Developing countries, in their pursuit of the Sustainable Development Goals, are intensifying their efforts to promote agricultural mechanization, with China at the forefront, through comprehensive policy revisions aimed at enhancing mechanization levels. This study employs multivalued treatment effect (MVTE) and quantile treatment effect (QTE) methodologies to assess the impact of agricultural mechanization on labor and land productivity while also examining the seffects across different farm sizes and moderating influences. Leveraging data from the 2016 China Labor-force Dynamics Survey, the findings indicate that semi-mechanized and full-mechanized farming, compared to no-mechanized farming, significantly boost labor and land productivity for both average and lower-to-mid-level productivity farmers. Disaggregated analysis by farm size shows that larger farms benefit significantly from semi-mechanized and full-mechanized farming in terms of increased labor and land productivity. However, the incremental benefits of varying levels of mechanization on productivity are observed only in labor productivity for farmers with mid- and upper-level productivity and for those in the largest farm size group. Additionally, land transfer status, government subsidies, and the intensity of clan culture are found to significantly influence the productivity effects of agricultural mechanization in China. Consequently, it is imperative to reorient agricultural policies towards enhancing the efficiency and cost-effectiveness of mechanization, especially considering the constraints on labor and land availability and the need to minimize the environmental impact of agricultural production. In light of these discussions, this paper presents policy implications for sustainable agricultural mechanization.

Adoption of diverse weed management practices is viewed as essential for slowing the spread of herbicide-resistant (HR) weeds. Yet, adoption of diverse tactics has remained low, while there has been explosive growth of resistant weeds. This study analyzes U.S.-farm-level data to identify factors affecting adoption of diverse weed management practices. This study uses directed acyclic graphs (DAGs) to consider how practice adoption is influenced by different causal pathways between farmer and farm characteristics and farmer awareness of and concern over HR weeds. This study then uses multiple regression analysis to estimate the direct and indirect pathways that influence practice adoption. Respondents relied more heavily on herbicide-based weed control methods than on mechanical or cultural methods. Concern over herbicide resistance increased the number of practices farmers adopted and the percentage of acres where farmers implemented these practices. Practice adoption was negatively associated with increasing levels of farmer risk aversion. Technological optimism—belief that new herbicides would soon be developed to counter HR weeds—discouraged diverse herbicide use practices that combat resistance, but encouraged use of some non-chemical weed control methods. Perceived weed dispersal externalities (from weed mobility) led to more diverse weed management, running counter to hypotheses that greater mobility reduces incentives for individual resistance management.

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