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
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.