Can the application of digital production technology promote large-scale farming? Evidence from Chinese rice farmers

Large-scale farming is crucial to improving agricultural competitiveness and increasing farmers’ income. With the rapid development of the digital economy, applying digital technology in agricultural production is regarded as a new source of enhancing agricultural competitiveness. However, few studies have discussed the potential link between the application of digital production technology and large-scale farming from a micro-perspective. Based on survey data from rice farmers in Jiangsu Province, China, in 2023, this study uses an endogenous switching model to analyze the impact of digital production technology on large-scale farming and its potential mechanisms. The results show that applying digital production technology significantly promotes large-scale farming at both the farm and plot levels. These findings remain robust after a series of robustness tests, including changing estimation methods and redefining dependent variables. Mechanism analysis reveals that digital production technology promotes large-scale farming through three pathways: reducing labor costs, mitigating disaster risks, and enhancing managerial capabilities. The heterogeneity analysis indicates that the impact of digital production technology on large-scale farming is more pronounced among farmers with high digital literacy or those in regions with advanced digital finance. This study emphasizes that promoting digital production technology is feasible for achieving large-scale farming.

1 Introduction

Promoting large-scale farming and concentrating farmland in the hands of more capable farmers has been widely regarded as a key strategy for enhancing agricultural competitiveness and increasing farmers’ incomes, particularly in nations dominated by smallholder farming (Duan et al., 2021; Yin et al., 2024). However, this transition faces significant challenges. The first challenge stems from the dual decline in both the quantity and quality of agricultural labor due to rapid industrialization and urbanization worldwide. These structure shifts have led a substantial number of young and middle-aged rural laborers to mi-grate from small-scale farming to industries and service sectors (Wang et al., 2021), posing a severe threat to food security (Ren et al., 2023).

Additionally, during the shift from small-scale to large-scale agriculture, it is common for farmers’ actual farm or plots size to fall short of their ideal sizes (Deininger et al., 2011). In some countries, farm or plot size are even gradually shrinking (Wang X. et al., 2024). A key factor driving these phenomenon remains the labor force reallocation across sectors in the industrialization and urbanization, which elevate non-agricultural wages and, in turn, increases labor costs—particularly wages for hiring agricultural workers—thereby raising the costs of large-scale farming. Moreover, scaling up agricultural operations inherently involves greater risk exposure and demands higher managerial capabilities from farmers (Zhao et al., 2024). As a result, overcoming the threefold barriers of high production costs, increased risks, and limited managerial capability has become an urgent issue in promoting large-scale farming and strengthening agricultural competitiveness.

A promising approach to addressing these challenges is the integration of digital technologies into agriculture (Zhang et al., 2023). With the rapid development of the digital economy, an increasing number of countries have launched digital agriculture initiatives (Trendov et al., 2018). Technologies such as the Internet of Things (IoT), drones, and artificial intelligence have rapidly permeated the agricultural sector, playing a crucial role in reducing labor intensity and enhancing management efficiency in agricultural production (Bakker and Ritts, 2018). China has been at the forefront of this digital transformation, making significant strides in the digitization of agriculture. By 2023, the digitalization level in agricultural production has reached 27.6%¹, reflecting substantial progress in this domain.

Among various digital technologies, drone has garnered particular attention in China’s grain production, where it is employed for monitoring meteorological and ecological disasters, as well as for precision pesticide and fertilizer application. Notably, the annual operation volume of agricultural (UAVs) in China exceeded 1.73 million square kilometers in 2024². The achievement is attributed not only to farmers’ independent purchase of UAVs but also largely to China’s unique socialized agricultural production services. This is because such services have largely alleviated the constraints on farmers’ purchase and use of UAVs caused by their relatively high average age and low digital literacy (Geng et al., 2024). UAV technology is of great significance for rice production, as it can not only alleviate the constraints of labor shortage on rice production but also increase rice yield through precision operations. In particular, greater yield increases have been achieved by applying pesticides with UAVs in large-area rice fields (Darith et al., 2024; Reji et al., 2025; Li et al., 2025). Given its widespread adoption and potential to transform agricultural practices, this paper focuses on examining the impact of drone technology on large-scale farming.

Relevant studies have found that the application of digital technologies in agriculture can promote agricultural technological innovation (Wang W. et al., 2024), improve agricultural total factor productivity (Haefner and Sternberg, 2020), facilitate more environmentally friendly agricultural practices (Mao et al., 2024), and increase farmers’ incomes (Xu J. et al., 2024). However, whether digital technologies can yield tangible benefits for smallholder farmers, and facilitate large-scale farming in smallholder-dominated agricultural systems remains an open question. Although this potential has been repeatedly highlighted in government policy and guidance documents, empirical evidence remains scarce. To date, few studies have explored the relationship between the digital technology adoption and large-scale farming from a micro-level perspective. This paper seeks to address this research gap.

To this end, this paper uses survey data collected from rice farmers in Jiangsu Province, China, in 2023 to theoretically and empirically analyze the impact and potential mechanisms of applying digital production technology on large-scale farming. Our research contributes to the existing literature in three key areas. First, this study expands the theoretical framework and empirical evidence regarding the economic impacts of digital technology, emphasizing that promoting digital production technologies offers a viable pathway to achieving large-scale farming. Second, to deal with the endogeneity issues, we adopt an endogenous switching model to causally examine the impact of digital production technologies on large-scale farming at both the farm and plot levels. Finally, we explore the potential mechanisms through which the application of digital production technologies influences large-scale farming, demonstrating that the adopting these technologies promotes large-scale farming by reducing labor costs, minimizing disaster risk losses, and enhancing the capabilities of farm operators. Furthermore, our heterogeneity analysis at both the farmer and regional levels, highlighting that the impact of digital production technology application is closely related to farmers’ digital literacy and the level of digital finance in their region (Cao et al., 2020; Mao et al., 2024). These findings comprehensively describe the relationship between digital production technology application and large-scale farming, providing insights for the formulation of more targeted policies.

The remainder of this paper is organized as follows: Section 2 provides an overview of the relevant literature, along with the theoretical analysis and our research hypotheses. Section 3 elaborates the data collection and empirical strategy employed. Section 4 presents the baseline results, robustness checks, mechanism analysis, and heterogeneity analysis. Section 5 concludes the study and discusses the policy implications.

2 Literature review and theoretical analysis

2.1 Literature review

Previous researches on the determinants of large-scale farming primarily falls into two categories: macro- and micro- factors. From the macro perspective, institutional factors play a crucial role. In China, the transfer of land-use rights has been identified as a key mechanism driving the formation of large-scale farming (Zhang et al., 2020). Other traditional macro-factors, such as development of agricultural labor market (Li et al., 2021), and the scale of village-level land consolidation (Wu, 2021) also influence farmers’ decisions to engage in large-scale farming. Some scholars argue that policy changes related to land property rights and land transfer regulations significantly impact large-scale farming (Ma et al., 2013), while others suggest that insecure property rights may, in fact, hinder the development of land transfer markets (Luo, 2018).

From a micro perspective, farmers are more likely to adopt large-scale farming decisions only when such expansion yields greater economic benefits, particularly for those whose primary source of income comes from agriculture (Marshall and Guillebaud, 1920). A key factor in this decision is the availability of farmers’ managerial capability or agricultural labor. Larger households tend to have a greater capacity for land cultivation due to the availability of internal labor (Clay and Johnson, 1992). Access to external labor and mechanized farming, enabled by the development of agricultural labor markets and the outsourcing of production services, also plays a significant role in facilitating large-scale farming (Zang et al., 2022). The adoption of machinery, in particular, serves as an effective substitute for labor shortages. Historical evidence from the United States suggests a similar pattern, where the large-scale farming has long dominated in the agricultural sector. In the 20th century, nearly one-third of farmers expanded their cultivation area as technology advancement in agricultural machinery reduced the labor constraints (Olmstead and Rhode, 2001). Other studies argue that the development of agricultural mechanization has reduced local agricultural output value, mainly because mechanization has promoted the conversion of cultivated land originally used for cash crops to food crops, and has also led to agricultural laborers losing their agricultural employment opportunities(Zou et al., 2024).

When shifting the attention from traditional factors to the role of digital production technology in smallholder-based economies, several new questions need to be uncovered. First, are smallholders willing to adopt digital production technology? Some researchers suggest that younger farmers, those in better health (Cai et al., 2023), individuals with higher educational attainment (Zheng et al., 2019), and households with higher incomes (Wachenheim et al., 2021)—particularly those whose primary income derives from agricultural production (Ren et al., 2023) and those who participate in cooperatives (Yue et al., 2023)—are more likely to embrace digital production technologies. However, a unified framework for analyzing farmers’ adoption decision remains lacking, necessitating rigorous empirical testing.

Second, can digital production technology facilitate the expansion of large-scale farming? Recent literature highlights the positive effects of agricultural digitalization on improving labor productivity (Vărzaru, 2025), and increasing land productivity by promoting the adoption of new technologies (Zhang and Zhu, 2025). However, limited evidence has been found regarding its impact on large-scale farming decisions and the underlying mechanisms.

Third, do digital technologies lead to divergent production decisions among farmers? Variations in farmers’ resource endowments may result in differences in their ability to effectively utilize digital production technologies, potentially influencing production efficiency (Rotz et al., 2019). Further research is needed to explore whether and how these disparities translate into heterogeneous effects on large-scale farming decisions.

Based on the analysis aforementioned, this paper investigates the impact of farmers’ digital technology adoption on large-scale farming, explores the underlying mechanisms, and examines the heterogeneous effects across different farmers and external environments. The findings of this paper aim to provide empirical insights for smallholder-based countries to advance agricultural digital production technology and promote large-scale cultivation, ultimately enhancing agricultural production efficiency.

2.2 Theoretical analysis

Farmers, as rational economic agents, make large-scale farming decisions by evaluating factor prices, production risks, and their own capabilities to maximize profits. According to the theory of induced technological change, technology advancements influence the relative prices of production factors, thereby altering both the quantity and structure of the inputs that farmers utilize (Ke et al., 2024). The agricultural digital production technology examined in this paper, i.e., Plant Protection Unmanned Aerial Vehicles, offer key advantages, including labor saving, disaster risk mitigation, and enhanced managerial capabilities. These features can substantially affect factor costs, input quantities, and their composition. Accordingly, this paper develops a theoretical framework to illustrate how digital production technology affects large-scale farming by reducing labor costs, mitigating disaster risk losses, and enhancing managerial capabilities (Figure 1).

Figure 1

Figure 1. Theoretical mechanism.

2.2.1 Mechanism I: reducing labor

The application of digital production technologies significantly contributes to saving labor and addresses the challenges posed by high labor costs in large-scale farming. By integrating digital technologies with mechanization, reliance on manual labor can be significantly reduced, particularly for labor-intensive tasks. For instance, drone technology for crop monitoring, pest control, and precise pesticide spraying not only enables real-time field and crop monitoring but also reduces the workload associated with manual inspections and pesticide application (Farooq et al., 2020). Additionally, conventional agricultural machinery, such as tractors, harvesters, and seeders, can be upgraded with sensors and intelligent control systems to facilitate autonomous driving and precision operations. These advancements reduce labor dependency, lower physical workloads (Strub et al., 2021), and enhance both the efficiency and safety of agricultural production.

Particularly during peak farming seasons, when labor shortages are pronounced, large-scale farmers often have to offer higher wages to attract workers (Xu D. et al., 2024). The rising cost of hired labor poses a substantial financial burden on large-scale farming, especially amid continuous wage increase. Although machinery and fertilizers can partially substitute labor in agricultural production, such substitution is constrained by technological limitation and agronomic conditions. Digital production technologies offer a more effective solution by reducing labor reliance and lowering hiring costs. Based on this analysis, we propose our hypothesis 1:

H1: The application of digital production technology facilitates large-scale farming by reducing labor costs.

2.2.2 Mechanism II: reducing disaster risk losses

As farming operations expand, the associated risks tend to escalate, leading to a greater dis-economy of scale and aggregation of risks for large-scale farmers compared to their small-scale counterparts. In traditional production practices, farmers often apply pesticides preventatively based on experience to reduce yield losses caused by pests and diseases (Zhao and Yue, 2020). However, this approach often results in excessive pesticide use and increased labor costs while failing to effectively address unexpected pest and disease outbreaks beyond prior experience, ultimately leading to sub-optimal preventive outcomes.

The application of digital production technology can effectively reduce disaster-related losses, particularly in large-scale farming. Technologies such as smart sensors and drones enables real-time monitoring of environmental conditions (Muangprathub et al., 2019), allowing for precise detection of potential risks. Additionally, these tools assist agricultural extension services in delivering timely information on meteorological and ecological disasters (Farooq et al., 2020). By providing accurate and timely data, digital production technologies can effectively mitigate agricultural risks (Deichmann et al., 2016), reduce the frequency and intensity of preventive pesticide applications, and enable farmers to implement targeted interventions in response to detected disasters, thereby enhancing their resilience (Grabs, 2020). Based on the preceding analysis, we propose our hypothesis 2:

H2: The application of digital production technology facilitates large-scale farming by reducing disaster risk losses.

2.2.3 Mechanism III: enhancing managerial capabilities

The application of digital production technology enhances farmers’ managerial capabilities, prompting them to expand their farming scale in alignment with their improved competencies. Specifically, digital production technology strengthens farmers’ ability to monitor and respond to agricultural risks, with drones playing an important role in pest and disease control. Compared to manual pesticide application, drones enables rapid large-scale spraying (Zizinga et al., 2024), effectively addressing the quick spread of pests and infestations (Schmale et al., 2008).

Besides, digital technology offers intelligent and precision-driven solutions (Keller et al., 2014), enhancing farmers’ capability for precision farming. For example, drones equipped with high-resolution cameras offer real-time insights into crop conditions, allowing farmers to accurately access needs of crops and soil (Lopez-Cueva et al., 2024). These advancements not only optimize resource utilization (Huang et al., 2009) but also create optimal conditions for crop growth, thereby increasing yield per unit area (Araújo et al., 2021). From the analysis above, we propose our hypothesis 3:

H3: The application of digital production technology facilitates large-scale farming by enhancing managerial capabilities.

2.2.4 Heterogeneity in farmers’ digital literacy and regional digital finance

Despite the benefits of digital production technology on large-scale farming, its utilization is non-negligibly constrained by farmers’ endowments, such as digital literacy and financial resources. On one hand, data-driven digital production technology is inherently technical and complex, requiring a high level of digital literacy. Farmers with higher digital literacy are better equipped to integrate those technologies into agricultural practices and address production challenges more effectively (Khanal and Mishra, 2016). On the other hand, digital financial services can alleviate the financial constraints associated with farming expansion. In particular, digital inclusive financial services in rural areas reduce borrowing barriers and transaction costs, enabling farmers to access formal credit more easily (Lin and Peng, 2025; Yu et al., 2024). Building on this analysis, we propose our hypothesis 4:

H4: The impact of digital production technology on large-scale farming is more pronounced among farmers with high digital literacy or those in regions with advanced digital financial services.

3 Study design

3.1 Model setting

Confounding variables that simultaneously affect farmers’ adoption of digital production technology and large-scale farming decisions may lead to self-selection bias. These confounders include observable factors, such as health and family size, as well as unobservable factors, such as individual ability. Since OLS regressions cannot fully control these confounders, we employ the Endogenous Switching Regression (ESR) model to estimate the impact of digital production technology. This model is implemented in two stages: the first stage uses a Probit regression model to estimate the probability of adopting digital production technology, while the second stage examines the determinants of farming scale separately for adopters and non-adopters. To account for heteroscedasticity issues in two-stage estimation, we adopt Full Information Maximum Likelihood (FIML) estimator (Lokshin and Sajaia, 2004).

In the first stage, a choice model is constructed to reflect farmers’ decision-making process regarding the adoption of digital production technology. Let $D_A^{\ast }$ denote the expected profit from adopting digital technology, and $D_N^{\ast }$ denote the expected profit from not adopting it. Both of them are unobservable. The profit difference between adoption and non-adoption is then defined as $D_i^{\ast }=D_A^{\ast }-D_N^{\ast }$ . If this difference is positive $(D_i^{\ast }>0)$ , farmers are more likely to adopt digital production technology, as represented by Equation 1.

$D_i^{\ast }=Z_i\alpha +{\delta }_i+{\mu }_i,\mathit{\text{with}}{D}_i=\{\begin{array}{cc}1,& D_i^{\ast }>0\\ 0,& \mathit{\text{otherwise}}\end{array}\text{ (1)}$

Where $D_i$ represents the observable component of the unobserved $D_i^{\ast }$ , when farmer i adopts the technologies. $Z_i$ is a vector of variables influencing technology adoption, including individual, household, and village characteristics. ${\delta }_i$ captures regional fixed effects, and ${\mu }_i$ is the error term. The parameter of interest, $\mathrm{\alpha }$ , is to be estimated.

In the second stage, we estimate two separate outcome equations for adopters and non-adopters of digital production technology, as specified in Equations 2a, 2b:

$\begin{array}{ll}{Y}_{\mathit{ki}}=X_i{\beta }_{\mathit{ki}}+{\delta }_i+{\epsilon }_{\mathit{ki}},\mathit{\text{if}}{D}_i=1& (2\text{a})\end{array}$

$\begin{array}{ll}{Y}_{\mathit{ni}}=X_i{\beta }_{\mathit{ni}}+{\delta }_i+{\epsilon }_{\mathit{ni}},\mathit{\text{if}}{D}_i=0& (2\text{b})\end{array}$

Where Y_ki and Y_ni denote the farming scale for adopters and non-adopters, respectively. X_i represents a vector of factors influencing farming scale, while ε_ki and ε_ni are the corresponding error terms. Notably, the control variables in the selection equation (Z_i) and the outcome equations (X_i) may overlap. However, for proper identification, at least one instrumental variable (IV) must be included in the selection equation that does not appear in the outcome equations. This valid instrument should meet the criteria that it influences the adoption of digital production technologies but not directly affect farming scale.

Furthermore, to address potential inconsistency in the error terms between the choice equation and the outcome equation μ_i, ε_ki and, ε_ni we adjust the outcome equations according to Equations 3a, 3b:

$\begin{array}{ll}{Y}_{\mathit{ki}}=X_i{\beta }_{\mathit{ki}}+{\delta }_i+{\rho }_k{\vartheta }_{\mathit{ki}}+{\upsilon }_{\mathit{ki}},\mathit{\text{if}}{D}_i=1& (3\text{a})\end{array}$

$\begin{array}{ll}{Y}_{\mathit{ni}}=X_i{\beta }_{\mathit{ni}}+{\delta }_i+{\rho }_n{\vartheta }_{\mathit{ni}}+{\upsilon }_{\mathit{ni}},\mathit{\text{if}}{D}_i=0& (3\text{b})\end{array}$

Where ρ_k represents the covariance between μ_i and ε_ki. ρ_n represents the covariance between μ_i and ε_ni. The inverse Mills ratios, ϑ_ki and ϑ_ni, are incorporated to correct for selectivity bias arising from unobserved factors. ϑ_ki and ϑ_ni denote error terms with an expected mean of zero.

Finally, we compare the observed outcomes of adopters with their counterfactual scenarios to estimate the Average Treatment Effect on the Treated (ATT), as shown in Equations 4a, 4b:

$\begin{array}{ll}E[Y_{\mathit{ki}}\mid D_i=1]X_i{\beta }_{\mathit{ki}}+{\delta }_i+{\rho }_k{\upsilon }_{\mathit{ki}}& (4\text{a})\end{array}$

$\begin{array}{ll}E[Y_{\mathit{ni}}\mid D_i=1]X_i{\beta }_{\mathit{ni}}+{\delta }_i+{\rho }_n{\upsilon }_{\mathit{ki}}& (4\text{b})\end{array}$

The ATT is given by Equation 5:

$\begin{array}{ll}[Y_{\mathit{ki}}\mid D_i=1]-E[Y_{\mathit{ni}}\mid D_i=1]=X_i({\beta }_{\mathit{ki}}+{\beta }_{\mathit{ni}})+{\vartheta }_{\mathit{ki}}({\rho }_k-{\rho }_n)& (5)\end{array}$

3.2 Data collection

This paper draws on survey data collected from rice farmers in Jiangsu Province by our research team in February 2023. Jiangsu was selected as the study area for four key reasons: First, Jiangsu is a core agricultural region in China and is undergoing a transition from small-scale farming to large-scale farming. As of September 2021, large-scale households managed 67% of the province’s arable land³. Second, Jiangsu is at the forefront of promoting digital production technologies in agriculture. In 2023, the province’s agricultural informatization level reached 51.2%, exceeding the national average by 23.6 percentage points⁴. Additionally, Jiangsu has the highest number of UAVs in China, making it a model for regions with lower levels of digital adoption. Third, Jiangsu is a key rice-producing region, with its rice cultivation and total output accounting for 7 and 10% of the national total, respectively⁵. This makes Jiangsu an ideal case for examining large-scale farming practices. Finally, Jiangsu, like many regions worldwide, faces labor shortage due to rural ro-urban migration and aging agricultural workforce. Studying this province provide valuable insights for other areas grappling with similar demographic shifts.

Given the representativeness of rice farmers in Jiangsu, our research team conducted a formal survey in February 2023 using a multi-stage sampling method. To account for regional variance in large-scale farming, digital production technology adoption, and economic development, we first selected cities from northern, central, and southern Jiangsu: Xuzhou, Yancheng, and Suqian in the north; Nantong and Yangzhou in the center; and Suzhou and Changzhou in the south. In the second stage, we randomly selected one county from each city, and three towns from each county. In the third stage, three villages were randomly chosen from each town, resulting in a total of 72 sample villages (Figure 2). Finally, we conducted face-to-face intervene with approximately 15 small-scale farmers and 6 large-scale farmers per village.

Figure 2

Figure 2. Research sample distribution map.

To ensure data accuracy and reliability, we provided professional training to surveyors, who were undergraduate and graduate students with backgrounds in agriculture and prior survey experience. The training covered questionnaire filling, effective, communication, and strategies for handling various field situations. During the survey, we implemented several quality measures, including ensuring that surveyors had a thorough understanding of the questionnaire, maintained a neutral and objective attitude, and training them to identify and address missing or anomalous data. After the survey, we conducted a rigorous data cleaning process, eliminating questionnaires with obvious errors, missing key information, or exhibiting anomalous patterns. Following this screening, we obtained 1,007 valid responses, establishing a solid dataset for this study.

3.3 Variable selection and descriptive statistics

3.3.1 Dependent variables

The dependent variables in this paper are farm scale and plot scale. In line with previous studies (Xu D. et al., 2024), farm scale is measured by the total rice planting area, while plot scale is represented by the size of the largest rice plot. Table 1 shows definitions and descriptive statistics for the main variables. It presents that the average farm size is 8.27 ha, and the average plot size is 1.53 ha. In contrast, the average farm size in China is only 0.52 ha, according to the third national agricultural census⁶. This substantial difference underscores the sample areas as model areas of large-scale rice farming in China.

Table 1

Table 1. Variable definitions and descriptive analysis.

3.3.2 Independent variables

The independent variable is measured by the use of Plant Protection Unmanned Aerial Vehicles (UAVs) for the following reasons. First, plant protection UAVs have become a pivotal tool in modern agriculture, significantly reducing production costs and increasing operational efficiency. Their adoption accelerates plant protection process while reducing labor requirements. Second, China boasts mature and world-leading drone technology, which has transitioned from the development phase to widespread adoption. Third, due to their high efficiency, precision, and intelligence, plant protection UAVs are among the most widely adopted digital production technologies by Chinese farmers. According to the 2023 survey data, 24% of the sampled households reported using UAVs (see Table 1). Given these factors, this study uses the adoption of plant protection UAVs as a proxy for farmers’ adoption of digital production technologies.

3.3.3 Mediator variables

The mediator variables in this paper encompass three aspects: agricultural labor costs, disaster risks, and managerial capabilities. Agricultural labor costs are measured by wages paid to hired laborers, considering both wage levels and the number of workers employed. Since digital technology can substitute manual labor, its adoption may reduce the demand for hired workers and lower wage expenses. To capture this effect, we surveyed farmers on the daily wages during peak seasons and self-employment wages when no hired labor was used, avoiding zero-value distortions. To assess the impact of drone technology on disaster risks, we used the frequency of preventive pesticide spraying, with a lower frequency is interpreted as a reduction in perceived risk due to DPT. Managerial capabilities are evaluated by asking farmers whether they believed their rice yield is higher than that of their neighbors. While this measurement method has a certain degree of subjectivity, our survey findings indicate that farmers generally have a basically accurate assessment of other farmers’ rice production capabilities within the same village.

3.3.4 Moderating variables

The moderating variables include farmers’ digital literacy and the level of digital financial development in their region. Digital literacy is measured by whether farmers use online channels to learn about digital production technology, serving as a direct indicator of their ability to acquire and apply such technology. However, it should be acknowledged that digital literacy needs to be measured more comprehensively from multiple aspects such as learning, social interaction, entertainment, and shopping (Zhou et al., 2024; Liu et al., 2024). In this paper, we selected “whether to learn production technologies online” to measure farmers’ digital literacy, mainly considering the direct impact of technical learning on farmers’ agricultural production planning. At the county level, digital financial development is accessed using the widely recognized Peking University Digital Inclusive Finance Index, which comprehensively reflects the penetration and quality of digital financial services in a region.

3.3.5 Control variables

Based on Mao et al. (2024) and Xu D. et al. (2024), this paper incorporates a range of micro- and macro-level factors that influence farmers’ decisions on the adoption of digital production technology and large-scale farming. These micro-level factors include gender and age of the household head, education level, health status, risk preferences, and restrictions on facility land use. At macro level, factors such as the distance from village to the town, village’s geographic characteristics, and its history of natural disasters are considered. Detailed definitions are provided in Table 1.

3.3.6 Instrumental variables

In the Endogenous Switching Model, we use the region’s average temperature and wind speed as instrumental variables. These variables influence the operational efficiency of drones for plant protection, but have little direct impact on large-scale farming, thus satisfying the relevance and exogeneity requirements for valid instrumental variables.

4 Empirical results

4.1 The estimated results of the ESR model

Table 2 present the results for factors influencing rice farmers’ adoption of digital production technology (DPT) and its impact on farm size and plot size. The results of the likelihood ratio test confirm a significant correlation between the selection equation and the outcome equations, validating the appropriateness of joint estimation.

Table 2

Table 2. ESR results of DPT adoption and its impact on farm size and plot size.

The second column presents the results of the Probit model used for the selection equation. The estimated coefficients of the instrumental variable (temperature) are all significantly positive, indicating that farmers in regions with relatively higher temperatures are more likely to adopt DPT. Second, several control variables show statistically significant effects, suggesting that individual characteristics influence farmers’ adoption decisions. For example, male household heads and those in better health are more inclined to adopt DPT, while increasing age reduces the likelihood of adoption. These results are consistent with previous studies (Cai et al., 2023).

The outcome equation in Table 2 shows that gender and terrain have statistically significant effects. In addition, age has a significant negative impact on the planting scale of both adopters and non-adopters, while health status has a consistent positive impact on the farm size and plot size of both the adopter group and the non-adopter group. Table 2 presents the factors influencing rice farmers’ adoption of digital production technologies (DPT) and the results of their impacts on farm size and plot size. The results of the likelihood ratio test confirm a significant correlation between the selection equation and the outcome equation, verifying the applicability of the joint estimation.

Table 3 presents the estimated ATT for adopters, demonstrating a statistically significant positive impact of digital production technology on both farm size and plot size. Specifically, the ATT for farm size is 0.121, and that of plot size is 0.099, indicating that the adoption of digital production technology by rice farmers leads to an increase in farm size and plot size by 37.53 and 46.82%, respectively. These findings confirm that digital production technology plays a crucial role in promoting large-scale farming.

Table 3

Table 3. ATT of DPT adoption on farm size and plot size.

4.2 Robustness tests

We employ two methods to check the robustness of the results in Table 4: altering the regression model and redefining the dependent variable. Table 4 presents the results based on propensity score matching (PSM) with radius matching. The PSM results reveal that the adoption of digital production technology leads to a 33.93% increase in farm size and a 44.16% increase in plot size. These estimates are consistent with the ESR model results in Table 4, reinforcing the robustness of our main findings.

Table 4

Table 4. Robustness test: PSM approach.

Additionally, we redefine the dependent variable using two binary indicators. “Farm size” is measured by “whether it is a large farm exceeding 3.33 ha,” and “plot size” is defined by “whether it is a large plot exceeding 0.67 ha.” Given the transformation of the dependent variables from continuous to binary, we employ the Endogenous Switching Probit (ESP) Model to account for potential endogeneity. The ESP model results, reported in Table 5, confirm that digital production technology significantly promotes large-scale farming. These findings further strengthen the reliability of our conclusions.

Table 5

Table 5. Robustness test: replace the dependent variable.

4.3 Mechanism analysis

We further investigated the mechanism through which digital production technologies affect large-scale agriculture (see Table 6) and illustrate the effects of DPT adoption on labor costs, disaster risk management, and operator capabilities.

Table 6

Table 6. Mechanism analysis.

First, DPT adoption has significantly negative correlation with the wage rates of hired labor during peak agricultural seasons, supporting the theoretical framework’s hypothesis that digital technology reduces labor costs. This result suggests that digital production technology enhances labor efficiency, mitigating the financial burden of high labor costs on large-scale farming, particularly during peak seasons. Therefore, our hypothesis (H1) is verified.

Second, there is a significant negative correlation between DPT adoption and the frequency of preventive pesticide applications by farmers. Given that a higher frequency of preventive spraying indicates greater perceived disaster risks, this result suggests that DPT adoption help reduce the disaster risk losses in large-scale farming. By enabling more accurate pest and disease monitoring and providing early warning systems, DPT reduces excessive pesticide applications and lowers potential crop losses due to disasters. This evidence validates our hypothesis (H2).

Finally, DPT adoption is significantly and positively correlated with farmers’ managerial capabilities. This correlation likely arises from the ability of DPT to help farmers enhance disaster monitoring and prevention management as well as improve their precision farming skills. By providing real-time data and analytical tools, DPT enables farmers to make more informed planting decisions, ultimately improving crop yields and quality. Collectively, these factors contribute to enhancing farmers’ managerial capabilities, supporting our hypothesis (H3).

4.4 Heterogeneity analysis

We also examine the heterogeneous impact of digital production technology on farm size and plot size across different groups of farmers, categorized by their levels of digital literacy. As shown in Tables 7, 8, the sample is divided into two groups: farmers with high digital literacy and those with low digital literacy. The results indicate that the impact of digital production technology on farm size and plot size varies with farmers’ digital literacy. Notably, the ATT in the high-literacy group is higher than that in the low-literacy group. This finding highlights the advantage of digitally literate farmers in leveraging digital production technology, supporting our hypothesis (H4).

Table 7

Table 7. Heterogeneity analysis: the moderating effect of digital literacy and digital finance on farm size.

Table 8

Table 8. Heterogeneity analysis: the moderating effect of digital literacy and digital finance on plot size.

Furthermore, to explore regional heterogeneity, we divide the samples into two groups based on the regional level of digital financial development. This classification allows us to examine the varying impact of DPT adoption on farm size and plot size across different regions. The results in Table 8 show that the ATT in the high-level group is higher than in the low-level group, suggesting that well-developed inclusive digital financial services effectively alleviate farmers’ financial constraints when expanding their farming scale, thereby enhancing the positive impact of digital production technology.

5 Conclusions and discussion

As the digital economy rapidly evolves, digital technologies are increasingly integrated into agricultural production, enhancing the management accuracy and optimizing input–output efficiency. This advancement serves as a vital strategy for developing countries to extends benefits of technology to rural areas and agriculture. Based on survey data collected in 2023 from rice farmers in Jiangsu Province, this paper examines the impact of digital production technology, i.e., drone technology, on large-scale farming. Its mechanisms and heterogeneity are further explored. The results are as follows. First, the application of digital production technology significantly promotes large-scale farming at both farm and plot levels. Second, this impact is gained by reducing labor costs, enhancing risk prevention and managerial capabilities. Digital production technologies enable real-time monitoring of crop conditions and improve operational accuracy, thereby fostering large-scale farming. Third, heterogeneity analysis indicates that the impact of digital production technology is more pronounced among farmers with higher digital literacy or those in regions with more advanced digital financial services.

The findings of this paper not only align with ongoing digital trends but also contribute to the integration of technology in agriculture, deepen our understanding of large-scale farming and land management, and offers insights for developing countries reliant on small-scale agriculture. To further facilitate the spread and adoption of digital technologies in agricultural production and promote large-scale farming, several policy implications could be drawn from the results above.

First, it is essential to promote the adoption of digital production technologies in agriculture through multiple channels. This includes incentivizing farmers to purchase digital equipment, and encouraging the development of socialized agricultural services to enhance the accessibility and efficiency of digital technologies.

Second, leveraging the opportunities presented by agricultural digitization, policymakers should strengthen both incentives and regulatory framework for land transfers. This would enable farmers to scale up their operations and transition towards large-scale farming.

Last but not least, efforts should be made to improve farmers’ digital literacy and enhance regional digital financial services. This can be achieved by providing targeted digital technology training, expanding financial service coverage, reducing service costs, and increasing access to credit and financing options. Such measures would strengthen the entrepreneurial capabilities of ‘core farmers’ and further drive agricultural modernization.

Data availability statement

The data used in the article are the survey data of the research team, and access to the article’s data is only available upon reasonable request.

Ethics statement

This study complies with ethical research standards regarding human participants.

Author contributions

XueZ: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft. YJ: Funding acquisition, Resources, Writing – review & editing. XuaZ: Supervision, Validation, Visualization, Writing – review & editing. YZ: Investigation, Supervision, Validation, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This manuscript was supported by the National Social Science Fund of China (NSSFC) Major Project (21&ZD101), the Yangzhou University Humanities and Social Sciences Research Foundation (xjj2024-05), and the General Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (2025JSYB1542).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

^https://www.digitalchina.gov.cn/2025/xwzx/qwfb/202510/t20251021_5220416.htm

^http://jl.people.com.cn/n2/2025/0909/c349771-41345562.html

^http://www.ghs.moa.gov.cn/ghgl/202109/t20210910_6376135.htm

^https://www.jiangsu.gov.cn/art/2024/2/28/art_46548_290.html

^https://tj.jiangsu.gov.cn/

^https://www.stats.gov.cn/

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