RESUMEN

Contrary to the expectations of promoters of organic agriculture, the adoption of the technology by smallholder farmers in Africa has been low and slow, for reasons not well understood. Existing studies on the topic mostly estimated the effect of some variables on the adoption of the technology. But adoption is characterized by complex and dynamic interactions of many interconnected factors, which existing studies overlooked. The underlying causal structures and feedback mechanisms that dynamically interact to affect the adoption of organic farming in urban and rural Africa are also not well known. To bridge these gaps, we used a system dynamics tool called participatory causal loop diagraming to map the underlying causal factors and feedback mechanisms driving the adoption of organic farming in rural and urban Nigeria. We conducted loop and network analyses of the group causal loop diagrams, which were created during the participatory system dynamics modeling workshops with the organic farmers in our study areas. Our findings underscore the importance of the knowledge of organic farming, demand- and supply-side-oriented awareness creation, and the economic viability of organic farming for widespread adoption of the technology. We suggested the potential leverages around which interventions can be built to boost the adoption rates of the technology.

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RESUMEN

Perennial crops offer the opportunity to harvest from the same plant many times over several years while reducing labor and seed costs, reducing emissions and increasing biomass input into the soil. We use system dynamics modeling to combine data from field experiments, crop modeling and choice experiments to explore the potential for adoption and diffusion of a sustainable agriculture technology in a risky environment with high variability in annual rainfall: the perennial management of pigeonpea in maize-based systems of Malawi. Production estimates from a crop model for the annual intercrop system and data from field experiments on ratooning for the perennial system provided the information to create a stochastic production model. Data from choice experiments posed by a farmer survey conducted in three Malawi districts provide the information for parameters on farmers' preferences for the attributes of the perennial system. The perennial pigeonpea technology appeared clearly superior in scenarios where average values for maize yield and pigeonpea biomass production were held constant. Adoption was fastest in scenarios where relatively dry growing seasons showcased the benefits of the perennial system, suggesting that perennial management may be appropriate in marginal locations. The potential for adoption was reduced greatly when stochasticity in yields and seasons combine with significant social pressure to conform. The mechanism for this is that low yields suppress adoption and increase disadoption due to the dynamics of trust in the technology. This finding is not unique to perennial pigeonpea, but suggests that a critical factor in explaining low adoption rates of any new agricultural technology is the stochasticity in a technology's performance. Understanding how that stochasticity interacts with the social dynamics of learning skills and communicating trust is a critical feature for the successful deployment of sustainable agricultural technologies, and a novel finding of our study.