Yield gap analysis framework applied to pasture-based livestock systems in Central Brazil

Context or problem Yield gap analyses for livestock systems may utilize different approaches, including mathematical models based on pasture carrying capacity concepts. Protocols typically used to estimate pasture carrying capacity in Brazil do not consider adequately the seasonal variability of forage production, mismatching the estimates of demand and supply. Objective or research question This study aimed to develop a protocol based on the concept of pasture carrying capacity for yield gap analysis of pasture-based beef cattle production systems on a regional scale and apply the protocol to estimate yield gap on pasture-based beef cattle systems in Central Brazil. Methods The framework gathered techniques such as homogeneous climatic zones definition; systematization of primary data with weather and soil information to run the models; scenario definition and assumptions; adaptation of the forage model to run long-term simulations; use of a pasture carrying capacity model, which estimates maximum (carrying capacity of systems with variable stocking rate) and critical stocking rates (carrying capacity limited by seasonal and interannual variability of forage production); and the use of national agricultural census databases to estimate actual stocking rate and calculate yield gaps. The protocol was applied to pasture-based beef cattle production systems under different management scenarios in Central Brazil. Results The maximum forage production and stocking rate increased with nitrogen fertilization and water availability. However, under cooler temperatures during winter months, forage production and critical stocking rates were less responsive to these factors. Mean yield gap for maximum stocking rate (difference between maximum and actual stocking rate) ranged from 5.81 to 5.12 animal units per hectare (AU ha−1) in the potential scenario, 4.18–2.9 AU ha−1 in the water-limited, and 2.73–1.43 AU ha−1 in the attainable scenario, while mean yield gap for critical stocking rate (difference between critical and actual stocking rate) varied from 5.44 AU ha−1 to 2.91 AU ha−1 in the potential scenario, 1.21–0 AU ha−1 in the water-limited, and 1.04–0 AU ha−1 in the attainable scenario. Conclusions Yield gap analysis of pasture-based beef cattle systems can be performed using long-term forage production simulations coupled with a model based on cumulative forage deficits to determine stocking rate metrics. Implications or significance The protocol allowed the identification and quantification of the gap size due to interaction of the main factors influencing forage production and pasture carrying capacity under several environmental and management conditions, and may be applied to support policy and investment decisions.