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Grazing in an Uncertain Environment: Modeling the Trade-Off between Production and Robustness

R. Sabatier, L. G. Oates, G. E. Brink, J. Bleier, R. D. Jackson
Agronomy journal 2015 v.107 no.1 pp. 257-264
continuous grazing, cool season grasses, grasslands, grazing lands, grazing management, intensive farming, livestock, livestock feeding, livestock production, models, overgrazing, quantitative analysis, rotational grazing, social impact, stochastic processes, uncertainty, Wisconsin
Concern with the environmental, economic, and social impacts of the post-WWII model of agricultural intensification has led to renewed interest in grazing as a feeding strategy for temperate livestock farming systems. Putting grass back at the core of livestock feeding not only requires technical knowledge but also reconsideration of the importance of uncertainty in management choices. We developed a simple stochastic model of grassland dynamics to quantify both robustness and production of alternative management strategies under continuous grazing and management-intensive rotational grazing. The model was calibrated on data from cool-season grasslands in south-central Wisconsin. We defined robustness as the probability that a given management strategy did not lead to overgrazing, while the production indicator was number of livestock unit days per hectare enabled by the grazing strategy. Robustness was strongly dependent on the timing and intensity of grazing, and the highest levels of production were incompatible with a high value of robustness. Beyond a certain threshold of production, we observed a trade-off between robustness and production, where robustness decreased regularly until the maximum possible production. This trade-off did not significantly differ between continuous grazing and rotational grazing. We identified key management practices that led to both high production and high robustness, but to attain these results will require not only acquisition of new technical knowledge but also a change in the way the system is managed: from controlling environmental variability with external inputs to understanding and managing stochastic systems in a way that reduces negative externalities while increasing production efficiencies.