Nikiema, Theodore, Katic, Pamela Giselle ORCID: https://orcid.org/0000-0001-7594-1081, Kiribou, Issaka Abdou Razakou, Chogou, Sylvain Kpenavoun and Ezene, Eugene C. (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 (IJAS), 24 (1):2674472. ISSN 1473-5903 (Print), 1747-762X (Online) (doi:10.1080/14735903.2026.2674472)

Preview

PDF (Open Access Article) 53653 KATIC_Forecasting_Future_Adoption_Rates_Of_Agroecological_Innovations_(OA)_2026.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

Implementation of agroecological innovations tends to be long-term processes, with the practices of one year often linked to those of previous years. However, previous studies have focused on understanding drivers of adoption at farm level, with adoption measured at a point in time. In this study, we use a decade of panel data from the Permanent Agricultural Survey in Burkina Faso from 2010 to 2020 and machine learning approaches, to model adoption rates of agroecological innovations at the provincial level as an autoregressive process. This modeling approach allows us to exploit the time series nature of our dataset to forecast future adoption rates. Our results showcase the potential of machine learning algorithms to improve the forecasting of agroecology adoption rates and provide a model that can be used as a base for proposing interventions to support the adoption of agroecological innovations. The LSTM model 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 targeted interventions and provides a foundation on which future studies can be built to predict and track agroecology adoption rates over time.

Item Type:	Article
Additional Information:	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 University of Greenwich, United Kingdom, for their support during our academic stay and the DSS of the Ministry of Agriculture of Burkina Faso for its support.
Uncontrolled Keywords:	agroecology, sustainable agriculture, climate change, time series, prediction, recurrent artificial neural network
Subjects:	Q Science > Q Science (General) S Agriculture > S Agriculture (General) T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Centre for Society, Environment and Development (CSED) Faculty of Engineering & Science > Natural Resources Institute > Centre for Society, Environment and Development (CSED) > Innovation & Learning in Agriculture
Last Modified:	02 Jun 2026 11:42
URI:	https://gala.gre.ac.uk/id/eprint/53653

Actions (login required)

View Item

Downloads