Contract farming has been shown to increase agricultural productivity and thus welfare of farmers in developing countries. However, studies that look at the potential environmental effects of contract farming remain quite scanty. This is however crucial, since contract farming may contribute to intensification in cultivation of the contracted crops, in terms of area and the intensity of inputs used. This study investigates the impact of participation in contract farming on sustainable farm practices, using a marginal treatment effects (MTEs) approach to account for potential selection bias and heterogeneity across households. The empirical results show significant heterogeneity in the effects of contract farming on the intensity of sustainable farm practice use. In particular, farmers with high propensity to participate in contract farming tend to have low probabilities of using sustainable farm practices. The findings of this study not only provide new insights into the heterogeneous effects of contract farming, but also entry points for further research to address the dual challenge of agriculture to produce sufficient food, while reducing the adverse impact on the environment.

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In this study, we employ a dynamic treatment effect approach to analyze heterogeneity in returns to farmers at different stages of adoption of a newly introduced

Examining the welfare impact of agricultural development interventions that incorporate diffusion of improved production technologies to farmers within extension delivery programs can be very challenging, because of the difficulty in disentangling the individual impacts of the production technology and the extension delivery program. Using recent farm level survey data from extension dissemination program of legume inoculant technology of 600 farmers in Ghana, we employ a recent methodological approach to investigate, simultaneously, the impact of the inoculant technology adoption and the extension program participation on farmers' productivity, efficiency and welfare. We decompose each of these impact measures into subcomponents whose causal paths can be traced to both the adoption of the production technology and the extension delivery program. We find that, in terms of yields and net revenue, direct contribution of improved technology adoption alone is 34 and 64%, respectively, and 35 and 66% indirectly due to improved farmer efficiency, leading to 36 and 74% improvement in farmers' welfare, respectively. On the other hand, direct contribution of extension delivery program participation alone is 66 and 36%, respectively, with 66 and 34% indirectly due to improved farmer efficiency, resulting in 64 and 26% improvement in farmers' welfare, respectively. Based on the findings, we recommend that policymakers should invest in research and development to produce yield enhancing agricultural technologies suitable for poor and degraded soil conditions in developing countries which can contribute immensely to poverty and food insecurity reduction. The development of new agricultural technologies must be pursued with vigorous provision of extension services to farmers to be able to exploit the full potentials of the new technologies.

We investigate the impact of farmers’ egocentric information network on technical efficiency and its distribution in the network, using observational data of 600 farmers from northern Ghana. We exploit community detection algorithms to endogenously identify homogeneous network communities with known structures to account for spatial heterogeneity, in a spatial stochastic frontier model that controls for social selection bias. The empirical results reveal that at the global network level, farmers’ technical efficiency strongly correlate with that of farmers in their egocentric networks. Our findings also show that farmers who are technically less efficient tend to depend on the more efficient farmers in their networks to improve efficiency. We further find that estimating spatial dependence of technical efficiency without accounting for spatial heterogeneity can lead to underestimation of technical efficiency of high (efficiency score >0.6) performing farmers, while overestimating that of medium (efficiency scores between 0.36–0.5) and low (efficiency scores between 0.1–0.35) performing farmers. The findings suggest that identifying central farmers in egocentric networks and improving their technical knowledge in a farmer-to-farmer extension organization, can contribute to improving the productivity of many farmers.