Proximate and Underlying Deforestation Causes in a Tropical Basin through Specialized Consultation and Spatial Logistic Regression Modeling

Wenseslao Plata-Rocha, Sergio Alberto Monjardin-Armenta, Carlos Eduardo Pacheco-Angulo, Jesus Gabriel Rangel-Peraza, Cuauhtemoc Franco-Ochoa and Zuriel Dathan Mora-Felix
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Wenseslao Plata-Rocha: Facultad de Ciencias de la Tierra y el Espacio, Universidad Autonoma de Sinaloa, Culiacan 80013, Mexico
Sergio Alberto Monjardin-Armenta: Facultad de Ciencias de la Tierra y el Espacio, Universidad Autonoma de Sinaloa, Culiacan 80013, Mexico
Carlos Eduardo Pacheco-Angulo: Facultad de Ciencias Forestales y Ambientales, Universidad de los Andes, Merida 5110, Venezuela
Jesus Gabriel Rangel-Peraza: Division de Estudios de Posgrado e Investigacion, Tecnologico Nacional de Mexico—Instituto Tecnologico de Culiacan, Culiacan 80220, Mexico
Cuauhtemoc Franco-Ochoa: Facultad de Ciencias de la Tierra y el Espacio, Universidad Autonoma de Sinaloa, Culiacan 80013, Mexico
Zuriel Dathan Mora-Felix: Division de Estudios de Posgrado e Investigacion, Tecnologico Nacional de Mexico—Instituto Tecnologico de Culiacan, Culiacan 80220, Mexico

Land, 2021, vol. 10, issue 2, 1-18

Abstract: The present study focuses on identifying and describing the possible proximate and underlying causes of deforestation and its factors using the combination of two techniques: (1) specialized consultation and (2) spatial logistic regression modeling. These techniques were implemented to characterize the deforestation process qualitatively and quantitatively, and then to graphically represent the deforestation process from a temporal and spatial point of view. The study area is the North Pacific Basin, Mexico, from 2002 to 2014. The map difference technique was used to obtain deforestation using the land-use and vegetation maps. A survey was carried out to identify the possible proximate and underlying causes of deforestation, with the aid of 44 specialized government officials, researchers, and people who live in the surrounding deforested areas. The results indicated total deforestation of 3938.77 km 2 in the study area. The most important proximate deforestation causes were agricultural expansion (53.42%), infrastructure extension (20.21%), and wood extraction (16.17%), and the most important underlying causes were demographic factors (34.85%), economics factors (29.26%), and policy and institutional factors (22.59%). Based on the spatial logistic regression model, the factors with the highest statistical significance were forestry productivity, the slope, the altitude, the distance from population centers with fewer than 2500 inhabitants, the distance from farming areas, and the distance from natural protected areas.

Keywords: survey design; deforestation; cross tabulation; proximate and underlying causes; spatial logistics regression; land-use change (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:10:y:2021:i:2:p:186-:d:497583

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