Identification of Influential Factors in the Adoption of Irrigation Technologies through Neural Network Analysis: A Case Study with Oil Palm Growers

Diana Martínez-Arteaga (), Nolver Atanacio Arias Arias, Aquiles E. Darghan and Dursun Barrios
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Diana Martínez-Arteaga: Colombian Oil Palm Research Center-Cenipalma, Bogotá 11121, Colombia
Nolver Atanacio Arias Arias: Colombian Oil Palm Research Center-Cenipalma, Bogotá 11121, Colombia
Aquiles E. Darghan: Department of Agronomy, Faculty of Agricultural Sciences, Universidad Nacional de Colombia, Bogotá 11132, Colombia
Dursun Barrios: Biogenesis Research Group, Department of Agriculture and Rural Development, Faculty of Agricultural Sciences, Universidad Nacional de Colombia, Bogotá 11132, Colombia

Agriculture, 2023, vol. 13, issue 4, 1-13

Abstract: Water is one of the most determining factors in obtaining high yields in oil palm crops. However, water scarcity is becoming a challenge for agricultural sustainability. Therefore, when the environmental supply of water is low, it is necessary to provide it to crops with the highest degree of efficiency. However, although irrigation technologies are available, for various reasons farmers continue to use inefficient irrigation systems, which causes resource losses. The objective of this study was to analyze the percentage of adoption of irrigation technologies for water management in oil palm crops and to classify the factors influencing their adoption by producers. The method for the classification of influential factors was based on multiple correspondence analysis and perceptron neural networks. The results showed that fewer than 15% of the producers adopt irrigation technologies, and the factors classified as influential in the adoption decision were the age of the palm growers, the size of the plantation, and the access to extension services. These results are the basis for the formulation of effective and focused extension strategies according to the characteristics of the producers and the local and technological specificity.

Keywords: farmers; agriculture extension; irrigation efficiency; perceptron neural networks; technology adoption (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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