Modeling grazing in the semi-arid rangelands of Lebanon using GRASIM
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Sustainable pasture management is critical for the economic viability of animal agriculture and particularly so in dryland areas. Simulation models serve as tools to evaluate management scenarios in order to find sustainable dryland grazing systems which account for the dynamic interplay of the components of a pasture grazing system. These management scenarios would be costly in both time and labor to test through field experiments. The GRAzing Simulation Model (GRASIM) was developed for the Midwest region of the United States. This study evaluated GRASIM's ability to simulate grazing in a semi-arid environment. Local weather, soil water, nitrogen, and growth data from a two-year experiment conducted in the Beqaa Valley, a semi-arid region in eastern Lebanon were used. The model performed well in predicting plant growth (R2 = 0.98), soil water contents, and soil nitrate leaching. Case studies were conducted using the GRASIM model to simulate grazing systems under semi-arid Bekaa area conditions to assess the impacts of key system factors including soil type, and stocking rate, grazing management regimes/grazing pressure, and water availability in terms of biomass production and water and nitrate leaching losses. Simulations showed significant negative impact of high grazing pressure (high stocking rate and/or continuous grazing) on biomass production, as well as positive impact of higher soil water availability on forage growth. Simulations also resulted in low nitrate leaching for all simulated scenarios due to vigorous plant uptake and high evapotranspiration relative to rainfall. In conclusion, the GRASIM model showed sufficient robustness to use in a decision-aid framework, around which better grazing management practices can be designed while accounting for key factors (weather, soil, plant, and animal). The current version of the model can be used via the internet at http://Pasture.ecn.purdue.edu/~grasim/. © 2007 American Society of Agricultural and Biological Engineers.
author list (cited authors)
El-Awar, F. A., Zhai, T., Mohtar, R. H., & Jabre, W.