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 intertemporal 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 re-sowing 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.

Multi-Stage Mathematical Programming Models and Their Applications in Agriculture

“…На третьем этапе осуществляется прогноз различных показателей аграрного производства, например, трудовых и материальных ресурсов, оплаты труда, урожайности и прочих, поскольку они имеют различные тенденции. В частности, для прогнозирования урожайности сельскохозяйственных культур в работе [10] использованы логистическая и асимптотическая функции (7), (8).…”

Modelling Chinese grassland systems to improve herder livelihoods and grassland sustainability

“…A key challenge in making decisions regarding the operational management of a grassland resource is its complexity and intractability. Decisions need to consider the interactions between grassland ecology, the use of technology to improve and manage the resource, environmental externalities, utilisation of the resource by grazing animals, and the short and longrun profitability of the whole farming system (MacLeod and McIvor 2008;Scott et al 2013;Behrendt et al 2016a;Kemp et al 2020a). In China, the identification of pathways to improve both grassland condition and the livelihoods of herders that depend on them, including the development of pragmatic criteria used to improve the management of grasslands, have been substantially researched (Kemp et al 2013(Kemp et al , 2018(Kemp et al , 2020Badgery et al 2020).…”

Parameter fitting for individual growth through data assimilation applied to beef cattle

“…In cattle farming, there are several decisions that depend on estimates of animal growth rates [5,7,1,38]. These include: (a) financial decisions based on forecast of expenses and revenues; (b) optimal economic slaughter time; (c) times of purchase and sale of animals;…”

“…(f) pasture stocking rates; (g) selection of animals for maintenance and disposal, and; (h) formation of animal batches. Managers of beef cattle production systems are challenged to make such decisions to maximize the economic return and minimize negative externalities, while still having to deal with seasonal variations in pasture productivity [7] and significant genetic heterogeneity between animals [54].…”