A Study on the Empowerment Effect of New Qualitative Productivity in Agriculture on Farmers' Income

A Study on the Empowerment Effect of New Qualitative Productivity in Agriculture on Farmers' Income | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Study on the Empowerment Effect of New Qualitative Productivity in Agriculture on Farmers' Income Fujun TIAN, Xinfeng PENG, Caibing Cao, Jiehui Xie, Yan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7060565/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Enhancing farmers' income stands as a vital pillar for realizing agricultural and rural modernization and promoting shared prosperity in China. Leveraging panel data from 30 Chinese provinces spanning the period of 2013 to 2022, this research utilizes fixed-effects, mediating-effects, and moderating-effects models to explore the mechanisms through which the new qualitative productivity in agriculture drives farmers' income growth. The findings reveal: (1) New agricultural productivity significantly boosts farmers' income, with more pronounced effects in eastern/western regions and grain production functional areas. (2) Urbanization and factor productivity serve as mediating factors in this process. (3) Infrastructure levels positively moderate the income-enhancing effect of new agricultural productivity, while basic resource conditions exert negative moderation. Based on these results, policy recommendations are proposed, including constructing adaptive technology diffusion systems, expanding public service provisions, implementing region-specific regulatory strategies, and adopting smart resource management, to leverage new agricultural productivity for sustainable income growth among farmers. Social science/Development studies Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences New Qualitative Productivity in Agriculture Farmers' Income Increase Technological Dividend Empowerment Effect Introduction As the foundational industry supporting national economic development, the quality improvement and efficiency enhancement in agriculture are not only crucial for safeguarding national food security but also serve as a key link in promoting rural economic revitalization and increasing farmers' incomes. China has always taken promoting farmers' income growth as the strategic basis of its work on the "three rural issues" (agriculture, rural areas, and rural residents). The 2023 Central Rural Work Conference explicitly proposed "leading the high-quality development of modern agriculture with new productive forces in agriculture," marking a systemic shift in agricultural technological innovation from a single yield-oriented approach to a focus on improving total factor productivity. China's No. 1 Central Document for 2025 first proposed "leading the agglomeration of advanced production factors through scientific and technological innovation and developing new productive forces in agriculture according to local conditions" [ 1 ] , signifying that agricultural modernization has entered a new development stage with technological drive as the core element. This strategic deployment not only provides new ideas for addressing long-standing issues such as low agricultural productivity and difficult income growth for farmers but also becomes an important driving force for promoting comprehensive rural revitalization and achieving common prosperity. Against the backdrop of a huge population and tightening resource-environment constraints, China's agriculture is facing the practical challenge of transforming from a traditional extensive model to a modern intensive one. In recent years, although the contribution rate of agricultural scientific and technological progress in China has exceeded 63%, and grain output has continuously reached new highs, farmers' income growth still faces multiple bottlenecks [ 2 ] . Traditional agricultural production relies on resource consumption and labor-intensive models, leading to diminishing marginal returns. Meanwhile, problems such as low technology conversion rates, unbalanced regional development, and insufficient non-agricultural employment opportunities have constrained the space for farmers to increase their incomes. In this context, how to promote the high-quality development of agriculture has become an epochal proposition that must be addressed. "New productive forces in agriculture" emphasize the deep integration of cutting-edge technologies such as biological breeding, intelligent agricultural machinery, and digital technology to reconstruct the combination of agricultural production factors and achieve a leap in total factor productivity. Cultivating and developing new productive forces in agriculture to promote farmers' income growth is an important foundation and urgent task for promoting common prosperity among rural residents. Literature Review In recent years, the new productive forces in agriculture and the issue of farmers' income growth have become important research topics, with many scholars conducting in-depth explorations from different perspectives and levels, yielding certain research achievements. How to ensure the stable and sustained growth of farmers' incomes has become a key point on the journey toward common prosperity for all [ 3 ] . Existing studies show that the mechanism of farmers' income growth is closely related to multiple factors such as agricultural technological innovation [ 4 ] , human capital accumulation [ 5 ] , industrial chain extension [ 6 ] , and reorganization of production factors [ 7 ] . In terms of increasing farmers' incomes, some scholars have proposed that agricultural development not only improves grain output and farmers' incomes but also generates significant poverty reduction spillover effects through the transfer of labor to non-agricultural sectors [ 8 ] . Previous research has confirmed that rural infrastructure is a core supporting factor driving agricultural modernization and farmers' income growth [ 9 ] . As a systematic transformative force integrating biotechnology, intelligent equipment, and digital management, new productive forces in agriculture are injecting momentum into the high-quality development of agriculture and rural areas through technological paradigm innovation and industrial ecology reconstruction [ 10 ] . On the one hand, it directly enhances agricultural operational efficiency by optimizing total factor productivity [ 11 ] , significantly improving the competitiveness of agricultural products and farmers' operational income. On the other hand, new agricultural formats spawned by new productive forces, such as smart farms and vertical agriculture, have broken traditional production boundaries [ 12 ] . While promoting intensive land management, they release rural labor, enabling farmers to obtain higher wage income through non-agricultural employment, skilled work, and other channels. It is worth noting that the optimization of factor allocation by new productive forces is not only reflected in the improvement of production efficiency but also reconstructs the value chain of agricultural products through digital means such as blockchain traceability and Internet of Things (IoT) management [ 13 ] , allowing farmers to deeply participate in high-value-added links such as post-harvest processing and brand marketing of agricultural products, forming a virtuous cycle of "technological penetration—value chain upgrading—income sharing." This dual mechanism provides a new path to solve the traditional dilemma of "increasing production without increasing income." However, existing studies still lack a systematic discussion on the income-increasing mechanism effect of new productive forces in agriculture: first, most literature focuses on single technology applications, ignoring their synergistic effects with elements such as institutional innovation, talent cultivation, and financial support; second, empirical analyses mostly focus on local regions, with insufficient discussion of regional differences. Based on this, this paper takes "how new productive forces in agriculture promote farmers' income growth" as the core issue, constructs an analytical framework of "technological penetration—structural transformation—institutional adaptation," and empirically tests the enabling effect of new productive forces in agriculture on farmers' income growth based on provincial panel data from 2013 to 2022. The theoretical contributions are as follows: first, revealing the synergistic income-increasing mechanism of promoting urbanization by releasing labor and improving total factor productivity to increase agricultural output value; second, identifying the regulatory paradox of infrastructure and resource endowments: road mileage strengthens the income-increasing effect by reducing technology diffusion costs, while the per capita effective irrigation area produces a restraining effect due to resource dependency inertia. The study aims to provide theoretical references for improving the theoretical framework of new productive forces in agriculture and promoting policy practice, so as to promote farmers' income growth and support the high-quality development of China's agricultural and rural modernization. Theoretical Analysis and Research Hypotheses 2.1Direct Effects of New Productive Forces in Agriculture on Enabling Farmers' Income Growth Based on the theory of total factor productivity (TFP) and marginal cost theory, the direct action mechanism through which new productive forces in agriculture enable farmers' income growth can be decomposed into three core dimensions in its theoretical logic:On the one hand, new productive forces in agriculture achieve a structural leap in total factor productivity. Through the transmission chain of technological innovation—factor collaboration—efficiency multiplication, new productive forces break through the ceiling of factor allocation efficiency in traditional agriculture [ 14 ] . Biotechnology such as gene editing and stress-resistant varieties optimizes crop genetic traits, enhancing light energy utilization and disaster resistance per unit of land; intelligent equipment breaks the productivity constraints of labor-intensive operations through mechanical substitution of human labor and precise operations; digital technology achieves precise delivery of mobile factors such as water, fertilizer, and pesticides through data-driven decision optimization. The synergistic effect of the three drives the agricultural production function toward a higher efficiency equilibrium point, forming an income-increasing path of "technological dividend→output expansion→income growth," which aligns with the core idea of technology endogenization driving output in the endogenous growth theory.On the other hand, new productive forces in agriculture continuously shift the marginal cost curve downward. New productive forces reconstruct the agricultural cost structure through factor substitution effects and scale effects: intelligent equipment's substitution of labor reduces the proportion of labor costs, while precise technologies reduce redundant input consumption, significantly decreasing the variable cost per unit of output. Additionally, intertemporal decision-making and risk management supported by digital technologies reduce implicit costs caused by production uncertainties [ 15 ] . According to marginal cost theory, when technological penetration shifts the long-run average cost curve (LAC) downward to the right, farmers' net income space under a given output expands, forming sustainable profit margin growth. Furthermore, new productive forces break through the homogeneous competition dilemma of traditional agricultural products through technology-empowered product differentiation [ 16 ] . Biofortification technologies endow agricultural products with functional premiums, while digital technologies construct quality signal transmission mechanisms to reduce market information asymmetry. According to the new structural economics theory [ 17 ] , this technology-driven product upgrading transforms farmers from price takers to bargaining subjects in niche markets, achieving value capture at the front end of the value chain through increased consumer willingness to pay, directly expanding operational income. In summary, the research hypothesis 1 is proposed: H1: New productive forces in agriculture have a positive impact on farmers' income. 2.2Indirect Effects of New Productive Forces in Agriculture on Enabling Farmers' Income Growth 2.2.1New Productive Forces in Agriculture, Urbanization, and Farmers' Income Growth Urbanization, as the core mediating variable connecting new productive forces in agriculture and farmers' income growth, follows a dual transmission logic of "factor reallocation (supply side)—demand upgrading (demand side)" in its mechanism: On the one hand, new productive forces in agriculture achieve non-agricultural labor transfer and wage income growth through factor reallocation. By substituting traditional labor with intelligent equipment, new productive forces in agriculture directly reduce agricultural employment density, and the released labor flows to urban non-agricultural sectors through the Todaro migration model [ 18 ] . Migrated laborers undergo skill training and occupational differentiation in cities, engaging in high-value-added positions in manufacturing, services, etc. Data from 2022 shows that the proportion of migrant workers in technical jobs increased from 28–41%, with their wage income growing at an average annual rate of 7.3%, significantly higher than the 4.1% growth rate of agricultural income. This process aligns with the Lewis turning point theory, where the transfer of agricultural surplus labor promotes income convergence between urban and rural areas [ 19 ] . On the other hand, new productive forces in agriculture achieve consumption market expansion and agricultural value chain reconstruction through demand upgrading. The increase in urban population proportion drives food consumption toward high-quality and branded transformation, and this demand upgrading forces the agricultural production side to undergo standardized transformation—for example, realizing transparent planting processes through IoT systems to meet the market rule of "high quality, high price." Urbanization gives birth to new formats such as central kitchens and cold chain logistics, promoting the extension of agricultural products from "field rough processing" to "terminal fine processing." Farmers invest in processing enterprises through cooperatives to share profits from post-harvest links, forming a closed loop of "technical efficiency—quality improvement—premium sharing." In summary, the research hypothesis 2 is proposed: H2: New productive forces in agriculture improve farmers' income by promoting urbanization. 2.2.2 New Productive Forces in Agriculture, Factor Productivity, and Farmers' Income Growth The per capita agricultural output value, as the core variable through which new productive forces in agriculture enable farmers' income growth, follows a collaborative transformation logic of "efficiency leap—resilience enhancement" in its mechanism: New productive forces in agriculture reconstruct traditional factor allocation models through technologies such as BeiDou navigation precision seeding and intelligent water-fertilizer integration systems [ 20 ] . They reshape the agricultural risk structure through environmental controllability and product differentiation: facilities technologies like intelligent greenhouses and plant factories shift agricultural production from natural dependency to artificial regulation. Through technical means such as automatic temperature-humidity management and disaster early warning systems, they significantly reduce the impact probability of extreme weather and plant diseases, smoothing the yield fluctuation curve. High-value-added agricultural products build quality signal transmission mechanisms relying on blockchain traceability, intelligent grading, and other technologies, forming a market isolation belt from traditional bulk agricultural products. This differentiation strategy reduces price elasticity and enhances demand stability, enabling farmers to maintain income resilience amid supply-demand fluctuations. When the leap in total factor productivity reduces the cost per unit of output, farmers' tolerance threshold for market price fluctuations expands accordingly, providing a safety margin for adopting "high input—high output" technologies. The enhanced income stability encourages farmers to extend the technology investment cycle, accelerate intelligent equipment updates and technological iteration, further consolidating efficiency advantages. In summary, the research hypothesis 3 is proposed: H3: New productive forces in agriculture increase farmers' income by improving factor productivity. 2.3The Moderating Role of Infrastructure Level in the Empowerment of New Productive Forces in Agriculture to Increase Farmers' Incomes Highway mileage, as a key infrastructure indicator, significantly enhances the transformation efficiency of new productive forces in agriculture to increase farmers' incomes through two mechanisms: reducing technical diffusion costs and reconstructing market network structures. On the one hand, the highway network accelerates technical diffusion to break through the technical penetration mechanism of spatial friction [ 21 ] .The highway network strengthens the penetration efficiency of new productive forces into agricultural production by shortening the technical accessibility radius and reducing technical service costs. An increase in highway density significantly reduces physical barriers to agricultural technicians' field services, transportation of intelligent equipment, and construction of digital technology base stations. A well-developed highway network promotes the downward deployment of market-oriented entities such as agricultural machinery cooperatives and agricultural technology enterprises to rural areas. On the other hand, highway infrastructure enhances market connectivity and realizes the income-increasing mechanism of value chain reconstruction [ 22 ] .Highway infrastructure reconstructs the distribution pattern of agricultural value chains through circulation link optimization and expansion of transaction counterparts. The improved traffic capacity of cold chain logistics vehicles shortens the transportation time for fresh agricultural products, effectively reducing the spoilage rate and directly expanding the effective supply radius of high-value-added agricultural products. Additionally, improved traffic accessibility enables farmers to more easily access multi-level sales channels such as wholesale markets and e-commerce platforms, increasing the proportion of terminal selling prices retained by farmers by reducing intermediate layers. Based on the above, the research hypothesis 4 is proposed: H4: The infrastructure level plays a positive moderating role in the process of new productive forces in agriculture affecting farmers' incomes. 2.4The Moderating Role of Basic Resource Conditions in the Empowerment of New Productive Forces in Agriculture to Increase Farmers' Incomes Per capita effective irrigation area, as a basic resource condition for agricultural production, may inhibit the income-increasing effect of new productive forces in agriculture through ecological threshold breakthrough and technological path dependence, with its negative moderating role embodied in the "resource curse—innovation blockage" dual trap. Firstly, when the per capita effective irrigation area breaks through the regional ecological carrying capacity threshold, the traditional extensive irrigation model fundamentally conflicts with the efficiency enhancement goal of new productive forces.Excessive reliance on irrigation area expansion leads to ecological degradation such as groundwater level decline and soil salinization, forcing farmers to increase environmental governance costs [ 23 ] , which directly erodes the benefits brought by new productive forces. In ecologically fragile areas, the yield-increasing effect of water-saving technologies experiences diminishing marginal returns due to total water resource constraints.Secondly, high dependence on irrigation area forms technological substitution stickiness, hindering the substitution process of new productive forces for traditional factors.The sunk costs of fixed assets under the traditional flood irrigation model are too high, suppressing farmers' willingness to adopt efficient water-saving technologies. Long-term reliance on manual irrigation results in farmers' lack of ability to operate intelligent irrigation equipment, forming a vicious cycle of "low-skilled labor—low-efficiency irrigation technology." Moreover, water resource shortages force farmers to prioritize basic irrigation needs, compressing their investment capacity in water-saving technologies, thus forming a vicious cycle of "water shortage→greater dependence on traditional irrigation→further water shortage." The failure to upgrade intelligent irrigation systems in a timely manner leads to persistently high water consumption per unit of agricultural products, accelerating the process of breaking through ecological thresholds. Based on the above, the research hypothesis 5 is proposed: H5: Basic resource conditions play a negative moderating role in the process of new productive forces in agriculture affecting farmers' incomes. Research Design 3.1Model Specification 3.1.1Baseline Regression Model $$\:{\text{I}\text{n}\text{c}}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{Anqp}_{it}+{\alpha\:}_{2}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}}_{it}+{\sigma\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{it}$$ 1 In Eq. ( 1 ): \(\:{\text{I}\text{n}\text{c}}_{it}\) represents the income level of farmers in province \(\:i\) in year \(\:t\) ; \(\:{Anqp}_{it}\) represents the level of new productive forces in agriculturein province \(\:i\) in year \(\:t\) ;The coefficient \(\:{\alpha\:}_{1}\) reflects the marginal effect of new productive forces in agriculture on farmers' income growth; \(\:Controlsit\) are control variables; \(\:{\alpha\:}_{0}\) is the constant term; \(\:{\alpha\:}_{i}\) is the province-specific effect; \(\:{\mu\:}_{t}\) is the time-specific effect; \(\:{\epsilon\:}_{it}\) is the random disturbance term. 3.1.2Mediation Effect Model To explore the potential mediating mechanisms through which new productive forces in agriculture affect farmers' income growth, this study takes urbanization and per capita agricultural output value as mediating variables for empirical testing. Building on Eq. ( 1 ), referring to the approach of Fang Wen [ 24 ] , the following regression models are constructed: $$\:{\text{I}\text{n}\text{c}}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{Anqp}_{it}+{\alpha\:}_{2}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}}_{it}+{\sigma\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{it}$$ $$\:\begin{array}{c}{M}_{it}={\beta\:}_{0}+{\beta\:}_{1}{\text{A}\text{n}\text{q}\text{p}}_{it}+{\beta\:}_{2}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}}_{it}+{\sigma\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{it}\\\:{\text{I}\text{n}\text{c}}_{it}={\gamma\:}_{0}+{\gamma\:}_{1}{M}_{it}+{\gamma\:}_{2}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}}_{it}+{\sigma\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{it}\end{array}$$ 2 3.1.3Moderation Effect Model Based on the previous mechanism analysis, it is found that infrastructure level and basic resource conditions play important roles in the empowerment of new productive forces in agriculture to increase farmers' incomes. Therefore, taking the two as moderating variables, the following models are constructed: $$\:{\text{I}\text{n}\text{c}}_{it}={\eta\:}_{0}+{\eta\:}_{1}{Anqp}_{it}+{\eta\:}_{2}{FT}_{it}+{\eta\:}_{3}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}}_{it}+{\epsilon\:}_{it}$$ $$\:{\text{I}\text{n}\text{c}}_{it}={\phi\:}_{0}+{\phi\:}_{1}{Anqp}_{it}+{\phi\:}_{2}{FT}_{it}+{\phi\:}_{3}({Anqp}_{it}\ast\:{FT}_{it})+{\phi\:}_{4}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}}_{it}+{\epsilon\:}_{it}$$ 3 3.2 Variable Description 3.2.1Dependent Variable The dependent variable is farmers' income growth, measured by the logarithm of rural residents' disposable income, following the approach of Li Qi [ 25 ] . This logarithmic transformation normalizes the data and mitigates heteroscedasticity, aligning with econometric modeling requirements. 3.2.2 Core Explanatory Variable The core explanatory variable is New Productive Forces in Agriculture (NPAF). Drawing from studies on constructing indicator systems for new productive forces [ 26 ] , NPAF is measured based on the three elements of productivity. Technological progress serves as the core driver for forming new agricultural production capabilities, rooted in the role of humans—particularly laborers. When evaluating laborers' individual contributions, data availability dictates considerations from three dimensions: conceptual understanding, technical competence, and production efficiency.From the perspective of objects of labor, this refers to the specific materials operated and processed by laborers in production, encompassing efficient use of production factors and environment-friendly management practices. It enhances food security by promoting green agricultural development, embodying the integration of technological progress and sustainable development [ 27 ] .Means of production for agriculture denote tangible and intangible resources relied on by laborers, including advanced agricultural machinery, intelligent equipment, IoT monitoring systems, big data analysis platforms (tangible tools), and agricultural scientific knowledge, management experience, information technology (intangible resources). These resources are essential for achieving production goals and transforming labor objects into practical outputs. Against the backdrop of urban-rural integration, improved infrastructure provides a solid foundation for applying these new tools, reducing energy consumption. Upgraded information infrastructure further optimizes resource allocation and management decision-making, significantly enhancing agricultural productivity. Based on previous research [ 28 ][ 29 ] , this study constructs an evaluation index system for NPAF, as detailed in Table 1 . Table 1 Evaluation Index System for New Productive Forces in Agriculture First-Level Indicators Second-Level Indicators Third-Level Indicators Indicator Attribute Laborers Labor Skills Educational Attainment of Rural Residents Positive Proportion of Rural Residents with Higher Education Positive Labor Productivity Per Capita Grain Output Positive Per Capita Output Value Positive Awareness of Laborers Labor Force Quality Positive Labor Force Structure Positive Objects of Labor Technological Progress Comprehensive Mechanization Rate of Grain Planting, Cultivating, and Harvesting Positive Food Security Grain Yield per Unit Area Positive Multiple Cropping Index Positive Volatility of Grain Output Negative Disaster Incidence Rate of Grain Crops Negative Green Development Chemical Fertilizer Application Intensity Negative Pesticide Application Intensity Negative Water Resource Utilization Efficiency Positive Grain Carbon Emission Negative Means of Production Infrastructure Agricultural Machinery and Equipment Positive Number of Observations from Agricultural Weather Stations Positive Energy Consumption Rural Energy Density Positive Diesel Oil Consumption Negative Technological Innovation Intensity of Agricultural R&D Expenditure Investment Positive 3.2.3 Control Variables Other factors potentially affecting new productive forces in agriculture are selected as control variables. Referencing the research of Zou Wei [ 30 ] , the following are included:(1)Rural Electricity Consumption: Electricity is an indispensable energy source in agricultural production, measured by the electricity consumption in rural areas.(2)Chemical Fertilizer Application Intensity: Measured by the amount of chemical fertilizers applied, which is a key input factor in agricultural production and directly influences grain yield and quality.(3) Human Capital Level: Represented by the average education level of farmers.(4) Disaster Incidence Rate: Expressed as the ratio of agricultural disaster-affected area to cultivated land area. 3.2.4Mediating Variables The mediating variables are selected as follows:(1)Urbanization: Urbanization promotes non-agricultural employment, thus increasing farmers' income. The urbanization rate is therefore chosen as the mediating variable.(2) Factor Productivity: Referencing the research of Liu Guangdong [ 31 ] , per capita agricultural output value is used as a proxy for factor productivity. 3.2.5 Moderating Variables The moderating variables are selected as follows:(1)Infrastructure Level: Infrastructure level can significantly enhance the transformation efficiency of new productive forces in agriculture to increase farmers' income through two mechanisms: reducing technical diffusion costs and reconstructing market network structures. Referencing the research of Bai Junhong [ 32 ] , highway mileage is used to characterize infrastructure level.(2)Basic Resource Conditions: Per capita effective irrigation area, as a basic resource condition for agricultural production, may inhibit the income-increasing effect of new productive forces in agriculture through ecological threshold breakthrough and technological path dependence. Referencing the research of Qiao Biao [ 33 ] , per capita effective irrigation area is used to characterize basic resource conditions. 3.3Data Source and Descriptive Statistics To ensure data reliability and consistency, relevant data from Hong Kong, Macau, Taiwan, and Tibet Autonomous Region were excluded during the sample screening process. The final research sample consists of panel data sets from 30 provincial administrative units in Mainland China spanning 2013–2022. Data are sourced from the China Statistical Yearbook, China Macroeconomic Database, China Environmental Database, etc. Partial missing data were imputed using the interpolation method. Meanwhile, given potential interference from dimensionality issues, logarithmic transformation and necessary unit calibration were performed on specific data to ensure standardization and comparability. Descriptive statistics for each variable are presented in Table 2 . Table 2 Descriptive Statistics of Variables Variable Sample Size Mean Standard Deviation Maximum Minimum Level of New Productive Forces in Agriculture 300 0.3235 0.0872 0.1473 0.5917 Per Capita Disposable Income of Rural Residents (CNY) 300 9.5477 0.3782 8.6285 10.5899 Total Power of Agricultural Machinery (10,000 kW) 300 7.6929 1.1370 4.5429 9.4995 Rural Electricity Consumption 300 4.9399 1.2617 1.5040 7.6064 Chemical Fertilizer Application Amount 300 4.7891 1.1706 1.5496 6.5738 Human Capital Level 300 8.0376 0.8761 5.8610 12.5995 Disaster Incidence Rate 300 12.7649 10.7942 0.4149 61.8273 Urbanization Rate 300 61.3861 11.3843 37.89 89.6 Per Capita Agricultural Output Value 300 9.2876 0.4470 8.1720 10.6278 Per Capita Effective Irrigation Area 300 0.1311 0.1038 0.0377 0.6001 Highway Mileage 300 11.7262 0.8523 9.4441 12.9126 Empirical Results and Analysis 4.1Empirical Results and Analysis Table 3 presents the regression results of the impact of new productive forces in agriculture on farmers' income growth. Specifically, Model (1) is the basic model considering only farmers' income growth as a single factor without any control variables; Model (2) adds relevant control variables based on Model (1) to comprehensively examine related influences; Model (3) further controls for individual and time factors based on Model (2) to obtain more accurate and robust regression results. The regression estimation of Model (3) shows that new productive forces in agriculture have a significantly positive correlation with farmers' income growth, with an impact coefficient of 0.242 and passing the 1% statistical significance test, indicating that the upgrading of this factor has a significant income-increasing effect on rural residents' income growth and verifying the value conversion capability of technological innovation in agricultural production. Specifically, driven by scientific and technological innovation, new productive forces in agriculture innovate agricultural production methods: on the one hand, they improve the yield and quality of agricultural products, enabling farmers to profit from premium sales; on the other hand, they give birth to new industries and business forms to expand income channels (e.g., rural e-commerce facilitates direct market access for agricultural products, opening new wealth paths for farmers and effectively improving their income levels). Accordingly, research hypothesis H1 is preliminarily verified. Table 3 Baseline Regression Results Variable Model (1) Model(2) Model(3) NPAF 2.114(0.459)*** 0.270(0.060)*** 0.242(0.060)*** HUMAN_CAP -0.001(0.003) AGRI_POWER 0.028(0.011)*** RURAL_ELEC 0.010(0.002)*** DISASTER_RATE -0.0001(0.0001) FERT_APPL -0.030(0.016)* YEAR_FE Not Controlled Controlled Controlled PROV_FE Not Controlled Controlled Controlled CONS 8.86(0.157)*** 9.661(0.017)*** 9.56(0.055)*** OBS 300 300 300 R² 0.127 0.998 0.998 4.2Robustness Tests To verify the stability of regression results, the following robustness tests are implemented: substitution of the dependent variable by using rural residents' per capita net income for estimation; addition of a control variable by additionally incorporating per capita agricultural plastic film usage to comprehensively consider impacts of different control variable selections; and endogeneity treatment by regressing the first lag of the core explanatory variable (new productive forces in agriculture) on farmers' income. The test results (see Table 4 ) show that the estimated coefficients of new productive forces in agriculture are all significantly positive, consistent with the baseline model results, further confirming the high robustness of previous research conclusions. Table 4 Robustness Test Results Variable Dependent Variable Substitution Control Variable Addition Endogeneity Treatment NPAF 0.1877(0.0887)** 0.2472(0.0602)*** 0.1867(0.0569)*** CONS 9.4370(0.0815)*** 9.5600(0.0553)*** 9.6851(0.0603)*** Control Variables Controlled Controlled Controlled YEAR_FE Controlled Controlled Controlled PROV_FE Controlled Controlled Controlled R² 0.9958 0.9980 0.9981 4.3Heterogeneity Test Although the above research has shown that new productive forces in agriculture have a positive effect on increasing farmers' income, given the significant differences in economic foundations and resource endowments across regions in China, whether new productive forces in agriculture produce the same income-increasing effect in different regions remains unknown. Therefore, this paper analyzes the heterogeneous impacts of new productive forces in agriculture on increasing farmers' income from two perspectives: geographical location and grain production functional zones. 4.3.1Geographical Zoning Heterogeneity Referencing existing research [ 34 ] , the 30 provinces across the country are divided into Eastern, Central, and Western regions for heterogeneity analysis. The research results (see Table 5 ) show that new productive forces in agriculture have a significantly positive impact on farmers' income in the Eastern and Western regions, but not in the Central region. Specifically, the coefficient of new productive forces in agriculture is significant at the 5% level in the Eastern region, with a marginal effect of 0.2679, while in the Western region, the coefficient is significant at the 1% level, with a marginal effect of 0.3845.The possible reasons lie in the "depression effect" and technological catch-up dividend in the Western region. The high marginal effect of new productive forces in Western agriculture mainly benefits from the late-mover advantage and policy preference. On the one hand, the foundation of agricultural modernization in the West is weak, and the contribution rate of traditional factors to income has long been low. The introduction of new productive forces can break through the "low-level equilibrium trap" and generate significant increasing marginal returns. On the other hand, policies such as the Western Development Strategy and targeted poverty alleviation have reduced the threshold for technological adoption through infrastructure investment and fiscal subsidies.The coefficient of new productive forces in agriculture in the Eastern region (0.2679) is significant but lower than that in the West, reflecting the characteristics of the deepening transformation phase. First, agriculture in the Eastern region has been highly integrated into the non-agricultural economy, with wage income accounting for more than 60% of farmers' income, diluting the marginal contribution of new productive forces to total household income. Second, technological application in the East faces a scale economy bottleneck in agriculture—most provinces have a facility agriculture coverage rate exceeding 40%, and further expansion is constrained by land fragmentation, making it difficult for new technologies to achieve cost spreading. Additionally, the export-oriented characteristics of Eastern agriculture exacerbate market risk transmission, inhibiting sustained technological investment by government departments or farmers. These structural contradictions make the income-increasing effect of new productive forces in the Eastern region exhibit the characteristics of "high significance, low elasticity".The core reason for the insignificant effect in the Central region is factor misallocation and policy goal conflict. As the core area of national food security, agriculture in the Central region is endowed with the dual goals of "ensuring production capacity" and "promoting income growth", but there is an inherent contradiction between the two: high-standard farmland construction policies prioritize ensuring food crops, restricting technological investment and industrial scale expansion for high-value economic crops. In summary, the geographical heterogeneity of new productive forces in agriculture is essentially the result of three-dimensional adaptability of "factor structure-institutional environment-market conditions". The Western region achieves technological catch-up by virtue of policy dividends and late-mover advantages; the Eastern region shows diminishing marginal returns due to structural rigidity; and the Central region falls into a transition lag dilemma due to institutional friction and goal conflict. Table 5 Heterogeneity Test Results by Geographical Zones Variable Eastern Central Western NPAF 0.2679(0.1160)** 0.1561(0.1191) 0.3845(0.0737)*** CONS 9.3630(0.1470)*** 8.9175(0.1932)*** 8.7578(0.1196)*** Control Variables Controlled Controlled Controlled YEAR_FE Controlled Controlled Controlled PROV_FE Controlled Controlled Controlled R² 0.6637 0.9318 0.7800 4.3.2Heterogeneity by Agricultural Functional Zones Drawing on existing research [ 35 ] , the 30 provinces are divided into grain main production areas and non-grain main production areas based on their food production functions. The test results (see Table 6 ) show that new productive forces in agriculture are significantly positive at the 1% level in both grain main production areas and non-grain main production areas, with marginal effects of 0.2909 and 0.2675, respectively.The possible reasons are as follows: Grain main production areas typically have the advantage of large-scale planting, with concentrated contiguous land and high mechanization levels, making it easier for new agricultural productive forces to achieve efficiency improvements through technological integration, hence the relatively significant marginal effect. Meanwhile, main production areas often undertake national food security strategic tasks, with strong policy support and a more mature supporting service system for technology promotion, which reduces the institutional costs for farmers to adopt new technologies.In non-grain main production areas, the agricultural structure is relatively diversified, with a higher proportion of cash crops and characteristic aquaculture. The income-increasing path of new productive forces may rely on industrial chain extension, and technological application needs to match market channel expansion capabilities. The slightly lower marginal effect may reflect the existence of transaction costs in the technology transformation process. Additionally, farmers in main production areas have long been engaged in grain planting, showing strong sensitivity and adaptability to production technology changes, while farmers in non-main production areas may have longer decision-making cycles for technology adoption due to high production decentralization and part-time employment, leading to regional differences in income elasticity under the same technical level. Table 6 Heterogeneity Test Results by Agricultural Functional Zones Variable Grain Main Production Areas Non-Grain Main Production Areas NPAF 0.2909(0.0939)*** 0.2675(0.0868)*** CONS 9.4006(0.2108)*** 8.6979(0.1051)*** Control Variables Controlled Controlled YEAR_FE Controlled Controlled PROV_FE Controlled Controlled R² 0.8898 0.2564 4.4Mediation Mechanism Test As shown in Table 2 , new productive forces in agriculture (NPAF) can empower farmers' income growth, but how NPAF influences farmers' income requires further verification. 4.4.1Mediating Variable: Urbanization From the perspective of urbanization, in Models 1 and 2 of Table 7 , NPAF and the urbanization coefficient show a significantly positive correlation, verifying the mediating transmission mechanism of urbanization between NPAF and farmers' income growth. The baseline regression Model (1) shows that a 1-unit increase in NPAF can drive a 12.4422-unit increase in the urbanization rate, highlighting the accelerating effect of technological innovation on urban-rural factor flow. Further testing the mediating path Model (2), the urbanization variable passes the significance test at the 1% statistical level, with a marginal impact coefficient of 0.0069, confirming that urbanization promotes farmers' income through agglomeration economic effects.NPAF improves agricultural production efficiency through technological innovation, releases rural labor resources, and promotes labor transfer to non-agricultural industries, accelerating urbanization. After the urbanization rate increases, the increase in non-agricultural employment opportunities and the optimization of urban-rural factor flow significantly enhance farmers' wage income and property income. Accordingly, research hypothesis H2 is verified. 4.4.2Mediating Variable: Factor Productivity As shown in Table 7 , coefficients of new productive forces in agriculture (NPAF) and factor productivity in Models 3 and 4 are both significant at the 1% level, confirming the theoretical pathway through which technological innovation transmits income-increasing dividends via factor productivity improvement. Specifically, the marginal output elasticity measurement shows that a 1-unit increase in NPAF can induce a 1.3729-unit growth in productivity, highlighting the driving effect of technological penetration on production factor optimization. Further mediation path testing verifies that factor productivity is significant at the 1% statistical level, with an output elasticity coefficient of 0.0640, confirming the transmission mechanism through which technology diffusion realizes farmers' income growth via economies of scale.New productive forces reconstruct the agricultural production function through improvements in factor substitution elasticity and leaps in total factor productivity, promoting "new combinations". Breakthrough innovations such as gene editing and digital-intelligent technologies disrupt the law of diminishing marginal returns of traditional factors, manifested as a leap in output value per unit of labor. Accordingly, research hypothesis H3 is verified. Table 7 Mediation Test Results for Factor Productivity Variable Model (1) Model(2) Model(3) Model(4) NPAF 12.4422(3.8010) *** 0.1552(0.0552) *** 1.3729(0.3259) *** 0.1536(0.0584) *** URBAN 0.0069(0.0009) *** Factor Productivity 0.0640(0.0109) *** Constant Term 58.4025(3.4934) *** 9.1587(0.0720) *** 4.9057(0.2995) *** 9.2496(0.0744) *** Control Variables Controlled Controlled Controlled Controlled YEAR_FE Controlled Controlled Controlled Controlled PROV_FE Controlled Controlled Controlled Controlled Observations 300 300 300 300 R² 0.9911 0.9984 0.9576 0.9982 4.5Further Discussion on Analysis Results 4.5.1Moderating Effect of Infrastructure Level To further verify the moderating effect of infrastructure construction level in the process of new productive forces in agriculture empowering farmers' income growth, an interaction term between infrastructure construction level and new productive forces in agriculture was introduced into the baseline regression model (specific results are shown in Table 8 ). The estimation results of Models (1) and (2) in Table 8 show that the regression coefficients of infrastructure level are both significant at the 1% significance level, with corresponding marginal effect values of 0.1113 and 0.0877, respectively. Meanwhile, the interaction term coefficients are significantly positive, with a marginal effect of 0.1209.The research results show that with the improvement of infrastructure construction level, the promoting effect of new productive forces in agriculture on farmers' income can be effectively strengthened. The empirical results verify the "circulation-diffusion enhancement effect" of highway mileage in the theoretical framework. The core reason lies in that infrastructure amplifies the empowering effect of new productive forces in agriculture on farmers' income growth through three mechanisms: reducing technology diffusion costs, expanding market radiation radius, and optimizing factor allocation efficiency. Accordingly, research hypothesis H4 is verified. Table 8 Moderating Effect Test Results of Infrastructure Level Variable Model (1) Model(2) NPAF 0.2157(0.0576)*** -1.1961(0.6482)* INFRA 0.1113(0.0217)*** 0.0877(0.0241)*** NPAF×INFRA 0.1209(0.0553)** Constant Term 8.5689(0.2013)*** 8.8764(0.2443)*** Control Variables Controlled Controlled YEAR_FE Controlled Controlled PROV_FE Controlled Controlled Observations 300 300 R² 0.9982 0.9982 4.5.2Moderating Effect of Basic Resource Conditions To further verify the moderating effect of basic resource conditions in the process of new productive forces in agriculture empowering farmers' income growth, the interaction term between per capita effective irrigation area and new productive forces in agriculture was incorporated into the baseline regression model (see Table 9 for details). In Models (1) and (2) of Table 9 , the regression coefficients of basic resource conditions both pass the significance test, with marginal effects of -0.0460 and 0.0436, respectively. Meanwhile, the interaction term coefficient between basic resource conditions and new productive forces in agriculture is significantly negative, with a marginal effect of -0.2633.This indicates that an increase in per capita effective irrigation area inhibits the income-increasing effect of new productive forces in agriculture. This negative moderating effect reveals the "resource curse" paradox of per capita effective irrigation area: in ecologically constrained regions, the expansion of irrigation area offsets the income-increasing potential of new agricultural productive forces through three mechanisms: environmental overload, technological lock-in, and factor misallocation. Accordingly, research hypothesis H5 is verified. Table 9 Moderating Effect Test Results of Basic Resource Conditions Variable Model (1) Model(2) NPAF 0.2335(0.0593)*** -0.3415(0.1420)** IRRIGATION -0.0460(0.0165)*** 0.0436(0.0258)* NPAF×IRRIGATION -0.2633(0.0595)*** Constant Term 9.3360(0.0984)*** 9.5410(0.1057)*** Control Variables Controlled Controlled YEAR_FE Controlled Controlled PROV_FE Controlled Controlled Observations 300 300 R² 0.9980 0.9982 Research Conclusions and Policy Recommendations 5.1Research Conclusions Based on panel data of 30 provinces in China from 2013 to 2022, this paper empirically analyzes the empowering effect of new productive forces in agriculture (NPAF), yielding key conclusions as follows. First, NPAF significantly drives farmers' income growth: baseline regression results confirm NPAF's income-increasing effect on farmers, which remains robust after a series of stability tests. Second, urbanization and factor productivity act as mediating mechanisms in NPAF's promotion of income growth, where technological innovation enhances agricultural efficiency, releases rural labor, and accelerates urban-rural factor flow to boost earnings. Third, notable spatial differentiation exists: western provinces demonstrate the strongest income-driven efficacy with a NPAF coefficient, followed by eastern regions, while central regions show no statistical significance—a gradient pattern closely tied to regional resource endowments and industrial upgrading stages. In grain production functional zones, NPAF significantly promotes income in both main and non-main areas, though the marginal effect is more pronounced in main production areas due to scale advantages and policy support. Fourth, infrastructure level exerts a positive moderating effect by reducing technology diffusion costs and expanding market reach, whereas basic resource conditions exhibit a negative moderating effect,reflecting a "resource curse" paradox through environmental overload and factor misallocation. These findings highlight the complex interrelationship among technology, institutions, and geography in shaping agricultural productivity and rural income dynamics. 5.2Policy Recommendations Facing dual challenges of tightening resource constraints and intensified market fluctuations, new productive forces in agriculture (NPAF) reconstruct the production function through technological and institutional innovations, optimizing value distribution along industrial chains while enhancing efficiency. Policies should facilitate the transformation of technological dividends into farmers' income through precise adaptation mechanisms, addressing the "efficiency-equity" coordination dilemma for sustainable income growth. Based on research conclusions, the following recommendations are proposed. First, construct an adaptive technology promotion system to improve penetration efficiency. Drive reforms of the technology promotion system at the county level, integrating grassroots agricultural technical resources through a dual-track mechanism of "agricultural machinery cooperatives + technology commissioners," combining technical guidance from agricultural stations with equipment service capabilities of market entities. Establish "technology stations" at the village level, dynamically adjusting technical solutions based on remote sensing monitoring and Households demand lists to ensure precise delivery of intelligent irrigation, precision fertilization, and other technologies. To address risks of household technology adoption, simultaneously build a risk hedging mechanism by developing "technology adoption index insurance," implementing tiered premium subsidies for high-risk technologies like blockchain traceability and smart greenhouses—government-subsidized in the first year, dynamically adjusted based on technical benefits in subsequent years—and offering 5%-10% income incentives for Households who continuously adopt new technologies for three years, fostering a positive cycle of "daring to use and using more technologies." On this basis, activate conditions for large-scale operations through land circulation and property rights reforms, standardizing circulation contracts by county-level property trading centers, prioritizing intelligent equipment like unmanned harvesters for plots over 200 mu, and piloting a "land contract right shareholding + technology dividend" model where household participate in cooperative dividends based on intelligent equipment usage and cost-saving efficiency, converting technological dividends into tangible asset-based income. Finally, establish a dynamic assessment mechanism for "technology penetration-income growth-ecological benefits," regularly releasing county-level technology adaptation indices, and promptly adjusting technical solutions or service providers in regions with insufficient promotion efficiency to ensure a full-chain closed loop from technology implementation to sustainability. Second, increase public service supply to promote non-farm employment and agricultural product sales. Build NPAF skills training bases at the county level, carrying out order-based training for positions like intelligent equipment operation and agricultural product e-commerce to ensure labor transfer is smooth, stable, and income-generating. Provide employment subsidies to enterprises absorbing agricultural transfer labor to incentivize non-farm job creation; deploy "central kitchen + cold chain logistics hubs" around highly urbanized city clusters, guiding processing enterprises through tax breaks to prioritize purchasing intelligent technology agricultural products, and establishing a premium pricing procurement list system. Implement a digital certification program for regional public brands of agricultural products, providing government guarantees for sales premiums of blockchain-traceable products. For enterprises absorbing agricultural transfer labor, besides employment subsidies, simultaneously implement a "social security connection plan" allowing migrant workers to convert rural pension insurance payment years into enterprise employee social security years, reducing insurance participation thresholds. Establish a premium revenue sharing mechanism requiring enterprises using government-guaranteed traceability codes to return part of the premium income to village collectives as technology diffusion funds for updating village-level digital facilities. Develop a "non-farm employment service one-code access" integrating functions like job recommendations, skills assessment, and rights appeal, enabling digital management of the entire employment process for migrant workers through county-level government affairs platforms. Third, adhere to regional differentiated strategies to crack the "central region collapse" dilemma. Establish NPAF technology adaptation funds to support R&D of water-saving and drought-resistant technologies suitable for local ecological conditions, addressing resource constraints and technology mismatch; strengthen the urbanization mediation mechanism by piloting a technology points household registration policy in the Yangtze River 中游 city cluster, incorporating indicators like intelligent agricultural machinery operation years and participation in agricultural digital services into the points system, opening special channels for guaranteed housing purchase and priority school placement for those meeting cumulative points, and matching employment positions through industrial transfer undertaking parks within city clusters to form a virtuous cycle of "technology empowerment-household registration incentives-factor agglomeration." Build NPAF innovation enclaves in the Yangtze River Delta and Pearl River Delta, promoting an "open competition" system for R&D projects in frontier fields like agricultural AI algorithms and synthetic biotechnology to expand technology spillovers. Establish an "ecological contribution-industry compensation" linkage mechanism, allowing counties undertaking ecological functions such as South-to-North Water Diversion water sources and the Yangtze River shelterbelt to convert ecological protection investments into carbon emission indicators proportionally, transferring them to central region high-energy consumption industry upgrading projects through carbon trading markets to realize the transformation of ecological value into industrial momentum.Fourth, implement intelligent resource management to break the "resource curse" paradox. Reconstruct the water resource management system with digital twin technology, establishing a dynamic coupling model of "soil moisture-crop water demand-intelligent water distribution" for precise irrigation regulation. Innovate water rights trading mechanisms by incorporating water savings from intelligent drip irrigation systems into carbon sink trading markets, allowing Households to obtain additional income through water-saving index transfers. In ecologically fragile areas, implement a dual-track system of "irrigation quota + ecological compensation," imposing tiered water prices and ecological taxes on overexploited areas to drive green transformation of agricultural production modes. Meanwhile, construct a full-chain ecological management system of "intelligent monitoring-warning response-restoration compensation," using UAV remote sensing and AI algorithms to assess soil health in real time, granting carbon point rewards to household adopting eco-friendly technologies like no-till direct seeding and integrated water-fertilizer management, promoting agriculture's shift from resource-consuming to ecological value-added. Declarations Funding This work was supported by the Major Project of Fujian Social Science Research Base, "Research on the Cultivation Mechanism of Rural Food Safety Governance Community Awareness" (FJ2022MJDZ021) and the Fujian Science and Technology Plan (2024R0026). Author Contribution T.F.J. was responsible for study conceptualization, theoretical framework development, and empirical methodology design, providing theoretical interpretations of results and policy implications. P.X.F. undertook data collection, model construction, statistical analysis, and initial manuscript drafting. C.B.C., X.J.H., and L.Y. assisted in literature review refinement, participated in data verification and analysis, contributed valuable insights during result discussions, and provided constructive feedback for manuscript revisions. All authors collaboratively discussed findings, formulated policy recommendations, and participated in multiple rounds of manuscript review and improvement. Data Availability The data that support the findings of this study are openly available in References Central Committee of the Communist Party of China and the State Council. Opinions on Further Deepening Rural Reforms and Solidly Promoting Comprehensive Rural Revitalization [N]. People's Daily, 2025-02-24(001). Xu Caiyao, Qian Chen, Kong Fanbin. Can Digital Rural Construction Narrow the Urban-Rural Income Gap? An Empirical Test Based on the Mediating Role of Agricultural Scientific and Technological Progress and Threshold Effect [J]. Journal of Agricultural Technology Economics, 2024(12): 4-24. Zhou J, Yang S. Common prosperity in the era of technological transformation: Digital inclusive finance, technological progress, and the income distribution of migrant workers[J]. Finance Research Letters, 2025: 107486. Caifeng T ,Jianping T ,Lan Y , et al.Dynamic Relationship between Agricultural Technology Progress, Agricultural Insurance and Farmers’ Income[J].Agriculture,2022,12(9):1331-1331. Xiaoyan D, Jiangnan Z, Xuelian G, et al. The impact of informatization on agri-income of China's rural farmers: ways for digital farming[J]. Frontiers in Sustainable Food Systems, 2024, 8: 1329674. Ruben R. From market-based development to value chain transformation: What markets can (not) do for rural poverty alleviation?[J]. Journal of Rural Studies, 2024, 109: 103328. Li C ,Zhang D ,Lu Q , et al.Production Process Outsourcing, Farmers’ Operation Capability, and Income-Enhancing Effects[J].Agriculture,2024,14(9):1448-1448. SHENG Y,ZHAO Y H,ZHANG Q,et al. Boosting rural labor offfarm employment through urban expansion in China[J]. World development,2022,151:105727. Bi J. Can rural areas in China be revitalized by digitization? A dual perspective on digital infrastructure and digital finance[J]. Finance Research Letters, 2024, 67: 105753. Lin L, Gu T, Shi Y. The influence of new quality productive forces on high-quality agricultural development in China: Mechanisms and empirical testing[J]. Agriculture, 2024, 14(7): 1022. Liu H, Li X. How digital technology can improve new quality productive forces?——Perspective of total factor agricultural carbon productivity[J]. Journal of Asian Economics, 2025, 98: 101921. You Liang, Tian Xiangyu. New Productive Forces in Agriculture: Realistic Logic, Connotative Analysis and Generation Mechanism [J]. Economic Issues, 2024(06): 27-35. Xiang Jiyou. Empowering Digital Rural Construction with New Productive Forces: Theoretical Logic and Path Analysis [J]. Guizhou Social Sciences, 2024(08): 148-154. Zhang Guanghui, Yang Zhen. New Productive Forces in Agriculture, Agricultural Socialized Services and Food Security [J]. Contemporary Economic Research, 2025(02): 68-78. Belhadi A, Kamble S, Subramanian N, et al. Digital capabilities to manage agri-food supply chain uncertainties and build supply chain resilience during compounding geopolitical disruptions[J]. International Journal of Operations & Production Management, 2024, 44(11): 1914-1950. Shi Bo, Hu Xijuan. How Do New Productive Forces Drive the Construction of a National Unified Market? A Study from the Perspective of Digital-Real Economy Integration [J]. Social Science Research, 2024(04): 56-66. Choruma D J, Dirwai T L, Mutenje M, et al. Digitalisation in agriculture: A scoping review of technologies in practice, challenges, and opportunities for smallholder farmers in sub-saharan africa[J]. Journal of agriculture and food research, 2024: 101286. Wu Zhongtao, Zhang Dan. The Impact of Urban-Rural Expected Income Gap on Rural Population Migration—Based on the Todaro Model [J]. Journal of Northwest University (Philosophy and Social Sciences Edition), 2013, 43(04): 74-79. Giles J, Park A, Zhang J. Labor market developments in China: A neoclassical view[J]. China Economic Review, 2023, 78: 102048. Zheng Yangyang, Liao Feng. Digital Transformation of New Agricultural Business Entities Empowers New Productive Forces in Agriculture [J]. Journal of Sichuan Agricultural University, 2024, 42(06): 1179-1185+1202. .Li Lanbing, Yan Li, Huang Jiuli. Accessibility of Transportation Infrastructure and Manufacturing Growth in Non-Core Cities: Market Power, Productivity and Allocation Efficiency [J]. Economic Research Journal, 2019, 54(12): 182-197. Shevtsova Y, Díaz-Lanchas J, Persyn D, et al. Trade spillover effects of transport infrastructure investments: a structural gravity analysis for EU regions[J]. Regional Studies, 2025: 1-16. Wichelns D ,Oster J .Sustainable irrigation is necessary and achievable, but direct costs and environmental impacts can be substantial[J].Agricultural Water Management,2006,86(1):114-127. Fang ,Wen ,Hau .Mediation Effects In 2-1-1 Multilevel Model: Evaluation Of Alternative Estimation Methods[J].Structural Equation Modeling: A Multidisciplinary Journal,2019,26(4):591-606. Li Qi, Tang Yuehuan, Ren Xiaojing. E-Commerce Development, Spatial Spillover and Farmers' Income Growth [J]. Journal of Agricultural Technology Economics, 2019(04): 119-131. Cui J, Du D. New quality productive forces, urban-rural integration and industrial chain resilience[J]. International Review of Economics & Finance, 2025: 104245. Hoffman nivian,Jones Kelly.Improving food safety on the farm:Experimental evidence from Kenyao nincentive sand subsidies for technology adoption[J].World Development,2021(143):105406. Lei Xunping, Qiu Ronbin. Evaluation of China's Food Security and Diagnosis of Obstacle Factors Based on Entropy Weight TOPSIS Model [J]. Journal of China Agricultural University, 2022, 27(12): 1–14. Li Shengzhu, Xue Feng, Jiang Jingui. Study on the Impact of Agricultural Digitalization on New Productive Forces of China's Grain [J]. Journal of Agro-Forestry Economics and Management, 2024, 23(04): 435–445. Zou Wei, Zhang Zhenlin. The Mechanism of Agricultural Development Affecting Farmers’ Poverty Reduction and Income Increase—An Empirical Decomposition and Test Based on Income Structure [J]. China Population, Resources and Environment, 2024, 34(08): 179–192. Liu Guangdong, Cheng Jiumiao, Zhu Chuanmin, et al. Study on Regional Differences of China’s "Three Rural Issues" [J]. Research of Agricultural Modernization, 2006(02): 126–130. Bai Junhong, Wang Lindong. Does Innovation Drive Promote the Improvement of Economic Growth Quality? [J]. Studies in Science of Science, 2016, 34(11): 1725–1735. Qiao Biao, Fang Chuanglin. Dynamic Coupling Model of Coordinated Development between Urbanization and Ecological Environment and Its Application in Arid Areas [J]. Acta Ecologica Sinica, 2005(11): 211–217. Xia H, Yu H, Wang S, et al. Digital economy and the urban–rural income gap: Impact, mechanisms, and spatial heterogeneity[J]. Journal of Innovation & Knowledge, 2024, 9(3): 100505. Tian C, Li L, Liao B. Evaluation of the green development effects of heterogeneous agricultural cleaner production place-based policy[J]. Journal of Cleaner Production, 2024, 442: 140969. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7060565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":512242127,"identity":"6d902626-f9e2-4e29-ba0f-6fc6786d96fe","order_by":0,"name":"Fujun TIAN","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Fujun","middleName":"","lastName":"TIAN","suffix":""},{"id":512242129,"identity":"a4cf12aa-f0a6-4a08-9021-d1f8f9cd49f7","order_by":1,"name":"Xinfeng PENG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACfvkD6R8SeP7LsbE3EKlFcgbDM4YPMszGfDwHiNRicIPxGeMMG+ZEOYkEYrXcbk57zJPDlsAm+XjjDYYam2jCDrtzLN2Y5wxPHpt0WrEFw7G03AZCWvgO5CRI8/ZIFLNJ55hJMDYcJqyF4UD+B2nefwaJbZJniNQicCMhTXIGT0JimwQPkVokew4kG3zgOWDMxgP0SwIxfuFnb0h8kMBzQE6+/fDGGx9qbIjwCxIwIDpqkLSQqmMUjIJRMApGBgAAgPE/J4Y1XYsAAAAASUVORK5CYII=","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Xinfeng","middleName":"","lastName":"PENG","suffix":""},{"id":512242132,"identity":"85dca54e-615a-48d3-a021-811584a208c7","order_by":2,"name":"Caibing Cao","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Caibing","middleName":"","lastName":"Cao","suffix":""},{"id":512242133,"identity":"f2818b90-e9ef-4be2-b1d1-10d8b5260860","order_by":3,"name":"Jiehui Xie","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Jiehui","middleName":"","lastName":"Xie","suffix":""},{"id":512242134,"identity":"2d80b2f4-0f9a-48c7-b399-3d60e9c1d2bc","order_by":4,"name":"Yan Liu","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-07 02:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7060565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7060565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94612338,"identity":"8b3688d9-6915-400f-9490-59a48e363d4e","added_by":"auto","created_at":"2025-10-29 02:09:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1466481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7060565/v1/d23067a4-c805-44c1-93c5-414de886a00d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study on the Empowerment Effect of New Qualitative Productivity in Agriculture on Farmers' Income","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the foundational industry supporting national economic development, the quality improvement and efficiency enhancement in agriculture are not only crucial for safeguarding national food security but also serve as a key link in promoting rural economic revitalization and increasing farmers' incomes. China has always taken promoting farmers' income growth as the strategic basis of its work on the \"three rural issues\" (agriculture, rural areas, and rural residents). The 2023 Central Rural Work Conference explicitly proposed \"leading the high-quality development of modern agriculture with new productive forces in agriculture,\" marking a systemic shift in agricultural technological innovation from a single yield-oriented approach to a focus on improving total factor productivity. China's No. 1 Central Document for 2025 first proposed \"leading the agglomeration of advanced production factors through scientific and technological innovation and developing new productive forces in agriculture according to local conditions\" \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, signifying that agricultural modernization has entered a new development stage with technological drive as the core element. This strategic deployment not only provides new ideas for addressing long-standing issues such as low agricultural productivity and difficult income growth for farmers but also becomes an important driving force for promoting comprehensive rural revitalization and achieving common prosperity.\u003c/p\u003e\u003cp\u003eAgainst the backdrop of a huge population and tightening resource-environment constraints, China's agriculture is facing the practical challenge of transforming from a traditional extensive model to a modern intensive one. In recent years, although the contribution rate of agricultural scientific and technological progress in China has exceeded 63%, and grain output has continuously reached new highs, farmers' income growth still faces multiple bottlenecks\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Traditional agricultural production relies on resource consumption and labor-intensive models, leading to diminishing marginal returns. Meanwhile, problems such as low technology conversion rates, unbalanced regional development, and insufficient non-agricultural employment opportunities have constrained the space for farmers to increase their incomes. In this context, how to promote the high-quality development of agriculture has become an epochal proposition that must be addressed. \"New productive forces in agriculture\" emphasize the deep integration of cutting-edge technologies such as biological breeding, intelligent agricultural machinery, and digital technology to reconstruct the combination of agricultural production factors and achieve a leap in total factor productivity. Cultivating and developing new productive forces in agriculture to promote farmers' income growth is an important foundation and urgent task for promoting common prosperity among rural residents.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eIn recent years, the new productive forces in agriculture and the issue of farmers' income growth have become important research topics, with many scholars conducting in-depth explorations from different perspectives and levels, yielding certain research achievements. How to ensure the stable and sustained growth of farmers' incomes has become a key point on the journey toward common prosperity for all\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Existing studies show that the mechanism of farmers' income growth is closely related to multiple factors such as agricultural technological innovation\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, human capital accumulation \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, industrial chain extension\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, and reorganization of production factors\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In terms of increasing farmers' incomes, some scholars have proposed that agricultural development not only improves grain output and farmers' incomes but also generates significant poverty reduction spillover effects through the transfer of labor to non-agricultural sectors\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Previous research has confirmed that rural infrastructure is a core supporting factor driving agricultural modernization and farmers' income growth\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. As a systematic transformative force integrating biotechnology, intelligent equipment, and digital management, new productive forces in agriculture are injecting momentum into the high-quality development of agriculture and rural areas through technological paradigm innovation and industrial ecology reconstruction\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. On the one hand, it directly enhances agricultural operational efficiency by optimizing total factor productivity\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, significantly improving the competitiveness of agricultural products and farmers' operational income. On the other hand, new agricultural formats spawned by new productive forces, such as smart farms and vertical agriculture, have broken traditional production boundaries\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. While promoting intensive land management, they release rural labor, enabling farmers to obtain higher wage income through non-agricultural employment, skilled work, and other channels. It is worth noting that the optimization of factor allocation by new productive forces is not only reflected in the improvement of production efficiency but also reconstructs the value chain of agricultural products through digital means such as blockchain traceability and Internet of Things (IoT) management \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, allowing farmers to deeply participate in high-value-added links such as post-harvest processing and brand marketing of agricultural products, forming a virtuous cycle of \"technological penetration\u0026mdash;value chain upgrading\u0026mdash;income sharing.\" This dual mechanism provides a new path to solve the traditional dilemma of \"increasing production without increasing income.\"\u003c/p\u003e\u003cp\u003eHowever, existing studies still lack a systematic discussion on the income-increasing mechanism effect of new productive forces in agriculture: first, most literature focuses on single technology applications, ignoring their synergistic effects with elements such as institutional innovation, talent cultivation, and financial support; second, empirical analyses mostly focus on local regions, with insufficient discussion of regional differences. Based on this, this paper takes \"how new productive forces in agriculture promote farmers' income growth\" as the core issue, constructs an analytical framework of \"technological penetration\u0026mdash;structural transformation\u0026mdash;institutional adaptation,\" and empirically tests the enabling effect of new productive forces in agriculture on farmers' income growth based on provincial panel data from 2013 to 2022. The theoretical contributions are as follows: first, revealing the synergistic income-increasing mechanism of promoting urbanization by releasing labor and improving total factor productivity to increase agricultural output value; second, identifying the regulatory paradox of infrastructure and resource endowments: road mileage strengthens the income-increasing effect by reducing technology diffusion costs, while the per capita effective irrigation area produces a restraining effect due to resource dependency inertia. The study aims to provide theoretical references for improving the theoretical framework of new productive forces in agriculture and promoting policy practice, so as to promote farmers' income growth and support the high-quality development of China's agricultural and rural modernization.\u003c/p\u003e"},{"header":"Theoretical Analysis and Research Hypotheses","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.1Direct Effects of New Productive Forces in Agriculture on Enabling Farmers' Income Growth\u003c/h2\u003e\u003cp\u003eBased on the theory of total factor productivity (TFP) and marginal cost theory, the direct action mechanism through which new productive forces in agriculture enable farmers' income growth can be decomposed into three core dimensions in its theoretical logic:On the one hand, new productive forces in agriculture achieve a structural leap in total factor productivity. Through the transmission chain of technological innovation\u0026mdash;factor collaboration\u0026mdash;efficiency multiplication, new productive forces break through the ceiling of factor allocation efficiency in traditional agriculture \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Biotechnology such as gene editing and stress-resistant varieties optimizes crop genetic traits, enhancing light energy utilization and disaster resistance per unit of land; intelligent equipment breaks the productivity constraints of labor-intensive operations through mechanical substitution of human labor and precise operations; digital technology achieves precise delivery of mobile factors such as water, fertilizer, and pesticides through data-driven decision optimization. The synergistic effect of the three drives the agricultural production function toward a higher efficiency equilibrium point, forming an income-increasing path of \"technological dividend\u0026rarr;output expansion\u0026rarr;income growth,\" which aligns with the core idea of technology endogenization driving output in the endogenous growth theory.On the other hand, new productive forces in agriculture continuously shift the marginal cost curve downward. New productive forces reconstruct the agricultural cost structure through factor substitution effects and scale effects: intelligent equipment's substitution of labor reduces the proportion of labor costs, while precise technologies reduce redundant input consumption, significantly decreasing the variable cost per unit of output. Additionally, intertemporal decision-making and risk management supported by digital technologies reduce implicit costs caused by production uncertainties\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. According to marginal cost theory, when technological penetration shifts the long-run average cost curve (LAC) downward to the right, farmers' net income space under a given output expands, forming sustainable profit margin growth.\u003c/p\u003e\u003cp\u003eFurthermore, new productive forces break through the homogeneous competition dilemma of traditional agricultural products through technology-empowered product differentiation \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Biofortification technologies endow agricultural products with functional premiums, while digital technologies construct quality signal transmission mechanisms to reduce market information asymmetry. According to the new structural economics theory \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, this technology-driven product upgrading transforms farmers from price takers to bargaining subjects in niche markets, achieving value capture at the front end of the value chain through increased consumer willingness to pay, directly expanding operational income.\u003c/p\u003e\u003cp\u003eIn summary, the research hypothesis 1 is proposed:\u003c/p\u003e\u003cp\u003eH1: New productive forces in agriculture have a positive impact on farmers' income.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2Indirect Effects of New Productive Forces in Agriculture on Enabling Farmers' Income Growth\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1New Productive Forces in Agriculture, Urbanization, and Farmers' Income Growth\u003c/h2\u003e\u003cp\u003eUrbanization, as the core mediating variable connecting new productive forces in agriculture and farmers' income growth, follows a dual transmission logic of \"factor reallocation (supply side)\u0026mdash;demand upgrading (demand side)\" in its mechanism:\u003c/p\u003e\u003cp\u003eOn the one hand, new productive forces in agriculture achieve non-agricultural labor transfer and wage income growth through factor reallocation. By substituting traditional labor with intelligent equipment, new productive forces in agriculture directly reduce agricultural employment density, and the released labor flows to urban non-agricultural sectors through the Todaro migration model\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Migrated laborers undergo skill training and occupational differentiation in cities, engaging in high-value-added positions in manufacturing, services, etc. Data from 2022 shows that the proportion of migrant workers in technical jobs increased from 28\u0026ndash;41%, with their wage income growing at an average annual rate of 7.3%, significantly higher than the 4.1% growth rate of agricultural income. This process aligns with the Lewis turning point theory, where the transfer of agricultural surplus labor promotes income convergence between urban and rural areas\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOn the other hand, new productive forces in agriculture achieve consumption market expansion and agricultural value chain reconstruction through demand upgrading. The increase in urban population proportion drives food consumption toward high-quality and branded transformation, and this demand upgrading forces the agricultural production side to undergo standardized transformation\u0026mdash;for example, realizing transparent planting processes through IoT systems to meet the market rule of \"high quality, high price.\" Urbanization gives birth to new formats such as central kitchens and cold chain logistics, promoting the extension of agricultural products from \"field rough processing\" to \"terminal fine processing.\" Farmers invest in processing enterprises through cooperatives to share profits from post-harvest links, forming a closed loop of \"technical efficiency\u0026mdash;quality improvement\u0026mdash;premium sharing.\"\u003c/p\u003e\u003cp\u003eIn summary, the research hypothesis 2 is proposed:\u003c/p\u003e\u003cp\u003eH2: New productive forces in agriculture improve farmers' income by promoting urbanization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 New Productive Forces in Agriculture, Factor Productivity, and Farmers' Income Growth\u003c/h2\u003e\u003cp\u003eThe per capita agricultural output value, as the core variable through which new productive forces in agriculture enable farmers' income growth, follows a collaborative transformation logic of \"efficiency leap\u0026mdash;resilience enhancement\" in its mechanism:\u003c/p\u003e\u003cp\u003eNew productive forces in agriculture reconstruct traditional factor allocation models through technologies such as BeiDou navigation precision seeding and intelligent water-fertilizer integration systems\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. They reshape the agricultural risk structure through environmental controllability and product differentiation: facilities technologies like intelligent greenhouses and plant factories shift agricultural production from natural dependency to artificial regulation. Through technical means such as automatic temperature-humidity management and disaster early warning systems, they significantly reduce the impact probability of extreme weather and plant diseases, smoothing the yield fluctuation curve. High-value-added agricultural products build quality signal transmission mechanisms relying on blockchain traceability, intelligent grading, and other technologies, forming a market isolation belt from traditional bulk agricultural products. This differentiation strategy reduces price elasticity and enhances demand stability, enabling farmers to maintain income resilience amid supply-demand fluctuations. When the leap in total factor productivity reduces the cost per unit of output, farmers' tolerance threshold for market price fluctuations expands accordingly, providing a safety margin for adopting \"high input\u0026mdash;high output\" technologies. The enhanced income stability encourages farmers to extend the technology investment cycle, accelerate intelligent equipment updates and technological iteration, further consolidating efficiency advantages.\u003c/p\u003e\u003cp\u003eIn summary, the research hypothesis 3 is proposed:\u003c/p\u003e\u003cp\u003eH3: New productive forces in agriculture increase farmers' income by improving factor productivity.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3The Moderating Role of Infrastructure Level in the Empowerment of New Productive Forces in Agriculture to Increase Farmers' Incomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHighway mileage, as a key infrastructure indicator, significantly enhances the transformation efficiency of new productive forces in agriculture to increase farmers' incomes through two mechanisms: reducing technical diffusion costs and reconstructing market network structures.\u003c/p\u003e\u003cp\u003eOn the one hand, the highway network accelerates technical diffusion to break through the technical penetration mechanism of spatial friction\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.The highway network strengthens the penetration efficiency of new productive forces into agricultural production by shortening the technical accessibility radius and reducing technical service costs. An increase in highway density significantly reduces physical barriers to agricultural technicians' field services, transportation of intelligent equipment, and construction of digital technology base stations. A well-developed highway network promotes the downward deployment of market-oriented entities such as agricultural machinery cooperatives and agricultural technology enterprises to rural areas.\u003c/p\u003e\u003cp\u003eOn the other hand, highway infrastructure enhances market connectivity and realizes the income-increasing mechanism of value chain reconstruction\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.Highway infrastructure reconstructs the distribution pattern of agricultural value chains through circulation link optimization and expansion of transaction counterparts. The improved traffic capacity of cold chain logistics vehicles shortens the transportation time for fresh agricultural products, effectively reducing the spoilage rate and directly expanding the effective supply radius of high-value-added agricultural products. Additionally, improved traffic accessibility enables farmers to more easily access multi-level sales channels such as wholesale markets and e-commerce platforms, increasing the proportion of terminal selling prices retained by farmers by reducing intermediate layers.\u003c/p\u003e\u003cp\u003eBased on the above, the research hypothesis 4 is proposed:\u003c/p\u003e\u003cp\u003eH4: The infrastructure level plays a positive moderating role in the process of new productive forces in agriculture affecting farmers' incomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4The Moderating Role of Basic Resource Conditions in the Empowerment of New Productive Forces in Agriculture to Increase Farmers' Incomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePer capita effective irrigation area, as a basic resource condition for agricultural production, may inhibit the income-increasing effect of new productive forces in agriculture through ecological threshold breakthrough and technological path dependence, with its negative moderating role embodied in the \"resource curse\u0026mdash;innovation blockage\" dual trap.\u003c/p\u003e\u003cp\u003eFirstly, when the per capita effective irrigation area breaks through the regional ecological carrying capacity threshold, the traditional extensive irrigation model fundamentally conflicts with the efficiency enhancement goal of new productive forces.Excessive reliance on irrigation area expansion leads to ecological degradation such as groundwater level decline and soil salinization, forcing farmers to increase environmental governance costs\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, which directly erodes the benefits brought by new productive forces. In ecologically fragile areas, the yield-increasing effect of water-saving technologies experiences diminishing marginal returns due to total water resource constraints.Secondly, high dependence on irrigation area forms technological substitution stickiness, hindering the substitution process of new productive forces for traditional factors.The sunk costs of fixed assets under the traditional flood irrigation model are too high, suppressing farmers' willingness to adopt efficient water-saving technologies. Long-term reliance on manual irrigation results in farmers' lack of ability to operate intelligent irrigation equipment, forming a vicious cycle of \"low-skilled labor\u0026mdash;low-efficiency irrigation technology.\" Moreover, water resource shortages force farmers to prioritize basic irrigation needs, compressing their investment capacity in water-saving technologies, thus forming a vicious cycle of \"water shortage\u0026rarr;greater dependence on traditional irrigation\u0026rarr;further water shortage.\" The failure to upgrade intelligent irrigation systems in a timely manner leads to persistently high water consumption per unit of agricultural products, accelerating the process of breaking through ecological thresholds.\u003c/p\u003e\u003cp\u003eBased on the above, the research hypothesis 5 is proposed:\u003c/p\u003e\u003cp\u003eH5: Basic resource conditions play a negative moderating role in the process of new productive forces in agriculture affecting farmers' incomes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Research Design","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1Model Specification\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1Baseline Regression Model\u003c/h2\u003e\u003cp\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{I}\\text{n}\\text{c}}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{Anqp}_{it}+{\\alpha\\:}_{2}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}}_{it}+{\\sigma\\:}_{i}+{\\mu\\:}_{t}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{I}\\text{n}\\text{c}}_{it}\\)\u003c/span\u003e\u003c/span\u003erepresents the income level of farmers in province \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Anqp}_{it}\\)\u003c/span\u003e\u003c/span\u003erepresents the level of new productive forces in agriculturein province \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e;The coefficient\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003ereflects the marginal effect of new productive forces in agriculture on farmers' income growth;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Controlsit\\)\u003c/span\u003e\u003c/span\u003eare control variables;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003eis the constant term;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the province-specific effect;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003eis the time-specific effect;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the random disturbance term.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2Mediation Effect Model\u003c/h2\u003e\u003cp\u003eTo explore the potential mediating mechanisms through which new productive forces in agriculture affect farmers' income growth, this study takes urbanization and per capita agricultural output value as mediating variables for empirical testing. Building on Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), referring to the approach of Fang Wen\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, the following regression models are constructed:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{I}\\text{n}\\text{c}}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{Anqp}_{it}+{\\alpha\\:}_{2}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}}_{it}+{\\sigma\\:}_{i}+{\\mu\\:}_{t}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{M}_{it}={\\beta\\:}_{0}+{\\beta\\:}_{1}{\\text{A}\\text{n}\\text{q}\\text{p}}_{it}+{\\beta\\:}_{2}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}}_{it}+{\\sigma\\:}_{i}+{\\mu\\:}_{t}+{\\epsilon\\:}_{it}\\\\\\:{\\text{I}\\text{n}\\text{c}}_{it}={\\gamma\\:}_{0}+{\\gamma\\:}_{1}{M}_{it}+{\\gamma\\:}_{2}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}}_{it}+{\\sigma\\:}_{i}+{\\mu\\:}_{t}+{\\epsilon\\:}_{it}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3Moderation Effect Model\u003c/h2\u003e\u003cp\u003eBased on the previous mechanism analysis, it is found that infrastructure level and basic resource conditions play important roles in the empowerment of new productive forces in agriculture to increase farmers' incomes. Therefore, taking the two as moderating variables, the following models are constructed:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\text{I}\\text{n}\\text{c}}_{it}={\\eta\\:}_{0}+{\\eta\\:}_{1}{Anqp}_{it}+{\\eta\\:}_{2}{FT}_{it}+{\\eta\\:}_{3}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\text{I}\\text{n}\\text{c}}_{it}={\\phi\\:}_{0}+{\\phi\\:}_{1}{Anqp}_{it}+{\\phi\\:}_{2}{FT}_{it}+{\\phi\\:}_{3}({Anqp}_{it}\\ast\\:{FT}_{it})+{\\phi\\:}_{4}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variable Description\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1Dependent Variable\u003c/h2\u003e\u003cp\u003eThe dependent variable is farmers' income growth, measured by the logarithm of rural residents' disposable income, following the approach of Li Qi \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This logarithmic transformation normalizes the data and mitigates heteroscedasticity, aligning with econometric modeling requirements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Core Explanatory Variable\u003c/h2\u003e\u003cp\u003eThe core explanatory variable is New Productive Forces in Agriculture (NPAF). Drawing from studies on constructing indicator systems for new productive forces \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, NPAF is measured based on the three elements of productivity. Technological progress serves as the core driver for forming new agricultural production capabilities, rooted in the role of humans\u0026mdash;particularly laborers. When evaluating laborers' individual contributions, data availability dictates considerations from three dimensions: conceptual understanding, technical competence, and production efficiency.From the perspective of objects of labor, this refers to the specific materials operated and processed by laborers in production, encompassing efficient use of production factors and environment-friendly management practices. It enhances food security by promoting green agricultural development, embodying the integration of technological progress and sustainable development \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.Means of production for agriculture denote tangible and intangible resources relied on by laborers, including advanced agricultural machinery, intelligent equipment, IoT monitoring systems, big data analysis platforms (tangible tools), and agricultural scientific knowledge, management experience, information technology (intangible resources). These resources are essential for achieving production goals and transforming labor objects into practical outputs. Against the backdrop of urban-rural integration, improved infrastructure provides a solid foundation for applying these new tools, reducing energy consumption. Upgraded information infrastructure further optimizes resource allocation and management decision-making, significantly enhancing agricultural productivity.\u003c/p\u003e\u003cp\u003eBased on previous research\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, this study constructs an evaluation index system for NPAF, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation Index System for New Productive Forces in Agriculture\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst-Level Indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond-Level Indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThird-Level Indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndicator Attribute\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eLaborers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLabor Skills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEducational Attainment of Rural Residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion of Rural Residents with Higher Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLabor Productivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer Capita Grain Output\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer Capita Output Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAwareness of Laborers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLabor Force Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLabor Force Structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003eObjects of Labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnological Progress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComprehensive Mechanization Rate of Grain Planting, Cultivating, and Harvesting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eFood Security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGrain Yield per Unit Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple Cropping Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVolatility of Grain Output\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDisaster Incidence Rate of Grain Crops\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eGreen Development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChemical Fertilizer Application Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePesticide Application Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWater Resource Utilization Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGrain Carbon Emission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eMeans of Production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eInfrastructure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgricultural Machinery and Equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Observations from Agricultural Weather Stations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEnergy Consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural Energy Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiesel Oil Consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnological Innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntensity of Agricultural R\u0026amp;D Expenditure Investment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Control Variables\u003c/h2\u003e\u003cp\u003eOther factors potentially affecting new productive forces in agriculture are selected as control variables. Referencing the research of Zou Wei\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, the following are included:(1)Rural Electricity Consumption: Electricity is an indispensable energy source in agricultural production, measured by the electricity consumption in rural areas.(2)Chemical Fertilizer Application Intensity: Measured by the amount of chemical fertilizers applied, which is a key input factor in agricultural production and directly influences grain yield and quality.(3) Human Capital Level: Represented by the average education level of farmers.(4) Disaster Incidence Rate: Expressed as the ratio of agricultural disaster-affected area to cultivated land area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4Mediating Variables\u003c/h2\u003e\u003cp\u003eThe mediating variables are selected as follows:(1)Urbanization: Urbanization promotes non-agricultural employment, thus increasing farmers' income. The urbanization rate is therefore chosen as the mediating variable.(2) Factor Productivity: Referencing the research of Liu Guangdong\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, per capita agricultural output value is used as a proxy for factor productivity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.5 Moderating Variables\u003c/h2\u003e\u003cp\u003eThe moderating variables are selected as follows:(1)Infrastructure Level: Infrastructure level can significantly enhance the transformation efficiency of new productive forces in agriculture to increase farmers' income through two mechanisms: reducing technical diffusion costs and reconstructing market network structures. Referencing the research of Bai Junhong\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, highway mileage is used to characterize infrastructure level.(2)Basic Resource Conditions: Per capita effective irrigation area, as a basic resource condition for agricultural production, may inhibit the income-increasing effect of new productive forces in agriculture through ecological threshold breakthrough and technological path dependence. Referencing the research of Qiao Biao\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, per capita effective irrigation area is used to characterize basic resource conditions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3Data Source and Descriptive Statistics\u003c/h2\u003e\u003cp\u003eTo ensure data reliability and consistency, relevant data from Hong Kong, Macau, Taiwan, and Tibet Autonomous Region were excluded during the sample screening process. The final research sample consists of panel data sets from 30 provincial administrative units in Mainland China spanning 2013\u0026ndash;2022. Data are sourced from the China Statistical Yearbook, China Macroeconomic Database, China Environmental Database, etc. Partial missing data were imputed using the interpolation method. Meanwhile, given potential interference from dimensionality issues, logarithmic transformation and necessary unit calibration were performed on specific data to ensure standardization and comparability. Descriptive statistics for each variable are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of New Productive Forces in Agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita Disposable Income of Rural Residents (CNY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.5477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.6285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.5899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Power of Agricultural Machinery (10,000 kW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.6929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.5429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.4995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural Electricity Consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.9399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.6064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemical Fertilizer Application Amount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.7891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.5496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.5738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman Capital Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.0376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.8610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.5995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisaster Incidence Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.7649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.7942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.8273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrbanization Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.3861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.3843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e89.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita Agricultural Output Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.2876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.1720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.6278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita Effective Irrigation Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighway Mileage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.7262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.4441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.9126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Empirical Results and Analysis","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1Empirical Results and Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the regression results of the impact of new productive forces in agriculture on farmers' income growth. Specifically, Model (1) is the basic model considering only farmers' income growth as a single factor without any control variables; Model (2) adds relevant control variables based on Model (1) to comprehensively examine related influences; Model (3) further controls for individual and time factors based on Model (2) to obtain more accurate and robust regression results. The regression estimation of Model (3) shows that new productive forces in agriculture have a significantly positive correlation with farmers' income growth, with an impact coefficient of 0.242 and passing the 1% statistical significance test, indicating that the upgrading of this factor has a significant income-increasing effect on rural residents' income growth and verifying the value conversion capability of technological innovation in agricultural production. Specifically, driven by scientific and technological innovation, new productive forces in agriculture innovate agricultural production methods: on the one hand, they improve the yield and quality of agricultural products, enabling farmers to profit from premium sales; on the other hand, they give birth to new industries and business forms to expand income channels (e.g., rural e-commerce facilitates direct market access for agricultural products, opening new wealth paths for farmers and effectively improving their income levels). Accordingly, research hypothesis H1 is preliminarily verified.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel (1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.114(0.459)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.270(0.060)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.242(0.060)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHUMAN_CAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGRI_POWER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028(0.011)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRURAL_ELEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010(0.002)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDISASTER_RATE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0001(0.0001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFERT_APPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.030(0.016)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYEAR_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Controlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePROV_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Controlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCONS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.86(0.157)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.661(0.017)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.56(0.055)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOBS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.2Robustness Tests\u003c/h2\u003e\u003cp\u003eTo verify the stability of regression results, the following robustness tests are implemented: substitution of the dependent variable by using rural residents' per capita net income for estimation; addition of a control variable by additionally incorporating per capita agricultural plastic film usage to comprehensively consider impacts of different control variable selections; and endogeneity treatment by regressing the first lag of the core explanatory variable (new productive forces in agriculture) on farmers' income. The test results (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) show that the estimated coefficients of new productive forces in agriculture are all significantly positive, consistent with the baseline model results, further confirming the high robustness of previous research conclusions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDependent Variable Substitution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl Variable Addition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEndogeneity Treatment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1877(0.0887)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2472(0.0602)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1867(0.0569)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCONS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.4370(0.0815)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.5600(0.0553)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.6851(0.0603)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYEAR_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePROV_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.3Heterogeneity Test\u003c/h2\u003e\u003cp\u003eAlthough the above research has shown that new productive forces in agriculture have a positive effect on increasing farmers' income, given the significant differences in economic foundations and resource endowments across regions in China, whether new productive forces in agriculture produce the same income-increasing effect in different regions remains unknown. Therefore, this paper analyzes the heterogeneous impacts of new productive forces in agriculture on increasing farmers' income from two perspectives: geographical location and grain production functional zones.\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1Geographical Zoning Heterogeneity\u003c/h2\u003e\u003cp\u003eReferencing existing research\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, the 30 provinces across the country are divided into Eastern, Central, and Western regions for heterogeneity analysis. The research results (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) show that new productive forces in agriculture have a significantly positive impact on farmers' income in the Eastern and Western regions, but not in the Central region. Specifically, the coefficient of new productive forces in agriculture is significant at the 5% level in the Eastern region, with a marginal effect of 0.2679, while in the Western region, the coefficient is significant at the 1% level, with a marginal effect of 0.3845.The possible reasons lie in the \"depression effect\" and technological catch-up dividend in the Western region. The high marginal effect of new productive forces in Western agriculture mainly benefits from the late-mover advantage and policy preference. On the one hand, the foundation of agricultural modernization in the West is weak, and the contribution rate of traditional factors to income has long been low. The introduction of new productive forces can break through the \"low-level equilibrium trap\" and generate significant increasing marginal returns. On the other hand, policies such as the Western Development Strategy and targeted poverty alleviation have reduced the threshold for technological adoption through infrastructure investment and fiscal subsidies.The coefficient of new productive forces in agriculture in the Eastern region (0.2679) is significant but lower than that in the West, reflecting the characteristics of the deepening transformation phase. First, agriculture in the Eastern region has been highly integrated into the non-agricultural economy, with wage income accounting for more than 60% of farmers' income, diluting the marginal contribution of new productive forces to total household income. Second, technological application in the East faces a scale economy bottleneck in agriculture\u0026mdash;most provinces have a facility agriculture coverage rate exceeding 40%, and further expansion is constrained by land fragmentation, making it difficult for new technologies to achieve cost spreading. Additionally, the export-oriented characteristics of Eastern agriculture exacerbate market risk transmission, inhibiting sustained technological investment by government departments or farmers. These structural contradictions make the income-increasing effect of new productive forces in the Eastern region exhibit the characteristics of \"high significance, low elasticity\".The core reason for the insignificant effect in the Central region is factor misallocation and policy goal conflict. As the core area of national food security, agriculture in the Central region is endowed with the dual goals of \"ensuring production capacity\" and \"promoting income growth\", but there is an inherent contradiction between the two: high-standard farmland construction policies prioritize ensuring food crops, restricting technological investment and industrial scale expansion for high-value economic crops.\u003c/p\u003e\u003cp\u003eIn summary, the geographical heterogeneity of new productive forces in agriculture is essentially the result of three-dimensional adaptability of \"factor structure-institutional environment-market conditions\". The Western region achieves technological catch-up by virtue of policy dividends and late-mover advantages; the Eastern region shows diminishing marginal returns due to structural rigidity; and the Central region falls into a transition lag dilemma due to institutional friction and goal conflict.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity Test Results by Geographical Zones\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2679(0.1160)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1561(0.1191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3845(0.0737)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCONS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.3630(0.1470)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.9175(0.1932)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.7578(0.1196)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYEAR_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePROV_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2Heterogeneity by Agricultural Functional Zones\u003c/h2\u003e\u003cp\u003eDrawing on existing research \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, the 30 provinces are divided into grain main production areas and non-grain main production areas based on their food production functions. The test results (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) show that new productive forces in agriculture are significantly positive at the 1% level in both grain main production areas and non-grain main production areas, with marginal effects of 0.2909 and 0.2675, respectively.The possible reasons are as follows: Grain main production areas typically have the advantage of large-scale planting, with concentrated contiguous land and high mechanization levels, making it easier for new agricultural productive forces to achieve efficiency improvements through technological integration, hence the relatively significant marginal effect. Meanwhile, main production areas often undertake national food security strategic tasks, with strong policy support and a more mature supporting service system for technology promotion, which reduces the institutional costs for farmers to adopt new technologies.In non-grain main production areas, the agricultural structure is relatively diversified, with a higher proportion of cash crops and characteristic aquaculture. The income-increasing path of new productive forces may rely on industrial chain extension, and technological application needs to match market channel expansion capabilities. The slightly lower marginal effect may reflect the existence of transaction costs in the technology transformation process. Additionally, farmers in main production areas have long been engaged in grain planting, showing strong sensitivity and adaptability to production technology changes, while farmers in non-main production areas may have longer decision-making cycles for technology adoption due to high production decentralization and part-time employment, leading to regional differences in income elasticity under the same technical level.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity Test Results by Agricultural Functional Zones\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrain Main Production Areas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Grain Main Production Areas\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2909(0.0939)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2675(0.0868)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCONS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.4006(0.2108)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.6979(0.1051)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYEAR_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePROV_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.4Mediation Mechanism Test\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, new productive forces in agriculture (NPAF) can empower farmers' income growth, but how NPAF influences farmers' income requires further verification.\u003c/p\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003e4.4.1Mediating Variable: Urbanization\u003c/h2\u003e\u003cp\u003eFrom the perspective of urbanization, in Models 1 and 2 of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, NPAF and the urbanization coefficient show a significantly positive correlation, verifying the mediating transmission mechanism of urbanization between NPAF and farmers' income growth. The baseline regression Model (1) shows that a 1-unit increase in NPAF can drive a 12.4422-unit increase in the urbanization rate, highlighting the accelerating effect of technological innovation on urban-rural factor flow. Further testing the mediating path Model (2), the urbanization variable passes the significance test at the 1% statistical level, with a marginal impact coefficient of 0.0069, confirming that urbanization promotes farmers' income through agglomeration economic effects.NPAF improves agricultural production efficiency through technological innovation, releases rural labor resources, and promotes labor transfer to non-agricultural industries, accelerating urbanization. After the urbanization rate increases, the increase in non-agricultural employment opportunities and the optimization of urban-rural factor flow significantly enhance farmers' wage income and property income. Accordingly, research hypothesis H2 is verified.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e4.4.2Mediating Variable: Factor Productivity\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, coefficients of new productive forces in agriculture (NPAF) and factor productivity in Models 3 and 4 are both significant at the 1% level, confirming the theoretical pathway through which technological innovation transmits income-increasing dividends via factor productivity improvement. Specifically, the marginal output elasticity measurement shows that a 1-unit increase in NPAF can induce a 1.3729-unit growth in productivity, highlighting the driving effect of technological penetration on production factor optimization. Further mediation path testing verifies that factor productivity is significant at the 1% statistical level, with an output elasticity coefficient of 0.0640, confirming the transmission mechanism through which technology diffusion realizes farmers' income growth via economies of scale.New productive forces reconstruct the agricultural production function through improvements in factor substitution elasticity and leaps in total factor productivity, promoting \"new combinations\". Breakthrough innovations such as gene editing and digital-intelligent technologies disrupt the law of diminishing marginal returns of traditional factors, manifested as a leap in output value per unit of labor. Accordingly, research hypothesis H3 is verified.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Test Results for Factor Productivity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel (1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.4422(3.8010)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1552(0.0552)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3729(0.3259)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1536(0.0584)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eURBAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0069(0.0009)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor Productivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0640(0.0109)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.4025(3.4934)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.1587(0.0720)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9057(0.2995)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.2496(0.0744)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYEAR_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePROV_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.5Further Discussion on Analysis Results\u003c/h2\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003e4.5.1Moderating Effect of Infrastructure Level\u003c/h2\u003e\u003cp\u003eTo further verify the moderating effect of infrastructure construction level in the process of new productive forces in agriculture empowering farmers' income growth, an interaction term between infrastructure construction level and new productive forces in agriculture was introduced into the baseline regression model (specific results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The estimation results of Models (1) and (2) in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e show that the regression coefficients of infrastructure level are both significant at the 1% significance level, with corresponding marginal effect values of 0.1113 and 0.0877, respectively. Meanwhile, the interaction term coefficients are significantly positive, with a marginal effect of 0.1209.The research results show that with the improvement of infrastructure construction level, the promoting effect of new productive forces in agriculture on farmers' income can be effectively strengthened. The empirical results verify the \"circulation-diffusion enhancement effect\" of highway mileage in the theoretical framework. The core reason lies in that infrastructure amplifies the empowering effect of new productive forces in agriculture on farmers' income growth through three mechanisms: reducing technology diffusion costs, expanding market radiation radius, and optimizing factor allocation efficiency. Accordingly, research hypothesis H4 is verified.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerating Effect Test Results of Infrastructure Level\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel (1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2157(0.0576)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.1961(0.6482)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINFRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1113(0.0217)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0877(0.0241)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u0026times;INFRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1209(0.0553)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.5689(0.2013)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.8764(0.2443)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYEAR_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePROV_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section3\"\u003e\u003ch2\u003e4.5.2Moderating Effect of Basic Resource Conditions\u003c/h2\u003e\u003cp\u003eTo further verify the moderating effect of basic resource conditions in the process of new productive forces in agriculture empowering farmers' income growth, the interaction term between per capita effective irrigation area and new productive forces in agriculture was incorporated into the baseline regression model (see Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e for details). In Models (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the regression coefficients of basic resource conditions both pass the significance test, with marginal effects of -0.0460 and 0.0436, respectively. Meanwhile, the interaction term coefficient between basic resource conditions and new productive forces in agriculture is significantly negative, with a marginal effect of -0.2633.This indicates that an increase in per capita effective irrigation area inhibits the income-increasing effect of new productive forces in agriculture. This negative moderating effect reveals the \"resource curse\" paradox of per capita effective irrigation area: in ecologically constrained regions, the expansion of irrigation area offsets the income-increasing potential of new agricultural productive forces through three mechanisms: environmental overload, technological lock-in, and factor misallocation. Accordingly, research hypothesis H5 is verified.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerating Effect Test Results of Basic Resource Conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel (1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2335(0.0593)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.3415(0.1420)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRRIGATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0460(0.0165)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0436(0.0258)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAF\u0026times;IRRIGATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.2633(0.0595)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.3360(0.0984)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.5410(0.1057)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYEAR_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePROV_FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControlled\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Research Conclusions and Policy Recommendations","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e5.1Research Conclusions\u003c/h2\u003e\u003cp\u003eBased on panel data of 30 provinces in China from 2013 to 2022, this paper empirically analyzes the empowering effect of new productive forces in agriculture (NPAF), yielding key conclusions as follows. First, NPAF significantly drives farmers' income growth: baseline regression results confirm NPAF's income-increasing effect on farmers, which remains robust after a series of stability tests. Second, urbanization and factor productivity act as mediating mechanisms in NPAF's promotion of income growth, where technological innovation enhances agricultural efficiency, releases rural labor, and accelerates urban-rural factor flow to boost earnings. Third, notable spatial differentiation exists: western provinces demonstrate the strongest income-driven efficacy with a NPAF coefficient, followed by eastern regions, while central regions show no statistical significance\u0026mdash;a gradient pattern closely tied to regional resource endowments and industrial upgrading stages. In grain production functional zones, NPAF significantly promotes income in both main and non-main areas, though the marginal effect is more pronounced in main production areas due to scale advantages and policy support. Fourth, infrastructure level exerts a positive moderating effect by reducing technology diffusion costs and expanding market reach, whereas basic resource conditions exhibit a negative moderating effect,reflecting a \"resource curse\" paradox through environmental overload and factor misallocation. These findings highlight the complex interrelationship among technology, institutions, and geography in shaping agricultural productivity and rural income dynamics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e5.2Policy Recommendations\u003c/h2\u003e\u003cp\u003eFacing dual challenges of tightening resource constraints and intensified market fluctuations, new productive forces in agriculture (NPAF) reconstruct the production function through technological and institutional innovations, optimizing value distribution along industrial chains while enhancing efficiency. Policies should facilitate the transformation of technological dividends into farmers' income through precise adaptation mechanisms, addressing the \"efficiency-equity\" coordination dilemma for sustainable income growth. Based on research conclusions, the following recommendations are proposed.\u003c/p\u003e\u003cp\u003eFirst, construct an adaptive technology promotion system to improve penetration efficiency. Drive reforms of the technology promotion system at the county level, integrating grassroots agricultural technical resources through a dual-track mechanism of \"agricultural machinery cooperatives\u0026thinsp;+\u0026thinsp;technology commissioners,\" combining technical guidance from agricultural stations with equipment service capabilities of market entities. Establish \"technology stations\" at the village level, dynamically adjusting technical solutions based on remote sensing monitoring and Households demand lists to ensure precise delivery of intelligent irrigation, precision fertilization, and other technologies. To address risks of household technology adoption, simultaneously build a risk hedging mechanism by developing \"technology adoption index insurance,\" implementing tiered premium subsidies for high-risk technologies like blockchain traceability and smart greenhouses\u0026mdash;government-subsidized in the first year, dynamically adjusted based on technical benefits in subsequent years\u0026mdash;and offering 5%-10% income incentives for Households who continuously adopt new technologies for three years, fostering a positive cycle of \"daring to use and using more technologies.\" On this basis, activate conditions for large-scale operations through land circulation and property rights reforms, standardizing circulation contracts by county-level property trading centers, prioritizing intelligent equipment like unmanned harvesters for plots over 200 mu, and piloting a \"land contract right shareholding\u0026thinsp;+\u0026thinsp;technology dividend\" model where household participate in cooperative dividends based on intelligent equipment usage and cost-saving efficiency, converting technological dividends into tangible asset-based income. Finally, establish a dynamic assessment mechanism for \"technology penetration-income growth-ecological benefits,\" regularly releasing county-level technology adaptation indices, and promptly adjusting technical solutions or service providers in regions with insufficient promotion efficiency to ensure a full-chain closed loop from technology implementation to sustainability.\u003c/p\u003e\u003cp\u003eSecond, increase public service supply to promote non-farm employment and agricultural product sales. Build NPAF skills training bases at the county level, carrying out order-based training for positions like intelligent equipment operation and agricultural product e-commerce to ensure labor transfer is smooth, stable, and income-generating. Provide employment subsidies to enterprises absorbing agricultural transfer labor to incentivize non-farm job creation; deploy \"central kitchen\u0026thinsp;+\u0026thinsp;cold chain logistics hubs\" around highly urbanized city clusters, guiding processing enterprises through tax breaks to prioritize purchasing intelligent technology agricultural products, and establishing a premium pricing procurement list system. Implement a digital certification program for regional public brands of agricultural products, providing government guarantees for sales premiums of blockchain-traceable products. For enterprises absorbing agricultural transfer labor, besides employment subsidies, simultaneously implement a \"social security connection plan\" allowing migrant workers to convert rural pension insurance payment years into enterprise employee social security years, reducing insurance participation thresholds. Establish a premium revenue sharing mechanism requiring enterprises using government-guaranteed traceability codes to return part of the premium income to village collectives as technology diffusion funds for updating village-level digital facilities. Develop a \"non-farm employment service one-code access\" integrating functions like job recommendations, skills assessment, and rights appeal, enabling digital management of the entire employment process for migrant workers through county-level government affairs platforms.\u003c/p\u003e\u003cp\u003eThird, adhere to regional differentiated strategies to crack the \"central region collapse\" dilemma. Establish NPAF technology adaptation funds to support R\u0026amp;D of water-saving and drought-resistant technologies suitable for local ecological conditions, addressing resource constraints and technology mismatch; strengthen the urbanization mediation mechanism by piloting a technology points household registration policy in the Yangtze River 中游 city cluster, incorporating indicators like intelligent agricultural machinery operation years and participation in agricultural digital services into the points system, opening special channels for guaranteed housing purchase and priority school placement for those meeting cumulative points, and matching employment positions through industrial transfer undertaking parks within city clusters to form a virtuous cycle of \"technology empowerment-household registration incentives-factor agglomeration.\" Build NPAF innovation enclaves in the Yangtze River Delta and Pearl River Delta, promoting an \"open competition\" system for R\u0026amp;D projects in frontier fields like agricultural AI algorithms and synthetic biotechnology to expand technology spillovers. Establish an \"ecological contribution-industry compensation\" linkage mechanism, allowing counties undertaking ecological functions such as South-to-North Water Diversion water sources and the Yangtze River shelterbelt to convert ecological protection investments into carbon emission indicators proportionally, transferring them to central region high-energy consumption industry upgrading projects through carbon trading markets to realize the transformation of ecological value into industrial momentum.Fourth, implement intelligent resource management to break the \"resource curse\" paradox. Reconstruct the water resource management system with digital twin technology, establishing a dynamic coupling model of \"soil moisture-crop water demand-intelligent water distribution\" for precise irrigation regulation. Innovate water rights trading mechanisms by incorporating water savings from intelligent drip irrigation systems into carbon sink trading markets, allowing Households to obtain additional income through water-saving index transfers. In ecologically fragile areas, implement a dual-track system of \"irrigation quota\u0026thinsp;+\u0026thinsp;ecological compensation,\" imposing tiered water prices and ecological taxes on overexploited areas to drive green transformation of agricultural production modes. Meanwhile, construct a full-chain ecological management system of \"intelligent monitoring-warning response-restoration compensation,\" using UAV remote sensing and AI algorithms to assess soil health in real time, granting carbon point rewards to household adopting eco-friendly technologies like no-till direct seeding and integrated water-fertilizer management, promoting agriculture's shift from resource-consuming to ecological value-added.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Major Project of Fujian Social Science Research Base, \"Research on the Cultivation Mechanism of Rural Food Safety Governance Community Awareness\" (FJ2022MJDZ021) and the Fujian Science and Technology Plan (2024R0026).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.F.J. was responsible for study conceptualization, theoretical framework development, and empirical methodology design, providing theoretical interpretations of results and policy implications. P.X.F. undertook data collection, model construction, statistical analysis, and initial manuscript drafting. C.B.C., X.J.H., and L.Y. assisted in literature review refinement, participated in data verification and analysis, contributed valuable insights during result discussions, and provided constructive feedback for manuscript revisions. All authors collaboratively discussed findings, formulated policy recommendations, and participated in multiple rounds of manuscript review and improvement.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are openly available in\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Central Committee of the Communist Party of China and the State Council. Opinions on Further Deepening Rural Reforms and Solidly Promoting Comprehensive Rural Revitalization [N]. People\u0026apos;s Daily, 2025-02-24(001).\u003c/li\u003e\n \u003cli\u003eXu Caiyao, Qian Chen, Kong Fanbin. Can Digital Rural Construction Narrow the Urban-Rural Income Gap? An Empirical Test Based on the Mediating Role of Agricultural Scientific and Technological Progress and Threshold Effect [J]. Journal of Agricultural Technology Economics, 2024(12): 4-24.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Zhou J, Yang S. Common prosperity in the era of technological transformation: Digital inclusive finance, technological progress, and the income distribution of migrant workers[J]. Finance Research Letters, 2025: 107486.\u003c/li\u003e\n \u003cli\u003eCaifeng T ,Jianping T ,Lan Y , et al.Dynamic Relationship between Agricultural Technology Progress, Agricultural Insurance and Farmers\u0026rsquo; Income[J].Agriculture,2022,12(9):1331-1331.\u003c/li\u003e\n \u003cli\u003eXiaoyan D, Jiangnan Z, Xuelian G, et al. The impact of informatization on agri-income of China\u0026apos;s rural farmers: ways for digital farming[J]. Frontiers in Sustainable Food Systems, 2024, 8: 1329674.\u003c/li\u003e\n \u003cli\u003eRuben R. From market-based development to value chain transformation: What markets can (not) do for rural poverty alleviation?[J]. Journal of Rural Studies, 2024, 109: 103328.\u003c/li\u003e\n \u003cli\u003eLi C ,Zhang D ,Lu Q , et al.Production Process Outsourcing, Farmers\u0026rsquo; Operation Capability, and Income-Enhancing Effects[J].Agriculture,2024,14(9):1448-1448.\u003c/li\u003e\n \u003cli\u003eSHENG Y,ZHAO Y H,ZHANG Q,et al. Boosting rural labor offfarm employment through urban expansion in China[J]. World development,2022,151:105727.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBi J. Can rural areas in China be revitalized by digitization? A dual perspective on digital infrastructure and digital finance[J]. Finance Research Letters, 2024, 67: 105753.\u003c/li\u003e\n \u003cli\u003eLin L, Gu T, Shi Y. The influence of new quality productive forces on high-quality agricultural development in China: Mechanisms and empirical testing[J]. Agriculture, 2024, 14(7): 1022.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Liu H, Li X. How digital technology can improve new quality productive forces?\u0026mdash;\u0026mdash;Perspective of total factor agricultural carbon productivity[J]. Journal of Asian Economics, 2025, 98: 101921.\u003c/li\u003e\n \u003cli\u003eYou Liang, Tian Xiangyu. New Productive Forces in Agriculture: Realistic Logic, Connotative Analysis and Generation Mechanism [J]. Economic Issues, 2024(06): 27-35.\u003c/li\u003e\n \u003cli\u003eXiang Jiyou. Empowering Digital Rural Construction with New Productive Forces: Theoretical Logic and Path Analysis [J]. Guizhou Social Sciences, 2024(08): 148-154.\u003c/li\u003e\n \u003cli\u003eZhang Guanghui, Yang Zhen. New Productive Forces in Agriculture, Agricultural Socialized Services and Food Security [J]. Contemporary Economic Research, 2025(02): 68-78.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Belhadi A, Kamble S, Subramanian N, et al. Digital capabilities to manage agri-food supply chain uncertainties and build supply chain resilience during compounding geopolitical disruptions[J]. International Journal of Operations \u0026amp; Production Management, 2024, 44(11): 1914-1950.\u003c/li\u003e\n \u003cli\u003eShi Bo, Hu Xijuan. How Do New Productive Forces Drive the Construction of a National Unified Market? A Study from the Perspective of Digital-Real Economy Integration [J]. Social Science Research, 2024(04): 56-66.\u003c/li\u003e\n \u003cli\u003eChoruma D J, Dirwai T L, Mutenje M, et al. Digitalisation in agriculture: A scoping review of technologies in practice, challenges, and opportunities for smallholder farmers in sub-saharan africa[J]. Journal of agriculture and food research, 2024: 101286.\u003c/li\u003e\n \u003cli\u003eWu Zhongtao, Zhang Dan. The Impact of Urban-Rural Expected Income Gap on Rural Population Migration\u0026mdash;Based on the Todaro Model [J]. Journal of Northwest University (Philosophy and Social Sciences Edition), 2013, 43(04): 74-79.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Giles J, Park A, Zhang J. Labor market developments in China: A neoclassical view[J]. China Economic Review, 2023, 78: 102048.\u003c/li\u003e\n \u003cli\u003eZheng Yangyang, Liao Feng. Digital Transformation of New Agricultural Business Entities Empowers New Productive Forces in Agriculture [J]. Journal of Sichuan Agricultural University, 2024, 42(06): 1179-1185+1202.\u003c/li\u003e\n \u003cli\u003e.Li Lanbing, Yan Li, Huang Jiuli. Accessibility of Transportation Infrastructure and Manufacturing Growth in Non-Core Cities: Market Power, Productivity and Allocation Efficiency [J]. Economic Research Journal, 2019, 54(12): 182-197.\u003c/li\u003e\n \u003cli\u003eShevtsova Y, D\u0026iacute;az-Lanchas J, Persyn D, et al. Trade spillover effects of transport infrastructure investments: a structural gravity analysis for EU regions[J]. Regional Studies, 2025: 1-16.\u003c/li\u003e\n \u003cli\u003eWichelns D ,Oster J .Sustainable irrigation is necessary and achievable, but direct costs and environmental impacts can be substantial[J].Agricultural Water Management,2006,86(1):114-127.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Fang ,Wen ,Hau .Mediation Effects In 2-1-1 Multilevel Model: Evaluation Of Alternative Estimation Methods[J].Structural Equation Modeling: A Multidisciplinary Journal,2019,26(4):591-606.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Li Qi, Tang Yuehuan, Ren Xiaojing. E-Commerce Development, Spatial Spillover and Farmers\u0026apos; Income Growth [J]. Journal of Agricultural Technology Economics, 2019(04): 119-131.\u003c/li\u003e\n \u003cli\u003eCui J, Du D. New quality productive forces, urban-rural integration and industrial chain resilience[J]. International Review of Economics \u0026amp; Finance, 2025: 104245.\u003c/li\u003e\n \u003cli\u003eHoffman nivian,Jones Kelly.Improving food safety on the farm:Experimental evidence from Kenyao nincentive sand subsidies for technology adoption[J].World Development,2021(143):105406.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Lei Xunping, Qiu Ronbin. Evaluation of China\u0026apos;s Food Security and Diagnosis of Obstacle Factors Based on Entropy Weight TOPSIS Model [J]. Journal of China Agricultural University, 2022, 27(12): 1\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eLi Shengzhu, Xue Feng, Jiang Jingui. Study on the Impact of Agricultural Digitalization on New Productive Forces of China\u0026apos;s Grain [J]. Journal of Agro-Forestry Economics and Management, 2024, 23(04): 435\u0026ndash;445.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Zou Wei, Zhang Zhenlin. The Mechanism of Agricultural Development Affecting Farmers\u0026rsquo; Poverty Reduction and Income Increase\u0026mdash;An Empirical Decomposition and Test Based on Income Structure [J]. China Population, Resources and Environment, 2024, 34(08): 179\u0026ndash;192.\u003c/li\u003e\n \u003cli\u003eLiu Guangdong, Cheng Jiumiao, Zhu Chuanmin, et al. Study on Regional Differences of China\u0026rsquo;s \u0026quot;Three Rural Issues\u0026quot; [J]. Research of Agricultural Modernization, 2006(02): 126\u0026ndash;130.\u003c/li\u003e\n \u003cli\u003eBai Junhong, Wang Lindong. Does Innovation Drive Promote the Improvement of Economic Growth Quality? [J]. Studies in Science of Science, 2016, 34(11): 1725\u0026ndash;1735.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Qiao Biao, Fang Chuanglin. Dynamic Coupling Model of Coordinated Development between Urbanization and Ecological Environment and Its Application in Arid Areas [J]. Acta Ecologica Sinica, 2005(11): 211\u0026ndash;217.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Xia H, Yu H, Wang S, et al. Digital economy and the urban\u0026ndash;rural income gap: Impact, mechanisms, and spatial heterogeneity[J]. Journal of Innovation \u0026amp; Knowledge, 2024, 9(3): 100505.\u003c/li\u003e\n \u003cli\u003eTian C, Li L, Liao B. Evaluation of the green development effects of heterogeneous agricultural cleaner production place-based policy[J]. Journal of Cleaner Production, 2024, 442: 140969.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"New Qualitative Productivity in Agriculture, Farmers' Income Increase, Technological Dividend, Empowerment Effect","lastPublishedDoi":"10.21203/rs.3.rs-7060565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7060565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnhancing farmers' income stands as a vital pillar for realizing agricultural and rural modernization and promoting shared prosperity in China. Leveraging panel data from 30 Chinese provinces spanning the period of 2013 to 2022, this research utilizes fixed-effects, mediating-effects, and moderating-effects models to explore the mechanisms through which the new qualitative productivity in agriculture drives farmers' income growth. The findings reveal: (1) New agricultural productivity significantly boosts farmers' income, with more pronounced effects in eastern/western regions and grain production functional areas. (2) Urbanization and factor productivity serve as mediating factors in this process. (3) Infrastructure levels positively moderate the income-enhancing effect of new agricultural productivity, while basic resource conditions exert negative moderation. Based on these results, policy recommendations are proposed, including constructing adaptive technology diffusion systems, expanding public service provisions, implementing region-specific regulatory strategies, and adopting smart resource management, to leverage new agricultural productivity for sustainable income growth among farmers.\u003c/p\u003e","manuscriptTitle":"A Study on the Empowerment Effect of New Qualitative Productivity in Agriculture on Farmers' Income","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 12:00:05","doi":"10.21203/rs.3.rs-7060565/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10f4cf80-70d5-41f4-b9eb-d4951a508280","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54406106,"name":"Social science/Development studies"},{"id":54406107,"name":"Business and commerce/Economics"},{"id":54406108,"name":"Social science/Economics"},{"id":54406109,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-10-28T19:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 12:00:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7060565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7060565","identity":"rs-7060565","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}