Strategies for achieving sustainability equilibrium between human activity and nature are essential to the global and local scenario. Our work highlights the importance of restoring the stability between economic activity and nature. Developing indicators to monitor this equilibrium is necessary to reduce uncertainty in formulating strategies, decisions, or actions. The Matrix decomposition analysis (MDA) adapts the Leontief input-output equations for the disaggregated structural decomposition of key performance indicators (KPI). At the farm level, three experiments denominated “marginal exponentiation” are proposed to compare the MDA with the Data envelopment analysis (DEA) and Stochastic frontier analysis (SFA). RMarkdown was used for methodological operationalization. Data science steps are coded in specific chunks, applying a layered process with modeling and successive analyses such as descriptive statistics, correlation, cluster, and Linear discriminant analysis (LDA). Given the results, we may argue that the MDA is a Leontief partial equilibrium model that produces indicators with dual interpretation, enabling measurement of the dynamic equilibrium of the sustainable ecosystem variables. The method offers a new ranking system that detects relative changes in the use of resources correlated with efficiency analysis. MDA might provide a new robust ranking system capable of detecting relative changes in the use of resources that can be applied to input-output relationships of many organizations. We found that MDA can identify if a given ecosystem is in equilibrium and that the instability can be caused by the excessive use of resources or abnormal productivity.