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Angel Saul Cruz-Ramírez Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecua-rias y Forestales, Centro Nacional de Investigación Disciplinaria en Conservación y Mejoramiento de Ecosistemas
Gabino Alberto Martínez Gutiérrez Instituto Politécnico Nacional, Centro Interdisciplinario de Investiga-ción para el Desarrollo Integral Regional, Unidad Oaxaca
Isidro Morales-García Instituto Politécnico Nacional, Centro Interdisciplinario de Investiga-ción para el Desarrollo Integral Regional, Unidad Oaxaca
María del Rosario López-García Secretaría del Trabajo y Previsión Social.
Carlos Roman Castillo-Martínez Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecua-rias y Forestales, Centro Nacional de Investigación Disciplinaria en Conservación y Mejoramiento de Ecosistemas

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Resumen

Objective: The aim of this study was to compare forecasting models for the planted area of agave mezcalero from 2023 to 2027, using data from the 1982–2022 period through autoregressive integrated moving average (ARIMA) models, in order to promote the efficient management of natural resources.


Design/methodology/approach: To validate the forecasting efficiency, an out-of-sample comparison was conducted between ARIMA models and simple forecasting methods such as the mean and naïve approaches.


Findings: The results identified a growth scenario for the planted area of agave mezcalero during the 2023–2027 period. The most accurate model, the autoregressive (4,0,0), projected that the maximum planted area would be reached in 2024, ranging from 22,723 to 60,280 hectares, followed by a gradual decline starting in 2025.


Research limitations/implications: The predictions are constrained by the availability and quality of the databases. The main limitation faced by the study was the lack of historical data.


Originality/value/conclusions: It is concluded that the ARIMA model can be an efficient tool for estimating the planted area of agave intended for mezcal production. The cultivation of agave mezcalero is currently in a context of overproduction. The information generated may be useful for planning and investment decisions by the industry, agave producers, and government authorities.

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