A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia.

ABSTRACT

Agroecological systems are potential solutions to the environmental challenges of intensive agriculture. Indigenous communities, such as the Kamëntsá Biyá and Kamëntsá Inga from the Sibundoy Valley (SV) in Colombia, have their own ancient agroecological systems called chagras. However, they are threatened by population growth and expansion of intensive agriculture. Establishing new chagras or enhancing existing ones faces impediments such as the necessity for continuous monitoring and mapping of agroecological potential. However, this method is often costly and time consuming. To address this limitation, we created a digital map of the Biodiversity Management Coefficient (BMC) (as a proxy of agroecological potential) using Machine Learning. We utilized 15 environmental predictors and in-situ BMC data from 800 chagras to train an XGBoost model capable of predicting a multiclass BMC structure with 70% accuracy. This model was deployed across the study area to map the extent and spatial distribution of BMC classes, providing detailed information on potential areas for new agroecological chagras as well as areas unsuitable for this purpose. This map captured footprints of past and present disturbance events in the SV, revealing its usefulness for agroecological planning. We highlight the most significant predictors and their optimal values that trigger higher BMC status.