Evaluating agricultural flood control benefits by an optimal programming technique

The general objective of this study is to estimate the difference in farm organization and net income where the likelihood of potential flood damage vary. The specific objectives are to; (1) adjust the production functions of crops grown in the flood plain to reflect varying levels of potential flood damage, (2) use the adjusted production functions to estimate organization and income for each respective level of potential flood damage for different resource situations, and (3) use these estimates to demonstrate a procedure for computing potential farm income for different levels of flood damage, i.e., benefits from flood control. Estimates of production functions adjusted for potential flood damages are assumed to reflect the farmer’s image of floodplain land productivity. These estimates were tabulated from a combination of farmer experiences, yield estimates from agronomic research, and certain hydrologic data. The adjusted production functions were necessary to reflect the reduced income of different levels of flood damage, i.e., the cost of incurring the flood risk. The adjusted production function developed for different levels of flood damage will reflect differences in farm organization and net income. This procedure has been suggested for estimating benefits from enhancement land use which result in changes in farm organization and net income. Estimates of organization and net income changes should prove useful guides to farmers having floodplain land, as well as provide a basis for evaluating agricultural benefits of different levels of flood control. The production response of farmers to installation of flood control measures is of major importance in estimating the economic feasibility of a water resource development project. The benefit accruing to agriculture is the difference in net income between the optimum use of the floodplain land with natural flooding risk and that obtained with different levels of flood control.