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Prediction of Resin Production in Copal Trees ( Bursera spp.) Using a Random Forest Model

Author

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  • Julio César Buendía-Espinoza

    (Maestría en Agroforestería para el Desarrollo Sostenible, Departamento de Suelos, Universidad Autónoma Chapingo, Carretera México-Texcoco Km 38.5, Texcoco 56230, Estado de México, Mexico)

  • Elisa del Carmen Martínez-Ochoa

    (Academia de Ciencias Básica, Centro de Bachillerato Tecnológico No.3, Texcoco, Empedradillo S/N, San Diego, Texcoco 56200, Estado de México, Mexico)

  • Rosa María García-Nuñez

    (Maestría en Agroforestería para el Desarrollo Sostenible, Departamento de Suelos, Universidad Autónoma Chapingo, Carretera México-Texcoco Km 38.5, Texcoco 56230, Estado de México, Mexico)

  • Selene del Carmen Arrazate-Jiménez

    (Maestría en Agroforestería para el Desarrollo Sostenible, Departamento de Suelos, Universidad Autónoma Chapingo, Carretera México-Texcoco Km 38.5, Texcoco 56230, Estado de México, Mexico)

  • Alejandro Sánchez-Vélez

    (Departamento de Ciencias Forestales, Universidad Autónoma Chapingo, Carretera México-Texcoco Km 38.5, Texcoco 56230, Estado de México, Mexico)

Abstract

Non-timber forest products (NTFPs) are essential for community development, but their enormous demand has posed a serious threat to trees growing in their natural habitat. Copal resin is one of these products, which has a great deal of religious and ceremonial significance in Mexico and around the world. Resin extraction from a tree depends on its morphological and physiological characteristics, as well as its physical and sanitary condition. In this study, a methodology was proposed for determining the yield and health status of Copal trees, and a random forest (RF) model was developed to explain their resin production based on their morphological and condition characteristics. The experiment was conducted in the Agua Escondida watershed in Puebla, Mexico. With the training data, the average accuracy of the model was 99%, with a Kappa index of 98%, which is considered an excellent level of agreement beyond chance, and with the validation data, the average accuracy was 71% and 47%, which is considered a good level of agreement beyond chance. Tree condition was the most important factor affecting resin production in Copal trees, followed by stem diameter (33 and 38 cm), height (2 and 2.5 m), and diameter of secondary branches (from 8 to 15, 22 and 32 cm).

Suggested Citation

  • Julio César Buendía-Espinoza & Elisa del Carmen Martínez-Ochoa & Rosa María García-Nuñez & Selene del Carmen Arrazate-Jiménez & Alejandro Sánchez-Vélez, 2022. "Prediction of Resin Production in Copal Trees ( Bursera spp.) Using a Random Forest Model," Sustainability, MDPI, vol. 14(13), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8047-:d:853623
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
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