Bioeconomic evaluation of feedings strategies in the yearling beef cattle system in Mozambique

2021 
Abstract The application of feeding strategies (FS) to meet nutrient requirements of beef cattle grazing on native pastures during the dry season, are required to improve the productivity of production systems in tropical regions. The objective of this study was to evaluate the bioeconomic effects of different FSs applied to yearling bulls in Mozambique, using modeling and simulations as tools to support decision making. A simple deterministic simulation model was developed, assuming initial body weight (120 kg), average daily gain (ADG), feedstuffs, and production costs as inputs. FSs were simulated for a total of 120 days within four ADG systems: 0.000 kg (S000), 0.200 kg (S200), 0.400 kg (S400), and 0.600 kg/d (S600), and three diets were simulated for the positive and maintenance ADG systems, totaling 12 FS. The effects of 12 FS combinations were analyzed and a sensitivity analysis was performed. The effect of the change in the inputs of the model (feedstuffs purchase and calf purchase price) showed the sensitivity of the model to economic parameters (Gross Margin and Net Profit). The negative ADG (-0.200 kg) system (S-200) had the highest labor cost. Corn bran, considering its availability and low cost in the studied region, is a promising feedstuff for concentrates. Effective operational cost (EOC) was higher than 99% in all FSs. S200, S000, and FS5 within the S200 system resulted in negative net profit (NP) values. Net profit proportionally increased as ADG increased. FS12 (Hyparrhenia rufa, corn bran, and Sesbania sesban) of S600 promoted the highest ratio (NP/total operation cost) (0.48), and consequently, the highest profitability (32.37%). In general, the simulation model shows that, in native and communal pasture beef production systems in regions of Africa with similar production conditions, the productivity of yearling beef cattle during the dry season may be improved by applying feeding strategies.
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