| dc.description.abstract | Agricultural technologies have long been promoted by governments and development organizations as
effective ways to increase farm productivity and reduce poverty. However, adoption of many seemingly
beneficial technologies remains low. Empirical adoption studies attempt to identify the motivation for
adoption based on differences in characteristics between adopters and non-adopters. This study investi gates variables that regularly explain adoption across technologies and contexts using a meta-analysis of
367 regression models from the published literature. We find that, on average, farmer education, house hold size, land size, access to credit, land tenure, access to extension services, and organization member ship positively correlate with the adoption of many agricultural technologies. Technologies in the
categories of improved varieties and chemical inputs are adopted more readily on larger farms, which
casts doubt on the scale-neutrality of these technologies. Agricultural credit can positively influence
adoption, but researchers should measure whether farmers are credit constrained, rather than simply
whether or not they have access to credit. While extension services may substitute for education in
the case of improved varieties, the two variables appear to be complementary for natural resource man agement technologies. Land tenure can encourage adoption of natural resource management techniques,
and we find it to be most influential in the adoption of technologies with long planning horizons, such as
erosion control methods. Unsurprisingly, although some patterns are identified when results are aver aged, most adoption determinants vary widely by technology, cultural context, and geography. Based
on these observations, we provide some recommendations for adoption researchers and policy makers,
but, given the variability of the results, conclude that efforts to promote agricultural technologies in
the developing world must be adapted to suit local agricultural and cultural contexts.
2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND | en_US |
