Beef production benefits significantly from research aimed at enhancing profitability and productivity, with simulation providing a means to achieve faster results and reduced costs. This paper outlines the development and validation of simulation models for full-cycle production, specifically designed to assess production systems within the Cerrado biome of Brazil. Using published data, models were created for six production systems: one modal system (MS), characterized by less intensive practices – such as first calving at four years and slaughter at 48 months, accompanied by higher mortality – and five improved breeding systems (IBSs) that implement progressively intensive practices. Enhancements included various feed strategies, culminating in a system that enables slaughter at 19 months of age. The models were constructed utilizing the R software scripts that incorporated a Leslie matrix to represent an age-structured model with probabilities of producing female calves and survival rates, facilitating stochastic simulation predictions. The results were evaluated through correlation and regression analyses. Key simulated variables in these models included counting variables, such as the total number of herd animals, harvest steers, and cull cows, as well as weight variables for harvest heifers, harvest steers, and mature cows. The analysis revealed a robust and positive correlation (ranging from 0.93 to 0.99) (p < 0.01), providing evidence that our results accurately replicate all evaluated systems. We employed regression analyses to compare the observed data with the simulations. The significant linear regression for all the evaluated variables (p < 0.01) and the high determination coefficients suggest that the variables can indeed be predicted through simulation. This study illustrates that simulation models may serve to forecast cattle production across diverse systems, holding promise for application in the Brazilian Cerrado and beyond.
Keywords:
evolution herd; modeling; stochastic; validation
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