Cassava production in Africa is constrained by number of biotic factors, including cassava mosaic disease (CMD). In response to this challenge, the PlantVillage Nuru application, which employs artificial intelligence for CMD diagnosis, provides farmers with the ability to independently detect the disease. This study examines the factors influencing the adoption of the innovative Nuru application by farmers in Benin. Data were randomly collected from 305 farmers in three Agricultural Development Poles (PDAs 5, 6 and 7). A binary logit model was used to analyze the determinants of adoption. The results show that, despite the potential of the Nuru application, the adoption rate remained relatively low at 14.1%. The key drivers of adoption were found to be participation in CMD training, disease knowledge, ownership of an Android smartphone, education level and practice of crop association. These findings emphasize the necessity of intensifying farmers’ training and raising awareness about CMD. Effective strategies to reach and train a significant number of farmers are crucial. Enhancing Nuru adoption can lead to more effective CMD management and improved cassava production, which will have a positive impact on food security in Africa and strengthen the resilience of farming communities against biotic challenges.

Transboundary diseases, such as cassava mosaic disease (CMD), represent a significant risk to food security and the livelihoods of millions of households in sub-Saharan Africa. To address this issue, awareness campaigns have been conducted targeting farmers and stakeholders within the cassava secor. The objective of this study is to quantify the impact of these awareness campaigns on the knowledge of CMD, the adoption of management practices, and the incidence of the disease in cassava fields. A random sample of 305 farmers and 77 cassava fields in Benin was selected for data collection. To account for potential selection bias associated with observable characteristics, we applied Propensity Score Matching (PSM). The results indicate that farmers who participated in the training demonstrated significantly higher levels of CMD knowledge and were more likely to adopt a greater number of management practices, which ultimately led to a reduction in the prevalence of the disease in their fields. However, CMD symptoms were still prevalent in the majority (61,04) of cassava farms, regardless of participation in the campaigns, due to the lack of healthy planting material and the abundance of whiteflies. These findings suggest that awareness campaigns can significantly improve farmers' knowledge and encourage behavioural changes in the identification and adoption of sustainable CMD management practices. It also shows the need to provide famers with healthy cuttings for more effective disease management.

Cassava Mosaic Disease (CMD) is a major constraint to cassava production in Côte d’Ivoire, causing significant yield and income losses for smallholder farmers. Despite its high prevalence, farmers’ knowledge and understanding of the disease remain limited. To address this issue, the Central and West African Virus Epidemiology (WAVE) Regional Center of Excellence provided capacity building for farmers in the major cassava growing regions. This study assesses the impact of the WAVE’s trainings and awareness campaigns on farmers’ knowledge of the disease and the management methods they adopted. Mixed socio-agronomic data were collected from 290 farmers, and CMD epidemiological parameters were assessed in 82 farms. Data were analysed using propensity score matching (PSM), followed by a Tobit regression model to assess the determinants and intensity of adoption of CMD management practices, using Stata. The results showed that trained farmers had a better understanding of CMD compared to untrained farmers. On average, trained farmers adopted 2.36 disease management practices (DMPs) compared to 1.55 DMPs for untrained farmers. Participation in WAVE’s training sessions and a sound knowledge of CMD positively influenced both the adoption and intensity of adoption of DMPs. However, there was no significant difference in CMD incidence between beneficiary areas (54.55%) and non-beneficiary areas (54.95%), likely due to the unavailability of disease-free planting material, inadequate agricultural practices, and high populations of whiteflies (Bemisia tabaci). This study shows the importance of awareness campaigns in the sustainable management of crop diseases in general and CMD in particular and suggests the need to train farmers on disease management and provide them with healthy planting materials.

This study examines the impact of cassava producers’ participation in inclusive agribusiness models (IAMs) on allocative efficiency in Benin, providing new microeconomic evidence on smallholder market integration in Sub-Saharan Africa. Using a sample of 1167 farmers across seven departments, we combine Stochastic Frontier Analysis (SFA) with an Endogenous Switching Regression (ESR) model to estimate efficiency while correcting for observable and unobservable selection bias. The results reveal moderate average allocative efficiency (0.646), indicating that producers remain about 35 percentage points below the cost-minimizing input allocation. IAM participants display higher allocative efficiency (0.700) than non-participants (0.620). The ESR model yields a positive and statistically significant treatment effect: participation is associated with an increase of 0.150 points in allocative efficiency among beneficiaries (ATT), while the potential effect for non-participants is smaller (ATU = 0.060). These results are corroborated by an Inverse-Probability-Weighted Regression Adjustment (IPWRA) robustness check, which yields a closely comparable treatment effect (ATT = 0.132). This ATT–ATU asymmetry reveals treatment-effect heterogeneity and positive selection on gains, as producers with the largest expected efficiency gains are the most likely to participate. Within IAMs, ordinary cooperative membership and the existence of a sales contract are associated with higher allocative efficiency, whereas extension contact and meeting participation, although the strongest predictors of participation, are associated with lower efficiency among participants, pointing to time-allocation trade-offs. The findings offer actionable evidence for designing inclusive value chain policies targeting the most marginalized smallholders and addressing structural constraints limiting efficiency across producer groups.