Impact Assessment Methodologies

Climate change presents a challenge for researchers attempting t o quantify its impact due t o the global scale of likely impacts, the diversity of agricul- ture systems, and the decades-long time scale. Current climatic, soil, and socioeconomic conditions vary widely across the world. Each crop and crop variety has specific clinlatic tolerances and optima. It is not possible t o model world agriculture in a. way t h a t captures the details of plant response in every location. T h e availability of d a t a with the necessary geographic detail currently is the primary limitation, rather than computational capa- bility or basic understanding of crop responses t o climate. A specific problem has been how t o incorporate the detailed knowledge of plant response into aggregate assessments of regional a.ssessments. In general, compromises are necessary in developing quantitative a.nalyses a t regional scales.

There a.re two basic approa.cl1es t o eva.lua.ting crop and farmer response to changing clima.te that l ~ v e ma.de tlifferent compronlises. These are (1) structural modeling of the agronomic response of plants and the eco- nomic/management decisions of farmers based on theoretical specifications and controlled experimental evidence and (2) reliance on the observed re- sponse of crops and farmers t o varying climate.

For the first approach, sufficient structure and detail are needed t o repre- sent specific crops and crop varieties whose responses t o different conditions are known tllrougll detailed experiments. Similar detail on farm manage- ment allows direct modeling of the timing of field operations, crop choices, and how these decisions affect costs and revenues. These approaches typi- cally model a representative crop plant or farm. Both in the case of economic models of farm decisions and in the case of crop response models, the origi- nal purpose of these models was t o improve understanding of how the crop grows or how a farmer manages. In the case of models of a representative farm, one might hope t o offer prescriptive advice for the farmer: where farm operations differ from the profit ma.xiining (or cost minimizing) model re- sults, it provides guidance for how fariners inight improve farm performance.

In both cases, the idealized representation of the crop and farm operation tends t o give results that differ markedly from the actual experience on farms operating under real world conditions. This may reflect the fact that farmers do not operate a.s profit nla,simizers (they could improve their per- formance) or that the models fail to consider some of the factors that the farmer takes into account, such a.s risk, lack of immediate employment al- ternatives, or other considerations. Because of the idealized nature of them models, many analysts consider theill t o provide evidence of the potential production or potential profitability. Imposiilg climate change on these mod- els gives estimates of how potential production may change due t o climate change. Using these results as indicative of how climate will actually affect agriculture thus rests on the a.ssumption that the change in the potential represents the change likely t o be actua.lly experienced. Many approaches of this type have used detailed crop response models requiring daily weather records. For aggregate analyses iiifereilces concerning large areas and di- verse production systems must be made from a relatively few sites and crops because of the complexity of the models and the need for detailed d a t a on weather over a decade or more. This is the basic approach of Fischer et al.

(1994) reported elsewhere in this volume.

T h e work of Leemans and Solomon (1993) is in a similar vein, choosing much simpler represelltations of crop/cliinate interactions, but is still related t o basic agronomic representation of crop growth in response t o temperature

and precipitation. The adva,nta.ge of t,heir approach is that, because of the miiliinal amouilts of clima.tic data (illcan niontllly d a t a on temperature and precipitation), the d a t a exist t o apply the crop models a t a resolution of 0.5' latitude

x

T h e second approach, relying on observed responses of crops and farm- ers, provided some of the ea.rliest estimates of the potential effects. T h e simplest example of this approach is t o observe the current climatic bound- aries of crops and t o redraw tliese boundaries for a predicted changed climate (e.g., Rosenzweig, 1985). In a similar vein, researchers have applied statisti- cal analysis of d a t a across geographic areas t o separate climate from other factors (e.g., different soil quality, va,rying economic conditions) t h a t explain regional productioil differences and have used these t o estimate the poten- tial agricultural impacts of cliillate change (e.g., Mendelsohn et al., 1994).

An advantage of using direct evidence froin observed production is t h a t the d a t a reflect how fa.rmers operating under commercial conditions and crops growing under such conditions actually respond t o geographically varying climatic conditions. Here, the inost recent work uses extremely reduced form models (e.g., Mendelsohn et rrl., 1994) although estimation of more detailed structural models is possible. Darwin et al. (1995) use revealed evidence from geographic variation in climate in a global model, allocating production and input use t o cliinatically determined land classes based on current production patterns. C!lima.te cllange impacts are then simulated by altering the distributioil of land cla,sses a,nd assuming t h a t when an area's land class changes, its uilderlying production level changes t o t h a t of the new land class.' T h e advantage of these a.pproaches is tliat the response of crops and farmers is based on actual respoilse under current operating conditions rather than an idealized view of how crops and farmers respond. The basic caveat associated with this approa,ch is that one must have faith t h a t land currently producing one set of outputs can change t o the new set of out- puts once climate changes. Whether these types of approaches accurately

'The Darwin et al. (1995) approach links the basic agricultural productivity of land classes, described by a production function, with a computable general equilibrium model of t h e world economy. Thus, actual production in a region or land class depends on generate estimates of the market impact and realized production under new equilibrium prices.

ca.pture the productivity impact depends on how well they control for other factors (such as soil quality) and ~vhet,her farmers can adjust their produc- tion as climate changes. This latter coilsideratioll leads t o the interpretation t h a t these approaches capture the long-run equilibrium response t o climate change and may not capture adjustmellt costs associated with changing t o new crops and productioil practices.

3. Crop Response Estimates for Different