Annelise Aila Gomes Lobo*, Ana Laura Januário Lelis, Augusto Hauber Gameiro, Télis Adolfo Cumbe, Aluisio Fernando Alves Ferreira, Anderson de Souza Gallo, Mariana Campana and Jozivaldo Prudêncio Gomes de Morais

Department of Biotechnology and Plant and Animal Production, Studies Group and Work in Agriculture and Livestock Farming, Federal University of Sao Carlos, Brazil

*Corresponding Author: Annelise Aila Gomes Lobo, Department of Biotechnology and Plant and Animal Production, Studies Group and Work in Agriculture and Livestock Farming, Federal University of Sao Carlos, Brazil.

Received: May 21, 2024; Published: July 31, 2024

Abstract

Creating decision-making models to optimise tropical ruminant landscapes requires a sophisticated understanding of modelling structures. While deterministic forecasting simplifies predictions, it ignores system variability, contrasting with the richer insights provided by probabilistic forecasting. By considering variables such as supplementary nutrition and system maintenance costs, this study aims to elucidate the key elements of modelling frameworks essential for redesigning tropical ruminant landscapes in order to guide stakeholders toward socially, environmentally and economically viable landscape management strategies. Despite their simplicity, deterministic models are important for making profitable decisions in the redesign of productive landscapes, taking into account the importance of their additional elements, such as sensitivity analysis, dimensional analysis and simulated scenarios, which are emphasised to strengthen the robustness of the model and the integration of relevant parameters for sustainable landscapes.

Keywords: Decision-Making; Deterministic Model; Productive Landscapes; Robustness; Simulated Scenarios; Tool

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Citation

Citation: Annelise Aila Gomes Lobo.,et al.“Features Mathematical Modeling in a Sustainable Silvopastoral System - A Further Experts’ Opinion". Acta Scientific Agriculture 8.8 (2024): 60-63.

Copyright

Copyright: © 2024 Annelise Aila Gomes Lobo., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.