Forecasting future adoption rates of agroecological innovations using machine learning: perspectives from Burkina Faso’s 45 provinces from 2010 to 2020

Publication Type

Journal Article

Name of Author

• Theodore Nikiema, Institute of Mathematics and Physics Sciences, University of Abomey Calavi, Cotonou, Benin
• Pamela Giselle Katic, Natural Resources Institute,University of Greenwich, Chatham, UK
• Issaka Abdou Razakou Kiribou, Africa Centre of Excellence for Climate Smart Agricultural and Biodiversity ConservationHaramaya University, Dire Dawa, Ethiopia
• Sylvain Kpenavoun Chogou, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
• Eugene C. Ezin, Institute of Training and Research in Computer Science, University of Abomey Calavi, Cotonou, Benin

Publication Date (Issue Year)

2026

Journal Name

INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY

Abstract

Implementation of agroecological innovations tends to be long-term processes, withthe practices of one year often linked to those of previous years. However, previousstudies have focused on understanding drivers of adoption at farm level, with adoptionmeasured at a point in time. In this study, we use a decade of panel data from thePermanent Agricultural Survey in Burkina Faso from 2010 to 2020 and machine learningapproaches, to model adoption rates of agroecological innovations at the provinciallevel as an autoregressive process. This modeling approach allows us to exploit the timeseries nature of our dataset to forecast future adoption rates. Our results showcase thepotential of machine learning algorithms to improve the forecasting of agroecologyadoption rates and provide a model that can be used as a base for proposinginterventions to support the adoption of agroecological innovations. The LSTMmodel reached a R² of 75% compared to 27% for the ARIMA family baseline model.The framework we proposed allows the identification of priority areas for targetedinterventions and provides a foundation on which future studies can be built to predictand track agroecology adoption rates over time.

Keywords

Agroecology, sustainableagriculture, climate change, time series, prediction;recurrent artificial neuralnetwork

Rsif Scholar Name

Theodore Nikiema

Rsif Scholar Nationality

Burkina Faso

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

Université d'Abomey-Calavi, Benin

Funding Statement

This work was supported by the Partnership for Skills in Applied Sciences, Engineering, and Technology (PASET)Regional Scholarship Innovation Funds (RSIF). We are grateful to the Natural Resources Institute (NRI) of the Universityof Greenwich, United Kingdom, for their support during our academic stay and the DSS of the Ministry of Agriculture ofBurkina Faso for its support.

Recommended Citation

Nikiema, T., Giselle Katic, P., Razakou Kiribou, I., Kpenavoun Chogou, S., & C. Ezin, E. (2026). Forecasting future adoption rates of agroecological innovations using machine learning: perspectives from Burkina Faso’s 45 provinces from 2010 to 2020. INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY https://doi.org/10.1080/14735903.2026.2674472