Optimising management to achieve sustainable economic yields from grasslands

To achieve the objective of sustainably managing grasslands there is a need to consider the complex interactions within the system that predominantly determine the productive capacity of the grassland resource, as well as the risks and returns from its management. To address this complex problem a dynamic grasslands simulation model was developed that accounted for interactions between management; being seasonal stocking rate, supplementary feeding and the decision to sow grasslands; and the inter-temporal dynamics of soil fertility, erosion, pasture growth, grassland composition, and livestock performance under climatic uncertainty. To derive a solution to this complex problem requires the development of a bioeconomic model and the treatment of the grassland resource, in this instance, as an exploitable renewable resource. The developed bioeconomic model was solved using a Seasonal Stochastic Dynamic Programming method that allowed for the identification of the optimal seasonal decision rules for different initial states of the grassland resource (soil fertility, grassland composition and biomass). These decision rules account for the potential effect of climatic uncertainty on maximising economic returns over the long term. Their application through Monte Carlo simulation using 40 years of climate data results in the convergence of future grassland states towards an economically optimal and sustainable outcome. The production from these states represents the sustainable economic yields achievable from the grasslands given economic objectives and climatic uncertainty. The model is applied to a case study scenario from the Northern Tablelands of NSW, Australia, with implications drawn and discussed for the case study region.