Farmers’ preferences for adopting agroforestry in the Eastern Province of Rwanda: A choice experiment

Abstract

Agroforestry is recognized as an effective land restoration strategy in Rwanda. To effectively implement agroforestry practices, it is crucial to examine and address the preferences of farmers. However, there is limited knowledge concerning farmers’ preferences for different agroforestry characteristics and their level of willingness to engage in such systems. This paper examines farmers’ preferences for adopting agroforestry in the Eastern Province of Rwanda using a discrete choice experiment (DCE) conducted among 248 households. The analysis conducted at plot level included a total of eight attributes: number of fruit trees, number of non-fruit trees, root system and canopy, change in maize yield resulting from tree planting, extension assistance, additional labor, distance to the tree nursery, and the cost of tree seedlings. Results indicate that farmers are generally willing to adopt agroforestry practices. They prefer agroforestry systems that incorporate non-fruit and particularly fruit trees, trees with smaller canopies, agroforestry systems with potential to increase maize yields, and regular extension assistance. However, farmers dislike increases in the cost of tree seedlings. Furthermore, findings reveal two distinct latent classes of plots, encompassing 34.5 % (Class 1) and 65.5 % (Class 2) of the plots. Farmers in Class 1 possess plots that are farther away from their homesteads and are indifferent towards the number of trees to be planted but prefer trees that increase maize yields. Farmers in Class 2 exhibit a strong inclination towards planting a greater number of trees that increase maize yields and have a small canopy. The proximity to tree nurseries and the availability of low-cost tree seedlings contribute to the likelihood of tree planting in this class. This study recommends aligning agroforestry-related policies and interventions with the preferences and needs of the farmers, taking into account plot characteristics.