Institutional Change and Agricultural Modernization: The Impact of Land Certification on Agricultural Technology Adoption

Abstract

The adoption of agricultural technologies is paramount for enhancing global agricultural productivity and sustainability. However, widespread implementation faces significant challenges, particularly in developing regions. Using data from the China Land Economic Survey (CLES), this study examines how land certification reform affects farmers’ technology adoption behavior from an institutional perspective. Results demonstrate that land certification significantly increases agricultural technology adoption rates, with more pronounced effects observed among households possessing greater human and physical capital. A mechanistic analysis reveals that land certification facilitates technology adoption through three pathways: (1) improving credit accessibility, (2) strengthening long-term investment incentives, and (3) expanding the production and operational scale. These findings highlight land tenure security as a fundamental institutional driver of agricultural modernization, deepen the understanding of the interaction between institutions and innovation in agriculture, and offer actionable insights for integrating property rights reforms with technological advancements.

1. Introduction

Agricultural modernization stands as a core proposition in global economic transformation and human sustainable development. Under the triple challenges of population expansion, climate change, and resource constraints, the global agricultural system urgently requires a productivity revolution to achieve the arduous goal of “feeding more people with fewer resources.” The World Resources Institute’s report Creating a Sustainable Food Future (2019) warns that persisting with the current agricultural paradigm will result in a global food supply gap of 740 trillion calories and an agricultural land demand shortfall of 593 million hectares by 2050 [1]. Faced with these dual deficits in the food supply and agricultural land, the traditional model of agricultural productivity development proves to be unsustainable, necessitating the construction of a more efficient and sustainable agricultural productivity growth model to advance agricultural modernization. As a technological carrier for the reorganization of agricultural production factors, the application of agricultural technology enhances the productivity and timeliness of agricultural operations, improves the effective utilization of resources, and propels the agricultural system toward a high-efficiency, sustainable modern agriculture.

During the Green Revolution of the 1960s–1980s, developing countries achieved breakthrough growth in agricultural output through modern inputs, irrigation technology innovations, scientific applications of fertilizers and pesticides, and seed improvements, with annual growth rates of 3.1% (rice), 5.1% (wheat), and 3.8% (maize), respectively [2]. Despite the notable achievements brought about by technological innovations such as the Green Revolution, agricultural technology adoption globally confronts formidable challenges, particularly in developing countries where agricultural technological advancements have yet to achieve large-scale dissemination [3]. In Ethiopia, for instance, only 30–40% of smallholder farmers apply chemical fertilizers, with an average application rate of 37–40 kg per hectare—far below the recommended standard [4]. In India, agriculture sustains 17% of the global population, contributes 20% to the country’s GDP, and employs nearly half of its workforce. However, only 47% of agricultural operations are mechanized, a rate that lags behind other developing nations [5]. Therefore, an in-depth analysis of the influencing factors and pathways of farmers’ technology adoption behavior holds critical policy relevance for formulating effective agricultural extension policies and facilitating agricultural modernization.

The current study examines the determinants of technology adoption by farmers along three dimensions: individual characteristics, household conditions, and the social environment. First and foremost, in terms of individual characteristics, existing studies focus on the impacts of gender disparities [6,7,8], age structures [9], educational attainment [10], and health status [11]. Secondarily, the household income [12], labor force structure [13], and farmland scale [14] constitute key variables influencing agricultural technology adoption within household conditions. Finally, in the social environment, agricultural technology adoption is also influenced by factors such as the regional culture [15], technical training [16], and policy extension [17]. In practice, these multifaceted factors face a core challenge: farmers’ willingness and capacity to adopt technologies are often thwarted by prohibitive costs. These financial barriers persist throughout the technology lifecycle, clashing fundamentally with smallholder farming systems. These multi—layered factors interweave to jointly construct the micro—foundations of agricultural technology adoption decisions.

Furthermore, it is worth noting, however, that agricultural technology adoption is not an isolated economic behavior but is deeply embedded in institutional environments [18]. Essentially, technology adoption represents farmers’ rational choice to weigh costs and benefits under specific institutional constraints and incentives. Formal institutions, anchored in legal norms and policy regulations, use mandatory rules to define the boundaries and standards for agricultural technology applications. Informal institutions, conversely, rely on soft constraints such as social norms and interpersonal networks to subtly influence farmers’ value orientations, continuously shaping and guiding their technology adoption choices. Formal and informal institutions complement each other, collectively shaping farmers’ cognitive logic and risk perception of technology application, thereby exerting systematic and sustained impacts on their technology adoption decisions.

The current research has acknowledged the influence of institutional environments on agricultural technology adoption. In the realm of formal institutions, Omotilewa, Ricker, and Ainembabazi (2019) further indicate that when the effectiveness of a new agricultural technology is uncertain, one-time subsidies help generate demand for the technology, enabling farmers to experiment with it and learn from experience before making investments [19]. In the domain of informal institutions, Han et al. (2022) emphasize that social capital exerts a significantly positive impact on farmers’ willingness to adopt new agricultural technologies, with social networks, trust, and participation playing pivotal roles in such decisions [20]. However, existing studies predominantly focus on the short-term incentive effects of policy tools and the social network influences of informal institutions, overlooking property rights institutions—the core foundational institutions of agricultural production. As the bedrock of agricultural factor allocation, property rights institutions not only determine farmers’ dominance and income rights over resources like land and technology but also systematically influence long-term technological investment behaviors through deeper mechanisms such as stabilizing expectations and reducing transaction costs. Gao et al. (2021) demonstrate that land certification clearly defines and secures property rights, facilitating the reallocation of land and labor to more efficient farms, thereby generating positive aggregate effects [21]. This shows that the stability and clarity of property rights systems form the institutional bedrock for sustained agricultural technology adoption.

Against this backdrop, this study takes China’s rural land certification reform as its policy context and employs the property rights theory in new institutional economics to systematically explore the impact mechanism of land property rights clarification on farmers’ technology adoption behaviors. This breakthrough in the research perspective not only helps deepen the theoretical understanding of agricultural technology diffusion mechanisms and broaden the research scope of property rights institutions and technology adoption but also provides scientific foundations and practical guidance for deepening agricultural institutional reforms and optimizing technology extension policies, thus possessing significant theoretical and practical implications for promoting high-quality agricultural development.

2. Institutional Background and Theoretical Analysis

2.1. Institutional Background of China’s Rural Land Certification

From the late 1970s to the early 1980s, China implemented the household contract responsibility system based on the collective ownership of rural land, establishing a dual separation of rural land ownership and management rights. Under this system, the essence of rural land collective ownership remained unchanged, while farmers obtained the right to operate contracted land for profit. This significantly mobilized farmers’ enthusiasm for agricultural management and promoted rapid agricultural growth. However, with the development of the rural economy and the increasing frequency of land transfers, issues such as the unclear legal definition of rural land contractual management rights and the absence of a registration and certification system for such rights gradually emerged, triggering contradictions and disputes. Thus, there was an urgent need to legally protect farmers’ land contractual management rights.

To stabilize farmers’ land contractual management rights and ensure the security of land ownership, the Chinese government introduced a series of policies. In 2008, the Third Plenary Session of the 17th Communist Party of China Central Committee explicitly proposed “completing rural land rights ascertainment, registration, and certification work,” setting the direction for land certification. Since 2010, the Chinese government has repeatedly deployed and required the certification and registration of homesteads and collective construction land use rights. Subsequently, relevant departments successively issued policy documents to steadily advance rural land certification, registration, and certification work, incorporating work funds into local fiscal budgets with supplementary funding from the central government.

As a key policy to clarify land rights and protect the legitimate rights of landholder, land certification refers to the legal determination of the ownership of land rights, use rights, and other property rights. The policy comprises three main components: (1) Precision Certification and Registration: Using modern surveying and mapping technologies to accurately certify and register 1.48 billion mu of contracted land for 230 million rural households nationwide, establishing digital archives containing plot coordinates, ownership information, and transfer records; (2) Legal Certification Issuance: Issuing legally binding contractual management right certificates to farmers, creating a dual guarantee of “rights public notification + legal certification”; (3) National Digital Platform Construction: Building a unified national land management information platform to achieve the full-process digital management of public notifications, certificate printing, and other functions.

Since 2008, after years of experimentation, pilot implementation, promotion, and full-scale rollouts, China essentially completed the rural land certification process in 2020. This achievement realized the three-way separation of rural land ownership rights, contracting rights, and management rights, ultimately establishing a robust rural collective land property rights system.

2.2. Theoretical Analysis of Land Certification’s Impact on Agricultural Technology Adoption Behavior

2.2.1. Land Certification and Agricultural Technology Adoption

Land certification refers to the institutional arrangement that clarifies rural land ownership rights, contracting rights, and management rights through legal procedures to enhance land tenure security. Agricultural technology adoption denotes farmers’ choices and applications of new agricultural technologies. The impact of land certification on agricultural technology adoption essentially stems from the behavioral incentives for farmers arising from the enhanced tenure security. As an institutional arrangement that legally defines land ownership and clarifies farmers’ contractual management rights, land tenure certification directly promotes the adoption of agricultural technologies by enhancing the certainty of property rights and the exclusivity of benefits. On one hand, land tenure certification provides farmers with secure land use rights, eliminating the risks of technology application arising from ambiguous ownership and reducing the uncertainty of adoption [22]. On the other hand, clearly defined rights strengthen farmers’ exclusive claims to land outputs, preventing free riding by others and thereby increasing their incentive to adopt new technologies [23]. This direct effect manifests in farmers’ greater willingness to bear the upfront costs of technology adoption and their ability to form stable expectations about the benefits, ultimately shifting agricultural technology adoption from short-term opportunism to long-term rational decision-making.

Based on this, we propose Hypothesis 1: land certification can enhance farmers’ agricultural technology adoption behaviors.

2.2.2. The Mechanism of Land Certification on Agricultural Technology Adoption

In recent years, significant progress has been made in China’s rural land certification efforts, with over 95% of contracted farmland nationwide having undergone certification, registration, and title issuance. This institutional transformation has not only reshaped the structure of rural land property rights but has also triggered profound shifts in the allocation of agricultural production factors [24]. As a core driver of agricultural productivity improvement and modernization, agricultural technology adoption exhibits a notable interactive relationship with changes in land institutions. The existing research indicates that property rights stability constitutes a critical variable influencing the agricultural productive investment, and land certification, through clarifying land tenure and granting farmers long-term property rights expectations, which may exert substantial impacts on decisions regarding agricultural technology adoption [25,26].

Against this backdrop, this paper draws on property rights economics and agricultural technology diffusion theories to systematically analyze the internal logic of how land certification influences agricultural technology adoption from three dimensions: the credit acquisition capacity, long-term investment willingness, and production and operational scale. The aim is to provide a theoretical foundation for deepening rural land institutional reforms and advancing the innovative application of agricultural technologies. The research mechanism is shown in Figure 1.

(1)

Land Certification, Credit Acquisition Capacity, and Agricultural Technology Adoption Behavior

As a fundamental pillar of economic development, the property rights system profoundly influences the resource allocation efficiency and the economic behaviors of market participants [27]. In agriculture, land—the core productive factor—undergoes institutional changes to its property rights that exert far-reaching effects on farmers’ production and management decisions, capital acquisition capabilities, and technology adoption behaviors. Under traditional agricultural management models, land as farmers’ most critical productive asset has struggled to meet financial institutions’ basic requirements for collateral due to issues such as unclear tenure boundaries and incomplete property rights, leaving farmers facing severe financing constraints. Modern agricultural technologies, however, exhibit distinct capital-intensive characteristics: their upfront investments require substantial financial support, which farmers often cannot secure due to limited financing channels, thereby failing to meet the capital demands of technological adoption. This institutional barrier has made it difficult for farmers to convert land assets into effective financing tools, severely restricting their ability to access external capital and impeding the technological upgrading and modernization of agricultural production.

Land certification reconstructs the rural land property rights system through legal registration procedures, transforming traditionally ambiguous land rights into complete property rights protected by law [28]. Certified land not only clarifies farmers’ tenure boundaries but also achieves a quantifiable and verifiable valuation of land assets through the establishment of digital cadastral archives and standardized assessment systems. This process significantly enhances the legality, stability, and liquidity of land as collateral, thereby improving farmers’ credit acquisition capacity [29]. Studies have shown that certified land contract and management rights possess statutory mortgage and guarantee functions, effectively reducing financial institutions’ risk control costs and providing critical financial support for farmers to adopt modern agricultural technologies [30,31]. Consequently, land certification enhances farmers’ ability to access credit, thereby exerting a positive effect on agricultural technology adoption behavior.

Based on this, Hypothesis 2 is proposed: land certification positively affects agricultural technology adoption behavior by enhancing farmers’ credit acquisition capacity.

(2)

Land Certification, Long-Term Investment Willingness, and Agricultural Technology Adoption Behavior

Based on property rights economics theory, the stability of property rights constitutes a core determinant of economic agents’ investment decisions [32]. As a fundamental economic institution, property rights not only define economic agents’ rights to possess, use, and benefit from resources but also provide institutional safeguards for long-term investment decisions [33]. Given the protracted production cycles and slow return on investment in agriculture, ambiguous property rights tend to incentivize short-term farming practices, discouraging long-term investments such as soil improvement or irrigation infrastructure—investments that may fail to yield returns due to land reallocation or expropriation. Concurrently, technology adoption often requires complementary long-term inputs and learning costs; under conditions of tenure insecurity, farmers lack the incentive to adopt new technologies, trapping agricultural production in a low-level equilibrium.

Land certification establishes a long-term property rights system, transforming farmers’ de facto land tenure from an uncertain state into a formally secured and stable arrangement. This enhancement of tenure security strengthens farmers’ long-term confidence in land management, shifting their operational approach from short-term exploitative practices toward sustainable long-term management [34]. Moreover, the property rights incentives created by certification reduce the risk costs associated with technology adoption. This lowers the barriers for farmers to undertake upfront investments and bear risks, thereby increasing their willingness to allocate resources for agricultural technology upgrading and application. Consequently, this drives a fundamental transformation in agricultural production—from traditional extensive methods toward modern intensive practices [35,36]. Importantly, such long-term investment behavior, grounded in tenure stability, not only facilitates the effective diffusion of agricultural technologies but also propels a paradigm shift in agricultural production—from experience-based traditional methods to technology-driven approaches.

Based on this, Hypothesis 3 is proposed: land certification positively influences agricultural technology adoption behavior by strengthening farmers’ long-term investment willingness.

(3)

Land Certification, Production and Operational Scale, and Agricultural Technology Adoption Behavior

Drawing on transaction cost theory, a well-defined and stable property rights system can significantly reduce information search costs, negotiation costs, and default risks in market transactions, thereby improving the resource allocation efficiency [37]. As the core measure of rural property rights reform, land certification provides farmers with legally binding land contractual management rights certificates, establishing an institutional foundation for the market-oriented transfer of land resources. Before the implementation of land certification policies, ambiguous land ownership boundaries and irregular transfer procedures led to frequent ownership disputes and high transaction costs, causing farmers to maintain small-scale, fragmented operations to avoid potential losses from tenure conflicts. At the same time, small-scale farming limited the scale effects of technology adoption, reducing farmers’ incentives to implement mechanization and precision agriculture technologies, as the low marginal returns from dispersed operations could not cover learning costs and equipment investments [38].

Land certification clarifies property rights boundaries and strengthens legal protections, significantly reducing land-related transaction costs and creating conditions for expanding production and operational scales. After certification, standardized property registration reduces information asymmetry, lowering the difficulty of the contract negotiation and enforcement. This enables farmers to consolidate land resources through leasing, equity participation, or other arrangements, facilitating moderately scaled operations. At the same time, the emergence of scaled land operations increases the returns on technology adoption. Larger-scale operators are more inclined to introduce advanced technologies—such as smart agricultural machinery and water-saving irrigation systems—to reduce per-unit production costs and enhance competitiveness [39].

Based on this, Hypothesis 4 is proposed: land certification promotes agricultural technology adoption behavior by expanding the production and operational scale.

3. Research Design

3.1. Data Source

The data used in this paper were derived from the China Land Economic Survey (CLES) conducted between 2020 and 2022. The CLES primarily consists of survey data from 104 villages and 2600 rural households randomly selected across 13 prefecture-level cities in Jiangsu Province. In 2020, the project launched a baseline survey in Jiangsu Province. Based on the establishment and investigation of rural fixed observation points, a PPS (Probability Proportional to Size) sampling method was employed as follows: 26 survey counties/districts were selected from the 13 prefecture-level cities, 2 sample townships were drawn from each county/district, 1 administrative village was selected from each township, and 50 households were randomly sampled from each village. This yielded a total sample of 52 administrative villages and 2600 households.

In 2021, the project conducted a follow-up survey, completing tracking in 12 prefecture-level cities with an average tracking rate of 63.8%. For households that could not be tracked, other households from the same village were substituted to maintain the total sample size. In 2022, follow-up surveys were completed in 6 prefecture-level cities, 12 counties, and 24 villages, collecting a total of 1203 household questionnaires and 24 village questionnaires, with a household tracking rate of 56.4%. Again, non-tracked households were replaced to sustain the sample size. Over the three survey periods, the dataset comprises 124 village-level observations, 6251 household-level records, and 1101 plot-level datasets. The CLES data cover multiple dimensions, including land markets, agricultural production, land inputs, and rural finance, enabling the effective measurement of the explanatory variables, dependent variables, and other constructs in this study. After data processing to exclude samples with missing critical information, a final sample of 4936 valid observations remained.

3.2. Variables Description

(1)

Dependent Variable

Agricultural technology adoption (ATA) refers to the actual application behavior of agricultural producers in adopting modern agricultural knowledge and material inputs during production processes to improve production efficiency, reduce production costs, or enhance the added value of agricultural products. Drawing on relevant studies [40], we employ a hierarchical scoring method to measure agricultural technology adoption, using the number of distinct technology types applied by farmers as an indicator. Specifically, agricultural technologies encompass six key areas: improved seed services, soil testing and fertilization, crop cultivation management, pest and disease control, mechanized production, and value-added processing technologies for agricultural products. These technologies represent the current focus of China’s agricultural extension system, and they exhibit synergistic effects that demonstrate the systematic nature of technology adoption while reflecting core characteristics of modern agriculture. The scoring system is structured as follows: farmers who adopt none of these technologies receive an APTA score of 0; those adopting one technology score 1; the adoption of two technologies scores 2; and so on, with a maximum score of 6 assigned to farmers who have adopted all six technologies.

(2)

Explanatory Variable

Land certification (LC), the explanatory variable in this study, refers to the process of confirming and determining rural land ownership, use rights, and other rights, with the aim of stabilizing rural land relations by clarifying land ownership and usage rights. Following relevant research [41], land certification is operationalized as a binary dummy variable: households are coded as 1 if their land has been certified and the land certification certificate has been issued and 0 if the certification process remains incomplete.

(3)

Mechanism Variables

Based on theoretical analysis, we select the credit acquisition capacity, long-term investment willingness, and production scale as mechanism variables to explore the causal pathways through which land certification influences farmers’ agricultural technology adoption. The credit acquisition capacity measures the accessibility and scale of agricultural production funds for households, operationalized by the actual loans obtained from banks and rural commercial banks. The long-term investment willingness reflects farmers’ subjective propensity to make long-term fixed asset investments and sustain operational activities in agriculture. This is measured by farmers’ attitudes toward land redistribution after the current contract expires, specifically whether they support reallocation or maintaining the existing distribution. The scale of the production and operation reflects the spatial extent and resource capacity of farmers’ agricultural production activities, measured by the actual land area under their management.

(4)

Instrumental Variable

When exploring the impact of land certification on farmers’ agricultural technology adoption behavior, potential endogeneity issues, such as reverse causality or omitted variables, may arise. Therefore, drawing on Pan et al.’s (2024) research [24], we select the village certification duration as an instrumental variable to address endogeneity. Specifically, the village certification duration is defined as the time interval from the issuance of the first land contract management certificate in the village to the survey year. This variable satisfies the core requirements for instrumental variables: on one hand, the village certification duration is closely correlated with the progress of land certification—the earlier the certificates were issued, the earlier the village initiated land certification, implying a longer duration of clarified land tenure and thus a strong correlation with the core explanatory variable, land certification; on the other hand, the village certification duration does not directly influence farmers’ agricultural technology adoption behavior but exerts its effect exclusively through land certification, satisfying the exogeneity condition. Consequently, the village certification duration effectively mitigates endogeneity in the model, ensuring that the estimated impacts of land certification on farmers’ agricultural technology adoption behavior are more reliable and robust for causal inference.

(5)

Control Variables

We control for factors across three dimensions: individual characteristics, household characteristics, and land characteristics. First, in terms of individual characteristics, we control for the household head’s gender, age, household registration (hukou), education level, health status, and the number of days spent on agricultural production in the current year. These factors significantly influence farmers’ receptivity to new agricultural technologies and their decision-making capabilities—for example, households with higher education levels may be more inclined to adopt and apply new technologies, while extensive agricultural experience shapes their judgment and selection of different technologies [42,43]. Second, regarding household characteristics, we consider the total household labor force, non-agricultural income, and household agricultural assets. The size of the labor force affects the scale of agricultural operations; the proportion of non-agricultural income reflects the household’s dependence on the agricultural economy; and agricultural assets influence the household’s ability to bear the costs of adopting new technologies [44,45,46]. Finally, for land characteristics, we control for the distance from roads, soil fertility, soil type, and slope. The proximity of land to roads affects the crop selection and agricultural product marketing costs, thereby influencing farmers’ willingness to adopt quality-enhancing technologies [47]; the soil fertility and type directly impact planting technology choices, as high-fertility soils and crop-adaptive soil types are more suitable for high-yield and efficient planting technologies [48]; and the slope limits the application of large-scale, mechanized agricultural technologies [49]. Additionally, due to the mixed cross-sectional dataset, we include year fixed effects.

Table 1. Descriptive statistics.

Variables	Define	Mean	Std
ATA	Number of ATA adopted by farmers	0.44	0.94
LC	0 = not certified, 1 = certified	0.93	0.25
CAC	Logarithm of bank loans in the past year (yuan)	2.86	5.11
LIW	1 = Support land reallocation 2 = Support maintaining current allocation	0.51	0.5
POS	Total area of production and operation scale (mu)	18.24	67.04
VCD	Village certification duration (year)	3.59	1.63
Gender	0 = Female, 1 = Male	0.72	0.45
Age	Age	61.33	11.32
Hukou	0 = Non-agricultural hukou, 1 = Agricultural hukou	0.96	0.2
Education	Years of education	7.13	3.98
Health	1 = Disabled, 2 = Poor, 3 = Fair, 4 = Good, 5 = Excellent	3.98	1.07
Days	Days engaged in agricultural labor in the past year	74.83	98.32
Agrilabor	Number of family members engaged in agricultural labor	1.43	1.02
In_income	Logarithm of household non-farm wage income (yuan)	4.68	5.09
In_deposits	Logarithm of household deposits (yuan)	6.31	5.26
Assets	Number of productive assets	0.61	2.26
Slope	1 = Depression land, 2 = Flat land, 3 = Slope land, 4 = Other	2.02	0.43
Span	Distance to nearest road (km)	1.55	16.33
Soil	1 = Sandy soil; 2 = Loam; 3 = Clay soil; 4 = Other	2.67	20.09
Fertility	1 = Poor, 2 = Fair, 3 = Good	2.43	0.62

reports the descriptive statistics for the relevant variables.

3.3. Model Construction

In this study, our dependent variable—agricultural technology adoption—is an ordinal categorical variable. Drawing on Xu et al.’s (2024) research [40], we employ an ordered probit regression model for analysis. The ordered probit model effectively addresses the ordinal categorical nature of the dependent variable by establishing a relationship between the latent continuous variable and the observed ordered categorical outcomes, thereby the enabling estimation of the impact of independent variables on the probability of households being at different agricultural technology adoption levels [50]. The model is expressed as follows:

$${AT{A}_{i,t}}^{*}={\beta }_0+{\beta }_{1}L{C}_{i,t}+{\beta }_2{X}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(1)

In Model 1, ${AT{A}_{i,t}}^{*}$ represents the agricultural technology adoption status of individual i at time t; $L{C}_{i,t}$ reflects the land certification status of individual i at time t; ${\beta }_1$ is the parameter of the primary interest, indicating the impact of the land certification on farmers’ agricultural technology adoption; $X_{i,t}$ denotes a vector of control variables, encompassing factors at the individual, household, and land characteristic levels; ${\mu }_i$ represents individual fixed effects that do not vary over time; ${\delta }_t$ denotes time fixed effects that are constant across individuals; and ${\epsilon }_{i,t}$ is the stochastic error term.

The ordered probit model defines the actual observed values of agricultural technology adoption behavior ${AT{A}_{i,t}}^{*}$ as being transformed into ordinal categories through a set of unknown thresholds $K_1$, $K_2$, $K_3$, $K_4$, and $K_5$, which are estimated via the maximum likelihood estimation (MLE). Specifically, when ${AT{A}_{i,t}}^{*}$ ≤ $K_1$, the observed number of adopted agricultural technologies is 1 ($AT{A}_{i,t}$ = 1). When ${AT{A}_{i,t}}^{*}$ falls between two consecutive thresholds, the observed value corresponds to the respective number of adopted technologies. When ${AT{A}_{i,t}}^{*}$ exceeds the highest threshold $K_5$, the observed number of adopted technologies is 6 ($AT{A}_{i,t}$ = 6), as specified in Model 2:

$${AT{A}_{i,t}}^{*}=\left\{\begin{array}{c}1,if {AT{A}_{i,t}}^{*}\le K_1 \\ 2,if K_1<{AT{A}_{i,t}}^{*}\le K_2\\ 3,if K_2<{AT{A}_{i,t}}^{*} \le K_3 \\ 4,if K_3<AT{A}_{i,t}^{*} \le K_4 \\ 5,if K_4<AT{A}_{i,t}^{*} \le K_5 \\ 6,if AT{A}_{i,t}^{*}>K_5 \end{array}\right.$$

(2)

Thus, the probability of the observed number of agricultural technology categories can be derived from Equations (3)–(8).

$$P(AT{A}_{i,t}=1)=\Phi \left(K_1-X\beta \right),$$

(3)

$$P(AT{A}_{i,t}=2)=\Phi \left(K_2-X\beta \right)-\Phi \left(K_1-X\beta \right),$$

(4)

$$P(AT{A}_{i,t}=3)=\Phi \left(K_3-X\beta \right)-\Phi \left(K_2-X\beta \right),$$

(5)

$$P(AT{A}_{i,t}=4)=\Phi \left(K_4-X\beta \right)-\Phi \left(K_3-X\beta \right),$$

(6)

$$P(AT{A}_{i,t}=5)=\Phi \left(K_5-X\beta \right)-\Phi \left(K_4-X\beta \right),$$

(7)

$$P(AT{A}_{i,t}=6)=1-\Phi \left(K_5-X\beta \right) ,$$

(8)

where $\Phi$($·$) denotes the cumulative distribution function, and $X\beta$ represents the linear combination of explanatory variables and their coefficients. These probabilities represent the likelihood of each agricultural technology adoption behavior being observed, which is conditional on the explanatory variables and model parameters.

Moreover, to investigate the mediating role of the credit accessibility, long-term investment intention, and production operation scale in the relationship between land certification and agricultural technology adoption, we developed the following mediation models:

$$Me{\mathrm{diate}}_{i,t}={\beta }_0+{\beta }_{1}Certi{f}_{i,t}+{\beta }_2{X}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(9)

$$Adoptio{n}_{i,t}={\beta }_0+{\beta }_{1}Certi{f}_{i,t}+{\beta }_{2}Me{\mathrm{diate}}_{i,t}+{\beta }_3{X}_{i,t}+{\mu }_i+{\delta }_t+{\epsilon }_{i,t}$$

(10)

where $Me{\mathrm{diate}}_{i,t}$ denotes the mediating variables, specifically the credit accessibility, long-term investment intention, and production operation scale, with other variables being consistent with Model 1. First, we assess the impact of land certification on the mediating variables. Subsequently, we simultaneously include both land certification and mediating variables to evaluate their effects on agricultural technology adoption, thus examining the effect mechanisms through which land certification affects agricultural technology adoption.

4. Results

4.1. Baseline Regression Results

Using an ordered probit regression model, we examined the impact of land certification on farmers’ agricultural technology adoption behavior.

Table 2. Baseline regression results.

Variables	(1)	(2)	(3)
	ATA	ATA	ATA
LC	0.194 **	0.207 **	0.217 **
	(0.079)	(0.098)	(0.100)
Gender		0.308 ***	0.292 ***
		(0.067)	(0.068)
Age		−0.001	−0.002
		(0.003)	(0.003)
Hukou		0.029	0.052
		(0.135)	(0.137)
Education		0.048 ***	0.043 ***
		(0.008)	(0.008)
Health		−0.001	−0.008
		(0.029)	(0.029)
Days		0.001 **	0.001 *
		(0.0004)	(0.0004)
Agrilabor		0.126 ***	0.117 ***
		(0.027)	(0.027)
In_income		0.019 ***	0.032 ***
		(0.006)	(0.007)
In_deposits		0.095 ***	0.091 ***
		(0.011)	(0.011)
Slope		−0.058	−0.070
		(0.085)	(0.096)
Span		−0.003	−0.003
		(0.003)	(0.003)
Soil		−0.038	−0.022
		(0.030)	(0.030)
Fertility		0.047	0.062
		(0.044)	(0.045)
Fixed effects	No	No	Yes
Observations	4936	4936	4936

*** p < 0.01, ** p < 0.05, and * p < 0.1; standard errors in parentheses.

presents the regression results. Specifically, Column 1 shows that without control variables, the coefficient for the land certification variable (LC) is 0.194 (significant at p < 0.05), indicating that certified households adopt 0.194 more technology categories on average than uncertified households. As control variables and fixed effects are added in Columns 2 and 3, the coefficient increases from 0.207 to 0.217 (both significant at p < 0.05), suggesting that controlling for confounding factors isolates the true effect of land certification. This implies that land certification not only directly promotes technology adoption but may also operate through indirect channels. Overall, the results confirm Hypothesis H1, indicating a positive effect of land certification on agricultural technology adoption.

4.2. Robustness Tests

To ensure the reliability of our research conclusions, we employ two methods to conduct robustness checks on the baseline regression results. First, we modify the measurement approach of the dependent variable: assigning a value of 1 if farmers adopt any agricultural technology in production and 0 if they adopt none. This binary variable enables a more direct examination of the impact of land certification on farmers’ shift from complete non-adoption to adopting at least one technology [51], while the binary choice model also allows us to assess whether the effect of land certification remains consistent across different model specifications. Second, considering the potential influence of age on farmers’ decision-making, we exclude samples where household heads are over 70 years old and re-run the regression analysis. The robustness test results are presented in

Table 3. Robustness test.

Variables	(1)	(2)	(3)
	ATA	ATA	ATA
LC	0.217 *	0.079 **	0.313 **
	(0.113)	(0.036)	(0.126)
Control variables	control	control	control
Fixed effects	Yes	Yes	Yes
Observations	4936	4936	3798

*** p < 0.01, ** p < 0.05, and * p < 0.1; standard errors in parentheses.

.

In Table 3, Column 1 reports the baseline regression results, while Columns 2 and 3 present the results after redefining the dependent variable and excluding samples with household heads over 70 years old, respectively. The results indicate that land certification continues to exert a significant positive effect on agricultural technology adoption under both the redefined dependent variable and the adjusted sample.

4.3. Endogeneity Analysis

Despite including relevant control variables and fixed effects and conducting robustness tests in the baseline regression, the potential endogeneity arising from omitted variables may still bias our results. To address this issue, we use the village certification duration as an instrumental variable (IV) and employ a two-stage least squares (2SLS) regression.

Table 4. Instrumental variable regression results.

Variables	(1)	(2)
	LC	ATA
	2SLS—First Stage	2SLS—Second Stage
LC		0.133 *
		(0.069)
VCD	0.031 ** (0.014)
Control variables	Yes	Yes
F-statistic in the first stage	53.1 ***
Observations	4936	4936

*** p < 0.01, ** p < 0.05, and * p < 0.1; standard errors in parentheses.

presents the IV regression results.

The first-stage regression in Table 4 reveals a significant positive association between the instrumental variable (VCD) and land certification, indicating that longer village certification durations are associated with higher probabilities of land certification. The Kleibergen—Paap F-statistic of 53.1 (p < 0.01) rejects the null hypothesis of weak instruments, thereby confirming the IV’s validity. In the second-stage results, land certification remains a significant positive predictor of agricultural technology adoption (ATA). These findings suggest that after addressing the endogeneity, land certification continues to exert a robust positive effect on farmers’ technology adoption behavior.

4.4. Heterogeneity Analysis

To explore how land certification influences agricultural technology adoption behavior across farm households with varying characteristics and uncover deeper factors affecting their adoption decisions, we conduct a heterogeneity analysis across two dimensions: human capital (education level) and physical capital (financial reserves). Human capital is measured by the householder’s years of schooling, while physical capital is proxied by the household’s savings deposits. Households with differing education levels vary in their knowledge absorption, information access, and cognitive comprehension of new technologies, while those with disparate financial reserve levels exhibit distinct capacities to assume new technology costs and risks—these variations may yield heterogeneous effects of land certification on technology adoption across groups.

Table 5. Heterogeneity analysis.

Variables	(1)	(2)
	ATA	ATA
LC	0.105 ***	0.026 ***
	(0.008)	(0.007)
Education	0.024 *
	(0.011)
In_deposits		0.019 ***
		(0.003)
LC*Education	0.071 ***
	(0.013)
LC * In_deposits		0.013 *
		(0.008)
Control variables	control	control
Fixed effects	Yes	Yes
Observations	4936	4936

*** p < 0.01, ** p < 0.05, and * p < 0.1; standard errors in parentheses.

presents the results of our heterogeneity analysis.

In Column 1 of Table 5, the estimation reveals that the interaction term between the land certification and education level (LC*Education) has a coefficient of 0.071, which is significant at the 5% level. This suggests that land certification exerts a stronger promotional effect on agricultural technology adoption among households with higher education levels, indicating that the land certification and education level synergistically encourage such households to adopt agricultural technologies more proactively. This may stem from better-educated farmers’ enhanced ability to comprehend the clarified property rights and long-term income security afforded by land certification, thereby augmenting their awareness and trust in new technologies. Simultaneously, they possess superior capabilities to access and implement agricultural technologies, rendering them more inclined to experiment with and adopt novel agricultural technologies post-certification.

In Column 2, the interaction term between land certification and financial reserves has a coefficient of 0.013, which is significant at the 5% level, indicating that households with greater financial reserves demonstrate a more substantial increase in agricultural technology adoption following land certification. Land certification exhibits a more pronounced promotional effect on technology adoption among households with superior financial conditions. Households with ample financial reserves command stronger economic capacities to absorb the initial investment costs and potential risks of new technologies. Land certification further fortifies their confidence in the long-term utilization of land, prompting them to invest in agricultural technologies using their financial reserves, thereby amplifying agricultural technology adoption.

Overall, the heterogeneity analysis demonstrates that land certification generates heterogeneous effects on agricultural technology adoption across households with divergent human capital and physical capital characteristics. The abundance of both human and physical capital potentiates the positive effect of land certification on agricultural technology adoption.

5. Mechanism Analysis

The baseline regression confirmed that land certification significantly promotes farmers’ agricultural technology adoption, providing a solid foundation for subsequent research. To comprehensively understand the relationship between the two, the heterogeneity analysis focused on human capital and physical capital, systematically exploring how land certification influences technology adoption under different resource endowments. This analysis revealed the complexity and diversity of land certification effects. However, to deeply analyze the mechanisms through which land certification fosters technology adoption, focusing solely on heterogeneity proves insufficient. Therefore, we introduce a mediating variable analysis, concentrating on three key factors—the credit accessibility, long-term investment propensity, and production and operational scale—to uncover the pathways through which land certification affects agricultural technology adoption, thereby offering a more nuanced perspective on their internal relationship. The regression results for the mediating effects are reported in

Table 6. Mechanism regression results.

Variables	(1)	(2)	(3)	(4)
	ATA	CAC	LIW	POS
LC		0.420 ** (0.206)	1.617 *** (0.569)	0.059 ** (0.029)
CAC	0.021 ** (0.010)
LIW	0.014 ** (0.006)
POS	0.012 *** (0.004)
Control variables	Control	Control	Control	Control
Fixed effects	Yes	Yes	Yes	Yes
Observations	4936	4936	4936	4936

*** p < 0.01, ** p < 0.05, and * p < 0.1; standard errors in parentheses.

.

In Table 6, the first row of regression results presents the impact coefficients of the independent variable on the mediating variables. It is evident that land certification exerts a significant positive effect on farmers’ credit accessibility, long-term investment willingness, and production and operational scale. The impact coefficient of land certification on farmers’ credit accessibility is 0.420 (p < 0.05), indicating that land certification significantly enhances farmers’ credit accessibility. In terms of long-term investment willingness, the impact coefficient reaches 1.617 (p < 0.01), demonstrating a highly significant promoting effect on farmers’ long-term investment willingness. For the scale of production and the operational scale, the impact coefficient is 0.059 (p < 0.05), signifying that land certification substantially expands farmers’ land production and operational scale.

The second row of regression results indicates that farmers’ credit accessibility positively influences agricultural technology adoption behavior, with an impact coefficient of 0.021 (p < 0.05). This finding underscores the critical supporting role of credit in the process of agricultural technology adoption. From an economic behavior perspective, adopting agricultural technologies typically requires a substantial capital investment. Improved credit accessibility effectively alleviates farmers’ financial constraints, enabling them to cover the initial costs of implementing these new technologies. Abate et al. (2016) demonstrated in their research that institutional financial services significantly impact the adoption and intensity of agricultural technologies among Ethiopian farmers [52]. Their findings align with this study’s result, confirming that credit accessibility promotes agricultural technology adoption. Thus, Hypothesis H2 is validated.

The third column of the regression results reveals that the impact coefficient of the long-term investment willingness on agricultural technology adoption behavior is 0.014 (p < 0.05). This suggests that a stronger long-term investment willingness correlates with a higher propensity to adopt agricultural technologies. From the perspective of agricultural production logic, an enhanced long-term investment willingness reflects farmers’ stable expectations and strategic planning for agricultural production, rendering them more inclined to undertake upfront investments and assume risks associated with technology adoption. These findings resonate with the classic literature on agricultural technology diffusion [51]. Accordingly, Hypothesis H3 is validated.

The fourth column of the regression results shows that the production scale has a significant positive impact on agricultural technology adoption behavior, with a coefficient of 0.012 at the 1% statistical significance level. This suggests that the expansion of the production and operational scale can significantly promote farmers’ adoption of agricultural technologies. From the perspective of production scale economies, when land operation scale expands, farmers are more motivated to adopt technologies to improve production efficiency. Ma et al. (2023) demonstrate that the production scale promotes continuous and large-scale agricultural land operations [53], which in turn facilitates farmers’ adoption of mechanized farming methods and new green technologies, thereby driving agricultural technological progress. Therefore, Hypothesis H4 is supported.

6. Discussion

As the critical link connecting smallholders with modern agricultural development, agricultural technology adoption faces dual challenges from institutional constraints and production incentives, becoming a key bottleneck restricting high-quality agricultural development [54]. Land certification, as the core measure of rural property rights reform, profoundly influences farmers’ technology adoption behavior by reshaping their property rights expectations and resource allocation capabilities. Focusing on this fundamental institutional reform, we systematically examine the impact mechanism of land certification on farmers’ agricultural technology adoption.

First, our baseline regression verifies the promoting effect of land certification on agricultural technology adoption, which aligns with existing findings on how property rights security drives technology adoption. Abdulai et al. (2011) similarly demonstrate that land ownership tends to promote investments in soil improvement and natural resource management practices in Ghana [55]. Specifically, land certification facilitates agricultural technology adoption through two pathways: risk mitigation and incentive restructuring. At the risk mitigation level, land certification legally entrenches land contractual management rights, reducing farmers’ concerns about property rights instability and uncontrollable returns [56]. This enhanced security encourages farmers to undertake long-term technology investments. At the incentive restructuring level, land certification transforms farmers’ identity perception from temporary users to long-term holders, shifting their focus toward maximizing land productivity over extended periods [57]. Consequently, technology adoption becomes a core strategy for enhancing the land asset value.

Second, we identify three transmission channels—the credit access capacity, long-term investment willingness, and production and operational scale—through which land certification affects farmers’ agricultural technology adoption. In terms of credit access, land certification endows land management rights with implicit collateral attributes and credit enhancement functions, effectively addressing the dual challenges of collateral shortages and information asymmetry in rural financial markets [58]. Our findings align with Ma et al. (2017), who confirmed that strengthening property rights positively influences farmers’ access to formal credit [59]. However, while Ma et al. (2017) focused on the impact of land certification on the overall credit volume, we specifically link this credit improvement to technology adoption outcomes. Regarding the long-term investment willingness, land certification reduces farmers’ temporal discount rate for land rights by clarifying contract terms and resetting psychological accounting frameworks. This enhances their stable expectations of future returns, incorporating technology investments into long-term asset allocation strategies [60]. Our study provides micro-level behavioral evidence to support the classic proposition of “property rights incentivizing investment,” advancing beyond early research that merely examined how property rights security affects investment decisions. Instead, we offer deeper insights into the decision-making process itself [29]. Concerning the production scale, land certification reduces negotiation costs and legal risks in land transfers by clearly defining property boundaries and legalizing rights transfers. This facilitates the concentration of land resources among farmers with stronger technology adoption capabilities, creating favorable conditions for a scaled technology application [61]. While consistent with related studies’ inferences [35], our research particularly highlights the critical role of optimized land allocation in improving technological efficiency.

Overall, as an official institutional arrangement, land certification rectifies institutional defects, such as property rights ambiguity, alleviating technology adoption constraints across the financial markets, decision-making psychology, and factor markets. These results echo the core arguments of new institutional economics regarding how effective institutions reduce transaction costs and incentivize economic behavior [62]. They also verify that in China’s rapidly transforming rural context, clearly defining and strengthening property rights can effectively activate farmers’ endogenous motivation to adopt modern agricultural technologies. This provides crucial institutional economic evidence for achieving an organic integration between smallholders and modern agricultural development.

7. Conclusions

In the global agricultural modernization process, the transformation of agricultural development from being factor-driven to innovation-driven has emerged as a pivotal trend. Agricultural technology, serving as a core engine for enhancing production efficiency and optimizing resource allocation, holds strategic significance in connecting smallholder farmers to modern agricultural systems. The existing research has examined this phenomenon across multiple dimensions, such as factor inputs and policy incentives, yet the underlying mechanisms of smallholders’ technology adoption behavior through the lens of property rights institutions remain underexplored. Land certification, as a cornerstone of rural property rights system reform, profoundly influences farmers’ agricultural technology adoption by reshaping their property rights expectations and resource allocation capacities.

Utilizing data from the China Land Economic Survey (CLES), we analyzed the impact of land certification on agricultural technology adoption and uncovered its pathways through credit accessibility, long-term investment propensity, and the production and operational scale. This comprehensive study aims to deepen theoretical understandings of the nexus between property rights system reform and agricultural technological progress while providing empirical evidence for optimizing institutional designs that integrate smallholders into modern agriculture. Our results reveal that land certification significantly enhances farmers’ agricultural technology adoption. Mechanistically, land certification alleviates financial constraints by improving credit accessibility, promotes technological investment by bolstering the long-term investment propensity, and achieves economies of scale by expanding production and the operational scale—these three pathways jointly shape farmers’ technology adoption behavior.

These findings not only validate the critical role of property rights institutions in promoting agricultural technology dissemination and adoption but also provide empirical evidence for better aligning rural property rights reforms with agricultural extension policies. More importantly, the identified mechanisms through which land certification influences technology adoption offer valuable insights for other developing countries facing similar development challenges. While historical contexts, ownership structures, and policy environments vary across nations, ensuring farmers’ access to clear, secure, and legally protected land rights remains a universal prerequisite for incentivizing the adoption of long-term, capital-intensive agricultural technologies. Nevertheless, successful policy implementation requires contextual adaptation, particularly through complementary measures including well-functioning financial services, effective agricultural extension systems, and market mechanisms that facilitate appropriate land transfers [63].

Considering these findings and the current realities, we propose the following policy recommendations for consideration and implementation:

(1)

Establish a diversified policy-supported financing system: The central and local governments should jointly establish a risk compensation fund to reimburse financial institutions for a portion of the losses incurred from agricultural technology loans, thereby effectively mitigating lenders’ risks.

(2)

Optimize agricultural technology subsidy policies: Design differentiated subsidies for technologies requiring long-term investment (e.g., soil improvement and smart agriculture) via direct subsidies and tax incentives to mitigate farmers’ investment risks and enhance their propensity to adopt long-term technologies.

(3)

Fortify land circulation service systems: Invest in land circulation service platforms to improve the information dissemination, price evaluation, and contract management. Establish incentive mechanisms for land circulation, such as policy support for households achieving technology-adaptive operational scales, to facilitate a large-scale technology application.

This study has limitations that warrant attention in future research. First, the data source (CLES) exhibits regional coverage limitations; subsequent studies could incorporate multi-source data and expand samples to encompass more agricultural ecological zones and regions with diverse economic development levels, thereby enhancing generalizability. Second, this study focuses on credit, investment, and land circulation pathways while omitting an analysis of psychological and social factors (e.g., social network effects and policy perception biases). Future research could integrate behavioral economics theories to broaden analytical dimensions.