EC of a soil suspension to estimate the
salinity effect on plant growth. Therefore, an incubation experiment with four soils
differing in texture and amended with sodium chloride (NaCl) was conducted to assess
the effect of soil texture and osmotic potential (Os, calculated from EC and water
content) on CO2 release. The results of this study showed that, compared to saline soils
from the field, the decrease in CO2 release was greater in these soils suggesting that
the sudden increase in salinity leads to overestimation of the salinity effect compared
to saline soils in the field where salinity increases gradually. The relative decrease in
respiration was less when plotted against Os than if plotted against EC.
To investigate the importance of salinity compared to other soil properties in
soils from a salt-affected landscape, CO2 emission from the soils of India and Australia
with a wide range of EC and SAR with 2% (w/w) mature wheat residue was measured
over 120 days at constant temperature and soil water content. Cumulative CO2
emission from unamended and amended soils was related to soil properties by
stepwise regression models. Carbon dioxide release in salt-affected landscapes is
affected by EC, C availability (size of C pools) and clay content. Electrical conductivity
had a negative impact on CO2 release in soils of India and Australia, which shows the
universal effect of salinity on CO2 release, irrespective of climate and origin of salinity.
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Therefore, there is a need to add a decomposition rate modifier for salinity in the SOC
models for accurate prediction of SOC dynamics and CO2 release from salt-affected
soils.
The Rothamsted Carbon Model (RothC) was modified to take into account the
reduced plant inputs into salt-affected soils. Plant inputs were calculated based on a
generalised equation from the literature. The decomposition rate modifier for salt-
affected soils was based on the comparison of measured and modelled CO2 emissions
from wheat residue amended soils of India and Australia. The modelled CO2 emissions
were higher than measured CO2 emissions. In order to match the measured and
modelled CO2 emissions, rate modifiers ranging from 0.2-1 were introduced in the
model. After accounting for the laboratory effect due to soil disturbance, the impact of
salinity (calculated using Os) or sodicity (measured as SAR) on the rate of
decomposition was calculated. A significant positive relationship was found between
decomposition rate modifier and Os whereas SAR had no effect. Therefore, a
decomposition rate modifier due to salinity (as a function of Os) was introduced into
RothC.
The RothC with the plant input modifier and decomposition rate modifier was
used to estimate past SOC content when saline soils were non-saline and future SOC
content. These simulations were performed for the Indian and Australian sites. The
results showed that the modelled past SOC when the soils were non-saline was higher
than measured SOC of saline soils; thus these soils have lost SOC (31 t ha-1 for India
and 55 t ha-1 for Australia). On the other hand, simulations with the decomposition
rate modifier only, without taking into account the reduced plant input, suggest that
SOC of saline soils has increased since they became saline. Since SOC in saline soils is
lower than in non-saline soils, this shows that in order to accurately model SOC stocks
in saline soils, both reduced plant inputs and reduced decomposition rate have to be
taken into account. Overall SOC content was more strongly affected by reduced plant
inputs than by reduced decomposition rates. In addition, future SOC stocks of India
and Australia were simulated with and without modifiers from 2009-2100. In saline
soils of both regions, the simulation of SOC without modifiers showed that, compared
to the present SOC content, SOC would decrease by ≤15% by the year 2100, whereas
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simulations with decomposition rate modifier and plant input modifier indicate that
SOC would decrease by 39% for the Indian site and by 29% for the Australian site.
The key findings from the research are:
I. High resolution multispectral imagery with paddock-by-paddock approach
allowed accurate mapping of different levels of salinity severity.
II. In saline soils, osmotic potential is a better measure to assess the impact of salt
on microbial activity than EC, particularly when comparing soils of different
texture.
III. In soils from salt-affected landscapes, salinity and reduced carbon availability
determine CO2 emission.
IV. Two novel approaches were developed: (a) calculation of a decomposition rate
modifier from incubation experiments after taking into account the laboratory
effect and (b) calculation of past SOC content when saline soils were non-
saline.
V. The predictions of SOC stocks from saline soils have been overestimated by not
taking into account the negative effect of salt on decomposition rate and plant
inputs.
VI. For realistic modelling of SOC stocks and turnover in saline soils, both reduced
decomposition rate and reduced plant inputs need to be considered.
xi
DECLARATION
This work contains no material which has been accepted for the award of any other
degree or diploma in any university or other tertiary institution and, to the best of my
knowledge and belief, contains no material previously published or written by another
person, except where due reference has been made in the text.
I give consent to this copy of my thesis when deposited in the University Library, being
made available for loan and photocopying, subject to the provisions of the Copyright
Act 1968.
The author acknowledges that copyright of published works contained within this
thesis (as listed below) resides with the copyright holder(s) of those works.
I also give permission for the digital version of my thesis to be made available on the
web, via the university’s digital research repository, the Library catalogue, the
Australasian Digital Thesis Program (ADTP) and also through web search engines,
unless permission has been granted by the University to restrict access for a period of
time.
Raj Setia Date :
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LIST OF PUBLICATIONS
Setia, R., Marschner, P., Smith, P., Baldock, J., Chittleborough, D., Smith, J., 2010. Using salt-amended soils to calculate a rate modifier for salinity in soil carbon models, 19th World Congress of Soil Science, Soil solutions for a changing world, Brisbane, Australia. Setia, R., Lewis, M., Marschner, P., Raja Segaran, R., Summers, D., Chittleborough, D., 2011. Severity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imagery. Land Degradation & Development, DOI: 10.1002/ldr.1134. Setia, R., Marschner, P., Baldock, J., Chittleborough, D., 2010. Is CO2 evolution in saline soils affected by an osmotic effect and calcium carbonate? Biology and Fertility of Soils 46, 781-792. Setia, R., Marschner, P., Baldock, J., Chittleborough, D., Smith, P., Smith, J., 2011. Salinity effects on carbon mineralization in soils of varying texture. Soil Biology & Biochemistry 43, 1908-1916. Setia, R., Marschner, P., Baldock, J., Chittleborough, D., Verma, V., 2011. Relationships between carbon dioxide emission and soil properties in salt-affected landscapes. Soil Biology & Biochemistry 43, 667-674. Setia, R., Smith, P., Marschner, P., Baldock, J., Chittleborough, D., Smith, J., 2011. Introducing a decomposition rate modifier in the Rothamsted carbon model to predict soil organic carbon stocks in saline soils. Environmental Science & Technology, dx.doi.org/10.1021/es200515d. Setia, R., Smith, P., Marschner, P., Gottschalk, P., Baldock, J., Verma, V., Smith, J., 2011. Simulation of salinity effects on soil carbon: past, present and future carbon stocks. Agriculture, Ecosystems and Environment, Submitted.
CHAPTER 1
INTRODUCTION AND REVIEW OF LITERATURE
1.1 Introduction
Approximately 6.5 % of the world land area is affected by either salinity or sodicity
(Szabolcs, 1993) and Pessarakli and Szabolcs (1999) reported that salt-affected land
comprised 19 percent of the 20.8 billion hectares of arable land on earth. Saline and
sodic soils account for approximately 40 and 60 percent of world’s salt-affected land,
respectively (Tanji, 1990).
Salt accumulation in soils is a major threat to agricultural production and
ecosystem sustainability because it reduces plant growth and increases the risk of
erosion. On a global scale, the cost to agriculture productivity from salinity is estimated
at $US 12 billion a year and this cost is expected to increase with further increases in
the area affected by salt in the future (Ghassemi et al., 1995).
One important process affected by salinity and sodicity is soil organic matter (SOM)
turnover which is mainly mediated by microbial activity. Soil microorganisms carry out
crucial functions in all soils; most importantly, they are the key drivers of soil organic
matter decomposition and nutrient cycling. Stresses such as salinity will lead to the
inhibition or death of sensitive microorganisms and, may irreversibly, reduce essential
microbial activities and thereby affect SOM decomposition. Soil organic matter
turnover plays a key role in greenhouse gas emissions, soil structural stability, ion
exchange, water quality and ecosystem sustainability. In general, microbial activity and
plant inputs are lower in salt-affected soils (Rietz and Haynes, 2003). If inputs of soil
organic carbon (SOC) in salt-affected soils were similar to those in non-saline soils,
reduced decomposition rates may help in mitigating the green house effect by
increasing carbon (C) sequestration in these soils and reducing the emission of carbon
dioxide (CO2) into the atmosphere.
Reclamation of salt-affected soils would enhance soil quality, improve productivity
and SOC sequestration (Lal, 2001), but for a better understanding and quantification
of their SOC turnover and sequestration potential, integrated use of frontier
technologies such as remote sensing, modelling and sophisticated analytical methods
is required. Soil organic carbon models may help in predicting and understanding
2
future changes in SOC in response to changing climate, altered land use and different
land management practices. Soil organic carbon models have been used successfully to
predict changes in soil organic matter content in non-saline land in the short or long-
term plot and on different spatial scales: field (Jenkinson et al., 1987; Parton, 1996;
Franko et al., 1997; Li et al., 1997; Smith et al., 1997; Skjemstad et al., 2004; Gottschalk
et al., 2010), regional (Ardo and Olsson, 2003) as well as country (Smith et al., 2005;
Smith et al., 2007; Smith et al., 2010). However, these models have not been calibrated
for salt-affected soils. The combination of remote sensing and Geographical
Information System (GIS) data with process based models (Rothamsted Carbon Model,
RothC), CENTURY, (DeNitrification and DeComposition (DNDC), DAISY etc.) can provide
tools for understanding spatial C dynamics, but this type of integration has not been
used for studying SOC in salt-affected landscapes.
The following literature review will cover (1) properties of salt-affected soils
and how these soils can be mapped (2) effects of salinity and sodicity on SOC (3)
modelling of SOC and (4) integration of modelled SOC data with GIS (Figure 1).
Figure 1: Overview of topics covered in the literature review and how they relate
to soil organic carbon dynamics in salt-affected soils
Climate and ancillary data
Soil organic carbon dynamics
Remote Sensing Mapping of salt-
affected soils
Modelling
Soil organic matter
3
1.2 Properties of salt-affected soils
Salt accumulates when the loss of salt via leaching is less than the addition of salt
through rainfall or irrigation, or as a result of a rise of saline groundwater. The US
Salinity Laboratory Staff (1954) suggests the following three classes of salt-affected
soils:
(A) Saline soils: Salinity is measured in units of electrical conductivity (EC) using a
range of soil/water ratios: saturated paste of soil (ECe), 1:1, 1:2 or 1:5. The various EC
values can be converted to ECe using a multiplication factor based on soil texture
(Shaw, 1999). Saline soils have a high concentration of soluble salts and an electrical
conductivity of saturation extract (ECe) greater than 4 dS m-1 (Figure 2).
There are two forms of salinity:
(a) Primary Salinity caused by soluble salts originating from the weathering of primary
minerals, aeolian recycling or cyclic accession, where salts are transported inland by
winds off the ocean.
(b) Secondary salinity results from human activities such as irrigation and land clearing
in areas that are not irrigated (dryland salinity) which causes a change in the salt and
water balance.
In general, saline soils are flocculated but the high salt content in the soil has an
adverse affect on plant growth due to (1) low soil osmotic potential, causing water to
move from areas of lower salt concentration (plant tissue) into the soil where the salt
concentration is higher thereby inducing water stress in plants (2) specific ion toxicity,
and 3) ion imbalance (Ca2+, K+ and Na+) all of which disrupt plant metabolism.
(B) Sodic soils: Sodicity of a soil is expressed using the exchangeable sodium
percentage (ESP):
CEC
]100[Na ESP ex
in %
Where,
CEC = cation exchange capacity in cmol p (+) kg-1
[Na+] = exchangeable Na+ in cmol (+) kg-1.
4
Figure 2: Categories of salt-affected soils based on electrical conductivity (ECe) and sodium adsorption ratio (SARe) measured in saturated soil: water extract [Source: US Salinity Laboratory Staff (1954)]
For determination of ESP, CEC is required but its determination is laborious and
time consuming. Therefore, a convenient measure to express the sodicity of a soil
solution or of water is the sodium absorption ratio (SAR) which is calculated as follows:
0.5225:1
])[Mg]Ca ([
][Na SAR
Where, [Na+], [Ca2+], [Mg2+] = concentration of Na+, Ca2+ and Mg2+ in the soil
solution in mmol l-1. Note that SAR does not have units.
Sodic soils typically have a high pH (>8.5) and high (>13) SAR but ESP varies with
classification system. For example, the US system defines a sodic soil as that with an
ESP greater than 15 (Soil Survey Staff, 1998) whereas the recent Australian
classification system (Isbell, 2002) states that sodic soils have an ESP greater than 6
(Rengasamy, 2006). Sodium will replace Ca and Mg on the clay particles, which can
cause dispersion of clay and loss of soil structure. Dispersed clay can move into soil
pores, leading to blockage of pores, which, in turn, results in poor drainage and
reduced aeration (Rengasamy and Olsson, 1991) and hence, reduced plant growth. As
sodic soils dry out, they become very hard and the decrease of macrospores and pore
connectivity reduces the capacity of roots to penetrate the soil (Marschner, 1995).
NOTE: This figure is included on page 4 of the print copy of the thesis held in the University of Adelaide Library.
5
(C) Saline- sodic soils: These soils have an ECe greater than 4 dS m-1 and SAR greater
than 13 but their pH is less than 8.5. In general, these soils have good soil structure and
water movement through the soil profile is adequate. They can have the
characteristics of either a saline or a sodic soil, depending on whether sodium or
calcium dominates.
In general, soils are often first saline and then become sodic as Na replaces Ca
and Mg from the cation exchange sites which causes dispersion of clay particles and
thereby affects oxygen availability in the soils, but sodic soils that are also highly saline
contain high concentrations of electrolytes and these soils remain flocculated. This can
be explained by the three plate model of Quirk (2001). In a clay domain, clay crystals
overlap to a certain extent and slit-shaped pores form within the domain or where
crystals do not overlap. A repulsive pressure operates over the surface area of the slit-
shaped pores, while an attractive pressure operates over the surface area of the clay
crystals. A high concentration of monovalent cations such as Na+ results in the
accumulation of these dispersive cations in the slit-shaped pores and these ions do not
form extensive double layers as compared with the smaller double layers for cations of
higher valence (Sumner et al., 1998). Thus, the attractive force can override the
repulsive force in soil systems resulting in the flocculation.
In Australia, irrigation-induced salinity, dry-land salinity and dry saline land
salinity are wide spread. Dryland salinity commonly occurs at the foot of slopes and in
valley floors where the water table is shallow (Rengasamy, 2006). This salinity is
caused by agriculture by clearing of native vegetation which disturbs the water
balance. Cultivation of annual crops leads to lower evapotranspiration rates than
under native perennial plants, which results in rising of the ground water table and
dissolved salts and, hence dryland salinity. On the other hand, dry saline land or
transient salinity is not associated with the ground water table and involves surface
and sub-surface soil salinity. This type of salinity was first identified in 1942 by Herriot
and is also termed ‘magnesia patches’. Dry saline land generally occurs within duplex
or texture-contrast soils on upper or middle slopes, particularly when there is a
sandy/loamy A horizon over an impermeable, clay rich, sodic B horizon. It results from
seasonal movement of salt in the top soil due to evaporation and rainfall (Rengasamy,
2002).
6
1.3 Effect of salinity and sodicity on plant growth
Salinity inhibits plant growth and the activity of soil microorganisms as a result
of low water availability (due to low osmotic potential of the soil solution), ion toxicity
microbes), humidified SOM (HUM, humus) and inert organic matter (IOM) that is
resistant to biological transformations (Coleman and Jenkinson, 1996). The incoming
plant material is divided into DPM and RPM but the DPM/RPM ratio depends upon the
vegetation type (1.44 for crops and grasses, 0.25 for deciduous or tropical woodlands
and 0.67 for unimproved grassland and scrubland). Both DPM and RPM decompose to
form CO2, BIO and HUM (Figure 4). Biomass and humus further decompose into CO2,
biomass and humus pools but at a slower rate than DPM and RPM.
The amount of C (y) lost from each pool, except IOM, is described by first order
exponential decay:
y = y0 (1-e-abckt)
where y0 = initial amount of C in a particular pool; a, b and c are rate modifying factors
for temperature, moisture and plant retainment, respectively; k is the rate constant
for the given pool expressed per year and t is 1/12 to convert k to a monthly time-
step.
22
Figure 4: Structure of the RothC model
The rate modifying factor ‘b’ accounts for the soil moisture deficit which is a
function of soil clay content, monthly average rainfall and pan evaporation.
Decomposition in the RothC model is also sensitive to whether the soil is bare or
covered by vegetation. The modifier is 0.6 for actively growing vegetation (reduces
decomposition rates by 40%) and 1 for bare soil. The pools are defined by the rate
constant, proportion of BIO to HUM (fixed in the model) and the ratio of CO2 to BIO +
HUM which varies according to the clay content with lower clay content leading to
higher relative CO2 release. The rate constant ‘k’ is 10 yr-1 for DPM, 0.30 yr-1 for RPM,
0.66 yr-1 for BIO and 0.02 yr-1 for HUM.
The inputs required to run the model are divided into three types:
1. Soil data : Initial SOC ( t C ha-1), clay (%), depth of soil layer sampled (cm)
2. Land use and management data: soil cover, monthly input of plant residue (t
ha-1), and farm yard manure ( t ha-1).
3. Climate data: Monthly rainfall (mm), monthly open pan evaporation (mm) and
average monthly mean air temperature (°C)
These input parameters must be known to run the model but plant input is rarely
known and can be generated by the model by running it in inverse mode.
IOM
CO2
Organic inputs
DPM
RPM
HUM
BIO
HUM
BIO
CO2
23
1.12 Modifications of RothC for subsoils, waterlogged soils and Andosols
According to Coleman and Jenkinson (2005), RothC should be used cautiously
on subsoils, soil from tundra and taiga regions, soils on recent volcanic ash and is not
suitable for waterlogged soils. However, Jenkinson and Coleman (2008) also
parameterized RothC for subsoils after incorporating two parameters, p (for downward
movement of organic C) and s (for slow decomposition of SOM at depth). Shirato et al.
(2004) modified RothC for Andosols of Japan by changing the rate constant of humic
pool and setting inert organic matter to zero. They found that with the modified
RothC, there was a good agreement between measured and modelled SOC of long-
term experiments on these soils. Further, Shirato et al. (2005) modified the
decomposition rate constants of RothC by a factor of 0.2 in summer and 0.6 in winter
for better performance of this model for paddy soils of Japan. These studies suggest
that RothC can be parameterized for different conditions by adjusting rate constants or
adding additional parameters.
1.13 Measurable and modelled pools in RothC
The input pools in the model can be approximated to measurable pools either
by chemical analyses or decomposition studies of different plant residues. Smith et al.
(2002) showed that a measured pool is equivalent to a modelled pool only when it is
unique and non-composite. Most studies initialize RothC using only measured total soil
carbon and other input parameters, however there are few studies where the
conceptual pool of model were replaced with measured SOC pools. Skjemstad et al.
(2004) showed that RPM, HUM and IOM pools of RothC could be replaced by POC (C
associated with particles > 53 µm), humus (C associated with particles < 53 µm
excluding char-C) and char-C (condensed aromatic C), respectively. They found a good
agreement between measured and modelled pools after changing the decomposition
rate constant (k) of RPM from 0.30 to 0.15 per year and retaining the original rate
constant values for the other pools as proposed by Jenkinson et al. (1987). Similarly,
Zimmermann et al. (2006) used a fractionation scheme to divide SOC pools into five
fractions namely POC, DOC, C associated with silt plus clay (SSOC), C associated with
sand and stable aggregates (ASOC), and resistant C (RSOC). They found that POC+DOC
could be replaced with DPM + RPM, SSOC (minus RSOC) and ASOC with HUM+BIO and RSOC
24
with IOM. Shirato and Yokozawa (2006) identified only DPM and RPM fractions in
RothC by acid hydrolysis of plant materials. They divided C in plant material into three
pools: labile pool I (obtained by hydrolysis with 5 N H2SO4), labile pool II (obtained by
hydrolysis with 26 N H2SO4 and then with 2N H2SO4) and recalcitrant pool
(unhydrolyzed residue). They found that the labile pool approximated the DPM pool
and the labile pool II plus recalcitrant pool approximated the RPM pool in RothC.
Ludwig et al. (2003) compared measured and modelled pools of RothC using the 13C
technique. Total C and maize derived C in the <63 µm fraction were correlated with the
sum of modelled total and maize derived C in the humic pool, inert organic matter and
microbial biomass.
1.14 Integration of RothC with spatial data in the geographical information system
For identification of potential areas for C sequestration at regional scale,
information about spatial variation in climate, soil properties, vegetation, crops and
land use is required (Falloon et al., 1998; Smith et al., 2006). Many SOC models are
point-based and perform simulations of SOC and predictions for one site at a time. The
integration of the RothC with soil, land use and climate data in a GIS environment was
successfully illustrated by Fallon et al. (1998) for studying the changes in SOC after 50
and 100 years following afforestation of arable land in Hungary. Fallon et al. (2006)
studied SOC fluxes in mineral soils as a function of changes in climate, land use and
land management at a 1-km resolution in the UK. Smith et al. (2005, 2006) used a
similar approach for studying projected changes in SOC of European croplands,
grassland and forests in 18 x 18 km cells. Further, Smith et al. (2007) used the spatial
version of RothC to estimate changes in SOC stocks in soils of European Russia and the
Ukraine from 1990-2070 with four intergovernmental panel on climate change (IPCC)
scenarios. Similarly, Kamoni et al. (2007), Al-Admat et al. (2007), Bhattacharya et al.
(2007) and Cerri et al. (2007) predicted SOC stocks between 2000 and 2030 in Kenya,
Jordan, India and Brazilian Amazon, respectively. Jones et al. (2005) used RothC to
model the effect of future climate on global SOC stocks and predicted that SOC stocks
would remain unchanged until about 2050 and but decline sharply thereafter, resulting
in a decrease of SOC by 54 Pg C by 2100. In Australia, FullCAM (Richards, 2001) is used
for C accounting and can also be run in a spatial mode for studying the effect of land
25
use changes after integrating remote sensing data, climatic parameters and soil
information. FullCAM is an integration of several sub-models: RothC and CENTURY, the
empirical C tracking model CAMFor (Richards and Evans, 2000), the tree growth model
3PG (Landsberg and Waring, 1997) and the litter decomposition model GENDEC
(Moorhead et al., 1999).
1.15 Conclusions and knowledge gaps
In South Australia, dryland salinity and dry saline land salinity are forms of land
degradation of major significance, but information about the extent of the area
affected is scant. By providing fast, cost effective and time series data, remote sensing
can play an important role in detecting, mapping, and monitoring salt-affected surface
features. In this thesis, the ability of higher resolution multispectral satellite imagery as
a means of mapping salinity at a large scale will be assessed in the agricultural area
around Kadina, South Australia where dryland and dry land salinity are wide-spread.
Increasing soil salinity and sodicity affect SOC dynamics and can alter C stocks and
fluxes in the landscape, but the influence of salt on SOC stocks may vary according to
different types of salinity and climatic conditions. Therefore, an agricultural area in
Punjab (Muktsar district), India where salinity was induced by irrigation, will also
mapped and sampled on a 1:50,000 scale.
Although there are many studies on the effect of EC and SAR on SOM
decomposition, they mainly used soils to which salt was added. This may not reflect
the effect of EC and SAR on decomposition in soils that have been exposed to salt for
longer periods of time. To close this knowledge gap, SOM decomposition in field
collected salt-affected and salt amended soils (salinity is altered by adding soluble
salts) will be determined.
Compared to other models, RothC is quite simple and transparent model and
has been used successfully to predict changes in SOC in different parts of the world.
However, RothC has not been calibrated for salt-affected soils that cover large areas in
Australia and India. In this thesis, an approach will be described for development of a
decomposition rate modifier for salinity and its integration into the spatial version of
RothC. Further, the impact of reduced plant inputs in salt-affected soils on modelled
26
SOC stocks will also be evaluated. Lastly, the RothC, modified for salt-affected soils, will
be used to model regional SOC stocks.
Thus, the present study has the following aims:
1. to map salt-affected soils of selected regions of South Australia and India
2. to investigate the effect of various levels of EC and SAR on SOC turnover
3. to develop decomposition rate modifier for salt that can be included into
RothC for simulating SOC dynamics in salt-affected soils
4. to estimate regional SOC turnover in salt-affected soils by linking the
modified Roth C model to a GIS data base for India and Australia
1.16 Structure of thesis
A schematic diagram of outline of approaches used for studying SOC dynamics
in salt-affected soils by integrating remote sensing, incubation experiments, SOC
modelling and GIS is shown in Figure 5. For this purpose, two regions, one in India
(Muktsar, Punjab) and one in Australia (Kadina, South Australia) were selected. The
salt-affected soils were mapped on finer scale (~ 1:10, 000) for Kadina, South Australia
(Chapter 2) and on coarse scale (1:50,000 scale) for Muktsar, Punjab, India (Appendix
1) due to best available data.
The salt-affected soils contain calcium carbonate and estimation of CO2 release
in calcareous soils is problematic. Chapter 3 covers the influence of calcium carbonate
on CO2 release in saline soils in the presence of residues. Previous studies (Ben-Gal et
al., 2009; Rengasamy, 2010) suggested that the osmotic potential of soil solution may
be an appropriate parameter for assessing the effect of salinity on plant growth. The
question arose whether the effect of salinity on soil respiration may be different when
calculated with osmotic potential. Therefore, salt-amended soils of varying texture
were used in an incubation study to assess the effect of salinity on CO2 release as a
function of osmotic potential (Chapter 4). The results of this study showed that salt-
amended soils overestimate the salinity effect compared to saline soils from the field.
Chapter 5 covers the relationship between CO2 release and soil properties in salt-
affected landscapes of India and Australia. The results of this study suggest that among
27
all soil properties, EC was the main factor influencing for soil respiration. Therefore, EC
should be taken account into simulating SOC dynamics in these soils.
Currently SOC models do not take account into salinity and the plant growth is
lower in salt-affected soils. Chapter 6 covers the development of decomposition rate
modifier for salinity using CO2 release from soils of India and Australia. RothC, modified
after including a decomposition rate modifier and a plant input modifier, was used to
simulate SOC dynamics in soils collected from a field in South Australia. In order to
estimate the past SOC content from saline soils of India and Australia when these were
non-saline, a new method to model the change in SOC stocks that can be attributed to
the salinization of the soils was developed (Chapter 7) using RothC modified for saline
soils.
28
Figure 5: Schematic diagram of the structure of thesis
Australia
Calcium carbonate
Salt amended soils
Soil properties
(Chapter 3)
(Chapter 4)
(Chapter 5)
India
Past, present and future SOC stocks (India and Australia)
Remote Sensing (Satellite imagery)
Classification
Selection of soil sampling sites
Measurement of total soil organic carbon, EC and SAR
Incubation experiments for measuring CO2 emission
Effect of salt on CO2 emission
Compared with modelled CO2 (RothC) emission
Salt rate modifier for decomposition
RothC modified for saline soils Plant input modifier for salinity
(Chapter 2)
(Chapter 6)
(Chapter 7)
29
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39
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Wong, V.N.L., Dalal, R.C., Greene, R.S.B., 2008. Salinity and sodicity effects on respiration and microbial biomass of soil. Biology and Fertility of Soils 44, 943-953. Wong, V.N.L., Dalal, R.C., Greene, R.S.B., 2009. Carbon dynamics of sodic and saline soils following gypsum and organic material additions: A laboratory incubation. Applied Soil Ecology 41, 29-40. Yuan, B.C., Li, Z.Z., Liu, H., Gao, M., Zhang, Y.Y., 2007. Microbial biomass and activity in salt affected soils under arid conditions. Applied Soil Ecology 35, 319-328. Zimmermann, M., Leifeld, J., Schmidt, M., Smith, P., Fuhrer, J., 2006. Measured soil organic matter fractions can be related to pools in the RothC model. European Journal of Soil Science 58, 658-667.
42
CHAPTER 2
Severity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imagery Raj Setia1, Megan Lewis2, Petra Marschner1, Ramesh Raja Segaran2, David Summers2,3 and David Chittleborough2
1Soils, School of Agriculture, Food and Wine, The University of Adelaide, Adelaide SA 5005, Australia
2School of Earth and Environmental Sciences, The University of Adelaide, Adelaide SA 5005, Australia
3Natural Resource and Economic Decision Sciences, CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, SA 5064, Australia
The work contained in this chapter is published in Land Degradation & Development (With permission from Wiley-Blackwell, UK)
Setia, R., Lewis, M., Marschner, P., Raja Segaran, R., Summers, D., Chittleborough, D. (2011). Severity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imagery. Land Degradation & Development, DOI: 10.1002/ldr.1134
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
A Setia, R., Lewis, M., Marschner, P., Raja Segaran, R., Summers, D. & Chittleborough, D. (2011). Severity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imagery. Land Degradation & Development, Early View Online Version, Wiley Online Library
A NOTE:
This publication is included on pages 44-53 in the print copy of the thesis held in the University of Adelaide Library.
A It is also available online to authorised users at:
A http://dx.doi.org/10.1002/ldr.1134
A
54
CHAPTER 3
Is CO2 evolution in saline soils affected by an osmotic effect and calcium carbonate?
Raj Setia1*, Petra Marschner1, Jeff Baldock1,2 and David Chittleborough3
1Soils, School of Agriculture, Food and Wine, The University of Adelaide, Adelaide SA5005, Australia 2CSIRO Land and Water, Glen Osmond SA 5064, Australia
3School of Earth and Environmental Sciences, The University of Adelaide, Adelaide SA5005, Australia
The work contained in this chapter is published in Biology and Fertility of Soils (With permission from Springer)
Setia, R., Marschner, P., Baldock, J., Chittleborough, D., 2010. Is CO2 evolution in saline soils affected by an osmotic effect and calcium carbonate? Biology and Fertility of Soils 46, 781-792.
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
A Setia, R., Marschner, P., Baldock, J. & Chittleborough, D. (2010). Is CO2 evolution in saline soils affected by an osmotic effect and calcium carbonate? Biology and Fertility of Soils, v. 46 (8), pp. 781-792
A NOTE:
This publication is included on pages 56-67 in the print copy of the thesis held in the University of Adelaide Library.
A It is also available online to authorised users at:
A http://dx.doi.org/10.1007/s00374-010-0479-3
A
68
CHAPTER 4
Salinity effects on carbon mineralization in soils of varying texture
Raj Setia1*, Petra Marschner1, Jeff Baldock1, 2, David Chittleborough3, Pete Smith4 and Jo Smith4
1Soils, School of Agriculture, Food and Wine, The University of Adelaide, Adelaide SA5005, Australia 2 CSIRO Land and Water, Glen Osmond SA 5064, Australia 3School of Earth and Environmental Sciences, The University of Adelaide, Adelaide SA5005, Australia
4Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen- AB24 3UU, Scotland, UK The work contained in this chapter is published in Soil Biology & Biochemistry
(With permission from Elsevier) Setia, R., Marschner, P., Baldock, J., Chittleborough, D., Smith, P., Smith, J. (2011). Salinity effects on carbon mineralization in soils of varying texture. Soil Biology & Biochemistry 43, 1908-1916
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
A Setia, R., Marschner, P., Baldock, J., Chittleborough, D., Smith, P. & Smith, J. (2011). Salinity effects on carbon mineralization in soils of varying texture. Soil Biology & Biochemistry, v. 43 (9), pp. 1908-1916
A NOTE:
This publication is included on pages 70-78 in the print copy of the thesis held in the University of Adelaide Library.
A It is also available online to authorised users at:
A http://dx.doi.org/10.1016/j.soilbio.2011.05.013
A
79
CHAPTER 5
Relationships between carbon dioxide emission and soil properties in salt-affected landscapes Raj Setia1*, Petra Marschner1, Jeff Baldock1,2 , David Chittleborough3 and Vipan Verma4
1Soils, School of Agriculture, Food and Wine, The University of Adelaide, Adelaide SA5005, Australia 2 CSIRO Land and Water, Glen Osmond SA 5064, Australia 3School of Earth and Environmental Sciences, The University of Adelaide, Adelaide SA5005, Australia
4Punjab Remote Sensing Centre, Ludhiana-141 004, Punjab, India
The work contained in this chapter is published in Soil Biology & Biochemistry (With permission from Elsevier)
Setia, R., Marschner, P., Baldock, J., Chittleborough, D., Verma, V., 2011. Relationships between carbon dioxide emission and soil properties in salt-affected landscapes. Soil Biology & Biochemistry 43, 667-674.
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
A Setia, R., Marschner, P., Baldock, J., Chittleborough, D. & Verma, V. (2011). Relationships between carbon dioxide emission and soil properties in salt-affected landscapes. Soil Biology & Biochemistry, v. 43(3), pp. 667-674
A NOTE:
This publication is included on pages 81-88 in the print copy of the thesis held in the University of Adelaide Library.
A It is also available online to authorised users at:
A http://dx.doi.org/10.1016/j.soilbio.2010.12.004
A
89
CHAPTER 6
Introducing a decomposition rate modifier in the Rothamsted carbon model to predict soil organic carbon stocks in saline soils Raj Setia1, Pete Smith2, Petra Marschner1*, Jeff Baldock1,3, David Chittleborough4 and Jo Smith2
1Soils, School of Agriculture, Food and Wine, The University of Adelaide, Adelaide SA5005, Australia 2 Institute of Biological and Environmental Sciences, 23 St Machar Drive, University of Aberdeen, Aberdeen- AB24 3UU, Scotland, UK 3 CSIRO Land and Water, Glen Osmond SA 5064, Australia 4School of Earth and Environmental Sciences, The University of Adelaide, Adelaide SA5005, Australia The work contained in this chapter is published in Environmental Science & Technology
(With permission from American Chemical Society) Setia, R., Smith, P., Marschner, P., Baldock, J., Chittleborough, D., Smith, J. (2011). Introducing a decomposition rate modifier in the Rothamsted carbon model to predict soil organic carbon stocks in saline soils. Environmental Science and Technology, dx.doi.org/10.1021/es200515d
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
Setia, R., Smith, P., Marschner, P., Baldock, J., Chittleborough, D. and Smith, J. (2011) Introducing a decomposition rate modifier in the Rothamsted carbon model to
predict soil organic carbon stocks in saline soils
Environmental Science and Technology, v.45 (15), pp. 6396–6403, August 2011
NOTE: This publication is included in the print copy of the thesis
held in the University of Adelaide Library.
It is also available online to authorised users at:
Simulation of salinity effects on soil organic carbon: past, present and future carbon stocks Raj Setia1, Pete Smith2, Petra Marschner1*, Pia Gottschalk3, Jeff Baldock1,4, Vipan Verma5 and Jo Smith2
1Soils, School of Agriculture, Food and Wine, The University of Adelaide, Adelaide SA5005, Australia 2 Institute of Biological and Environmental Sciences, 23 St Machar Drive, University of Aberdeen, Aberdeen- AB24 3UU, Scotland, UK 3 Potsdam Institute for Climate Impact Research, Telegrafenberg, P.O. Box 601203, 14412 Potsdam, Germany 4 CSIRO Land and Water, Glen Osmond SA 5064, Australia 5Punjab Remote Sensing Centre, Ludhiana-141 004, Punjab, India The work contained in this chapter has been submitted to Agriculture, Ecosystems & Environment.
NOTE: Statements of authorship appear in the print copy of the thesis held in the University of Adelaide Library.
102
A Setia, R., Smith, P., Marschner, P., Gottschalk, P., Baldock, J., Verma, V. & Smith, J. (2012) Simulation of salinity effects on past, present and future soil organic carbon stocks Environmental Science & Technology, v. 46 (3), pp. 1624-1631
A NOTE:
This publication is included on pages 102-127 in the print copy of the thesis held in the University of Adelaide Library.
A It is also available online to authorised users at:
A http://dx.doi.org/10.1021/es2027345
A
A NOTE
Published article is titled: 'Simulation of salinity effects on past, present and future soil organic carbon stocks'.
128
CHAPTER 8
CONCLUSIONS AND FUTURE RESEARCH
The global carbon (C) pool up to 1 m depth, comprising organic and inorganic C is 2300
Pg, of which 67.4% (1550 Pg) is organic and 32.6% (750 Pg) is inorganic (Lal, 2008).
Today, the soil organic carbon (SOC) content is lower than before human intervention
due to land use changes and the resulting carbon dioxide (CO2) emissions have played
an important role in enrichment of atmospheric carbon dioxide (Smith et al., 2008).
This is particularly the case for degraded soils, e.g. salt-affected soils. Hence, these
soils have a significant potential for increasing C sequestration in the above-ground
biomass and in the SOC pool through amelioration (Lal, 2001).
The total area of saline and sodic soils is 397 and 434 million ha, respectively
which is approximately 6.5 % of the world land area (Szabolcs, 1993). Adoption of soil
restorative measures in these soils requires the integrated use of frontier technologies
such as remote sensing, experiments and modelling which can be used to estimate the
potential of these soils for SOC sequestration.
Salinization may be quite localized and change from one season to another.
Therefore, it is necessary to map the spatial and temporal changes in its occurrence.
Such mapping can be accomplished in several ways. Since field and laboratory
methods are quite expensive and time-consuming, cheaper and less time-consuming
methods such as remote sensing can play an important role in identifying, mapping
and monitoring of soil salinity (Kalra and Joshi, 1996, Metternicht and Zinck, 2003;
Shrestha, 2006; Dutkiewicz et al., 2009).
The unsupervised classification of whole image of the Australian study area was
unable to discriminate between severity of salinity because the expression of salinity in
this area differs from paddock-to-paddock due to crop and management factors. The
paddock-by-paddock-approach takes into account this variability and could
differentiate three classes of salinity which were significant related with electrical
conductivity (EC) but not with sodium adsorption ratio (SAR). This clearly shows that in
salt-affected areas with small localised patches of salinity and differential land
management, the paddock-by-paddock approach can be used for accurate mapping.
129
With respect to the effect of salinity on decomposition rates, the studies
reported in this thesis highlighted two important points. Firstly, that incubation studies
where salt is added may overestimate the effect of salinity on microbial activity and
thus decomposition rates. And secondly, that the EC may not be a good measure for
the salinity stress experienced by microbes in saline soils. Addition of salt to soils will
cause an immediate osmotic stress to soil microbes which may not give them sufficient
time to adjust physiologically or via changes in community composition to the lower
osmotic potential. In the field, salinity develops more slowly and therefore allows for
adjustment. Therefore, to evaluate the effect of salinity on decomposition rates, saline
soils from the field should be used. The EC measured in fixed soil-to-solution ratio is a
poor indicator of the salt stress experienced by plants and microbes because as soils
dry, the salt concentration in the soil solution increases (decreasing osmotic potential).
Thus, the osmotic potential varies with water content in a given soil but also between
soils. Two soils of different texture may have the same EC1:5 (EC of 1:5 soil: water
suspension), but the osmotic potential is lower in the soil with lower water content.
The relative importance of soil properties may be quite different in salt-affected soils
to that in non-salt-affected soils. The regression analysis of the CO2 emission from non-
salt-affected soils and salt-affected soils collected from India and Australia showed that
cumulative CO2-C emission was significantly positively correlated with particulate
organic carbon (POC) and humus-C but significantly negatively related with EC and
significantly positively with osmotic potential. Therefore, salinity strongly affects
decomposition rates and should be taken account into SOC models for studying SOC
dynamics in salt-affected soils.
When modelling SOC turnover in soils, it is important to not only consider
decomposition rates but also plant inputs. There are many studies showing reduced
yield in saline soils, therefore, plant inputs into saline soils will be reduced. The
Rothamsted Carbon Model (RothC, Jenkinson et al. 1987, Coleman and Jenkinson
1996) is often used for estimations of future SOC stocks and CO2 emissions, but it had
not been calibrated for saline soils.
In the work described here, reduced plant inputs were calculated based on
data from Maas and Hoffman (1977) who compiled a list of threshold and slope values
for yield of various crops. The importance of considering reduced plant inputs into
130
saline soils was demonstrated by modelling past SOC stocks considering reduced
decomposition only or reduced decomposition and reduced plant inputs. Using the
reduced decomposition only, the past SOC content was lower than the present,
suggesting an increase in SOC due to salinity. On the other hand, including both
reduced decomposition and reduced plant inputs showed that saline soils had lost
considerable amounts of SOC since they became saline. The average loss of SOC due
to salinity was 55 t ha-1 for Australian soils and 31 t ha-1 for Indian soils. Since this and
other study showed that saline soils have lower SOC contents compared to non-saline
soils, only the latter simulation (reduced decomposition and reduced plant inputs) is
correct. The iterative approach to calculate past SOC stocks is novel and useful.
However its limitation is that it cannot be verified and it is not known when the now
saline soils were non-saline.
The modelling results with the decomposition rate modifier and plant inputs
modifier indicated a greater decrease in SOC in saline than in non-saline soils of India
and Australia in the future climate. Thus, the previous projections of the decrease of
SOC in saline soils have been underestimated; this also means that saline soils will emit
more CO2 than previously thought.
This thesis represents development of the following innovative and unique
approaches:
1. A paddock-by-paddock approach for accurate mapping of salinity for
smaller area using multispectral high resolution imagery.
2. Using osmotic potential (not EC) for estimating the effect of salinity on
microbial activity and soil respiration.
3. Calculation of a decomposition rate modifier for saline soils and its inclusion
in Roth C
4. Development of an approach to calculate plant inputs into saline soils and
its inclusion in Roth C
5. A novel approach for calculating past SOC stocks when saline soils were
non-saline.
131
Recommendations for future research work
1. The rate modifier for decomposition was derived from CO2 emission over 120
days in a single incubation experiment with 120 soils. The decomposition rate
modifier derived from this limited set of soils may not be generally applicable.
Additionally, there was no relationship between sodicity and CO2 emission in
this study, which may be due to the limited range of sodicity in the sampled
soils. Therefore, a similar incubation experiment should be conducted focussing
specifically on sodic soils. In order to preserve the soil structure, such an
experiment would have to be carried out with intact soil cores.
2. Due to the short incubation time (120 days), only easily decomposable (labile)
SOC pools changed. It was assumed that the same rate modifier applies for all
SOC pools. This issue could be addressed by analysing archived soil samples
from salt-affected areas taken at least 10 years previously and comparing the
SOC pools of these soils with those of present soils from the same location.
3. The plant input modifier used in modelling was based on a generalised
equation between EC and yield and did not take into account the differential
sensitivity of plants to salinity. Moreover, yield may not be directly reflected in
C inputs, particularly with respect to root-derived C. Root and shoot biomass of
common crop plants will need to be determined over a range of salinity and
sodicity levels to develop a more accurate equation for plant inputs into saline
soils.
4. To verify if the decomposition rate modifier is applicable to the field situation,
CO2 emission from salt-affected soils should be measured in the field,
accompanied by soil sampling to determine EC and water content.
References
Coleman K, Jenkinson D, 1996. RothC-26.3-A Model for the turnover of carbon in soil. RothC-26.3-A Model for the turnover of carbon in soil. In: Powlson, D., Smith, P., Smith, J. (Eds.), Evaluation of soil organic matter models using existing long-term datasets. NATO ASI SERIES, 38, 237-246.
132
Dutkiewicz A, Lewis M, Ostendorf B, 2009. Evaluation and comparison of hyperspectral imagery for mapping surface symptoms of dryland salinity. International Journal of Remote Sensing 30: 693-719.
Jenkinson D, Hart P, Rayner J, Parry L, 1987. Modelling the turnover of organic matter in long-term experiments at Rothamsted. Intecol. Bull 15: 1-8.
Kalra N, Joshi D, 1996. Potentiality of Landsat, SPOT and IRS satellite imagery, for recognition of salt affected soils in Indian Arid Zone. International Journal of Remote Sensing 17: 3001-3014.
Lal R, 2001. Potential of desertification control to sequester carbon and mitigate the greenhouse effect. Climatic Change 51: 35-72.
Lal R, 2008. Carbon sequestration. Philosophical Transactions of the Royal Society B: Biological Sciences 363: 815-830. Mass E, Hoffman G, 1977. Crop salt tolerance current assessment. Journal of the Irrigation and Drainage Division ASCE 504: 115-135.
Metternicht G, Zinck J, 2003. Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment 85: 1-20.
Shrestha R, 2006. Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degradation and Development 17: 677-689.
Smith P, Fang C, Dawson JJC, Moncrieff JB, 2008. Impact of global warming on soil organic carbon. Advances in Agronomy 97: 1-43.
Szabolcs I, 1993. Soils and salinisation. In: Pessarakli M, (ed.) Handbook of Plant and Crop Stress. Marcel Dekker, New York, 3–11.
133
APPENDIX -I
Mapping of salt affected soils in Muktsar district, Punjab, India
The multi-temporal geo rectified Resourcesat-I LISS-III data (spatial resolution = 23.5
m) acquired during spring (April 2005), autumn (October 2005) and winter (February
2006) of 2005-06 was used for mapping of salt-affected soils in Muktsar district. The
imagery was provided as four bands [green (0.52 - 0.59 µm), red (0.62 - 0.68 µm), near-
infrared (0.76 – 0.86 µm) and short-wave infrared (1.55 – 1.70 µm)]. Ancillary data in
the form of Survey of India (SOI) topographical maps, existing waste land data and
other published reports were used as reference data. Survey of India topographical
maps on 1:50,000 scale were used for identification of base features and for planning
of the ground truthing. Ground-truthing was performed by collecting 120 soil samples
over the study area of 2631 km2.
The methodology is based on on-screen interpretation using standard image
interpretation keys like tone, texture, size, pattern, association etc. which was
followed by ground truthing. To delineate indicators of salinity like reduced plant cover
and growth, halophytic vegetation, greater soil exposure and possibly brighter soil
surfaces, suitable band combinations were used where the signatures of particular
class were quite evident. The standard false colour composites (FCC) showed white
patches of varying tones in the red background indicating the salt efflorescence of salt
affected soils which showed higher digital values than non-salt-affected soils. Similar
tone was also observed for sandy soils prevalent in the area but these soils were
discriminated by associated features and ground truthing. The salt-affected soils were
mapped in the light of the ground observations, visual interpretation keys and
available soil parameters (electrical conductivity and sodium adsorption ratio) (Figure
1). The salt-affected soils occupy less than 1% area of district in various seasons and
the salinity was mainly induced by irrigation (secondary salinization). Some the patches
were observed along the twin canals, these canals are aligned across the natural
gradient of the area, obstructing the natural flow of water. Nearly 61% area of the
district has poor quality groundwater, with 37% of the area having saline to highly
saline, 19% saline-sodic and 5% area sodic waters (Sharma et al., 2003).
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Figure 1: Classified Resourcesat-I LISS-III data of the Muktsar district, Punjab, India.