Coffee Farming in the Sierra Norte Region of Puebla, Mexico: A Multivariate Analysis Approach to Productive Dedication

Zayner Edin Rodríguez-Flores, Cesar San-Martín-Hernández (), Victorino Morales-Ramos, Victor Hugo Volke-Haller, Juliana Padilla-Cuevas and Carlos Hernández-Gómez
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Zayner Edin Rodríguez-Flores: Department of Soil Science, College of Postgraduates in Agricultural Sciences Campus Montecillo, Montecillo, Texcoco 56264, MX, Mexico
Cesar San-Martín-Hernández: Department of Soil Science, College of Postgraduates in Agricultural Sciences Campus Montecillo, Montecillo, Texcoco 56264, MX, Mexico
Victorino Morales-Ramos: Department of Coffee Science and Technology, College of Postgraduates in Agricultural Sciences Campus Cordoba, Amatlan de los Reyes, Cordoba 94946, VER, Mexico
Victor Hugo Volke-Haller: Department of Soil Science, College of Postgraduates in Agricultural Sciences Campus Montecillo, Montecillo, Texcoco 56264, MX, Mexico
Juliana Padilla-Cuevas: Department of Soil Science, College of Postgraduates in Agricultural Sciences Campus Montecillo, Montecillo, Texcoco 56264, MX, Mexico
Carlos Hernández-Gómez: Department of Soil Science, College of Postgraduates in Agricultural Sciences Campus Montecillo, Montecillo, Texcoco 56264, MX, Mexico

Agriculture, 2025, vol. 15, issue 21, 1-16

Abstract: Puebla is Mexico’s third largest coffee-producing state, supporting more than 40,000 families in the Sierra Norte region alone. In this area, the heterogeneity of production, which ranges from traditional subsistence methods to technified models, and a significant difference in the level of dedication to production represent major challenges for the sustainability of coffee farming. This study aimed to classify coffee producers in the Tlaxcalantongo ejido, Xicotepec, Puebla, according to their level of productive dedication, using multivariate techniques such as hierarchical clustering, non-metric multidimensional scaling (NMDS), and Random Forest. Data were obtained from a structured questionnaire with 102 questions administered in person to 50 active producers. The cluster analysis found patterns and differences in the productive dedication of coffee growers that allowed them to be differentiated into two groups. Group 1 (8%) showed minimal fertilization practices and low operating expenses, reflecting significant differences in resource management. In contrast, producers in group 2 (92%) had a profile characterized by intensive fertilization practices, greater investment in inputs, and structured agronomic management. In the NMDS analysis, dimension 1 was significantly associated with the group of producers with low productive dedication and dimension 2 was significantly associated with the group with greater dedication, while the third dimension showed no clear differentiation between the groups. The variables that determined the productive dedication profiles were fertilization application, division, type, and expenditure.

Keywords: coffee; Coffea arabica; coffee growers typification; machine learning; Xicotepec (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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