Planning for an Optimal Mix of Agricultural and Wildlife Land Use

Abstract

A linear programming model was formulated to maximize profit of a small farm subject to constraints on the wildlife and agricultural subsystems. The model was developed to allocate the area among crops, fertilizer levels, and rotations, and to determine the average field size and proper spatial variety of crops. Application of the model to a hypothetical small farm showed that it was most efficient to have a large average field size, and, therefore, less area of edge. Wildlife constraints were then met by the type of crops planted and the area left unharvested. The marginal costs associated with fertilizer, crop minimums, and unharvested acreage constraints were high and produced a significant reduction in profit. J. WILDL. MANAGE. 43(2):493-502 Planning for an orderly environmental development will inevitably produce conflict between economic, recreational, and aesthetic uses of the land. Such a conflict is exemplified by the antagonism between agricultural and wildlife land use. Humans derive economic benefit from crops harvested, and aesthetic benefits such as recreation, education, and relaxation from observing or exploiting wildlife. However, an increase in benefits derived from the agricultural subsystem will often cause a decrease in benefits derived from the wildlife subsystem, and vice versa. The major objective of each subsystem may be at odds with that of the other. One purpose of an environmental plan is, therefore, to provide a satisfactory mix of 2 or more subsystems. a mix which will maximize the attainment of goals of 1 subsystem while maintaining an acceptable standard of the other subsystems. One methodology for finding optimal solutions to complex problems is through linear programming (Hillier and Lieberman 1967, Taha 1971). Problems consisting of hundreds of decision variables and constraints may be solved relatively quickly by computer-implemented linear programming (LP) packages (Spivey 1973). However, LP requires that objectives and constraints be expressed as linear functions of the decisions. Thus, interaction between decision variables cannot be explicitly included. Techniques for approximating nonlinear functions with linear models allow the use of LP in many cases (Dantzig 1963), and the efficiency of LP solution algorithms may then be exploited. I employed LP to produce a plan for the optimal mix of an agricultural-wildlife system. This system is exemplified by the especially difficult case of the owner of a small farm wishing to maximize profit while protecting specified wildlife levels. Choices of crop varieties, fertilizer levels, rotations, cropping regimes, and spatial arrangements are made to meet constraints of farm production and still achieve the desired results. The case study system is a hypothetical, yet realistic, situation which exemplifies interaction between agriculture and wildlife. While some generalities may be made from the results of this case study, the utility is more in the approach that is used. I am grateful to R. H. Giles for presenting me with the original idea for this work. J. Wildl. Manage. 43(2):1979 493 This content downloaded from 207.46.13.98 on Fri, 05 Aug 2016 04:03:35 UTC All use subject to http://about.jstor.org/terms 494 PLANNING AGRICULTURAL AND WILDLIFE LAND USE* Powers