A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model

This study addresses the critical challenge of the limited understanding of environmental factors influencing cocoa cultivation in Colombia, a region with significant production potential but diverse agroecological conditions. The fragmented nature of the existing agricultural data and the lack of targeted research hinder efforts to optimize productivity and sustainability. To bridge this gap, this research employs a data-driven approach, using advanced machine learning techniques such as supervised, unsupervised, and ensemble models, to analyze environmental datasets and provide actionable recommendations. By integrating data from official Colombian sources, as well as the NASA POWER database, and geographical APIs, the present study proposes a methodology to systematically assess environmental conditions and classify regions for optimal cocoa cultivation. The use of an assembled model, combining clustering with targeted machine learning for each cluster, offers a more precise and scalable understanding of cocoa establishment under diverse conditions. Despite challenges such as limited dataset resolution and localized climate variability, this research provides valuable insights for a more comprehensive understanding of the environmental conditions impacting cocoa plantation establishment in a given location. The key findings reveal that temperature, humidity, and wind speed are crucial determinants of cocoa growth, with complex interactions affecting regional suitability. The results offer valuable guidance for the implementation of adaptive agricultural practices and resilience strategies, enabling sustainable cocoa production systems. By implementing better practices, countries such as Colombia can achieve higher market shares under growing global cocoa demand conditions.