ANÁLISIS COMPARATIVO DE DOS HERRAMIENTAS PARA EVALUAR LA SOSTENIBILIDAD GANADERA EN EL CONTEXTO

Abstract

<p><strong>Background.</strong> In the future, the production of cattle will be increasingly affected by the reduction of natural resources, particularly land and water, and by the competition between food and feed, therefore, the evaluation of the sustainability of production systems, is increasingly important. <strong>Objective.</strong> The objective of this study was to identify the strengths and weaknesses of the Sustainability of Food and Agricultural Systems (SAFA) and Life Cycle Assessment (LCA) methodologies, as tools to assess the sustainability of cattle production systems. <strong>Methodology.</strong> The SAFA and LCA methodologies were theoretically evaluated and applied to three production systems for grazing cattle in the Mexican tropics. <strong>Results.</strong> The results show that the advantages of SAFA with respect to LCA are: having a friendlier and more agile format and not requiring expert evaluators for its management, however, it has the disadvantage that it does not allow for accurate identification, within 21 topics that it evaluates, of the results of each indicator (116 in total). This could hinder the proposal of measures to improve the performance of the systems. While LCA is a methodology by which objective, detailed and standardized evaluations are carried out, but the disadvantages of this tool are that it requires experts and can be more expensive than SAFA due to the use of specialized software. <strong>Implications.</strong> The results of this study help to identify the strengths and weaknesses of SAFA and LCA methodologies, allowing evaluators of the sustainable performance of livestock systems to decide which methodology suits their interests. <strong>Conclusion.</strong> It is concluded that both tools are useful for evaluating the sustainability of livestock systems but with different objectives and scopes. Likewise, the validity of the results in both methodologies depends on the quality, veracity and transparency of the data used.</p>