Conservation agriculture (CA) represents a paradigm shift towards more sustainable and climate-smart intensification of smallholder farming systems in southern Africa. This can only be achieved with reasonably fast, widespread, and sustained adoption of CA. However, many farmers are slow to adopt CA and when they do, they often do not continue using it and eventually dis-adopt. We combine duration models and quantile regression models to study how long farmers take to adopt conservation agriculture once they are trained; and to assess the distributional effects of the drivers of the persistence of adoption once a farmer adopts. Both models account for self-selection which makes adoption endogenous. We find that, on average, farmers take four years to adopt once trained and that there is a congruence between factors that reduce the duration to adoption and those that increase the persistence of adoption. Access to CA extension and credit, labor availability, education and hosting demonstrations increase the speed of adoption by 13–28 %. The duration from the first training, access to extension services, and farming experience increase the persistence of adoption, especially in the initial years. The findings point to the need for implementing multi-year CA promotional programs with medium-term time horizons that should prioritize enhanced training through community-embedded demonstrations and learning sites, and digital extension for extended reach.

https://doi.org/10.1016/j.techfore.2024.123689

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Adane Tufahttps://orcid.org/0000-0001-9801-6526

Arega Alenehttps://orcid.org/0000-0002-2491-4603

David Chikoyehttps://orcid.org/0000-0002-6047-9821

https://doi.org/10.1016/j.techfore.2024.123689

Plant Production and Health; Social Science and Agribusiness

Agribusiness; Agronomy; Climate Change; Farming Systems; Food Security; Smallholder Farmers

Adoption; Conservation Agriculture; Climate Smart Agriculture; Sustainable Intensification; Farming Systems

Africa; Southern Africa

Malawi; Zambia; Zimbabwe

Southern Africa Hub

Technological Forecasting and Social Change