Climate Change: Impact on Indian Agriculture


The Intergovernmental Panel on Climate Change (IPCC) in its fourth assessment report observed that, ‘warming of climate system is now unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising global sea level’ (Solomon et al., 2007). In India, several studies have shown that while temperature marked an increasing trend over the previous century, rainfall had no such significant change. However, at regional levels, the rainfall showed increasing or decreasing trends in the previous century. Agriculture being a climate-sensitive sector as also a sector that provides livelihood for more than 60% of Indians, over the past decade a large number of studies have tried to assess the impact of climate variability and climate change. This article reviews the existing literature that assesses the economic impacts and demarcates the emerging research directions.

Mall et al. (2006) provide an excellent review of climate change impact studies on Indian agriculture, mainly from the perspctive of physical impact. The available evidence shows a significant drop in yields of important cereal crops like rice and wheat under climate change conditions. However, biophysical impacts on some of the important crops like sugarcane, cotton and sunflower are yet to be studied adequately.

The economic impacts of climate change on agriculture have been studied extensively across the globe and this continues to be a hotly debated research problem. There are two broad approaches for assessing economic impacts—agronomic-economic approach and Ricardian approach. In the former, the physical impacts (in the form of yield changes and/or area changes) are introduced into an economic model exogenously as Hicks neutral technical changes. In the Indian context, Kumar and Parikh (2001a) have estimated the macro level impacts of climate change using such an approach. They showed that under doubled carbon dioxide concentration levels in the later half of twenty-first century, the gross domestic product would decline by 1.4 to 3% points due to climate change. More significantly, they also estimated an increase in the proportion of population in the bottom income groups of the society in both rural and urban India as a result of climate change.

Since the scope for incorporating adaptation into the agronomic-economic approach is rather limited, Mendeloshn, Nordhaus and Shaw proposed an alternative approach in their classic paper in American Economic Review in 1994. This approach, called the Ricardian approach, is based on the argument that, ‘by examining two agricultural areas that are similar in all respects except that one has a climate on average (say) 3o C warmer than the other, one would be able to infer the willingness to pay in agriculture to avoid a 3o C temperature rise’ (Kolstad, 2000). While all possible adaptations are accounted for in the impact estimation, the constant relative prices’ assumption could lead to bias in the results of this approach. For India, Kumar and Parikh (2001b) have used a variant of this approach and showed that a 2o C temperature rise and 7% increase in rainfall would lead to almost 8% loss in farm level net revenue. Kumar (2003) extended the analysis to include climate variation terms in the Ricardian approach and estimated that a 5% increase in climate variation, along with the above-mentioned climate change scenario, would result in an almost 10% drop in the farm level net revenue.

The results of the two broad approaches outlined above correspond to what could be termed as the ‘naive’ and the‘clairvoyant’ farmer, respectively. While the estimates from agronomic-economic approach account for adaptation only in an insignificant manner, the Ricardian approach treats the farmer as though he has perfect foresight. In the Ricardian approach, farmers are assumed to identify instantaneously and perfectly any change in the climate, evaluate all associated changes in market conditions and then modify their actions so as to maximize profits. These assumptions also imply that the agricultural system is ergodic, i.e., space and time are substitutable. Ergodic assumptions imply, for example, that skills, institutional and financial endowments for responding to, say, drought (that are typically refined in arid places) are assumed to be available for use by people in humid areas (where such resources are under-developed) immediately and in an essentially cost-less manner. Thus, the Ricardian estimates must be modified to include the influence of spatial features. Recent studies in the US have demonstrated that such refinements are essential to get accurate estimates of climate sensitivity (Polsky, 2004; Schlenker et al., 2006). Initial evidence from an ongoing analysis in India also supports the significant influence of inter-farmer communication on climate sensitivities.

The impact assessment literature has so far mainly been driven by the end-goal of justifying action to avoid climate change. However, mitigation of greenhouse gases represents only one side of the climate policy, with adaptation being the other half. The climate change literature has viewed vulnerability as ‘end-point’ of the impact analysis, i.e., the remaining impacts of climate change on, say, agriculture, after all adaptations are accounted for represented the vulnerability of agriculture to climate change. However, as O’Brien et al. (2004) argue, vulnerability can also be conceptualized as the starting point of analysis as is the case with several other disciplines (e.g., food security and disaster management) that focus on vulnerability. Kumar et al. (2007) demonstrate important methodological similarities and scope for synergy between the vulnerability assessments in poverty and recent literature on climate change.

For the adaptation assessment, the existing tools used for impact assessment may not be useful. For instance, as mentioned above, if the aggregate impact assessment shows that inter-farmer communication has a significant influence on climate sensitivity, it remains to be analyzed what kind of communication and communication exists between who would result in reducing the climate sensitivities. Two important changes in research direction are essential for achieving this objective. Firstly, the future climate change projections must be complemented with local level information and knowledge, for it is at the local level where most of the adaptation would take place. This enables more comprehensive assessment of the system’s vulnerability and also helps in ‘mainstreaming’ the climate change policies. Secondly, new tools and approaches must be explored to carry out the adaptation assessment. One emerging tool in this context is agent-based modeling for analyzing social interactions of agents. Their use in the context of climate policy issues is relatively new but growing (Downing et al., 2001; Ziervogel et al., 2005).


Downing, T.E., S. Moss, and C. Pahl-Wostl (2001): ‘Understanding Climate Policy Using Participatory Agent-based Social Simulation’, in S. Moss and P. Davidsson (eds.) Multi-agent-based Simulation, Springer Verlag, New York.

Kolstad, C. (2000): Environmental Economics, Oxford University Press, New Delhi.

Kumar, K.S.Kavi, and Jyoti Parikh (2001a): ‘Socio-economic Impacts of Climate Change on Indian Agriculture’, International Review of Environmental Strategies, 2(2), pp. 277-293.

Kumar, K.S.Kavi, and Jyoti Parikh (2001b): ‘Indian Agriculture and Climate Sensitivity’, Global Environmental Change, 11(2), pp. 147-154.

Kumar, K.S. Kavi (2003): Vulnerability of Agriculture and Coastal Resources in India to Climate Change, Research report submitted to Ministry of Environment and Forests, GoI, New Delhi.

Kumar, K.S. Kavi, R.J.T. Klein, C. Ionescu, J. Hinkel, R. Klein (2007): ‘Vulnerability to Poverty and Vulnerability to Climate Change: Conceptual Framework, Measurement and Synergies in Policy’, paper presented at WIDER Conference on Fragile States – Fragile Groups: Tackling Economic and Social Vulnerability, 15-16 June, Helsinki.

Mall, R.K., R. Singh, A. Gupta, G. Srinivasan, and L.S. Rathore (2006): ‘Impact of Climate Change on Indian Agriculture: A Review’, Climatic Change, 78, pp. 445-478.

Mendelsohn, R., W. Nordhaus, and D.G. Shaw (1994): ‘The Impact of Global Warming on Agriculture: A Ricardian Analysis’, American Economic Review, 84(4), 753-771.

O’Brien, K., S. Eriksen, A. Schjolden, and L. Nygaard (2004): What’s in a word? conflicting interpretations of vulnerability in climate change research. Working Paper 2004:04, Centre for International Climate and Environmental Research Oslo, University of Oslo, Norway.

Polsky, C. (2004): ‘Putting space and time in Ricardian climate change impact studies: Agriculture in the US Great Plains, 1969-1992’, Annals of the Association of American Geographers, 94(3), pp. 549-564.

Schlenker, W., W.M. Hanemann and A.C. Fisher (2005): ‘Will US agriculture really benefit from global warming? Accounting for irrigation in the Hedonic approach’, American Economic Review, 95(1), pp. 395-406.

Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.) (2007): Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, 996 pp.

Ziervogel, G., M. Bithell, R. Washington, and Tom Downing (2005): ‘Agent-based Social Simulation: A Method for Assessing the Impact of Seasonal Climate Forecast Applications among Smallholder Farmers’, Agricultural Systems, 83(1), pp. 1-26. q   

Dr. K.S. Kavi Kumar

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