Farm Business Management: An holistic stochastic decision-making model

Abstract

Farm Business Management requires an holistic perspective to understand, manage and direct the purposeful use of the natural resources, and aim for individual and social rewards. A new perspective on physical and financial sustainability is a key component of the overall management strategy of the farm system. This research attempts to integrate different schools of systems thinking in order to build a method of farm planning which, because it uses a holistic view of farm resource management, is more useful to the decision maker at the farm level. In order to achieve this integration a case-study was used to identify the system components, using conceptual decision models (i.e. soft systems methodologies in the form of conceptual mapping) and optimal decision models (i.e. hard systems methodologies, here stochastic mathematical programming) to learn more about the operation of the system, aiming to enhance/enrich decision making by improving on-farm information processes. Conceptual mapping was initially used to have a basic understanding of the farm system. Later stages defined system relationships and critical points from the perspective of the farmer. This exercise showed that systems analysis, while objective, cannot ignore the expectations and immediate objectives of the individual decision maker. Consideration of these facts allows the analyst to construct different levels of conceptual maps, ranging from simplistic models to higher levels of detail on specific purposes of the system. A mathematical programming model with stochastic characteristics which capture the technical and attitudinal risks of the farm system was constructed in order to simulate farm performance. The exercise showed that stochastic mathematical programming techniques, when integrated to soft systems methodologies in farm resource management, become a valuable information tool that enable the decision maker to have a better perspective of the farm system, for short-term planning purposes and contingency situations. Quantitative techniques for planning, rather than becoming deductive tools for prescribing system behaviour, should be understood as an opportunity to identify the farm system components, dynamics and purposes more thoroughly and contribute to managing the elements within the system that generate instability and chaos through the use of accepted algorithms of risk analysis and risk management. By doing this, decision making is clearly enhanced and the value of strategic management is reinforced.