The impacts of adopting agricultural innovation and technology are widely examined. This paper synthesises 154 studies, which yielded a total of 600 estimates of the impact of adopting agricultural innovation and technology on production, social and economic outcomes. Using meta‐regression analysis, the empirical results show that the reported impact of adopting agricultural innovation and technology rises significantly over time, notwithstanding a significant publication bias. Whether studies find significant impacts of adopting agricultural innovation and technology depends on the use of experimental research designs, parametric method, endogenous switching regression and region. Our results show a large bias in the literature towards agricultural innovations and technologies that focus on high‐yielding varieties and thus, neglect other forms of complementary innovations and technologies.

Although adoption of agricultural innovations has been extensively examined in the literature, its impact on indicators of farm production and household welfare measures remains ambiguous in the context of sub-Saharan Africa (SSA). This study contributes to the literature by conducting a meta-regression analysis on 92 studies published between 2001 and 2015 in the SSA region. Overall, empirical results from the meta-analysis suggest that adoption of agricultural innovations has a positive and significant effect on indicators of farm production and household welfare measures. However, the magnitude of the impact is relatively small, which also suggests a weak relationship.

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PurposeThe lack of theoretical and pragmatic way of measuring agricultural commercialization has been responsible for the inconsistent results for the impact of agricultural commercialization on household welfare. This study makes use of an input-based market participation approach that utilizes household preplanting production decision to stratify farming households according to production orientation.Design/methodology/approachThe study estimates a system of input and consumer demand equations. It augments traditional input and consumer demand equations with an additional variable based on an endogenous switch, which measures the probability of being a commercial farming household. Empirical evidence suggests that market orientation is an important determinant of the level of traded input and hence, market participation. Predicted probabilities obtained from the endogenous switch are used to stratify households into subsistence and commercial agricultural households.FindingsResults of the relative effect of commercial agriculture on the level of household food security support the claim that production orientation does affect the relationship between the relative share of food expenditure to the household total expenditures and the logarithm of household expenditure for this part of sub-Saharan Africa.Research limitations/implicationsAs in the case of all generalized method of moments studies, the results depend on the robustness of the instruments. However, search for better instruments may run afoul of Leamer's ad hoc specification search with nonexperimental data.Originality/valueThis paper is original in its formulation of an endogenous switch between subsistence and commercial agriculture. This switch is estimated as a latent variable following a logit form.

This study contributes to the emerging literature on household food security resilience by harmonizing food security indicators which capture different dimensions of food security, to compute a household food security resilience index that simultaneously reflects different dimensions of food security status. The estimated Poisson model had characteristics of the household, household head, sources of income, asset asset-related variables and other intuitive variables that could affect household food security resilience. Noteworthy in the results are the positive effect of income diversification, household savings, employment of household heads in the non-agricultural sector, gender, job stability, and education on household food security resilience. On the other hand, household size, dependent ratio, household head living away from the family, and being a seasonal worker or a daily paid worker are some variables that negatively impact household food security resilience. Although the negative coefficient of the income-generating sources of household food security resilience seems counterintuitive, this result shows that increase in the number of household businesses could impose time and resource constraints that could ultimately affect the food and nutritional security of the household over time.