Mitigating climate change impact on soybean productivity in India: a simulation study

Abstract

Field experiments with soybean were conducted over a period of 1990–1998 in diverse Indian locations ranging in latitude, longitude, and elevation. These locations provided a wide range of environments for testing and validation of the crop growth (CROPGRO) model considered in this study with observed changes in soils, rainfall and other weather parameters. Model predicted satisfactorily the trends of days to flowering, maturity and grain yields. The deviations of simulated results were within ±15% of the measurements. Validated CROPGRO model has been used to simulate the impact of climate change on soybean production in India. The projected scenarios for the Indian subcontinent as inferred from three state-of-the-art global climate models (GCMs) have been used in the present study. There was a decrease (ranging between about 10 and 20%) in soybean yield in all the three future scenarios when the effect of rise in surface air temperature at the time of the doubling of CO2 concentration was considered. The results obtained on the mitigatory option for reducing the negative impacts of temperature increases indicate that delaying the sowing dates would be favorable for increased soybean yields at all the locations in India. Sowing in the second season would also be able to mitigate the detrimental effects of future increases in surface temperature due to global warming at some locations.

Full text

Agricultural and Forest Meteorology 121 (2004) 113–125
Mitigating climate change impact on soybean
productivity in India: a simulation study
R.K. Malla,∗,M.Lalb, V.S. Bhatiac, L.S. Rathored, Ranjeet Singhd
aCentre for Systems Simulation, Indian Agricultural Research Institute, New Delhi 110012, India
bIndian Institute of Technology, New Delhi 110016, India
cNational Research Centre for Soybean, Indore 452017, India
dNational Centre for Medium Range Weather Forecasts, Lodi Road, New Delhi 110003, India
Received 19 March 2003; accepted 19 June 2003
Abstract
Field experiments with soybean were conducted over a period of 1990–1998 in diverse Indian locations ranging in latitude,
longitude, and elevation. These locations provided a wide range of environments for testing and validation of the crop growth
(CROPGRO) model considered in this study with observed changes in soils, rainfall and other weather parameters. Model
predicted satisfactorily the trends of days to flowering, maturity and grain yields. The deviations of simulated results were
within ±15% of the measurements.
Validated CROPGRO model has been used to simulate the impact of climate change on soybean production in India. The
projected scenarios for the Indian subcontinent as inferred from three state-of-the-art global climate models (GCMs) have
been used in the present study. There was a decrease (ranging between about 10 and 20%) in soybean yield in all the three
future scenarios when the effect of rise in surface air temperature at the time of the doubling of CO2concentration was
considered. The results obtained on the mitigatory option for reducing the negative impacts of temperature increases indicate
that delaying the sowing dates would be favorable for increased soybean yields at all the locations in India. Sowing in the
second season would also be able to mitigate the detrimental effects of future increases in surface temperature due to global
warming at some locations.
© 2003 Elsevier B.V. All rights reserved.
Keywords: Global climate models; Impact mitigation; Climate change; Soybean; Sowing dates; Crop simulation model
1. Introduction
Crop growth and yield under normal conditions
are largely determined by weather during the grow-
ing season. Even with minor deviations from the nor-
∗Corresponding author. Present address: Central Ground Water
Board, A2/W3 Curzon Road Barracks, Kasturba Gandhi Marg,
New Delhi 110001, India. Tel.: +91-11-2338-1089
(O)/+91-11-2005-3312 (R); fax: +91-11-2338-8310.
E-mail address: mall raj@rediffmail.com (R.K. Mall).
mal weather, the efficiency of externally applied in-
puts and food production is seriously impaired. The
increasing CO2concentration in the atmosphere and
the anticipated climate change due to global warm-
ing are also likely to affect future global agricultural
production through changes in rate of plant growth
(Lemon, 1983; Cure and Acock, 1986) and transpi-
ration rate (Morison, 1987; McNaughton and Jarvis,
1991; Jacobs and DeBruin, 1992).
Soybean [Glycine max (L.) Merrill] ranks first
among the oilseeds in the world and has now found
0168-1923/$ – see front matter © 2003 Elsevier B.V. All rights reserved.
doi:10.1016/S0168-1923(03)00157-6

114 R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125
a prominent place in India. It has seen phenomenal
growth in area and production in India in the past
decade (Paroda, 1999). Area under soybean culti-
vation has steadily increased over the years from
the level of 0.50millionha (Mha) in 1979–1980 to
5.86Mha in 1997–1998. The growth in production
and productivity has been from 0.28milliontonnes in
1979–1980 to 6.72milliontonnes in 1997–1998 and
570kg/ha in 1979–1980 to 1150kg/ha in 1997–1998,
respectively (SOPA, 1999). This increasing trend of
fast adaptation of the crop by the farmers in India
points out that soybean is going to be the future
leading commercial venture in the country. Its cultiva-
tion has also brought about positive socio-economic
changes in the life of farmers in some parts of India
(Tiwari et al., 1999). There is still substantial scope
to increase both area and productivity of soybean in
India. The current estimated growth in area coverage
is 10Mha and, by 2010 a.d., productivity enhance-
ment will be about 1500kg/ha such that production
of 15milliontonnes by 2010 can be expected in India
(Holt et al., 1997). Soybean has a good potential to
get involved in the intercropping (Jat et al., 1998)
Fig. 1. Locations of the selected sites in India considered in the study.
as well as crop sequences, as it is a short duration
(85–125 days) leguminous crop.
Future climatic change is likely to have substan-
tial impact on soybean production depending upon
the magnitude of variation in CO2and temperature.
Increased temperature significantly reduces the grain
yield due to accelerated development and decreased
time to accumulate grain weight (Seddigh and Joliff,
1984a,b;Baker et al., 1989). There have been a few
studies in India and elsewhere aimed at understand-
ing the nature and magnitude of gains/losses in yields
of soybean crop at different sites under elevated at-
mospheric CO2conditions and associated climate
change (Adams et al., 1990; Sinclair and Rawlins,
1993; Haskett et al., 1997; Lal et al., 1999).
In this study, an attempt has been made: (i)to
evaluate the performance of CROPGRO model under
different seasons, weather, locations, management,
and sowing dates; (ii) to know the yield potential of
soybean; and (iii) to explore the possibilities of em-
ploying different mitigating options to alleviate the
climate change impacts on soybean production un-
der different climate change scenarios inferred from

R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125 115
the state-of-the-art global climate models (GCMs)
in the major soybean growing area in India using
CROPGRO-soybean simulation model. The long-term
observed daily weather data on rainfall, maximum
and minimum temperatures and solar radiation at
the selected stations in India, namely Coimbatore,
Dharwad, Ludhiana, Hissar, Pantnagar, Delhi, Pune,
Hyderabad, Ranchi, Indore, Raipur, Jabalpur and
Gwalior have been used in this study. The geographi-
cal location of these stations is shown in Fig. 1.
2. Data and methodology
2.1. The CROPGRO-soybean model
Crop growth simulation models which share a
common input and output data format have been de-
veloped and embedded in a software package called
the Decision Support System for Agrotechnology
Transfer (DSSAT) (Tsuji et al., 1994; Jones et al.,
1994; Hoogenboom et al., 1994). The models under
DSSAT umbrella include CROPGRO for soybean.
Its major components are vegetative and reproduc-
tive development, carbon balance, water balance
and nitrogen balance. A detailed description of the
modified version of CROPGRO-soybean model is
provided in Boote et al. (1996). The model uses
empirical functions to compute daily canopy gross
photosynthesis in response to CO2concentration,
air temperature and daily canopy evapotranspiration.
Canopy photosynthesis is computed at hourly time
steps using leaf-level photosynthesis parameters and
hedgerow light interception calculations (Boote and
Pickering, 1994). Photosynthesis and evapotranspira-
tion algorithms also take into account the changes in
daily canopy photosynthesis under elevated CO2con-
centration and temperature conditions (Curry et al.,
1990a,b). The model simulates the potential, water
and nutrient limited yields of soybean.
2.2. Input data
The model requires input data on soil, crop and
weather for its calibration and validation in different
environments. Weather (solar radiation, maximum
and minimum temperatures and rainfall) and soil
(albedo, first stage evaporation, drainage, USDA Soil
Conservation Service Curve Number for runoff and
layer-wise information and saturation, field capacity,
wilting point, texture and hydraulic conductivity) and
crop management data (dates of sowing, plant and
row spacing, irrigation, fertilizer, etc.) were collected
for each of the locations under study. The details on
weather data used in this study are given in Table 1.
2.3. Evaluation of the crop model
2.3.1. Genetic coefficients
To simulate a crop variety, the crop model requires
15 genetic coefficients. The genetic coefficients of the
‘Bragg’ variety of soybean for the model were esti-
mated by repeated iterations in the model calculations
until a close match between simulated and observed
phenology, growth and yield was obtained. All calibra-
tion data required to derive genetic coefficients were
obtained from field experiment conducted at Indore
during 1995 and 1996 using random block design. In
this field experiment, the soybean crop was sown at
row spacing of 35cm and the seed depth was main-
tained as 5cm. A net 20kg of urea was applied as
basal dose at the time of sowing. Plant population was
kept as 25 plants/m2. The genetic coefficients deter-
mined in the model using the identical conditions as
in the field experiment for ‘Bragg’ variety of soybean
are presented in Table 2. These coefficients were used
in the subsequent validation and application.
2.3.2. The model validation
A large number of field experiments have been
conducted in India where the effect of different
agro-ecological factors such as season, weather, sow-
ing dates and variety has been studied on growth
and yield of soybean crop in different locations. This
database included all relevant information (including
the different management practices adapted and the
location specific weather conditions) obtained from
field experiments conducted between 1980 and 1998
in major soybean producing states of India and had
representations varying from Hissar in north India to
Coimbatore in South India (Table 1).
2.4. The climate change scenarios
Climate change is no longer a distant scientific
prognosis but is becoming a reality. The anthropogenic

116 R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125
Table 1
Mean simulated potential yields of variety Bragg and their coefficients of variation (CV%), period of weather data used and average
seasonal temperature for current climatic conditions at selected locations in India
Location Period Latitude
(◦N) Longitude
(◦E) Yield
(kg/ha) CV % Seasonal average
temperature, max/min (◦C)
Ludhiana (LUDH) 1974–1998 30.9 75.8 4200 5.3 33.8/24.6
Hissar (HISR) 1969–1996 29.1 75.5 4400 4.3 33.4/24.1
Pantnagar (PANT) 1970–1997 29.0 79.3 3900 4.6 32.2/24.4
Delhi (DELH) 1969–1998 28.3 77.1 4300 9.4 33.2/23.4
Gwalior (GWLR) 1965–1988 26.1 78.1 3300 11.9 33.5/23.6
Ranchi (RANC) 1986–1995 23.3 85.2 4000 5.9 29.2/22.3
Jabalpur (JBLP) 1969–1997 23.1 79.9 4050 5.2 30.8/22.9
Indore (INDR) 1985–1995 22.7 71.8 3550 9.4 32.1/23.5
Raipur (RAIP) 1971–1997 21.2 81.6 3800 7.1 31.6/22.5
Pune (PUNE) 1985–1997 18.3 73.5 4200 5.2 29.1/20.8
Hyderabad (HYDE) 1975–1997 17.4 78.4 4000 4.9 29.9/22.2
Dharwad (DHAR) 1990–1998 15.3 75.6 4000 8.1 30.8/21.2
Coimbatore (COIM) 1964–1994 11.0 77.0 3800 5.9 31.2/22.1
increases in emissions of greenhouse gases and
aerosols in the atmosphere result in a change in the
radiative forcing and a rise in the Earth’s temperature.
The bottom-line conclusion of the Third Assessment
Report of the Intergovernmental Panel on Climate
Change (IPCC, 2001) is that the average global sur-
face temperature will increase by between 1.4 and
3◦C above 1990 levels by 2100 for low emission
scenarios and between 2.5 and 5.8◦C for higher emis-
Table 2
Genetic coefficients of cultivar ‘Bragg’ obtained in calibration experiment
Description Genetic coefficients
Development aspects
Critical short day length (h) 11.81
Slope of relative response of development to photoperiod (h) 0.32
Time between plant emergence and flower appearance (photothermal days) 19.5
Time between first flower and first pod (photothermal days) 10.0
Time between first flower and first seed (photothermal days) 15.0
Time between first seed and physiological maturity (photothermal days) 30.5
Time between first flower and end of leaf expansion (photothermal days) 17.0
Seed filling duration for pod cohort at standard growth conditions (photothermal days) 24.9
Time required for cultivar to reach final pod load under optimal conditions (photothermal days) 10.9
Growth aspects
Maximum leaf photosynthesis rate at 30◦C and high light (mg CO2/m2s) 1.0
Specific leaf area of cultivar under standard growth conditions (cm2/g) 350.0
Maximum size of full leaf (three leaflets) (cm2) 170.0
Maximum fraction of daily growth that is partitioned to seed +shell 1.0
Maximum weight per seed (g) 0.16
Average seed per pod under standard growing conditions 2.1
sion scenarios of greenhouse gases and aerosols in
the atmosphere. Over land regions of the Indian sub-
continent, the projected area-averaged annual mean
surface temperature rise by the end of 21st century
has been estimated to range between 3.5 and 5.5◦C
depending upon the future trajectory of anthropogenic
radiative forcing (Lal et al., 2001). The projected
temperature increase has a large seasonal and spa-
tial dependency over India. During the monsoon

R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125 117
season, the temperature rise over south India is pro-
jected to be less than 1.5◦C by 2050s while the
increase in surface temperature is more pronounced
over north, central and east India (∼2◦C).
Globally averaged precipitation is projected to in-
crease based on an ensemble of simulations performed
with the state-of-the-art GCMs, but at the regional
scale both increases and decreases have been pro-
jected. Over the Indian subcontinent, a marginal in-
crease of about 7–10% in area-averaged annual mean
precipitation has been projected by the end of this
century (Lal, 2001). During the monsoon season, an
increase in area-averaged precipitation of only about
3–5% over the land regions has been projected by
2050s. Moreover, the standard deviation of future pro-
jections of area-averaged monsoon rainfall centered
around 2050s is not significantly different relative to
the present-day atmosphere implying thereby that the
year-to-year variability in mean rainfall during the
monsoon season may not significantly change in the
future. More intense rainfall spells are, however, pro-
jected over the land regions of the Indian subcontinent
in the future thus increasing the probability of extreme
rainfall events in a warmer atmosphere.
Other environmental factors such as cloudiness and
solar radiation at the earth’s surface will also change
but the GCMs are less consistent in their predictions,
particularly on a regional basis (Mitchell et al., 1995;
IPCC, 2001). Climate models, in general, are sub-
ject to several uncertainties, which are especially pro-
nounced at regional scales. Models are also known
to be inadequate in their representation of physical
processes related to rainfall. It should be noted here
that the projected changes in climatic elements by the
end of the 21st century is sensitive to assumptions
concerning future concentrations of greenhouse gases
and aerosols. Because there is still considerable uncer-
tainty in our understanding of how the climate system
varies naturally and reacts to emissions of greenhouse
gases and aerosols, current estimates of the magnitude
of future warming are subject to future adjustments
(either upward or downward). These caveats need to
be kept in view while interpreting the possible impacts
associated with the projected climate change scenar-
ios presented here.
Climate change scenarios for the selected regions
of the Indian subcontinent were developed using
three widely known GCMs namely, Goddard Insti-
tute of Space Studies Model (GISS-2; Russell and
Rind, 1999), Geophysical Fluid Dynamics Labora-
tory Model (GFDL-R30; Knutson et al., 1999) and
United Kingdom Meteorological Office, Hadley Cli-
mate Prediction Centre Model (UKMO, HadCM3,
Mitchell et al., 1998). For the crop growth model used
in this study, the probable changes in surface air tem-
perature during the growing season were estimated
at the selected sites in the region following standard
regionalization techniques suggested by IPCC (Carter
et al., 1999; Mearns et al., 2001). Probable changes in
precipitation, cloudiness and solar radiation under the
climate changes scenarios were not taken into con-
sideration in this analysis in view of the significant
uncertainties associated with non-linear, abrupt and
threshold rainfall events projected by GCMs over the
Indian subcontinent.
3. Results and discussion
3.1. The model validation
The correct estimation of crop phenology is very
crucial for the successful validation of crop growth
simulation models at a specific site. Observed duration
to flowering of soybean crop at selected sites in India
varies from 30 days (in Pune) to 60 days (in Hissar),
whereas simulated duration in our model validation
exercises ranged from 30 days (in Jabalpur) to 59 days
(in Hissar). Similarly, the observed duration to matu-
rity of soybean crop at selected sites varies from 89
days (in Coimbatore) to 134 days (in Ludhiana). The
simulated duration to maturity ranged from 84 days (in
Jabalpur) to 131 days (in Hissar). The analysis further
suggested that the root mean square error (RMSE) and
the mean bias error (MBE) in the model simulated du-
ration to flowering were significantly small (RMSE =
2.9, MBE =−0.6). The RMSE and MBE for the
model simulated duration to maturity of crop were
also negligible (RMSE =4.3, MBE =0.5). RMSE
provides information on the performance of a model
by allowing a term by term comparison of the actual
difference between the simulated and observed values.
MBE provides information on the performance of a
model by over-estimation or under-estimation; a posi-
tive value gives the average amount of over-estimation
in the estimated values and vice versa. The simulated

118 R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125
durations to flowering as well as maturity were within
15% error line. This exercise confirmed that the se-
lected crop growth model was able to simulate the ob-
served flowering and maturity periods reasonably well
for most treatments and at all sites selected in this
study (Fig. 2a and b).
Fig. 2c depicts a close correspondence between
simulated and observed grain yields across all treat-
ments. Observed grain yields ranged from 235kg/ha
(Delhi) to 3788kg/ha (Pune) depending upon the lo-
cation whereas simulated grain yields ranged from
367kg/ha (Delhi) to 3588kg/ha (Pune). It is evident
from Fig. 2c that model predicted grain yields within
±15% of the measured yields (RMSE =154, MBE =
3.2). In general, the predicted yields were relatively
higher than the observed yields in the years with low
yields indicating the model’s inability to simulate crop
growth when there is extreme stress. Considering that
the field measurements too generally have ±10–15%
error and that the treatments covered widely varied
weather conditions it is assumed that the model is
adequate to simulate the effects of climate change
on soybean yields in diverse agro-environments
of India.
3.2. Predicted potential yield under present-day
climate
The model simulates the potential yield of soybean,
mainly driven by solar radiation and temperature and
on varietal characteristics. In predicting the potential
yields, it is assumed that the crop has no water and
nitrogen stress and is free from any insect, pest and
disease effects. Dates of sowing for each location
were chosen based on the local practice. The potential
soybean yields for the selected 13 sites under current
climatic conditions along with their coefficients of
variation (CV%) are shown in Table 1. The seasonal
average daily temperature during the soybean grow-
ing period is also included in this table. Simulated
potential yields ranged from 3300kg/ha (at Gwalior)
to 4400kg/ha (at Hissar).
3.3. Performance of soybean under climate
change scenarios
We performed a set of simulations to examine the
sensitivity of soybean productivity to the enhanced
surface air temperature at the selected sites using the
CROPGRO model wherein daily maximum and min-
imum surface air temperature changes as obtained
in the GFDL, GISS and UKMO climate model pro-
jections for South Asia region for the present-day
conditions and also at the time of doubling of CO2
have been considered. These temperature changes
were superimposed on the observed daily maximum
and minimum temperature data series for all the
years considered in our simulations. The results of
simulation for three GCM scenarios are presented
in Table 3. The UKMO and GISS models simu-
late somewhat higher surface air temperatures than
the observed weather records for the present-day at-
mosphere (equivalent CO2concentration ∼350ppm
by volume) and as a consequence the simulated
grain yields show a decline in crop yield for differ-
ent GCM-generated climate (it ranged from 13% in
GFDL simulated present-day atmosphere to 21% in
UKMO simulated present-day atmosphere. The yields
in soybean are found to decline almost identically
for the climate change scenarios as inferred from
all the three GCMs for the case when a doubling of
CO2with respect to the present-day atmosphere oc-
curs. The simulated decline in crop yield was from
12% (GFDL model climate) to 21% (UKMO model
climate) under the doubled CO2climate change sce-
nario. The total above ground biomass was most
affected under the UKMO model-generated climate
scenario under both the levels of CO2. The matu-
rity day of the crop also extended by 2 to 5 days in
duration for both the CO2levels in our crop model
simulations (Table 3).
All the GCM projected climate change scenar-
ios (at the time of doubling of CO2concentrations)
predicted decreased yields (Table 4) for almost all
locations. Mean decline in yields across different
scenarios ranged from 14% in Pune (west India) to
23% in Gwalior (central India). Decline in soybean
yield is found to be less in west and south India as
compared to other parts of the country. The mean
yield was found to be significantly affected under
UKMO model-generated climate scenarios for both
current and doubled CO2atmosphere. In view of this,
the assessment of the options to mitigate the nega-
tive impact of the climatic change will be dealt in
the next section with special reference to the UKMO
model-generated climate change scenarios.

R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125 119
80
90
100
110
120
130
140
80 90 100 110 120 130 140
Observed, days to maturity
Simulated, days to maturity
(b)
20
30
40
50
60
70
20 30 40 50 60 70
Observed, days to flowering
Simulated, days to flowering
(a)
+15%
-15%
0
1000
2000
3000
4000
0 1000 2000 3000 4000
Observed yield, kg/ha
Simulated yield, kg/ha
Indore Jabalpur Coimbatore Dharwad
Ludhiana Hissar Pantnagar Raipur
Delhi Pune Ranchi
(c)
Fig. 2. Comparison of simulated and measured (a) duration to flowering, (b) duration to maturity and (c) yields across data sets varying in
seasons, weather, locations, nitrogen and water management, and sowing dates. Also shown are 1:1 line (solid) and error lines of ±15%
(dashed).

120 R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125
Table 3
Some crop growth parameters of the soybean variety ‘Bragg’as simulated by crop simulation model for two CO2levels and temperature
increase projected in selected GCMs (values represent average of 13 selected locations in India)
CO2level Crop growth parameters Tobs GFDL GISS UKMO
Present Yield (kg/ha) 3950 3450 3200 3100
Change in yield 0% −13% −19% −21%
Total above ground biomass (kg/ha) 7300 6900 6750 6650
Change in above ground biomass 0% −5% −7% −8%
Maturity duration (days) 109 111 113 114
Change in maturity duration 0% 1% 3% 4%
Doubled Yield (kg/ha) 5400 4750 4450 4250
Change in yield 0% −12% −18% −21%
Total above ground biomass (kg/ha) 10000 9450 9200 9100
Change in above ground biomass 0% −5% −7% −8%
Maturity duration (days) 109 111 113 114
Change in maturity duration 0% 1% 3% 4%
3.4. Mitigation strategies
While agriculture may benefit from carbon dioxide
fertilisation and an increased water efficiency of some
plants at higher atmospheric CO2concentrations,
these positive effects are likely to be negated due to
thermal and water stress conditions associated with
climate change. Thermal stress significantly affects
the agricultural productivity when it occurs in criti-
cal life stages of the crop (Rounsevell et al., 1999).
Increase the temperature reduces the total duration of
crop by inducing early flowering and shortening grain
Table 4
Changes in potential soybean yields (kg/ha) for three GCM scenarios in selected locations in India
Site 2 ×CO2aGFDL yield Percent
change GISS
yield Percent
change UKMO
yield Percent
change Mean
Ludhiana 5600 5000 −11 4700 −16 4600 −18 −15
Hissar 5850 5000 −15 4650 −20 4350 −26 −20
Pantnagar 5720 5100 −11 4850 −15 4750 −17 −14
Delhi 5800 5000 −13 4650 −20 4500 −22 −18
Gwalior 4450 3700 −17 3400 −24 3250 −27 −23
Ranchi 4950 4250 −14 3850 −22 3700 −25 −20
Jabalpur 5600 4900 −12 4600 −17 4450 −20 −17
Indore 4950 4250 −14 3850 −22 3700 −25 −20
Raipur 5300 4550 −13 4250 −20 4050 −23 −19
Hyderabad 5700 5000 −11 4750 −16 4650 −18 −15
Pune 5950 5350 −10 5100 −15 4950 −17 −14
Dharwad 5050 4400 −12 4100 −17 3950 −20 −16
Coimbatore 5650 5000 −11 4750 −16 4550 −19 −15
Mean 5400 4750 −12 4450 −18 4250 −21 −18
aIn this column, the temperature effect is not included and the yields are for the 2 ×CO2alone.
fill period (Iglesias et al., 1996). The shorter the crop
duration, the lower is the yield per unit area; a rise
in temperature should therefore lead to a fall in agri-
cultural production in a warmer atmosphere. Reports
of heat-stressed crops have become common in the
recent years in India. Even irrigated crops suffer from
high evaporation losses and heat stress. Under these
conditions, photosynthesis declines and the plant
switches from a growth path to a survival mode thus
reducing yields. A clear understanding of the relation-
ship between climatic variability, crop management
and agricultural productivity is critical in assessing

R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125 121
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Raipur
0
1000
2000
3000
4000
5000
6000
7000
8000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Jabalpur
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Indore
0
1000
2000
3000
4000
5000
6000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Gwaliar
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Delhi
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Yield (kg/ha)
L
ud
hi
a
n
a
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Julian day
Yield (kg/ha)
Present 2 x CO2
UKMO (2 x CO2)
Hissar
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Julian day
Yield (kg/ha)
Pantnagar
Fig. 3. Effect of varying planting dates on soybean yields [under present-day CO2and thermal conditions (solid line), under doubled CO2
conditions (dashed line) and under changed thermal conditions as projected in UKMO Model with doubled CO2conditions (dotted line)]
at selected locations in India.

122 R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Pune
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Hyderabad
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Yield (kg/ha)
Coimbatore
0
1000
2000
3000
4000
5000
6000
7000
1 51 101 151 201 251 301 351
Julian day
Yield (kg/ha)
Dharwad
0
1000
2000
3000
4000
5000
6000
7000
8000
1 51 101 151 201 251 301 351
Julian day
Yield (kg/ha)
Present 2 x CO2
UKMO (2 x CO2)
Ranchi
Fig. 3. (Continued)
the impacts of climatic variability and change on crop
production, the identification of adaptation strate-
gies and appropriate management practices, and the
formulation of mitigating measures to minimize the
negative effects of climatic variability including ex-
treme events on agricultural productivity (Reilly,
1995). Considering the importance of soybean as a
major cash crop in India, the key focus in this study
is to identify measures in order to reduce the poten-
tial negative effects of climate change on soybean
productivity.
3.4.1. Crop variety tolerance to temperature
Studieshaveshownthatresponses to climate change
are strongly variety specific(Wang et al., 1992). A stu-
dy by Easterling et al. (1993) explored how hypothet-
ical new varieties would respond to climate change.
The present simulation analysis, however, considers
the variety characteristics to be almost the same in the
future as at present. In reality, it is likely that the plant
breeding research will develop newer high yielding
varieties under the projected climatic conditions, thus
alleviating the climate change impact to some extent.

R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125 123
3.4.2. Sowing date and seasonal changes
Planting date is one of the important management
practices influencing soybean yield. Fig. 3 illustrates
the changes in simulated potential yield at selected
locations in India under varying sowing dates in a cal-
endar year for the present-day climatic conditions and
current level of CO2in the atmosphere (solid line),
under doubled CO2atmosphere (dashed line) and
for thermal stress conditions as projected in UKMO
Global Climate Model in a doubled CO2atmosphere
(dotted line). It is clearly evident that the present
choices of sowing dates (Julian days 152–183 corre-
sponding to June–July months) of soybean are most
appropriate in terms of maximum crop yield in dif-
ferent parts in India. Proper sowing date adjustments
will, however, be necessary for efficient utilisation of
natural resources under the climate change scenarios.
The simulation results suggest that for central Indian
stations (Raipur, Jabalpur, Indore and Gwalior), the
sowing of soybean crop may have to be delayed from
June and first week of July to first fortnight of August,
such that the adverse impacts of thermal stress due
to projected climate change could be avoided during
reproductive growth of the crop. Results also suggest
that seasonal shift in sowing of soybean crop to De-
cember will be beneficial in terms of higher yields par-
ticularly in north India (Delhi, Ludhiana, Hissar and
Pantnagar).
In view of findings reported above, potential adap-
tation options for sustained soybean productivity in
India include adjustment in cropping calendar and
crop rotation, development and promotion of use of
high yielding varieties and sustainable technological
applications. Delayed sowing date for soybean crop
at all locations in India should be most effective
in mitigating the thermal effects of climate change.
Since soybean is a short duration leguminous crop,
it has a good potential to get involved in the inter-
cropping as well as crop sequences. However, under
the circumstances, it might not be feasible to grow
second crop in the subsequent season at some loca-
tions, thus resulting in overall reduction in the food
productivity at these locations. Therefore, delay in
the planting dates for soybean crop must be decided
on the basis of the temporal rainfall distribution pat-
tern at any particular region. It may be noted here
that increasing number of recent studies have also
recommended the effectiveness of agronomic adap-
tation strategies including adjustments in planting
dates in coping with climate-induced yield losses in
different regions of the globe (see Rosenzweig and
Iglesias, 1998; Yates and Strzepek, 1998; Parry et al.,
1999; Winters et al., 1999; Darwin and Kennedy,
2000).
4. Limitation of the analysis
The findings reported here depend on the many
assumptions built into the crop simulation models.
For example, most of the relationships relating the
effect of temperature and CO2on the plant pro-
cesses are derived from experiments in which the
crop’s environment was changed for only part of
the season; acclimation of the crop to changes in its
environment is not taken account of in the model.
Studies have shown that in some crops growing un-
der enhanced CO2condition, there is initially a large
response, but over time, this response declines and
approaches that of crops growing under current CO2
levels.
As regards the climate change scenarios inferred
from global climate models, uncertainties are asso-
ciated with imperfect knowledge and/or represen-
tation of physical processes, limitations due to the
numerical approximation of the model’s equations,
simplifications and assumptions in the models and/or
approaches, internal model variability, and inter-model
or inter-method differences in the simulation of cli-
mate response to given forcing. Reducing the wide
range of uncertainty inherent in projections of global
and regional climate change will require major ad-
vances in our scientific understanding on the subject
in the years to come. Projections about the probabil-
ity, frequency, and severity of extreme weather events
should be carefully evaluated. Current GCMs have
only limited ability to predict changes in the inter-
annual and intraseasonal variability of the weather
or the frequency of the catastrophic events such as
hurricanes, floods, or even the intensity of monsoons,
all of which can be just as, or more, important in
determining crop yields as the average climatic data.
Nevertheless, despite these limitations, this study
marks significant progress in our understanding of
how future climates may affect soybean production
in India.

124 R.K. Mall et al./Agricultural and Forest Meteorology 121 (2004) 113–125
5. Conclusions
The crop simulation model used in this study has
been able to simulate the trends in grain yield and
phenology as measured in field experiments. The
observed variance in the results could be due to in-
adequate initialisation of the model and the lack of
information on the possible yield losses due to pests.
The simulation experiments were performed for rec-
ommended irrigation schedules and following the
nitrogen requirements for optimum yield. It is pos-
sible that some degree of water or nitrogen stresses
over different years influenced the field experiments
in some cases. The precision with which field mea-
surements used in this analysis were taken was not
quantitatively known but is expected to be usually be-
tween ±10 and ±15%. Considering this and also that
the treatments in this study widely varied in terms of
prevailing weather conditions at selected locations,
particularly in terms of surface air temperatures and
the management practices, it can be concluded that
the crop simulation model was adequate to simulate
the likely effects of climate change on soybean yields.
In general, the simulation results indicate that in-
creasing temperature levels could pose a serious threat
in decreasing the growth of soybean crop and hence
the yield. The thermal stress on the soybean crop at
selected sites in India due to projected regional cli-
mate change as inferred from three GCMs namely the
GFDL, GISS and UKMO could reduce the yield of
soybean crop by about 12, 18 and 21% respectively
compared to no temperature change under doubled
CO2concentration.
Our findings suggest that delaying the sowing dates
of soybean crop should be able to mitigate the detri-
mental effect of thermal stress due to climate change.
Also, soybean sowing in the second season, i.e. in the
month of December could be favorable for higher yie-
lds particularly at north Indian stations. However, the
proposed shift in soybean production from the current
main season to a second season may necessitate addi-
tional planning and change in management practices.
Acknowledgements
The first author wishes to express his sincere thanks
to Prof. J.T Ritchie, Michigan State University, USA
and Prof. L.A. Hunt, University of Guelph, Canada
for suggestions and useful advice during their visit to
India. The weather data used in this study were made
available by the India Meteorological Department and
agro-meteorological observatory of State Agricultural
Universities.
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