Climate variability is one of the limiting factors to increasing per capita food production for most smallholder farmers in Africa. The adoption and diffusion of climate smart agriculture technologies, as a way to tackle this barrier, has become an important issue in the development policy agenda for sub-Saharan Africa. This paper examines the adoption decisions for climate smart agriculture technologies using cross sectional household data, collected in 2014 from 619 farm households, in 2 districts of southern Malawi. In contrast to other studies that analyse technology adoption decisions separately, we analyse all four adoption decisions simultaneously using the multivariate probit method. This not only improves the precision of the estimation results and provides consistent standard errors of the estimates, but also enables us to analyse the interrelations between the four adoption decisions. This study shows how the estimation results, and particularly the estimated correlation coefficients, can be utilized to gain a deep insight into the interrelations between the different adoption decisions. The study reveals that gender, age, location, farmer type, level of education, livelihood status/ off-farm participation, land size and source/ownership, household income, household expenditure, anticipated weather pattern, climate variability knowledge/signs, access to credit, all influence the adoption decision of Climate Smart Technologies either positively or negatively.
Date of publication:
RUFORUM Journal Articles
Climate Smart Agriculture FAO Project, CABMACC, Carnegie Cooperation of New York; RUFORUM