As a sustainable and holistic approach to agriculture, agroecology has received considerable attention in recent years because of its potential to address the environmental, economic, and social challenges of agricultural systems. In order to identify key trends, influential authors, impactful journals, and emerging research themes surrounding the agroecological adoption topic, we performed a bibliometric analysis based on metadata from documents dealing with the topic and methods employed for its assessment over the period of January 1990 to July 2023, extracted from the Web of Science database. Based on the metadata of more than 1280 articles, our analyses show that the “identification of agroecology adoption determinants” and the “development of knowledge for food sovereignty” are among the trendiest research topics, while farming systems management and biodiversity issues are basic themes. The issue of analyzing agricultural productivity is more often linked to the impact of climate change. Also, we find that machine learning methods are not yet widely used to model the process of farm agroecological adoption. The contribution of African countries to the topic remains marginal in terms of documents produced, despite the predominance of traditional agriculture on the continent.

Background: Food insecurity affects a large proportion of the world's population, with enormous disparities to the disadvantage of low-income countries, especially those in sub-Saharan Africa. In this context, public decision-makers, funders, and NGOs urgently need streamlined indicators on households in precarious situations to better orient their policies and aid. However, existing food security monitoring tools focus on isolated dimensions of the problem, and some remain static. We aim to fill these gaps by focusing on a composite metric of food security to explore associations and identify variables with predictive relevance for food security outcomes using machine learning techniques. Methodology: This paper applies machine learning methods to a panel dataset from Burkina Faso to explore which agroecological practices are associated with improvements in food security and have an impact on the prediction of food security outcomes. Results: Overall, the trained models recorded accuracies varying from 64% for the Multi-Layer Perceptron model to 72% for the assembly models, with learning times generally < 1 min, except for the SVM model, which recorded around 30 min, suggesting good potential for these models to identify food-insecure households. Our results show that agroecological intensification through a combination of multiple agroecological practices, such as livestock and crop species diversification, plays a key role in predicting households’ food security status. Indeed, households practicing agroecology with good species diversification recorded a food insecurity rate of 6.6%, compared with 15% for conventional households, and pairwise differences in the prevalence of food security are significant (p-values most often < 0.01). The partial dependence plots show that the probability of belonging to the food-secure class increases by 3% as species richness increases or as livestock are integrated. Conclusion: The performance of the algorithms using a simplified measure of food security suggests that machine learning model-driven approaches could significantly improve food insecurity crisis response.