Evaluating heterogeneity in agroforestry adoption and practices within small-holder farms in Kenya

Abstract

Understanding the structure, densities and utilization of tree populations in agricultural landscapes is useful in determining the species influencing agroecosystem function. Our study evaluated agroforestry adoption and practices within smallholder farms in a former large-scale maize growing area of Trans Nzoia County, Rift Valley Province, Kenya. This was followed by investigation of factors influencing heterogeneity in the adoption and practices. The factors include: household resource endowment, land tenure and time under current management. Five settlement schemes which were formerly large estates dominated by maize mono-cropping were selected for the current study. Tree inventories of the farms were obtained through transect walks across each settlement. A total of 123 farms were assessed representing households of different resource endowment levels, tenure and number of years under current management. Different analyses were carried out including farm size and tree number, tree density, tree diversity and utilization of the dominant tree species. In total, we identified 44 tree/shrub species, 24 of which were indigenous and the rest exotic. However, the exotic tree species dominated strongly in abundance with Eucalyptus spp. being the most frequent taxon and constituting 34.6% of all trees. Species richness was found to be low compared to other agricultural landscapes in the region. Resource constrained households were found to prefer fruit tree species and maintained high tree diversity on their farms. Households with secure tenure had higher tree diversity than those without who had higherspecies richness and opted for fast growing fodder and fertilizer/firewood trees. Younger farms had fewer trees but higher species richness than older farms. The study, therefore, explains heterogeneity in agroforestry adoption in terms of variation in household resource endowment, land tenure and time under current management levels