关键词: 农业机械化, 农民收入, 农机作业水平, 农机社会化服务, 双重机器学习

Abstract:

[Objective] Agricultural mechanization is an important foundation of agricultural modernization, but the simultaneous constraints of land fragmentation and rural labor outflow have made its income-increasing effect increasingly uncertain. Most previous studies rely on a single indicator, such as total machinery power, however this cannot reflect the multi-dimensional character of mechanization. In addition, the mediating routes of mechanization's impact on income have not been fully elucidated. Many empirical analyses are based on parametric linear models, which may be subject to high-dimensional control variable biases and misspecification of nonlinear interactions. Therefore, it is important to elucidate the causal effects, transmission channels, and contextual dependencies of agricultural mechanization on farmers' income for the formulation of evidence-based policies to support sustained income growth and high-quality agricultural development. [Methods] The double machine learning (DML) framework with support vector machines was used as the basic learner, and used panel data from 31 Chinese provinces during 2008–2023. The main explanatory variable was the comprehensive mechanization level (CML). The main outcome was rural per capita disposable income, while wage income and net operating income were also conducted for structural decomposition. A wide range of control variables were incorporated to account for agricultural output, fiscal support, catastrophe risk and socio-economic factors. For robustness, winsorization was adopted, sample splits (1:2 and 1:7) were altered, municipalities were excluded; the learning technique was updated using random forest and gradient boosting; and instrumental variable estimation with non-agricultural earnings was applied. Mediation models were employed to examine if the strategy works through improving the agricultural operation level (AOL). Moderation models were used to test whether agricultural machinery socialized services, cooperative density, equipment structure, and urban-rural income gap strengthened the income-enhancing effect. Heterogeneity analyses were also carried out by grain-producing region, geography, and income level. [Results and Discussions] The benchmark estimation results showed that the level of agricultural mechanization had a significant positive impact on total income. For every 1% increase in the comprehensive level of agricultural mechanization, the per capita disposable income of farmers increased by approximately 0.18%. When income sources were broken down, mechanization had a considerable positive impact on net operating income, but a negative impact on pay income. The gain in operating income far exceeded the loss in wages. The mediation analysis showed that the agricultural operation level was an important route for mechanization to improve resource efficiency and reduce unit costs. The heterogeneity analysis showed three patterns: The income effect was significantly negative in major grain-producing areas but positive in non-grain-producing regions; positive in high-terrain relief areas but negative in low-relief plains; and statistically significant only in low-income provinces. The moderation tests showed that socialization services of agricultural machinery, cooperative density, equipment structure and urban-rural income gap positively moderated the income effect. Additional interaction analyses demonstrated that agricultural labor productivity, fiscal expenditure, grain production, financial development, and GDP had a positive moderating effect, however the crop disaster rate dramatically reduced the effect, emphasizing the restrictive role of natural risks. [Conclusions] The study confirms that agricultural mechanization remains an effective means for increasing farmers' income, but its benefits depend heavily on operational improvement, development of the service market, organizational density, equipment upgrading, and factor price incentives. The policy should be changed from expanding quantity to the structural optimization in grain-producing and plain areas, while promoting mechanization in non-grain-producing and mountainous areas. Full income gains will be unlocked through expanding socialized services, strengthening equipment structures and responding to wage signals. Further research should go deeper into interaction impacts of land transfer and machinery service markets and the micro-level mechanisms at the household or county levels, providing a more comprehensive picture of the diverse conditions that allow mechanization to benefit farmers.

Key words: agricultural mechanization, farmers' income, agricultural operation level, agricultural machinery socialized service, double machine learning