Exploring the agricultural landscape diversity-food security nexus: an analysis in two contrasted parklands of Central Senegal

• Published in: Agricultural systems 2022-02, Vol.196, p.103312, Article 103312
• Main Authors: Leroux, L., Faye, N.F., Jahel, C., Falconnier, G.N., Diouf, A.A., Ndao, B., Tiaw, I., Senghor, Y., Kanfany, G., Balde, A., Dieye, M., Sirdey, N., Alobo Loison, S., Corbeels, M., Baudron, F., Bouquet, E.
• Format: Article
• Language: English
• Subjects: Agroforestry; Data science; Food access; Food availability; Humanities and Social Sciences; Integrated landscape approach; Remote sensing
• ISSN: 0308-521X; 1873-2267
• DOI: 10.1016/j.agsy.2021.103312

Fostering diversity within agricultural systems can substantially contribute to improved food security among smallholder farmers. Agroforestry parklands are diverse agricultural landscapes where trees can provide an array of ecosystem services. Previous studies analyzing the agricultural landscape diversity-food security nexus in agroforestry parklands have only considered tree cover. We propose an original empirical approach that combines the analysis of spatial data on agricultural landscape diversity with agricultural field monitoring and household surveys. These three sources of data were used to scrutinize the direct and indirect contributions of agricultural landscape diversity to food availability and food access. Millet yield was used as a proxy for food availability, and household food access was approximated using the Household Food Insecurity Access Scale (HFIAS) indicator. Two contrasted agroforestry parklands of Central Senegal were chosen as case studies. Firstly, we used a Gradient Boosting Machine (GBM) algorithm to disentangle the relative contribution of landscape diversity, biophysical and crop management variables in explaining millet yield variability. Secondly, we investigated the pathways linking agricultural landscape diversity to HFIAS using a Correlation Network Analysis (CNA). The GBM model explained 77% and 84% of millet yield variability for the two parklands, respectively, with landscape diversity variables accounting for 53% and 47% of relative influence. Among the landscape diversity variables, tree species richness and tree density were the most important ones. Millet yield was positively associated with tree density in the Nioro site until a threshold of 5 trees/ha, and with tree species richness in the two sites. The CNA showed that greater tree cover and larger tree patches were moderately correlated with HFIAS. This suggests that tree species with large crown, as it the case for most fruit bearing tree species in the region, are the main species contributing directly to food access. Agricultural landscape diversity contributed mainly indirectly to household food access through an “agroecological pathway”, i.e. by the provision of ecosystem services regulating and supporting crop production. Using an integrated landscape approach relying on up-to-date remote sensing data and recent advances in data analysis methods, our study shows that tree species diversity matters as much as the amount of tree cover for the production