growth and yield responses of soybean in madhya pradesh ... · growth and yield responses of...

Growth and yield responses of soybean in Madhya Pradesh,

India to climate variability and change

M. Lala,*, K.K. Singhb, G. Srinivasanc, L.S. Rathoreb, D. Naidua, C.N. Tripathia

a Centre for Atmospheric Sciences, Indian Institute of Technology, New Delhi 110016, Indiab National Centre for Medium Range Weather Forecasting, New Delhi, India

c India Meteorological Department, New Delhi, India

Received 27 April 1998; accepted 24 September 1998

Abstract

This study is aimed at assessing the impact of thermal and moisture stresses associated with observed intraseasonal and

interannual variability in key climatic elements on the nature and extent of losses in growth and yield of soybean crop in

central India through the use of CROPGRO model. The crops are found to be more sensitive to higher cumulative heat units

during cropping season. The yields respond substantially to temporal variations in rainfall (associated with observed swings in

the continuity of monsoon). Prolonged dry spells at critical life stages of the soybean crop are found to adversely affect crop

development and growth and hence the yields at selected sites. We have also examined the plausible effects of future climate

change on soybean yields in the selected region based on simulations carried out for doubled atmospheric CO2 level and with

modi®ed weather variables using the available seasonal projections for the future. Our ®ndings on the response of elevated

CO2 concentrations in the atmosphere suggest higher yields (50% increase) for soybean crop for a doubling of CO2. However,

a 38C rise in surface air temperature almost cancels out the positive effects of elevated CO2 on the yield. Soybean crops at

selected site are more vulnerable to increases in maximum temperature than in minimum temperature. The combined effect of

doubled CO2 and anticipated thermal stress (likely by middle of the next century) on soybean crop is about 36% increase in

yield at the selected sites. A decline in daily rainfall amount by 10% restricts this yield gain to about 32%. De®cient rainfall

with uneven distribution during the monsoon season could be a critical factor for the soybean productivity even under the

positive effects of elevated CO2 in the future. # 1999 Elsevier Science B.V. All rights reserved.

Keywords: CROPGRO model; Soybean yields; CO2 effects; Climate change; Water and thermal stresses

1. Introduction

Indian agriculture has made great strides over the

last 50 years. The foodgrains production in India has

increased from 54.92 million tonnes in the 1949±50 to

198.96 million tonnes in 1996±97 with the per capita

availability improving from a low 395 g in 1951 to

about 500 g per day despite the population increase

from 361 millions to 950 millions today. However, in

recent years, the pace of the green revolution seems to

have started slowing down due to immense pressure on

India's land resources and indiscriminate addition of

restorer inputs such as inorganic fertilizers, pesticides

Agricultural and Forest Meteorology 93 (1999) 53±70

*Corresponding author. Tel.: +91-11-6960963; fax: +91-11-

6862037; e-mail: [email protected]

0168-1923/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved.

PII: S0168-1923(98)00105-1

etc. and their inef®cient use. To achieve long-term

sustainability in food production in the coming de-

cade, higher growth rates in crop yields must be

attained from diminishing per capita arable land

and scarce irrigation water resources.

The strong links between vagaries of the Indian

summer monsoon and agricultural productivity are

well known. Even with minor deviations from the

`normal' weather, the ef®ciency of externally applied

inputs and food production is seriously impaired.

Moisture stress due to prolonged dry spells or thermal

stress due to heat wave conditions signi®cantly affect

the agricultural productivity when they occur in cri-

tical life stages of the crop. Lack of our understanding

on the links between the climate variability and crop

productivity together with global warming and its

likely impact could seriously endanger sustained agri-

cultural production in the coming decades.

Recent studies on plausible changes in climate

explored by global climate models (GCMs) suggest

that, in addition to the thermal stress due to global

warming, stress on water availability in tropical Asia

is likely to be exacerbated in the future (IPCC, 1996a).

Studies also suggest a marked reduction in crop yields

in the arid and subhumid tropical regions (IPCC,

1996b). While these are long-term assessments

focussing on average effects over space and time, at

regional and local scales the effects of climate change

could be more adversely felt particularly in developing

countries like India which shares only 2% of the

world's geographical area but supports around 18%

of the world's population and over 15% of world's

livestock. In India, about 65% of gross cropped area

corresponds to the summer monsoon season (about

70% of the total annual rainfall in India occurs during

June±September) indicating its heavy dependence on

the monsoon rainfall. In order to ensure a balanced

growth and development in agriculture (during 1990±

1996 the growth rate is 2.37% per annum), a com-

prehensive understanding and assessment of the likely

impact of climate variability/change on our agricul-

tural productivity is warranted.

There have been a few studies both in India and

elsewhere aimed at understanding the nature and

magnitude of gains and/or losses in yield of particular

crops at selected sites under elevated atmospheric CO2

conditions and associated climatic change (e.g., Abrol

et al., 1991; Sinha and Swaminathan, 1991; Aggarwal

and Sinha, 1993; Aggarwal and Kalra, 1994; Gang-

adhar Rao and Sinha, 1994; Mearns et al., 1996; Riha

et al., 1996; Lal et al., 1998). These studies have been

mainly con®ned to cereal crops namely wheat and

rice. In this study, an attempt has been made to assess

the effects of climate variability and change on the

productivity of soybean, a leguminous crop in the state

of Madhya Pradesh, India using CROPGRO±soybean

simulation model (IBSNAT, 1989).

In recent years, soybean [Glycine max (L.) Merrill]

has emerged as one of the major rainy season cash

crops in central India. The state of Madhya Pradesh

has distinguished itself as a `Soya State' on account of

its largest share in area (77%) and production (72%) of

soybean in India. The growth in area, production and

productivity of soybean in Madhya Pradesh has been

from 1.21 mha in 1986±1987 to 3.70 mha in 1995±

1996, from 0.67 million tonnes in 1986±1987 to 2.90

million tonnes in 1995±1996 and 560 kg haÿ1 in

1986±1987 to 784 kg haÿ1 in 1995±1996, respectively

(SOPA, 1996). This trend of fast adoption of soybean

by the farmers is indicative of its potential to emerge

as a leading commercial crop in future. Soybean is

also ideal for intercropping as well as crop sequences

as it is a short duration (85±130 days) crop and is

comparatively tolerant to drought (Lawn, 1982) and

excessive soil moisture conditions (Wright et al.,

1988). Its better ability to ®x nitrogen, low phosphor-

ous requirement and tolerance to low pH and high

levels of aluminium (Tanaka, 1983) make it a suitable

choice for adoption in a wider area. Compared to

sorghum and corn, soybean ± an edible oil generating

legume ± has been reported to fetch higher price and

net returns (Soni et al., 1990).

The ability of the CROPGRO±soybean model to

simulate realistically the observed soybean yields in

the region during the past decade has been established

here using the daily weather data for four stations viz.,

Indore, Gwalior, Jabalpur and Raipur located in the

state of Madhya Pradesh. The geographical location of

these stations is depicted in Fig. 1. The responses of

the crop growth and yield in the region to thermal and

moisture stresses due to observed intraseasonal and

interannual variability in key weather parameters have

been examined in this paper. We also report here our

®ndings on the possible impacts of climate change on

soybean yields in the selected region based on simula-

tions carried out using doubled atmospheric CO2 level

54 M. Lal et al. / Agricultural and Forest Meteorology 93 (1999) 53±70

and modifying the baseline weather variables with the

future regional projections as inferred from recent

GCM results.

2. Model description and it's validation

2.1. The model

Crop models which share a common input and

output data format have been developed and

embedded in a software package called the Decision

Support System for Agrotechnology Transfer (DSSAT).

The DSSAT itself (Jones, 1993; IBSNAT, 1994; Tsuji

et al., 1994) is a shell that allows the user to organize

and manipulate crop, soil and weather data and to run

crop models in various ways and analyze their outputs.

The models running under DSSAT include the CERES

model for rice, wheat, maize, sorghum, pearl millet

and barley; the CROPGRO (CROP GROwth) model

for bean, peanut and soybean.

CROPGRO is a generic, physiological, process-

oriented legume crop growth model. The major com-

ponents of the CROPGRO±soybean model are the

vegetative and reproductive development, carbon bal-

ance, water balance and nitrogen balance modules

which relate the ¯ow of mass and information between

different modules. The basic structure of the model,

including underlying differential equations, has been

explained in several other publications (Wilkerson

et al., 1983; Boote et al., 1985; Hoogenboom et al.,

1991). The model accounts for vegetative and repro-

ductive development, photosynthesis, respiration, par-

titioning e.g., growth of leaves, stems, roots, shells and

seeds, transpiration, root water uptake, soil evapora-

tion, soil water ¯ow, in®ltration and drainage (Wilk-

erson et al., 1983; Jones et al., 1991; Hoogenboom

et al., 1992).

CROPGRO±soybean model uses empirical func-

tions to compute daily canopy gross photosynthesis

in response to CO2 concentration, air temperature and

daily canopy evapotranspiration (Peart et al., 1989).

Photosynthesis and evapotranspiration algorithms

have been modi®ed to take into account the changes

in daily canopy photosynthesis under elevated CO2

concentration and temperature conditions (Curry

et al., 1990a, b).

2.2. Input data

The input data required to run the CROPGRO±

soybean model include daily weather data, soil albedo,

soil water drainage constant, ®eld capacity, wilting

point and initial soil moisture in different layers as

well as maximum root depth, crop genetic coef®cients

and management practices (plant population, plant

Fig. 1. Locations of selected stations in the state of Madhya Pradesh, India considered in this study.

M. Lal et al. / Agricultural and Forest Meteorology 93 (1999) 53±70 55

row spacing and nitrogen application). Other input

®les include chemical and physical description of the

soil pro®le with separate information for each horizon,

initial organic matter in the soil at the beginning of the

experiment, initial soil water content, nitrogen con-

centration and pH for each layer of the soil pro®le,

dates and amount of irrigation required for irrigation

management, dates, amount and types of fertilizer

required for fertilizer management, planting date

and depth, row and plant spacing and other informa-

tion for crop management, cultivar-speci®c character-

istics and genetic coef®cients, and crop speci®c

characteristics.

The long-term observed daily weather data on

maximum and minimum temperatures, solar radiation

(derived from sunshine hours data) and rainfall at the

selected stations, namely, Indore, Gwalior, Jabalpur

and Raipur have been used in this study. The optimum

dates of sowing are chosen as per the current ®eld

practices at the selected sites. Soil water holding

characteristics for selected sites and the period of

weather data used are given in Table 1. The terms

lower limit and drained upper limit correspond to the

permanent wilting point and ®eld capacity, respec-

tively (Ritchie et al., 1986). Total extractable soil

water is a function of soil physical characteristics

as well as rooting depth.

2.3. Genetic coefficients

Crop genetic input data, which explain how the life

cycle of a soybean cultivar responds to its environ-

ment, are not usually available and therefore, these

were derived iteratively following Hunt's method

(Hunt et al., 1993). This involved determining values

of the phenology coef®cients and then values of the

coef®cients describing growth and grain development.

Minimum crop data sets required for these calcula-

tions included dates of emergence, anthesis, maturity,

pod initiation and full pod, grain yield, above-ground

biomass, grain density and weight. To obtain the

desired level of agreement between simulated and

observed values of development and growth, the

model was run initially with genetic coef®cients

derived elsewhere for cultivar Bragg (Table 2) and

then re-run using a range of values of each coef®cient

in the order indicated above. All calibration data to

derive genetic coef®cients were obtained from plot

experiments at Jabalpur during 1993 and 1994. Soy-

bean variety considered in the model is JS7244 (early

maturing `Gaurav' variety with life cycle of 105 days),

one of the currently prevailing varieties in the state.

2.4. Model validation

Validation of the model is based on crop yield data

available from experimental sites at Jabalpur (for the

period 1987±1996 with marginally different sowing

dates in different years) and at Raipur for the period

1991±1997. At Jabalpur, irrigation was given in some

of the years during seed ®lling stage whenever severe

moisture stress was observed. In order to evaluate the

performance of CROPGRO model in simulating soy-

bean crop yields in response to historical climate

variability, a comparison of observed vs. model-simu-

lated yields for Jabalpur and Raipur has been made in

Fig. 2. Each of the series of data represent the same

agromanagement and other agricultural practices.

Table 1

Soil water holding characteristics at selected sites and period of weather data used

Station/period Location Soil depth

(cm)

Lower/drained

upper limit (mm)

Saturated water

content (mm)

Extractable water

content (mm)

Raipur (1971±97) 21.27 N 60 102/180 240 78

81.60 E

Gwaliora (1965±88) 26.15 N 90 126/234 333 108

78.14 E

Indore (1985±95) 22.72 N 80 152/264 344 112

71.80 E

Jabalpur (1969±97) 23.15 N 70 133/226 294 93

79.97 E

a Weather data beyond 1988 currently not available.


Correlation coef®cients of 0.90 and 0.93 between the

observed and model simulated yields for Jabalpur and

Raipur have been obtained, respectively. Although the

model realistically simulates the year to year varia-

tions in yields, deviations in simulated and observed

yields are perhaps due to unaccounted factors such as

soil micronutrient status, soil pH, pest or disease

incidences etc. The large deviation between observed

and simulated yields at Jabalpur in the years 1993 and

1994 are due to rust incidences reported at the experi-

mental site. The farm level yield data at Raipur for the

variety considered in this study were within

360 kg haÿ1 of the model simulated yields during

the period from 1991 to 1997.

CROPGRO model simulates interannual variability

in crop yields depending upon the daily weather

variables for each of the selected years. The long-

term mean soybean crop yields of 1234 kg haÿ1 for

Indore (SD �928 kg haÿ1), 1097 kg haÿ1 for Gwalior

(SD �660 kg haÿ1), 1407 kg haÿ1 for Jabalpur (SD

�830 kg haÿ1) and 1586 kg haÿ1 for Raipur (SD

�783 kg haÿ1) are simulated during the selected per-

iod. To establish validity of the model's applicability

at a regional scale, a comparison of the simulated

yields at selected sites with those reported at district

level has also been made. In view of the varietal

transition in the soybean crop in Madhya Pradesh

after 1990, average simulated yields for the years

1991±95 only has been considered here. Fig. 3 depicts

a comparison of observed and model-simulated mean

soybean yields at selected sites (observed as well as

simulated yields are for the period 1991±1995 but for

Gwalior, the simulated yields are for 1983±88). Keep-

ing in view that the applications of fertilizers are

identical at all the four locations in the model simula-

tions and that the changes in variety sown within each

district in actual practice are not accounted for, the

simulated yields are in close proximity to those

obtained in ®eld at selected stations.

2.5. The model experiments and climate change

scenarios

Our primary interest in this study was to explore the

impact of intraseasonal and interannual variability in

key climatic elements as observed in historical daily

weather data on the productivity of soybean crop in the

selected regions. To examine the crop growth char-

acteristics under contrasting thermal and water stress

conditions, years with extreme cumulative heat unit

and/or rainfall anomalies (deviation from long-term

cumulative daily maximum and minimum tempera-

Table 2

Genetic coefficient development for soybean cultivar JS7244 in the agroclimatic conditions of Madhya Pradesh

Growth and development aspects of the soybean crop Genetic coefficient

for Bragg

Genetic coefficient

for JS7244

Development aspects

Critical short day length (hour) 12.01 11.0

Slope of relative response of development to the photoperiod (hÿ1) 0.32 0.305

Time between plant emergence and flower appearance (photothermal days) 19.5 15.53

Time between first flower and first pod (-do-) 10.0 6.00

Time between first flower and first seed (-do-) 15.0 10.0

Time between first seed and physiological maturity (-do-) 35.3 26.0

Time between first flower and end of leaf expansion (-do-) 15.0 15.0

Seed filling duration (photothermal days) 20.6 23.06

Time required for cultivar to reach final pod load (-do-) 10.0 8.84

Growth aspects

Maximum leaf photosynthesis rate (minimal CO2 mÿ2 sÿ1) 0.95 0.90

Specific leaf area (cm2 gmÿ1) 350 370

Maximum size of full leaf (cm2) 170 170

Maximum fraction of daily growth partitioned to seed and shell 1.00 1.00

Maximum weight per seed (gm) 0.10 0.10

Average seed per pod 1.6 1.9


tures in degrees Celsius and cumulative rainfall in mm

during the crop growth period) were identi®ed. The

selected contrast years, weather anomalies (including

the climatological percentiles) and model-simulated

yields for each of the four stations considered in the

study are listed in Table 3. These climatologically

anomalous seasons enable us to identify the speci®c

impacts of intraseasonal variability in weather para-

meters on crop development and growth in our model

simulations.

We have also assessed the changes in productivity

of soybean crop in the selected region under condi-

tions of elevated atmospheric CO2 level and environ-

mental stress linked to future climatic changes through

sensitivity studies. Two key factors of the environ-

mental management namely the thermal and water

Fig. 2. Observed (yields at experimental sites) and model-simulated soybean yields at Jabalpur (for the years 1987±1996) and Raipur (for the

years 1991±1997).


stresses have been considered while simulating the

soybean yields. The response of soybean crop at

selected sites to change in CO2 concentration from

330 ppmv to 660 ppmv (doubled CO2 atmosphere) has

been examined. The response to thermal and water

stresses (for changes in mean surface air temperature

ranging from ÿ58C to 48C and for ÿ40% to �40%

change in total rainfall) on soybean yields have been

Fig. 3. Observed and model-simulated average soybean yields at selected sites in Madhya Pradesh (observed as well as simulated yields are

for the period 1991±1995 but for Gwalior, the simulated yields are for 1983±88). The solid bars indicate the observed and simulated standard

deviations in yield.

Table 3

Anomalies in key climatological parameters and simulated soybean yields at selected sites during contrasting years

Station Years Yield (kg haÿ1) ��Tx (8C) ��Tn (8C) ��R (mm)

Raipur 1979 307 162 (>0.99) 90 (>0.90) ÿ431 (<0.01)

1994 2181 ÿ144 (<0.05) ÿ110 (<0.15) 255 (>0.85)

1995 948 37 (<0.70) 43 (<0.65) 2 (<0.60)

Gwalior 1978 459 ÿ168 (<0.10) ÿ449 (<0.01) ÿ398 (<0.05)

1983 1963 3 (<0.50) ÿ25 (<0.35) 56 (<0.55)

1987 398 246 (>0.99) 246 (>0.99) ÿ341 (<0.10)

Indore 1987 803 226 (>0.99) 102 (>0.95) ÿ187 (<0.20)

1990 2716 ÿ11 (<0.01) 9 (<0.85) ÿ32 (<0.75)

1991 386 39 (<0.70) ÿ156 (<0.01) ÿ117 (<0.35)

Jabalpur 1979 129 299 (>0.99) 22 (>0.65) ÿ713 (<0.01)

1981 1101 59 (>0.70) ÿ10 (<0.45) ÿ657 (<0.02)

1991 431 31 (>0.55) ÿ40 (<0.25) 43 (>0.45)

Tx and Tn represent maximum and minimum temperatures and R is the rainfall.

The numbers in brackets are the climatological percentiles.


explored. Simulations of soybean crop yields have

also been performed under the combined in¯uence of

elevated CO2 concentrations (660 ppmv) and the pro-

jected regional climate change scenarios.

The spatial distributions of likely changes in surface

air temperature as simulated by the recent global

climate models for doubled CO2 atmosphere with

respect to the present-day climate reveal that the

simulated surface warming over the central plains

of India during monsoon season is likely to be

�1.08C (Lal et al., 1995). A decline (�0.58C) in

the diurnal temperature range is also simulated (Lal

et al., 1996). Over the central plains of India, the

model simulations suggest an average decrease of

about 1.0 mm dayÿ1 in summer monsoon rainfall

under the combined in¯uence of doubled CO2 and

sulphate aerosol forcings (Mitchell et al., 1995; Lal

et al., 1995; Meehl et al., 1996). These scenarios have

been used in the crop simulation experiments to assess

the vulnerability of soybean crops at selected sites in

India to future climate change. Our ®ndings on these

experiments are presented and discussed in the

following section.

3. Results and discussion

3.1. Intraseasonal and interannual climate

variability and simulated crop yields

Two contrast years 1979 and 1994 at Raipur are

characterized as warm season (�1628C day-time heat

unit anomaly) and cold season (ÿ1108C night-time

heat unit anomaly), respectively, during the selected

time period. The cumulative rainfall for the cropping

season was also signi®cantly below normal in the year

1979. Soybean yields of 307 kg haÿ1 for the year 1979

and 2181 kg haÿ1 for the year 1994 are obtained in our

model simulation. The soybean yield was simulated to

be 948 kg haÿ1 in year 1995 when no net thermal or

water stresses were present during the cropping sea-

son. However, the crop in 1995 suffered acute water

stress conditions from ®rst pod to harvesting stages

due to uneven distribution of rainfall during the crop-

ping season this year. The simulated partitioning

factors (fraction of total dry matter weight of a plant)

for various components of the crop from sowing to

harvesting stages for each of these 3 years are illu-

strated in Fig. 4. A larger partitioning of the photo-

synthetic assimilates to the stem and/or root at the

expense of pod and grain are obtained for the years

1979 and 1995 as compared to the year 1994. Parti-

tioning of the photosynthetic assimilates to pod and

grain was higher in 1994 which led to higher biomass

production and hence better crop yields in our simula-

tions. The dry matter weights for stem, leaf and grain

at few stages of the development and growth of

soybean crop reported from one experimental site in

Raipur for the years 1994 and 1995 are found to be

consistent with those simulated by the model (Fig. 5).

Even though the cumulative rainfall was close to long

term mean, lower yield in the year 1995 relative to the

year 1994 both in our simulation and in ®eld is

attributed to suppressed biomass production subse-

quent to the end leaf stage (29 September) as a

consequence of no rainfall which resulted in severe

water stress (de®ned as one minus ratio of actual to

potential evapotranspiration) during the later part of

the crop growth season (Fig. 6). The simulated crop

yield for the year 1995 shot up from 307 to

2652 kg haÿ1 when automatic irrigation was provided

to remove the constraints of temporal variations in rain

water availability. The water stress conditions during

¯owering and pod growth stages of the crop due to

pronounced negative cumulative rainfall anomaly in

1979 (rainfall was less than normal by 45%) combined

with signi®cantly higher heat unit anomalies consid-

erably slowed down the potential reproductive growth

leading to poor biomass production and low yields in

the year 1979. Higher than normal day-time tempera-

tures in the year 1979 led to lower leaf area index (ratio

of total leaf area to ground surface) while lower than

normal night-time temperatures in the year 1994 led to

higher leaf area index during the ®rst ¯ower to end leaf

stages of the crop development (Fig. 6 ± top panel).

At Gwalior, soybean yield of 1963 kg haÿ1 was

simulated in the year 1983 when no net thermal or

water stresses were present during the cropping sea-

son. The years 1987 and 1978 in the selected period

were characterized as warm (�2558C heat unit anom-

aly) and cold seasons (ÿ3098C heat unit anomaly),

respectively (Table 3). The model simulated soybean

yields of 398 kg haÿ1 for the year 1987 and

459 kg haÿ1 for the year 1978. Here again, a larger

partitioning of the photosynthetic assimilates to the

stem and root at the expense of pod and grain was


simulated for the years 1978 and 1987 as compared to

the year 1983. The cumulative crop duration rainfall

was below normal in both the years 1978 and 1987.

The crops were subjected to severe water stress which

led to increase in the amount of carbohydrates dis-

tributed to stem and roots. Leaf and grain growth

suppression resulted in a negative feedback on photo-

synthetic assimilation thereby further reducing bio-

mass production. The simulated crop yields shot up to

2713 and 1910 kg haÿ1 in 1978 and 1987, respec-

tively, when automatic irrigation switch was turned

on. Soybean yields of over 2500 kg haÿ1 have been

Fig. 4. Relative vegetative and reproductive partitioning factors (fraction of total dry matter weight of a plant) as a function of crop lifetime

for Raipur in selected years.


attained at experimental sites in Madhya Pradesh in

years with uniform distribution of rainfall during the

cropping season.

The features on crop development and growth

similar to those discussed above were also obtained

in our simulation using year-speci®c environmental

conditions as listed in Table 3 for two other sites. The

soybean yields at selected sites are found to be sensi-

tive to higher than normal cumulative heat units during

the cropping season. Moreover, water stress condi-

tions at critical life stages of the crop adversely affect

the development and growth and hence the crop yields

at the selected sites. This suggests that the temporal

variations in rainfall (associated with the observed

swings in the continuity of monsoon) during the

cropping season plays a dominant role in the inter-

Fig. 5. Observed (O) and simulated (S) dry matter weights of vegetative and reproductive components of soybean crop as a function of time

for Raipur in selected years.


annual variability of rainfed soybean crop productivity

at selected sites.

3.2. Elevated CO2 levels and crop productivity

CO2 is vital for photosynthesis and hence for plant

growth. The increase in atmospheric CO2 concentra-

tion should increase the rate of plant growth (Cure and

Acock, 1986). There are, however, important differ-

ences between the photosynthesis mechanisms of

different crop plants and hence in their response to

increasing CO2. Plants with nitrogen-®xing symbiont

(e.g., soybean, beans) under favourable environmental

conditions tend to bene®t more from enhanced CO2

supplies than other plants (Cure et al., 1988). Root/

shoot ratios often increase under elevated CO2 levels

favouring root capillaries and also contribute to soil

organic matter build-up (Mauney et al., 1992). Crop

plants show increased water-use ef®ciency under ele-

vated CO2 levels (due to stomatal closure), but water

Fig. 6. Biomass production (BIO) and leaf area index (LAI) (top panel) and cumulative rainfall, CRF and water stress, WS (bottom panel) as a

function of crop lifetime for Raipur in selected years (CRF is the accumulated rainfall amount from the date of sowing and WS is defined as

one minus the ratio of actual to potential evapotranspiration. These two parameters together identify the moisture deficient condition at

selected stages of crop growth).


consumption on ground area basis vs. leaf area basis is

also affected. Water use on a ground area basis can

actually increase if leaf area (canopy) increases.

On an average, a 50% increase in yield of soybean

has been obtained in our simulations for a doubling of

atmospheric CO2 from its current level of 330 ppmv.

This is essentially due to an increase in photosynthesis

rate and hence plant growth under the direct effects of

elevated CO2 concentration. Our results are consistent

with those reported elsewhere suggesting that a dou-

bling of atmospheric CO2 concentration from 330 to

660 ppmv might cause a 18±40% increase in yield of

soybean crop (Adams et al., 1990; Sinclair and Raw-

lins, 1993). In some studies yield increases upto 110%

have also been reported (Haskett et al., 1997).

The increased CO2 has an important effect on the

stomatal regulation also. Our simulation experiments

suggest that, increases beyond the current atmospheric

CO2 level only marginally reduce transpiration at leaf

level due to decreased stomatal aperture. It may be,

therefore, stated that higher CO2 concentration alone

will exert little or no effect on regional evapotranspira-

tion. Moreover, as a consequence of rise in surface air

temperature under elevated CO2 levels due to green-

house effect, evaporative demand may increase enhan-

cing water loss from vegetation without any

substantial compensation expected from CO2-induced

stomatal closure. Morison (1987) suggested that, as an

upper limit, doubling of CO2 concentration could

reduce the plant transpiration by about 40% (depend-

ing upon the humidity conditions around a leaf) as a

result of stomatal conductance changes and this may

lead to higher water use ef®ciency by the plant.

However, the decline in transpiration is subject to

atmospheric feedback at spatial scales (McNaughton

and Jarvis, 1991; Jacobs and DeBruin, 1992). The

recent literatures also suggest that, for C3 species, the

major effect of CO2 increase is in assimilation rather

than in transpiration (Goudriaan and Unsworth, 1990).

While CO2 enrichment of the atmosphere tends to

increase crop yields, the associated warming may

decrease or increase crop yields depending on envir-

onment. Therefore, interaction of CO2 effects with

other environmental factors, such as temperature,

rainfall etc. must also be examined. In the next section,

we shall examine the response of soybean crop to

thermal and moisture stresses both under current and

elevated CO2 conditions.

3.3. Sensitivity to thermal and water stresses

The biological processes in plants associated with

increased levels of CO2 would depend on the changes

in surface temperature. Higher temperatures could

accelerate plant development (early ¯owering) and

shorten the growth period (grain ®lling) and thus affect

the crop phenology and dry matter production (Butter-

®eld and Morison, 1992). Particularly, the enzyme

controlled biochemical portion of photosynthesis is

strictly dependent on temperature. Thus, the light use

ef®ciency could be affected below some threshold of

minimum temperature and above some threshold of

maximum temperature. Whigham and Minor (1978)

suggested that decrease in temperature would nor-

mally delay ¯owering in soybean by 2±3 days for

each decrement of 0.58C. Flower initiation was accel-

erated when mean temperature increased; however,

beyond a certain limit, temperature increase had an

adverse effect on the rate of node formation, internal

growth and ¯ower initiation. Jeyaraman et al. (1990)

also studied the in¯uence of temperature on the crop

growth stages and yield of soybean, and concluded

that production potential of soybean crop can be

enhanced when grown under maximum temperatures

of 31.2±31.68C and minimum temperatures of 20.4±

20.98C.

We performed a set of simulations to examine the

sensitivity of soybean productivity to surface tempera-

ture on at the selected sites using the CROPGRO

model wherein daily mean surface temperature

changes within a range from ÿ58C to �48C have

been considered under the current and elevated CO2

conditions. These temperature changes were super-

imposed on the observed daily maximum and mini-

mum temperature data series for all the years

considered in our simulations. The average yields

are higher by 14% for a mean temperature 38C below

the present-day climate for current atmospheric CO2

concentrations (Fig. 7) suggesting thereby that the

present-day temperatures at selected sites are already

higher in terms of optimal thermal condition for

attaining highest yields for the soybean variety con-

sidered here. Under doubled atmospheric CO2 con-

centration, a 22% increase in soybean yield is obtained

at this optimal temperature when the 50% increase in

yields due to doubling of CO2 are not included. The

maturity day of the crop extends by 3 days in duration


for both the CO2 levels at this optimal temperature.

Soybean yields show a marginal decline for mean

temperatures below 58C relative to the present-day

conditions.

The yields in soybean are found to decline more

signi®cantly for a rise in temperature with respect to

the present-day climate. When CO2 concentrations is

doubled but no changes in surface air temperature are

imposed, the soybean yield is 50% more than that

simulated for the current atmospheric CO2 concentra-

tions. The maturity day remains unchanged. When

daily mean temperatures are increased by 18C and

CO2 is doubled, the net increase in yield is limited to

only 36% and crop maturity shortens by a day. A rise

in temperature by 38C almost cancels out the positive

effects of elevated CO2 such that soybean yield

decreases by 1%. Maturity occurs 2 days earlier. A

rise in present-day temperature by 48C together with

doubling of CO2 leads to a net decline of 21% in

simulated soybean yield.

Probably the most important consequences for

agriculture would stem from a reduction in soil moist-

ure due to higher rates of evaporation from soil surface

exposed to higher temperatures. A decline in rainfall

and hence reduced water availability could put an

additional stress on the crop productivity in a region.

To consider the sensitivity of changes in monsoon

rainfall on soybean yields in the region under study,

we have also performed a series of simulations

wherein daily rainfall changes within a range from

ÿ40% to �40% have been considered under the

current and elevated atmospheric CO2 concentrations.

A 40% decline in daily rainfall reduces the soybean

yield by about 18% (due to constraints on the avail-

ability of water) while a 40% enhancement in daily

rainfall increases the yield by just 8% under present-

day atmospheric CO2 level when other environmental

conditions including the intraseasonal variability

remain unchanged (Fig. 8). A 40% decline in daily

rainfall in doubled CO2 atmosphere, however, reduces

the soybean yield by only 12% as a consequence of

increased water use ef®ciency. Thus, it appears that

the adverse impacts of rainfall decline on soybean

crops would be at least marginally reduced under

elevated CO2 levels.

3.4. Soybean yields under projected climate change

scenario

The surface air temperature over the selected sites is

likely to increase by about 18C during the soybean

cropping season when atmospheric CO2 would double

its current concentrations. Climate model simulations

also indicate that future temperature change linked to

Fig. 7. Response of changes in surface air temperature on soybean yields in Madhya Pradesh (averaged for selected stations) for the present-

day and doubled atmospheric CO2 conditions.


global warming might be characterized by a marked

asymmetry between day time maxima and night time

minima (Karl et al., 1991). The increase would be

more pronounced in the night-time temperature, thus

leading to a decline in diurnal temperature range

(DTR). Under doubled atmospheric CO2 conditions,

a 0.58C decline in DTR during the soybean cropping

season over the selected sites could occur by a change

in both maximum and minimum temperatures by

different magnitude (Lal et al., 1996). We have exam-

ined the implications of this change in DTR under

elevated CO2 conditions on the productivity of soy-

bean at selected sites.

A 50% increase in soybean yield is obtained in our

simulations for a doubling of atmospheric CO2. A rise

of 18C in minimum temperature with doubled CO2

limits the yield increases to 48% (Fig. 9). However, a

rise of 18C in maximum temperature with elevated

CO2 restricts the yield increase to only 40%. The

combined effect of enhanced CO2 and thermal stress

(maximum temperature is increased by 18C and mini-

mum temperature increased by 1.58C) on the soybean

crop productivity in the selected region is a net 35%

increase in current yields. A concurrent rise in max-

imum temperature by 38C and minimum temperature

by 3.58C cancels out the positive effects of elevated

CO2 such that a net 5% decrease in the current yields is

obtained in our simulations.

From above, it is evident that in a doubled CO2

atmosphere, soybean yield at selected sites should

increase as compared to current CO2 levels even under

moderate thermal stress condition projected for the

future based on available climate change scenarios.

The interannual variations in soybean crop yield are,

however, likely to be more pronounced under elevated

CO2 concentration than under current CO2 levels. The

standard deviation for yield is minimum for Gwalior

(�966 kg haÿ1) and maximum for Indore

(�1279 kg haÿ1) under doubled CO2 conditions.

Recent climate model simulations suggest the possi-

bility of more frequent extreme events e.g., heat

waves, droughts etc. in a warmer atmosphere (IPCC,

1998). However, we have assumed here that the nature

of observed intraseasonal variability in key climatic

elements remains unchanged which may not be valid

in the future.

In order to assess the impact of any future decline in

the observed rainfall on soybean crop over the region,

we have conducted another set of simulations con-

sidering a 10±50% decline in daily observed rainfall

Fig. 8. Response of changes in cumulative rainfall on soybean yields in Madhya Pradesh (averaged for selected stations) for the present-day

and doubled atmospheric CO2 conditions.


Fig. 9. Response on soybean yields in Madhya Pradesh under doubled CO2 conditions to changes in maximum and minimum temperatures

and a decline in diurnal temperature range.

Fig. 10. Response on soybean yields in Madhya Pradesh under doubled CO2 conditions to changes in maximum and minimum temperatures,

likely decline in diurnal temperature range and a decline in rainfall.


over the region. These simulations suggest that a

decline in rainfall with anticipated thermal stress

(maximum temperature is increased by �18C and

minimum temperature is increased by �1.58C) leads,

on an average, to a reduction in crop yield by nearly

5% for every 10% decline (Fig. 10). The positive

effects of elevated CO2 almost cancel out with

enhanced thermal stress (maximum temperature is

increased by �28C and minimum temperature is

increased by �2.58C) and reduction in rainfall by

40% and 50% such that soybean yield is up by only

�1% and down by ÿ6%, respectively. This suggests

that signi®cantly de®cient monsoon rainfall condi-

tions combined with thermal stress should adversely

affect the positive effect of elevated CO2 on the

soybean crop in Madhya Pradesh, India.

4. Limitations of the study

The primary thrust of most crop simulation models

is to analyze how the weather and genetic character-

istics can affect the potential crop yields under a

speci®ed management scheme. The nutrient factors

representing phosphorus, potassium and other essen-

tial plant nutrients are assumed to be in abundant

supply in the soil so as not to cause any extent of

stress over plant and currently excluded in models.

Investigations on the crop's response to adverse soil

conditions need attention. The study reported here

does not include the yield losses due to weeds, insects

and diseases. The rise in surface temperature, parti-

cularly during the humid monsoon season, may create

more conducive conditions for pest infection and

hence loss of crops. The prevalence of pests and

diseases could be among the major constraints for

achieving higher crop yields in tropical countries like

India.

The agricultural crop yields are sensitive to climate

variability experienced through the occurrence of

extreme events such as droughts, ¯oods and heat

waves. Since subtle interactions of climate variables

are responsible for differential crop growth, an

increase in the probability of extreme events may

adversely affect the crop productivity. The impact

of intraseasonal and interannual variability in pre-

sent-day climate and its impact on soybean yields is

accounted for in our simulations through use of a long

time-series of daily weather data. However, the nature

of interannual and intraseasonal variability may not

remain same under enhanced CO2 conditions. There is

considerable uncertainty regarding how climate varia-

bility will change under perturbed climate conditions

and hence not accounted for in this study.

5. Conclusions

CROPGRO model is able to simulate soybean

yields which are in fair agreement with the currently

reported yields at selected locations in Madhya Pra-

desh (Indore, Gwalior, Jabalpur and Raipur). The

interannual variability in simulated yields are also

in close proximity to observed farm level yields.

The soybean crops are found to be more sensitive

to higher than normal heat units. Water stress condi-

tions due to temporal variations in rainfall (associated

with observed swings in the continuity of monsoon)

during the cropping season are found to adversely

affect the crop development and growth at critical life

stages of the crop and hence the yields at the selected

sites.

Our ®ndings suggest higher yields for soybean crop

(50% for a doubling of CO2) under elevated CO2

levels essentially as a result of enhanced photosynth-

esis rate. However, a rise in the surface air temperature

due to doubling of CO2 results in reducing the total

duration of crop (and hence reduced productivity) by

inducing early ¯owering and shortening the grain ®ll

period. Soybean crops in the study region are found to

be more vulnerable to increases in maximum tem-

perature than in minimum temperature. While the

moisture stress is crucial for the soybean yield at

selected sites, the adverse impacts of likely rainfall

decline on soybean crops would be relatively less

pronounced under elevated CO2 levels.

The combined effect of doubled CO2 and antici-

pated thermal stress (Tmax � 18C and Tmin � 1.58C ±

that is likely at the selected sites by middle of next

century) on soybean crop is about 36% increase in

yield provided the total rainfall amount and its intra-

seasonal variability does not change. A decline in

daily rainfall amount by 10% should bring down this

gain in soybean yield to about 32%. The acute water

stress due to prolonged dry spells during monsoon

season could be a critical factor for the soybean


productivity even under the positive effects of elevated

CO2 in the future.

Acknowledgements

The World Meteorological Organization funded the

acquisition of DSSAT model software at NCMRWF,

New Delhi. The United Nations Development Pro-

gramme, New Delhi provided fellowship to one of the

authors (KKS) for DSSAT familiarization training

with Prof. J.T. Ritchie at Michigan State University.

The weather data used in this study were made avail-

able by the India Meteorological Department.

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