A stochastic model to study the control of grazing systems

Abstract

Most modelling research into grazing management has focused on planning. However, if target outcomes for pastures, animals and profit are to be realised, planning and control must be closely linked. Control is required if actual outcomes are likely to deviate from those planned. This may be because of: variability in uncontrollable variables (e.g., the weather); imprecision in implementation (including timeliness); inaccurate measurement of outcomes (i.e., sensor errors); and errors in prediction during the planning process. A dynamic grazing system model was used with a genetic optimising algorithm to simulate the outcomes for different control strategies for a sheep farmlet. The optimum control sequencing for pasture allowance, nitrogen application, lamb drafting weight and supplementation was investigated. Optimum pasture cover levels were similar to those recommended to farmers, except for winter when higher allowances were suggested. Sensor errors for pasture measurement were shown not to have a significant financial cost to farmers if recommended measurement techniques are closely followed. Keywords: grazing control, grazing management, grazing system model, pasture measurement, system optimisation