Using seasonal stochastic dynamic programming to identify optimal management decisions that achieve maximum economic sustainable yields from grasslands under climate risk

Abstract

There are significant challenges in managing the trade-offs between the production of pastures and grazing livestock for profit in the short term, and the persistence of the pasture resource in the longer term under stochastic climatic conditions. The profitability of using technologies such as grazing management, fertiliser inputs and the renovation of pastures are all influenced by complex inter-temporal relations that need to be considered to provide suitable information for managers to enhance tactical and strategic decision making. In this study pasture is viewed as an exploitable renewable resource with its state defined by total pasture quantity and the proportion of desirable species in the sward. The decision problem was formulated as a stochastic dynamic programming (SDP) model where the decision variables are seasonal stocking rate and pasture resowing and the objective is to maximise the present value of future economic returns. The solution defines the optimal seasonal decisions for all intervening states of the system as uncertainty unfolds. The model was applied to a representative farm in the high rainfall temperate pasture zone of Australia and the pasture states under which tactical grazing rest, low stocking rates and pasture re-sowing are optimal were identified. Results provide useful general insights as well as specific prescriptions for the case study farm. The framework developed in this paper provides a means of identifying optimal tactical and strategic decisions that achieve maximum sustainable economic yields from grazing systems.