Spatial Modeling of Resource Allocation and Agriculture Production under Environmental Risk and Uncertainty

Ermoliev, Y.M. & Fischer, G. (1993). Spatial Modeling of Resource Allocation and Agriculture Production under Environmental Risk and Uncertainty. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-93-011

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Abstract

Within the limits of the physical production potential, effective land performance is largely determined by anthropogenic factors, e.g., the availability of infra-structure, market access and a complex interaction of behavioral, socio-economic, cultural and technological conditions. Any discussion of interactions with the environment must come to grips with uncertainties and resulting risks. The paper outlines a possible approach to modeling agricultural production in a spatial setting under environmental risk and uncertainty. The methodology proposes the distinction of "compartments", i.e., geo-referenced collections of homogenous land units which are coherent in terms of natural resources and economic production conditions. The compartments interact through commodity markets and "trade" of mobile resources, compete for allocation of limited public resources, and are jointly affected by government policies, regulations and other regional constraints.

The proposed model incorporates two types of decisions: strategic ex-ante decisions and flexible adaptive ex-post decisions. The notion of strategic decisions is especially relevant when dealing with the risks of virtually irreversible impacts, such as placing a dam, clearing rainforests for agricultural purposes, or diverting agriculture land to urban and industrial uses. Adaptive ex-post decisions allow to correct the strategic decisions relying on the ability to learn from experience and to adapt to observed situations.

From a formal point of view, this process can be interpreted as a stochastic decomposition procedure, integrating individual agents through market clearing conditions and allocation of public resources in order to find strategic decisions that are robust and most effective in an uncertain setting.

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