数字农业试点对城乡土地利用效率差距的影响 基于700个中国县的双重机器学习证据

1. Introduction

China’s agricultural production is still characterized by the coexistence of smallholder-based household farming and expanding scale operations. Small and fragmented plots remain widespread, while land transfer and the development of commercial family farms have gradually promoted larger-scale and more specialized production [1]. This production structure creates practical constraints on the transition toward smart agriculture. Fragmented farmland, relatively high technology costs, gaps in digital infrastructure and technical capacity, and uncertainty about investment returns can restrict the adoption of precision and digital technologies [1,2]. Farmers do not uniformly reject agricultural modernization: evidence from China indicates broad openness to precision-agriculture technologies, but willingness and readiness to adopt them vary substantially with farm scale, perceived economic returns, access to information, financing, and service support [1]. Against this background, public smart agriculture programs can serve not only as technology-demonstration projects but also as institutional interventions that lower adoption barriers and facilitate the transition from traditional, decentralized production toward more digitalized, standardized, and intensive agricultural production.

Land use efficiency has become an important constraint on high-quality county development in China as extensive spatial expansion becomes less sustainable [3,4]. The issue is relational as well as aggregate: urban land benefits from denser concentrations of capital, technology, and industry, whereas rural land is more often constrained by lower technological intensity, fragmented management, and limited factor inputs [5,6,7,8]. The resulting within-county disparity can impede urban–rural factor integration, making improvement on the rural side central to efficiency-enhancing convergence.

Digital technologies, represented by the Internet of Things, big data, artificial intelligence, remote sensing, and intelligent machinery, provide a potential route for improving the precision of agricultural production and the productivity of rural land [9,10]. Since 2017, the Digital Agriculture Innovation and Application Base program has introduced precision production, digital management, and intelligent equipment through successive county-level pilots. This staggered policy setting makes it possible to examine whether a production-oriented digital intervention narrows the urban–rural land use efficiency gap and, crucially, whether convergence occurs through rural catch-up rather than urban contraction.

The adoption constraints observed in China have close international parallels. Evidence from European farms shows that precision-agriculture uptake varies with farm scale, expected returns, investment costs, and regional conditions [11,12]; U.S. evidence likewise documents strong scale gradients in adoption and links precision technologies to technical efficiency [13,14]. Research on smallholders in India and Sub-Saharan Africa highlights information quality, connectivity, affordability, skills, and advisory support as recurring adoption constraints [15,16,17]. These cross-country regularities provide a comparative benchmark for the Chinese case: scale, digital readiness, and complementary services are general boundary conditions, whereas the collective land system and the national pilot designation process are institutionally specific to China.

This study makes three contributions. First, it shifts attention from the level of agricultural productivity to convergence within the county urban–rural land use system and distinguishes rural catch-up from urban contraction. Second, it treats agricultural technological progress, capital allocation improvement, and agricultural industrial upgrading as complementary transmission channels rather than isolated mechanisms, clarifying their theoretical connections and the limits of comparing their relative magnitudes. Third, it uses DML to flexibly partial out high-dimensional and nonlinear observed confounding while treating the conventional TWFE event study as an auxiliary diagnostic in view of the staggered timing of treatment.

2. Literature Review

Existing research on digital agriculture identifies several routes through which technology can affect agricultural and land productivity. One strand emphasizes production efficiency: sensing, data analytics, variable-rate technologies, and other precision-agriculture tools can improve input targeting and technical efficiency [10,13,18,19,20]. A second strand focuses on information and coordination. Digital advice, data platforms, and agricultural knowledge networks can reduce information frictions, support technology adoption, and strengthen coordination across production and value chains [9,15,17,21,22,23,24]. A third strand highlights farm scale and fixed costs. Cross-regional evidence from Europe and the United States, together with reviews of small-scale precision farming, shows that farm size and the ability to spread technology costs materially condition adoption [11,12,14,25,26].

The same literature also identifies constraints that can limit or unevenly distribute digital agriculture gains. High up-front costs, limited skills, weak connectivity, and insufficient complementary services can slow adoption, especially among smaller farms and resource-constrained producers [12,15,16,25]. Data governance, trust, advisory capacity, and the organization of agricultural innovation systems can further shape whether digital tools are converted into effective production decisions [9,24,27,28,29]. These constraints imply that digitalization can widen rather than narrow performance differences when access to capital, infrastructure, skills, and services is highly unequal, a concern documented particularly in smallholder settings [16,30].

Comparative evidence therefore points to conditional rather than universal effects. European studies emphasize the roles of farm structure and adoption costs [11,12,19], U.S. evidence shows that adoption is closely related to farm scale [13,14], and field evidence from India and other developing-country settings shows that digital advisory services depend on trust, information quality, connectivity, and complementary support [17,21,22]. Evidence from Sub-Saharan Africa similarly identifies affordability, infrastructure, skills, and institutional support as central barriers for smallholders [15,16]. These results closely mirror the boundary conditions examined later in this study while also showing that the institutional route through which those constraints operate may differ across countries.

Against this international literature, the unresolved issue is not an absence of research on digital agriculture itself. Rather, three more specific gaps remain. First, most studies evaluate farm productivity, technology adoption, technical efficiency, income, or environmental performance, while the relative efficiency of urban and rural land within the same local land system is rarely the outcome of interest [9,11,13,19,20]. Second, technological progress, factor allocation, and rural industrial integration are often examined separately even though digital transformation can connect these processes through information, investment, and value-chain coordination [9,24,31,32]. Third, conventional linear specifications can be restrictive when policy selection and outcomes depend on high-dimensional and nonlinear county characteristics. Accordingly, this study examines whether the staggered Digital Agriculture Innovation and Application Base program changes the within-county urban–rural gap in land use efficiency, how the adjustment is distributed between the rural and urban sides, and which complementary conditions shape the observed response.

3. Institutional Background and Theoretical Analysis

3.1. Institutional Background

Smart agriculture construction is an important institutional arrangement formed against the background of the Digital China initiative and the in-depth advancement of the rural revitalization strategy. For a long time, Chinese agriculture has been dominated by traditional production methods, characterized by limited precision, low resource efficiency, and short industrial chains; rural land output has long lagged behind urban land, and the urban–rural gap in land use efficiency has been pronounced. To promote the digital transformation of agriculture and accelerate agricultural modernization, the relevant national authorities launched the construction of Digital Agriculture Innovation and Application Bases (pilots) in 2017. Relying on the Internet of Things, big data, artificial intelligence, and intelligent equipment, the bases carry out digital transformation in grain, cash crops, livestock and poultry, and aquaculture; explore models and pathways for smart agriculture; and are designated and announced in successive batches. In terms of policy content, smart agriculture construction is not only an agricultural technology-upgrading project but also carries a functional orientation toward optimizing agricultural factor allocation and promoting agricultural industrial upgrading. On the one hand, by introducing digital technologies and intelligent equipment, the program raises the precision and intensification of agricultural production and improves the allocation of land, water, fertilizer, and labor, providing technological support for higher rural land output. On the other hand, by developing digitalized, standardized, and scaled production, the program promotes the integration of agriculture with processing, logistics, and services and extends agricultural value chains, creating conditions for rural land to shift from inefficient to efficient use.

China’s land institutions are also central to interpreting the empirical design. Land in urban areas is state-owned, whereas rural and suburban land is generally collectively owned; farm households hold contractual and land management rights, and agricultural operation can be reorganized through lawful transfer or leasing of land management rights without transferring collective ownership [33,34]. National land survey and cadastral rules distinguish agricultural land from construction land and provide the classification framework from which the study’s rural agricultural land and urban construction land measures are assembled [35]. The county land-transfer rate is therefore a meaningful indicator of whether fragmented management rights have been consolidated into operating units that are better able to absorb machinery and precision technologies. “Formerly poverty-stricken counties” refers to counties previously included in China’s national poverty-targeting classification [36].

3.2. Theoretical Analysis

The urban–rural gap in land use efficiency is defined here as a within-county relative disparity in output density between two land use subsystems: urban construction land supporting secondary- and tertiary-industry output, and rural agricultural land supporting primary-industry output. Because the two subsystems produce different outputs on different land categories, the gap is a scale-free relative measure of output density rather than a like-for-like comparison of production technologies; what it captures is how unevenly economic output is generated per unit of land across the urban and rural subsystems of the same county. The formation and the narrowing of the gap can also operate through fundamentally different mechanisms on the two sides: urban land use efficiency is shaped primarily by agglomeration economies, industrial composition, and the management of construction land supply [3,5,6,7,8], whereas rural land use efficiency depends on agricultural technology, factor allocation, and the scale of operation [11,13,26,37]. The index itself is symmetric: a smaller gap could arise from higher rural efficiency, lower urban efficiency, or simultaneous changes on both sides, and these possibilities have different economic meanings. Because the Digital Agriculture Innovation and Application Base program directly targets agricultural production, its principal adjustment margin is expected to lie on the rural side: urban land already benefits from denser concentrations of capital, technology, and industry, whereas smart agriculture applications can release rural output previously constrained by information, technology, and factor allocation frictions. The theoretically relevant form of convergence is therefore rural catch-up rather than urban decline; Section 6.2.1 later separates the two sides empirically so that the side of adjustment is identified rather than assumed.

Hypothesis****H1.

Smart agriculture construction narrows the urban–rural gap in land use efficiency primarily through rural catch-up, that is, by increasing rural land use efficiency without requiring a decline in urban land use efficiency.

From the perspective of technological progress, the primary driver of rural catch-up in land use efficiency is the upgrading of agricultural technology. Agricultural production in rural areas has long relied on experience and traditional methods, with low technological content and extensive resource use, leaving the output potential of land unrealized. By introducing the Internet of Things, remote-sensing monitoring, big data analytics, and intelligent equipment into agricultural production, smart agriculture construction promotes crop-condition sensing, precision irrigation and fertilization, intelligent pest and disease control, and intelligent machinery operations, shifting agricultural production from extensive to precision modes. Precision production reduces the waste of water, fertilizer, and pesticides and raises resource efficiency on the one hand, while it increases yields and product quality, raising agricultural output per unit of land, on the other hand [20]. Evidence from precision-agriculture research links these tools to measurable gains in input targeting and technical efficiency [10,13,18,19], and digital advisory services can substantially improve the quality and reach of production information [22]. Meanwhile, demonstration projects and agricultural advisory networks can lower the threshold for surrounding farmers to learn and use modern technologies [23]. Cross-country studies nevertheless show that such gains depend on complementary skills, connectivity, and service support, so a public demonstration program that bundles equipment, data