Pnutgro

Punjab

Negative

Hundal and Kaur (1996)

Chickpea

CROPGRO-chickpea

All India

Negative

Mandal (1998)

Aggarwal and Kalra (1994), evaluated the WTGROWS crop simulation model to estimate the effect of climate change on productivity of wheat in India, and simulated normally sown crops at three levels of production (potential, irrigated and rainfed) with the assumption of CO2 level at 425 ppm and temperature rise options of 0, 1 and 2°C. At the 425 ppm CO2 concentration and no rise in temperature, grain yield at all levels of production increased significantly at all places. One degree celsius rise in mean temperature had no significant effect on the potential yields. Irrigated yields, however, showed a small increase in most places where current yields were greater than 3.5 tha-1. In the central and peninsular India, where current irrigated yields were between 2 and 4 tha-1, the response varied from significant decreases to significant increases. The rainfed yields, however, showed a significant increase. But, an increase of 2°C in temperature reduced potential yields at most places. In sub-tropical (above 23°N) environments, there was a small decrease in potential yields (1.5-5.8%), but in tropical locations, the decrease was 17-18%. In the same study, the mean simulated yield of wheat for current and changed climate scenario (2°C rise and CO2 level of 425 CO2 ppm) in different latitudinal ranges when evaluated, showed that the irrigated yields slightly increased for latitudes greater than 27°N, but reduced at all other places. The decrease in yield was much higher in lower latitude. Several locations, particularly where current rainfed yields were greater than 2 tha-1, showed a very significant increase in the rainfed yields with climate change. Depending upon the magnitude of temperature increase, crop duration, particularly the period up to anthesis, was reduced. Unless accompanied with suitable research and policy interventions, the reduction in productivity under changed climate might reduce wheat production options in central India (Aggarwal and Kalra 1994; Aggarwal 2000).

The impact of climate change on the productivity of sorghum [Sorghum bicolor (L.) Moench] in three diverse growing areas in India (i.e., Hyderabad, Akola and Solapur) was studied by Gangadhar Rao et al. (1995), using the CERES-sorghum simulation model (Ritchie and Alagarswamy 1989) under climate change scenarios generated by the three GCMs, namely Goddard Institute of Space Studies (GISS), Geophysical Fluid Dynamics Laboratory (GFDL), and United Kingdom Meteorology Office (UKMO). The simulated results indicated a decrease in yield and biomass of rainy season sorghum at Hyderabad and Akola under all climate change scenarios. The post rainy season sorghum, grown at Solapur on stored soil water, showed a marginal increase in yield. The positive effects of increased CO2, if any, were masked by the adverse effects of predicted increase in temperature, resulting in shortened crop growing seasons. For rice crop production in India, Mohandass et al. (1995) used ORYZA1 model developed by Kropff et al. (1994) to simulate under current and future climate scenarios and showed an increase in rice production under the GCMs scenarios used. This was mainly due to the fertilizing effect of the increased CO2 level more than any detrimental effects of increased temperatures. Large decreases in yields were predicted for second season crops at many of the locations due to high temperatures prevailing, but its overall effect on the rice production was small due to relatively low proportion of total rice produced. Uprety et al. (1996) concluded that with the type of climate in the northern belt of Indian subcontinent, viz., variation in temperatures and CO2 concentration, the production of Brassica crop (an oilseed crop) is likely to increase and to be shifted to relatively drier regions. Hundal and Kaur (1996) examined the climate change impact on productivity of wheat, rice, maize and groundnut crops in Punjab, using CERES-wheat (Godwin et al. 1989), CERES-rice (Singh et al. 1993), CERES-maize and "PNUTGRO" crop simulation models. If all other climate variables were to remain constant, temperature increase of 1, 2 and 3°C from present day condition, would reduce the grain yield of wheat by 8.1, 18.7 and 25.7%, rice by 5.4, 7.4 and 25.1%, maize by 10.4, 14.6 and 21.4% and seed yield in groundnut by 8.7,23.2 and 36.2%, respectively.

Lal et al. (1998) examined the vulnerability of wheat and rice crops in northwest India to climate change through sensitivity experiments with CERES-wheat and CERES-rice models and found that under elevated CO2 levels, yields of rice and wheat increased significantly (15 and 28% for a doubling of CO2). However, a 3°C and 2°C rise in temperature annulled the positive effect of elevated CO2 on wheat and rice, respectively. The combined effects of enhanced CO2 and imposed thermal stress on the wheat and rice crop were 21 and 4% increase, respectively, in yield for the irrigation schedule presently practiced in the region.

Chatterjee (1998) used CERES-sorghum model and observed that an increase in temperature consistently decreased the sorghum yields from the present day conditions. Increase in temperature by 1 and 2°C in sorghum decreased the grain yields by 7-12%, on an average. A further increase in temperature drastically reduced the yields by 18-24%, on an average. The magnitude of decrease in yield with increase in temperature was, in general, proportional to the increase in temperature in most years, indicating that there was no large interaction effect between yearly climatic variation and increase in temperature. Similarly, the small beneficial effect of still higher CO2 concentrations was nullified by further increase in temperature.

Mandal (1998) used the CROPGRO-chickpea model and observed that an increase in temperature up to 2°C did not influence potential yield as well as above ground biomass of chickpea significantly. Lal et al. (1999) projected 50% increased yield for soybean for a doubling of CO2 in Central India by using CROPGRO-soybean model. However, a 3°C rise in the surface air temperature almost nullified the positive effects of doubling of carbon dioxide concentration and reduced the total duration of the crop (and hence productivity) by inducing early flowering and shortening the grain fill period. Soybean crops in Central India are found to be more vulnerable to increase in maximum temperature than in minimum temperature. 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.

Sahoo (1999) used CERES-maize crop model and carried out simulation for irrigated and rainfed conditions. Rise in temperature decreased the maize yield in both the environments. At CO2 level of 350 ppm, grain yield decreased continuously with temperature rise till 4°C and the yield was decreased by about 30% over the present day condition. Effect of elevated carbon dioxide concentration on growth and yield of maize was established, but less pronounced when compared with crops, like wheat, chickpea and mustard crop. The beneficial effect of 700 ppm CO2 was nullified by an increase of only 0.6°C in temperature. Further increase in temperature always resulted in lower yields than control. For one of the IPCC scenario (an increase of 1.8°C temperature for India and 425 ppm CO2 by the year 2030), potential maize yields would be severely effected (about 18%).

The sensitivity experiments of the CERES-rice model to CO2 concentration changes conducted by Saseendran et al. (2000) indicated that an increase in CO2 concentration would lead to yield increase due to its fertilization effect and also enhance the water use efficiency over the Kerala state. The temperature sensitivity experiments had shown that for a positive change in temperature up to 5°C, there was a continuous decline in the yield. For every one-degree increment, the decline in yield was about 6%. Nevertheless, the physiological effect of ambient CO2 at 425-ppm concentration compensated for the yield losses due to increase in temperature up to 2°C.

The overall impacts of the climate change scenario for a 2°C rise in temperature and a 7% increase in precipitation, were negative and about 8.4% loss of the total farm level net-revenue for India, when the functional relationship between farm level net revenue and climate variables, introduced through linear, quadratic, and interaction terms, were estimated. Increases in temperature resulted in significant negative impacts, while higher precipitation considered under the scenario increased the net-revenue. Haryana, Punjab, and western Uttar Pradesh, which grow predominantly wheat in the winter season, experience the most negative effects, along with the coastal districts of Tamil Nadu. On the other hand, the eastern districts of West Bengal and parts of Bihar seem to benefit from the changes in future. When the impact of various climate change scenarios had been assessed on grain yields of rice with two popular crop simulation models - CERES-rice and ORYZA1N (Aggarwal et al. 1997) at different levels of management, Aggarwal and Mall (2002) showed that rice yields at current level of management (referred to application of 150 kg N ha-1 in 3 split doses and frequent irrigations, a common practice in irrigated rice growing areas in several parts of the country) changed with change in temperature and CO2. Increase of 1-2°C temperature without any increase in CO2 resulted in a 3-17% decrease in grain yield in different regions. In general, as the temperatures increased, rice yields in eastern and western India were less affected, moderately affected in north and severely affected in southern India. Although a doubling of CO2 resulted in 12-21% increases in yield in different regions, the beneficial effect of 450 ppm CO2 was nullified by an increase of 1.9-2.0°C in northern and eastern regions and by 0.9-1.0°C in southern and western regions. In the improved level of management (analogues to potential production environment), the beneficial effect of 450 ppm CO2 was nullified by an increase of 1.2-1.7°C in northern and eastern regions and by 0.9-1.0°C in southern and western regions. In another study, Aggarwal (2003) showed that the irrigated wheat and rice yields in north India would not be significantly affected due to direct effect until 2050. When the temperature increases become very large by 2070, the crops will show huge reduction in yields.

Impact of elevated CO2 and temperature on rice yield in eastern India was simulated by Krishnan et al. (2007), using the ORYZA1 and the INFOCROP-rice models. The crop and weather data from ten different sites viz., Bhubaneswar,

Chinsurah, Cuttack, Faizabad, Jabalpur, Jorhat, Kalyani, Pusa, Raipur and Ranchi, which differed significantly in their geographical and climatological factors, were used in these two models. For every 1°C increase in temperature, ORYZA1 and INFOCROP-rice models predicted average yield changes of -7.20 and -6.66%, respectively, at the current level of CO2 (380 ppm). But, increases in the CO2 concentration up to 700ppm led to the average yield increases of about 30.73% by ORYZA1 and 56.37% by INFOCROP-rice, respectively. When temperature was increased by about +4°C above the ambient level, the differences in the responses by the two models became remarkably small. For the GDFL, GISS, and UKMO scenarios, the ORYZA1 predicted the yield changes of -7.63, -9.38 and -15.86%, respectively while the INFOCROP did at -9.02, -11.30 and -21.35% for the corresponding scenarios. There were considerable differences in the yield predictions for individual sites, with declining trend for Cuttack and Bhubaneswar, but an increasing trend for Jorhat. These differences in yield predictions were mainly attributed to the sterility of rice spikelets at higher temperatures (Krishnan and Rao 2005).

Developing the climate change scenarios for the selected regions of the Indian sub-continent using three GCMs namely, Goddard Institute of Space Studies Model (GISS-2, Russell and Rind 1999), Geophysical Fluid Dynamics Laboratory Model (GFDL-R30, Knutson et al. 1999) and United Kingdom Meteorological Office -Hadley Climate Prediction Centre Model (UKMO - HadCM3, Mitchell et al. 1998), Mall et al. (2004) used the CROPGRO-soybean model to simulate the impact of climate change on soybean production in India. The probable changes in surface air temperature during the growing season were estimated at the selected sites in the region, following standard rationalization techniques suggested by IPCC (Mearns et al. 2001). Probable changes in precipitation, cloudiness and solar radiation under the climate changes scenarios were not taken into consideration, in view of the significant uncertainties associated with non-linear, abrupt and threshold rainfall events projected by GCMs over the Indian subcontinent. All the GCM projected climate change scenarios (at the time of doubling of CO2 concentrations) predicted decreased yields for almost all locations. Mean decline in yields across different scenarios ranged from 14% in Pune (West India) to 23% in Gwaliar (Central India). Decline in soybean yield was 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 the UKMO model generated climate scenarios for both current and doubled CO2 atmosphere. In general, the direct impacts of climate changes would be small on kharif crops, but kharif agriculture would become vulnerable due to increased incidence of weather extremes, such as change in rainy days, rainfall intensity, duration and frequency of drought and floods, diurnal asymmetry of temperature, change in humidity, and pest incidence and virulence. The rabi crop production might become more vulnerable due to larger increase in temperature, asymmetry of day and night temperature and higher uncertainties in rainfall. Apparently, the impacts of the climate change on Indian agriculture would be small in the near future, but in long run, the Indian agriculture may be seriously affected depending upon season, level of management, and magnitude of climate change.

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