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Agriculture is the mainstay of Cameroon's economy as it serves the purposes of food, livelihood and employment. Nevertheless, the country's agriculture is plagued by low productivity and inefficiency in production. One of the main reasons for low productivity is the inability of farmers to fully exploit available technologies and production techniques. An important research question that comes to mind is, what are the major factors that hinder the technical efficiency of smallholder farmers? This study thus aimed to determine the level of technical efficiency in the production of tomato in smallholder farms, relying on primary data collected using a structured survey instrument administered to 80 tomato farmers in the Buea municipality of Cameroon. Data was analyzed using descriptive statistics and a stochastic frontier analysis method in the Cobb-Douglas production function. The STATA.14 software was used to obtain both stochastic frontier estimates and the determinants of technical efficiency. The results indicate that farmers are not fully technically efficient with a mean technical efficiency score of 0.68 with one farmer operating on the frontier. The study also revealed that most of the farmers irrespective of the size of the holdings have shown technical inefficiency problems. The older farmers were observed with the best measures of technical efficiency. Education, age and the adoption and practice of agronomic techniques had a positive and significant influence on technical efficiency while the nearest distance to the extension agent had a rather negative influence on technical efficiency. The input-output relationship showed that the area of tomato cultivation and the quantity of improved seed used were positive and significantly related to output at the 5% level of probability. As a result, it is recommended that farmers should increase their farm size, use of improved seeds and the adoption and practice of novel techniques in production. More emphasis should be placed on extension agents as they have a significant role to play in terms of improving and augmenting farmers' education and information base through on farm demonstrations and result oriented workshops as all this will ensure increased production and productivity thereby increasing technical efficiency and achieving food self-sufficiency.

Enhancing agricultural productivity through the adoption of improved technologies presents a credible pathway to economic development and poverty reduction especially through increased commercialisation of production. We used a triple hurdle (TH) model to estimate the production and commercialisation of smallholder farmers in Ethiopia. In doing so, we account for the adoption of improved Cicer arietinum (chickpea) varieties on commercialisation using a three-wave panel data set. We estimate a correlated random effect model with a control function and find the adoption of improved chickpea to have a significant positive effect on smallholder commercialisation. Our findings that are robust over different specifications and identification strategies also support the role of transaction cost in driving market participation (MP). Finally, we argue that the TH model is a better fit to the commonly used double hurdle model for MP, when not all households in a population produce a particular crop.

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Governments and development agencies increasingly promote agro-clusters as a pathway to improving smallholder incomes and ensuring inclusive rural development through mitigating production and market risks. However, there is very limited empirical evidence to support this promise. We use a large farm household survey of about 4000 smallholder farmers in Ethiopia growing cereals like teff, maize, wheat, maltbarley and sesame to examine the relationship between agro-clusters and smallholder welfare and poverty. Using instrumental variable estimators, we establish a positive association between agro-clusters, household income and per capita income. Agro-clusters are also shown to reduce poverty and poverty gaps. Our results are robust over different agro-cluster proxies and alternative estimators, such as the augmented inverse probability weighting estimator. We also show that our findings are unlikely to be driven by omitted variable bias. Moving beyond average effects and in the interest of understanding heterogeneous effects, we use quantile regressions at different income levels. We find that agroclusters are associated with welfare gains for all households. However, the most significant gains are observed for the wealthier households. Despite this regressive association, our findings suggest that agro-clusters may be useful in making farming more profitable with significant welfare implications.

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