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

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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.

Introduction

Crop growth and yield under normal conditions are largely determined by weather during the growing season. Even with minor deviations from the normal weather, the efficiency of externally applied inputs and food production is seriously impaired. The increasing CO2 concentration in the atmosphere and the anticipated climate change due to global warming are also likely to affect future global agricultural production through changes in rate of plant growth (Lemon, 1983, Cure and Acock, 1986) and transpiration 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 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 cultivation has steadily increased over the years from the level of 0.50 million ha (Mha) in 1979–1980 to 5.86 Mha in 1997–1998. The growth in production and productivity has been from 0.28 million tonnes in 1979–1980 to 6.72 million tonnes in 1997–1998 and 570 kg/ha in 1979–1980 to 1150 kg/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 cultivation 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 10 Mha and, by 2010 a.d., productivity enhancement will be about 1500 kg/ha such that production of 15 million tonnes 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) as well as crop sequences, as it is a short duration (85–125 days) leguminous crop.

Future climatic change is likely to have substantial impact on soybean production depending upon the magnitude of variation in CO2 and temperature. Increased temperature significantly reduces the grain yield due to accelerated development and decreased time to accumulate grain weight (Seddigh and Joliff, 1984a, Seddigh and Joliff, 1984b; Baker et al., 1989). There have been a few studies in India and elsewhere aimed at understanding the nature and magnitude of gains/losses in yields of soybean crop at different sites under elevated atmospheric CO2 conditions 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 employing different mitigating options to alleviate the climate change impacts on soybean production under different climate change scenarios inferred from 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 geographical location of these stations is shown in Fig. 1.

Section snippets

The CROPGRO-soybean model

Crop growth simulation 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) (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 reproductive development, carbon balance, water balance and nitrogen balance. A detailed description of the modified version

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 maturity of soybean crop at selected sites varies from 89 days (in

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 CO2 on the plant processes 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 under enhanced CO2 condition, there is initially a large

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 inadequate initialisation of the model and the lack of information on the possible yield losses due to pests. The simulation experiments were performed for recommended irrigation schedules and following the nitrogen requirements for optimum yield. It is possible that some degree of water or nitrogen

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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