Digital Technology and Agricultural Production Agglomeration: Mechanisms, Spatial Spillovers, and Heterogeneous Effects in China

Abstract

Agricultural production agglomeration is pivotal for improving productivity and resource efficiency in the agricultural sector. The rapid advancement of digital technologies, including precision farming systems, agricultural big data analytics, and IoT-based monitoring tools, provides a new impetus to strengthen this trend. Through using a series of empirical models such as two-way fixed effects and spatial econometric models, this study systematically evaluated the impact of digital technology on agricultural production agglomeration based on panel data from 2012 to 2022 in China. The results indicated that digital technology significantly promotes agricultural production agglomeration by facilitating land transfers and enhancing rural human capital. Moreover, the estimated coefficient was 0.273 and significantly positive at the 5% level, and a series of robustness tests showed that the robustness of this argument was not interfered with by endogenous issues. Furthermore, the influence of digital technology on agricultural production agglomeration exhibits significant spatial spillover effects, with direct effects outweighing indirect ones. A heterogeneity analysis showed that the effect is more pronounced in non-grain-producing regions and in the eastern, central, and northeastern parts of China but remains statistically insignificant in the western region. Based on these findings, the study recommends advancing region-specific digital integration, expanding rural digital infrastructure, and fostering digital competencies to accelerate agricultural production agglomeration and promote sustainable agricultural development.

1. Introduction

Amidst several worldwide predicaments, agriculture—a vital sector that guarantees human existence and advancement—is experiencing unparalleled metamorphosis. By 2050, there will likely be more than 9.7 billion people. Therefore, to meet this demand, food production will need to increase by 70% [1]. However, many countries are struggling to keep pace due to declining productivity development, the rising consequences of climate change [2], increasing pressure on water and soil resources [3], rising production costs [4,5], and geopolitical uncertainties [6], which are disrupting supply chains [7]. In response to the global food crisis, countries such as the Netherlands have restructured their agricultural sectors, becoming leading exporters by utilizing greenhouse farming and high-tech agribusiness hubs [8]. Similarly, Korea has established agro-industrial complexes that integrate farming, research, and technology, fostering cooperation and knowledge-sharing across agricultural zones [9]. India has promoted crop-specific clusters, such as those for oilseeds and pulses, aiming to increase production and improve farmers’ incomes through promoted market linkages [10]. These innovations leverage precision agriculture and sustainable resource management despite limited land availability. In China, agricultural production agglomeration is also being recognized as an effective strategy to promote production efficiency and resource utilization [11,12], further conforming to the worldwide movement toward sustainable farming methods.

Regarding the question of how to promote agricultural production agglomeration, the existing research indicates that natural resource endowments, technology, market forces, and policies are critical factors influencing its continuous evolution [12,13,14], as evidenced by both theoretical and empirical studies. Traditionally, the formation of agricultural clusters has been explained through two main lenses. The first attributes clustering to differences in natural factors, such as geographical location and resource availability [15,16,17], while the second emphasizes the significance of economic geography and political factors, including market demands, transportation costs, and supportive policies [18,19,20]. Together, these factors contribute to the spatial concentration of agricultural activities, which leads to significant economic and ecological benefits. Economically, clustering optimizes the use of land, labor, and capital, fostering economies of scale, increasing productivity, and improving farmer incomes [11,21,22]. Ecologically, clustering encourages sustainable practices [13], reducing carbon emissions [23] and environmental pollution through precision agriculture and efficient resource management [24,25].

Given the speed at which digital technology is developing, it is increasingly permeating agricultural production and management systems, providing new mechanisms to promote clustering by facilitating the integration of essential resources such as land, capital, technology, and talent [26,27]. Digital tools, including IoT-based precision farming systems [28,29], AI-driven decision support platforms [30,31], blockchain-enabled traceability solutions [32], and drone-assisted field monitoring [33,34], enable more effective coordination among agricultural entities, promote specialization in operations, and drive economies of scale [35,36]. Moreover, regarding real-life applications of digital technologies in agriculture, a prior study found that the application of digital technologies provides relevant producers with accurate and real-time observations regarding different features influencing their productivity, such as plant health, soil quality, weather conditions, and pest and disease pressure [37,38].

Notably, according to the data provided by the Ministry of Agriculture and Rural Affairs of China (https://www.moa.gov.cn/ztzl/ymksn/gmrbbd/202311/t20231128_6441353.htm, accessed on 16 January 2025), the contribution rate of agricultural scientific and technological progress in China reached 62.4% in 2022. The support of digital technology has further enhanced the comprehensive agricultural production capacity. Digital-driven smart agriculture is developing rapidly, and the digitalization process of the agricultural industry is accelerating. For instance, the informatization rate of agricultural production reached 25.4% in 2021. The development of digital technology is promoting the two-way circulation of agricultural and industrial products and the continuous strengthening of e-commerce infrastructure. For example, the national rural online retail sales reached 2.17 trillion yuan in 2022.

Overall, these advancements, particularly through real-time data collection using sensors, cloud-based resource optimization, and automated machinery control, boost productivity at various stages of agricultural production, from planting to distribution, thereby strengthening the overall efficiency and organization of clustered development [39,40]. As a result, a major factor in the development and growth of agricultural production clusters is digital technology [41], fostering both economic growth and ecological sustainability [12,42]. Moreover, smart agriculture technological advancements also can encourage the spread of knowledge and innovation, further boosting the advantages of clustered agricultural production [43,44]. The aggregation of agricultural production factors, with technology as a key intangible asset, serves as a critical driver of clustering [45]. While scholars have acknowledged the contribution of technology to agricultural advancement, the specific relationship between digital technology and agricultural production clustering remains underexplored, presenting a gap in the literature that warrants further investigation.

There are still a series of important issues, including the question of whether digital technology affected the development of agricultural production agglomeration. Moreover, questions remain regarding the impact mechanism and whether there regional heterogeneity exists. All of these problems are the vital questions that stakeholders pay much attention to and urgently need to solve. Focusing on the issues, taking 29 Chinese provinces (municipalities and autonomous areas) between 2012 and 2022 as examples, this research contributes to filling the following three gaps by discussing the effect of digital technology on agricultural production agglomeration in China, so as to provide theoretical support for promoting agricultural production agglomeration and advancing agricultural modernization.

Overall, this research has significant theoretical and practical implications in addressing the challenges of coordinating smallholder development and overcoming the fragmentation of agricultural operations. Therefore, the contributions of this paper comprise three aspects.

First, on the theoretical front, most of the existing literature [12,13,14,15,16,17] explores the formation mechanisms of agricultural production agglomeration from a theoretical perspective, while empirical studies remain relatively scarce. On the basis of integrating digital technology and agricultural production agglomeration within the same research framework and constructing a series of econometric models such as mechanism testing models and spatial econometric models, this study quantitatively explored the impact of digital technology on agricultural production agglomeration, so as to provide a fresh perspective on how digital technology influences agricultural production agglomeration, thereby enriching and expanding the relevant theories of agricultural agglomeration.

Second, the internal mechanism of digital technology affecting agricultural production agglomeration is still less discussed in the existing literatures [39,40,41,42,43,44]. Thus, this paper identifies and tests the mechanism function of rural human capital and land transfer, so as to expand the relevant research on the influence path of digital technology on the development of the agricultural economy.

Third, with the acceleration of digital transformation, the existing literature [19,20,21,22,23,24,25,26,27,35,40] on how to adopt more accurate digital technologies to promote the development of agricultural output agglomeration in China is still less discussed. This research offers scientifically grounded strategies for upgrading and transitioning agricultural practices on the basis of theoretical and empirical analyses. In general, this research facilitates the use of digital technology to promote the level of agricultural production agglomeration and encourages the growth of clustered agriculture, thereby offering important policy insights for advancing superior agricultural expansion in China.

The rest of this paper is organized as follows. The relevant theoretical analysis and research hypotheses are given in Section 2. Section 3 provides the materials and methods, including some variables and data and their sources. Section 4 gives the relevant model settings, including a benchmark regression model, mechanism model, and spatial econometric model. Finally, Section 5 provides further results and a relevant discussion, and Section 6 shows the conclusions, implications, limitations, and future research directions.

2. Theoretical Analysis

2.1. Impact of Digital Technology on Agricultural Production Aggregation

Technological externalities, through efficiency gains and knowledge spillover, drive agricultural production agglomeration by enhancing productivity, increasing incomes, attracting participants, and supporting operational expansion [46]. Digital technology accelerates this process by providing critical external conditions for agricultural production. Firstly, smart agriculture and precision farming, powered by digital technology, maximize the distribution of production elements, increase the effectiveness of resource use, and boost agricultural productivity. This expands the production possibility frontier [47], increasing crop yields and incomes, empowering small farmers to scale up [48], and promoting production agglomeration. For example, cyber–physical systems (CPS) integrate IoT sensors, image analytics, and predictive modeling (e.g., for leaf area and biomass monitoring) to enable real-time field condition management. Such systems simultaneously mitigate scale operation risks while improving the contiguous plot management precision—providing technological foundations for agricultural production clustering. Furthermore, modern intelligent equipment aids agricultural producers in making more informed management decisions, coordinating key factors such as land and labor, reducing both operational and natural risks, and promoting more efficient resource use [49], thereby deepening the production agglomeration. Secondly, digital technology, in conjunction with financial technology, fosters digital inclusive financing, ensuring that credit is accessible to agricultural producers. This facilitates production expansion and strengthens agglomeration. Digital finance offers low-cost, convenient credit services, providing financial support for scaling up and promoting higher levels of agricultural production agglomeration [50,51]. Finally, digital technology’s incorporation into conventional industries has fostered fresh models for rural e-commerce, expanding trading markets, enhancing supply-demand matching [52], increasing the efficiency of agricultural product distribution, and boosting production agglomeration levels [17].

Therefore, this research puts forth:

Hypothesis H1.

Digital technology can promote the level of agricultural production agglomeration.

2.2. Mechanism Effects of Land Transfer and Human Capital

(1)

Mechanism of land transfer

By encouraging the aggregation of land and labor, digital technology can have an indirect impact on the total amount of agricultural produce. Firstly, digital technology can be used as a medium for disseminating information in a way that is more efficient, more comprehensive, and more accurate in obtaining credit records and client information [43,53], thereby lowering the costs of transactions involving land transfers on both the supply and demand sides. Secondly, relying on a digital technical platform for land transfer elements can aggregate the land transfer information resources on supply and demand, accelerate information sharing and circulation, expand the scope of land transfer transactions, and increase the effectiveness of allocating land resources [54], meeting the scale requirements of agricultural production aggregation. Finally, the development of digital technology can adjust farmers’ employment beliefs and patterns, increasing the opportunities for non-agricultural employment for farmers [55] and driving the probability of land transfer. It has the potential to influence agricultural production aggregation, offering material circumstances for agricultural production aggregation, which must be predicated on the concentration of land resources.

Therefore, we propose

Hypothesis H2a.

Digital technology promotes agricultural production agglomeration through the facilitation of land transfer.

(2)

Mechanism of human capital

Digital technology can increase the amount and caliber of rural labor and strengthen rural human capital. On the one hand, combining digital technology with conventional farming methods can give farmers job and entrepreneurial options, drive rural economic development, stimulate labor return, and compensate for rural human capital [56]. On the other hand, in the digital age, farmers may successfully raise the level of human capital in rural areas by improving their technical proficiency and knowledge literacy, as well as their comprehension of modern agricultural technology and advanced production information, thereby learning the knowledge and fundamental skills required for modern production at a lower cost with the help of communication infrastructure [57]. Moreover, the accumulation of human capital helps farmers adopt new technologies and make scientific management decisions, while human capital investment compatible with technology helps activate and mobilize resources such as land and capital [58], thereby bringing about the development of scale and aggregation of agricultural production.

Therefore, we propose

Hypothesis H2b.

Digital technology promotes agricultural production agglomeration through the improvement of rural human capital.

2.3. Spatial Spillover Effects

Digital technology’s ongoing use and integration in the agricultural sector has the potential to both stimulate local agricultural production aggregation and through diffusion effects encourage agricultural production aggregation in other regions [59]. Firstly, digital technology can facilitate cooperative exchanges between different regions in agricultural production. Through information transmission media such as the Internet and big data, agricultural production management entities in different regions can communicate without obstacles, thereby achieving spillover effects of technology and knowledge and driving advanced agricultural aggregation areas to influence backward areas [44,60]. Secondly, digital technology can maximize labor and capital allocation, stimulating cooperation and the division of work in more expansive areas and facilitating the formation of agricultural aggregation in various regions [61]. Lastly, agricultural regions with better digital technology applications often have greater degrees of economic development [62], which can create a “suction effect” that draws in capital and labor from surrounding areas, promoting the aggregation of agricultural production across a broader geographical range [60].

Therefore, we propose

Hypothesis H3.

Digital technology promotes agricultural production agglomeration through spatial spillover effects.

The logical diagram of the assumption relationship is shown in Figure 1.

3. Materials and Methods

3.1. Variables Description

(1)

Dependent variable: Agricultural production agglomeration

The research’s dependent variable is agricultural production agglomeration, which includes the clustering of production entities and resources engaged in crop production, as well as the concentration and scaling-up of crop outputs, including both economic and grain crops. Drawing on the prior research by Wang et al. (2023) [63] and Chang et al. (2023) [64] in agricultural production agglomeration, this paper adopts the geographic average agglomeration rate used for provincial-level analyses.

The formula employed to calculate the geographic average agglomeration rate is as follows:

$${Agg}_{it}={\displaystyle \frac{1}{k}}{\sum }_{j=1}^k{\displaystyle \frac{\frac{p_{ijt}}{p_{jt}}\times 100 }{r_{it}}}$$

(1)

In this formula, ${Agg}_{it}$ represents the agricultural production agglomeration in region $i$ in year $t$, $p_{ijt}$ signifies the output of crop $j$ in region $i$ in year $t$, $p_{jt}$ denotes the total output of crop $j$ nationwide in year $t$, and $r_{it}$ represents the rural area in region $i$ in year $t$. Due to data availability, the rural area is approximated by subtracting the urban area from the total area of each province. The value of $k$ varies across regions based on their agricultural resource endowments. For instance, if Beijing’s crop output includes fruits, vegetables, and grains in a given year, $k$ is set to 3. This method of measuring crop production agglomeration effectively reduces spatial errors caused by differences in regional land areas and provides an accurate representation of agricultural production dynamics across regions. Larger values of ${Agg}_{it}$ indicate a higher degree of agglomeration. For ease of interpretation, the outcomes are multiplied by 100.

(2)

Core explanatory variable: Digital technology

The primary explanatory variable in this research is digital technology. Currently, scholars mostly use a comprehensive indicator system to measure and evaluate this indicator. Therefore, by developing a thorough indicator system, this paper also assesses China’s degree of digital technology progress. With reference to previous research and the methodologies of scholars such as Wang et al. (2021) [65] and Zhou et al. (2023) [66], this paper develops a thorough indicator measurement system to gauge the degree of digital technology development. Three dimensions are considered: rural digital infrastructure, digital technology industry support, and digital technology development capabilities.

Table 1. Comprehensive index evaluation system of digital technology.

Dimensions	Components	Indicators	Attribute
	Rural Mobile Phone Penetration Rate	Per 100 Rural Households, the Average Number of Mobile Phones Owned	+
Rural Digital Infrastructure	The Quantity of Stations for Agricultural Meteorological Observation	Number of Agricultural Meteorological Observation Stations	+
	Density of Rural Delivery Routes	Density of Rural Delivery Routes	+
	Rural Computer Penetration Rate	The Average Number of Computers Owned by 100 Rural Households at Year-End	+
	Rural Broadband Access Density	Users of Rural Broadband (10,000 households)/Rural Population	+
	Length of Optical Cable Lines	Length of Optical Cable Lines	+
Industry Support	Scale of Information Transmission, Software, and IT Services	Legal Entities’ Share of Information Transmission, Software, and IT Services (%)	+
	Scale of E-commerce Enterprises	The Percentage of Businesses Involved in E-Commerce	+
	Urban Employment in Information Transmission, Software, and IT Services	Ratio of Urban Employment in Software, IT Services, and Information Transmission (10,000 people)	+
	Number of Websites for Every 100 Businesses	The proportion of businesses per 100 that have websites	+
Development Capability	Scale of E-commerce Development	E-commerce Sales (billion RMB)	+
	Scale of Telecommunications Industry	Total Telecommunications Business (billion RMB)	+
	Scale of Software Industry	Revenue from Software Businesses (10,000 RMB)	+
	Development of Digital Inclusive Finance	Index of Digital Inclusive Finance	+

Note: The “+” denotes that all indicators are positively correlated with the digital technology index. Higher values of these indicators reflect stronger digital technology development.

displays the selection of 14 secondary indicators for measurement.

(3)

Control variables

Based on the established agricultural economics literature, the five control variables were carefully selected as follows.

(1)

Fiscal Support for Agriculture (Fin): This variable is measured by the per capita fiscal expenditure on agriculture and forestry in rural areas. The government reduces production costs through investments in rural infrastructure and various agricultural subsidies, effectively preventing agricultural natural disasters and motivating farmers’ production efforts. This, in turn, enables the realization of large-scale and concentrated agricultural production.

(2)

Transportation infrastructure (Tra): This variable is measured by the ratio of the mileage of graded roads to the regional area. Transportation infrastructure has always been a vital part of the formation of agricultural production clusters. The improvement of rural transportation infrastructure not only facilitates the circulation of agricultural products, allowing them to enter broader markets, meet consumer demand, and expand sales reach, but also helps various agricultural operators obtain more market information. In addition, it attracts external capital and technology into rural areas, enhancing the agricultural production efficiency and contributing to the concentration level of crop production.

(3)

Agricultural mechanization (Mec): This variable is measured by the total agricultural power per capita. The continuous rise in the level of mechanization in rural areas can drive innovations in farming methods, alleviate labor shortages in rural areas, and improve the agricultural production efficiency, thereby promoting the formation and development of concentrated crop production.

(4)

Urban industrial structure (Str): This variable is determined by the proportion of the output value of the secondary and tertiary industries to the total output value in a region. The continuous improvement of the urban industrial structure plays an important role in rural development. On one hand, it promotes non-agricultural employment for the rural population in the region, facilitating the scale cultivation of crops. On the other hand, it can provide crucial elements such as technology and capital to support agricultural development, benefitting the agricultural production concentration through spillover effects.

(5)

Crop disaster rate (Cdr): This variable is expressed as the ratio of the area affected by crop disasters to the total sown area. The rate of land disasters refers to the proportion of land affected by natural disasters within a certain timeframe compared to the total land area. The extent of the land disaster directly impacts the stability and profitability of agricultural production. A higher land disaster rate can lead to a decrease in crop yields and quality, thereby affecting the agricultural output and quality. Additionally, land disasters can compromise the stability and sustainability of agricultural production, constraining long-term agricultural development.

(4)

Mechanism variables

The ratio of the transferred land area to the arable land area in each province serves as a representation of land transfer. Since there are significant differences in both administrative and arable land areas across provinces, relying solely on the transferred land area could lead to bias and affect the conclusions. Hence, the proportion of the transferred land area relative to the arable land area is used for a more standardized comparison across provinces [67]. The Central University of Finance and Economics and the Labor Economics Research Center compute the real per capita rural labor human capital (in ten thousand yuan) as a measure of rural human capital. This method offers a broader measure of human capital compared to traditional methods, which typically rely on years of education alone. It integrates factors such as education, health, training, and work experience, providing a more comprehensive and accurate reflection of rural human capital levels in China.

3.2. Data Description and Descriptive Statistics Analysis

We make use of panel data from 29 Chinese provinces (municipalities and autonomous areas) between 2012 and 2022. Because of data restrictions, the regions of Tibet, Macau, Taiwan, Shanghai, and Hong Kong are not included in this research. The investigation looks at how digital technology affects the agglomeration of agricultural output. The Peking University Digital Finance Research Center, the China Statistical Yearbook, and the China Electronic Information Statistical Yearbook are the sources of the data used in the digital technology indicators. The China Rural Statistical Yearbook is the main source of information on agricultural production agglomeration. Sources including the China Statistical Yearbook, China Rural Statistical Yearbook, China Household Survey Statistical Yearbook, and China Human Capital Report are used to gather additional control and mechanism variables. To handle missing data, we apply estimation techniques such as average annual growth rate calculations and linear interpolation. To maintain consistency and comparability over time, values for monetary-related variables are adjusted using the price index, using 2012 as the base year.

Table 2. A descriptive statistical analysis of variables.

Variables	N	Mean	Sd	Min	Max	Unit of Measurement
Agricultural agglomeration (Agg)	319	0.202	0.197	0.008	0.540	Unitless index (×100)
Digital technology (Dig)	319	0.193	0.116	0.0384	0.761	Unitless composite index
Fiscal support for agriculture (Fin)	319	0.389	0.276	0.0998	1.823	10,000 RMB per rural resident
Transportation infrastructure (Tra)	319	0.868	0.469	0.0716	2.085	km/km2 (road density)
Agricultural mechanization (Mec)	319	1.883	0.992	0.440	6.448	kW per rural laborer
Urban industrial structure (Str)	319	0.900	0.051	0.747	0.997	Ratio (secondary + tertiary sector output/GDP)
Crop disaster rate (Cdr)	319	0.143	0.112	0.00592	0.695	Ratio (affected area/sown area)
Land transfer (Lt)	319	0.226	0.254	0.0166	2.244	Ratio (transferred area/cultivable land)
Rural human capital (Rhc)	319	0.819	0.308	0.163	1.847	10,000 RMB per rural laborer

displays the descriptive statistics for the variables.

4. Model Setting

This paper uses a fixed-effects model for the regression analysis to investigate how digital technology affects agricultural production agglomeration. By establishing benchmark regression models, our goal is to determine how digital technology affects the farming methods in different geographical areas.

4.1. Benchmark Regression Model

Based on the theoretical analysis proposed in Section 2 of the paper and referring to the existing relevant studies such as [17,61], this paper establishes the following benchmark regression model to examine the direct effects of digital technology on agricultural production agglomeration:

$$A{gg}_{it}={\alpha }_0+{\alpha }_1{Dig}_{it}+{{\alpha }_{c}Control}_{it}+{\mu }_i{+v}_t+{\epsilon }_{it}$$

(2)

In Equation (2), $A{gg}_{it}$ represents the level of agricultural production agglomeration in region $i$ at time $t$; ${Dig}_{\mathrm{i}\mathrm{t}}$ represents the level of digital technology in region $i$ at time $t$; ${Control}_{\mathrm{i}\mathrm{t}}$ represents a set of other control variables affecting agricultural production agglomeration; ${\mu }_i$ and $v_t$ represent the individual fixed effects and time fixed effects, respectively; ${\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$ represents the random disturbance term.

4.2. Mechanism Testing Model

According to the previously discussed indirect mechanism analysis, two significant ways that digital technology affects agricultural production agglomeration are by encouraging land transfer and raising the standard of rural human capital. Therefore, to further empirically test these mechanisms, the mechanism testing model is set as follows:

$$M{echanism}_{it}={\alpha }_0+{\alpha }_1{Dig}_{it}+{{\alpha }_{c}Control}_{it}+{\mu }_i{+v}_t+{\epsilon }_{it}$$

(3)

In this formula, $M{echanism}_{it}$ represents the mechanism variables, which include the two indicators: land transfer and the degree of human capital in rural areas. Equation (2) is consistent with the definitions of the other variables.

4.3. Spatial Econometric Model

Since digital technology can generate spillover effects of technology and knowledge to other regions, the degree of agricultural production agglomeration in a particular area may be influenced by the advancement of digital technology in that area, as well as in other areas. Therefore, The spatial spillover effect of digital technology on the agglomeration of agricultural production is tested using the spatial econometric model, as follows:

$$\begin{gathered} A{gg}_{it}={\alpha }_0+{\alpha }_1{Dig}_{it}+{{\alpha }_{c}control}_{it}+\rho WA{gg}_{it} \\ +k_{1}W{Dig}_{it}+k_c{Wcontrol}_{it}+{\mu }_i{+v}_t+{\epsilon }_{it} \end{gathered}$$

(4)

In this equation, the dependent variable’s spatial correlation coefficient is denoted by $\rho$; the core explanatory variable and the control variables’ spatial correlation coefficients are denoted by $k_1 \mathrm{a}\mathrm{n}\mathrm{d} k_{\mathrm{c}}$, respectively. The spatial weight matrix is denoted by $W$ (constructed using spatial contiguity relationships). Other variables are defined in Equation (2).

5. Results and Discussion

5.1. Benchmark Regression Analysis

To prevent “spurious regression”, which could compromise the validity and accuracy of the estimation findings, a stationarity test must be performed prior to doing the full-sample baseline regression on the panel data. Therefore, this paper applies the LLC test to all selected variables.

Table 3. Stability test for each variable.

Variables	Test Method	t-Statistic	p-Value	Conclusion
Agg	LLC	−6.411	0.000 ***	Stationary
Dig	LLC	−5.013	0.000 ***	Stationary
Fin	LLC	−2.129	0.016 **	Stationary
Tra	LLC	−8.292	0.000 ***	Stationary
Mec	LLC	−13.829	0.000 ***	Stationary
Str	LLC	−14.0829	0.000 ***	Stationary
Cdr	LLC	−13.881	0.000 ***	Stationary
Lt	LLC	−3.583	0.000 ***	Stationary
Rhc	LLC	−2.7166	0.003 ***	Stationary

Note: Standard errors in parentheses: ** p < 0.05, *** p < 0.01.

displays the test results. All variables reject the null hypothesis at the 1% or 5% significance level, as shown in the table, suggesting that there are no unit root phenomena among the data. This confirms that the panel data are relatively stationary, ensuring the reliability of the subsequent regression analysis.

Benchmark Regression Results Analysis

Prior to carrying out the empirical regressions, the F-test and Hausman test both attest to the appropriateness of employing a fixed effects model. Therefore, in order to investigate the link between the independent and dependent variables, this research uses a two-way fixed effects model. To address issues of autocorrelation and heteroscedasticity, clustered standard errors are used.

The regression results, after adjusting for temporal and individual effects, are shown in

Table 4. Benchmark regression results.

Variables	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)	Model (6)
	Agg	Agg	Agg	Agg	Agg	Agg
Dig	0.308 * (1.85)	0.379 *** (2.90)	0.386 *** (3.28)	0.278 ** (2.49)	0.276 ** (2.49)	0.273 ** (2.45)
Fin		0.198 (1.59)	0.241 * (1.95)	0.257 ** (2.27)	0.252 ** (2.18)	0.249 ** (2.17)
Tra			0.146 (1.59)	0.144 (1.64)	0.148 (1.69)	0.149 * (1.71)
Mec				−0.031 ** (−2.29)	−0.032 ** (−2.38)	−0.031 ** (−2.31)
Str					−0.324 (−0.37)	−0.303 (−0.35)
Cdr						−0.039 (−1.34)
_cons	0.284 *** (12.18)	0.229 *** (5.91)	0.109 (1.23)	0.168 * (2.04)	0.456 (0.56)	0.443 (0.55)
N	319	319	319	319	319	319
R2	0.586	0.632	0.665	0.684	0.695	0.688
T	Yes	Yes	Yes	Yes	Yes	Yes

Note: Standard errors in parentheses: * p < 0.1, ** p < 0.05, *** p < 0.01.

. It should be noted that this paper merely adopts a double fixed effect model in the stepwise regression methods (namely, Models (1)–(6)) to explore the impact of digital technology on agricultural production agglomeration. In fact, there should be other similar models such as the random effects model, time fixed effect model, and individual fixed effect model. However, the relevant results of the above-mentioned models are not presented in this paper. The reasons for handling it this way are as follows. First, due to space limitations, it is difficult to present the results of all models one by one in this paper here. In fact, we authors have tried several other models (such as the random effects model, time fixed effect model, and individual fixed effect model) but found that these models were inferior to the bidirectional fixed effects model in terms of statistical significance and economic significance. For example, the time fixed effect model failed the significance test. The main reason is that the model was unable to effectively control the influence from regional individuals, resulting in the estimation results being affected by a large deviation. While the individual fixed effect model shows certain significance in some aspects, its economic explanation is unreasonable and deviates significantly from the existing theories and actual situations. Specifically, the influence of the core explanatory variable on the explained variable is inconsistent with the theoretical assumptions of this paper, and the estimation results are too extreme. Therefore, based on the limitations of these models, we infer that they are not suitable for this study, and only the benchmark regression results that best conform to the actual expectations are given. Second, as mentioned in Section 4.1, based on the theoretical analysis proposed in Section 2 of the paper and referring to the existing relevant studies such as [17,61], the model with considering individual fixed effects and time fixed effects is used here.

Specifically, Model (1) assesses the impact of digital technology on agricultural agglomeration without including control variables, showing that digital technology significantly promotes agricultural agglomeration. The study’s control variables are introduced one after the other in Models (2) through (6), and the positive correlation between digital technology and agricultural agglomeration is maintained throughout. The regression coefficient for digital technology in Model (6), which incorporates all control variables, is 0.273. This supports Hypothesis H1, which suggests that digital technology can raise the degree of agricultural output agglomeration by showing that the level of agricultural agglomeration increases by 0.273 units for every unit increase in digital technology.

In other words, these results indicate that the improvement of regional digital technology would be conducive to improving the level of agricultural agglomeration. The reason could be that with the construction of digital infrastructure, digital technology is being integrated into various scenarios of agriculture, e.g., digital technology can quickly deliver all kinds of information to farmers, digital financial inclusion using digital technologies provides easy financial support for agriculture, the application and development of digital technology could improve the accuracy of agrometeorological observation, and digital technology could boost sales of agricultural products, thereby promoting the development of new agricultural business models and playing an important role in agricultural economic growth.

Moreover, the coefficient of Fin is 0.249 and significantly positive at the 5% level, indicating that enhancing the fiscal support for agriculture was conducive to the improvement of agricultural agglomeration in the long term. A likely explanation for this involves the fact that by providing the necessary financial support for agricultural production, the fiscal policy encourages farmers to increase the agricultural input and improve the production efficiency. In addition, the coefficient of Tra is significantly positive at the 10% level, indicating that the improvement of the transportation infrastructure level would be conducive to promoting the agglomeration of agricultural production. Notably, the coefficients of Mec and Str all are negative, which implies that improving agricultural mechanization and promoting the transformation and upgrading of regional industries would not facilitate the agglomeration of agricultural production. However, it can be found that the coefficient of Cdr is negative, indicating that a decrease in the crop disaster rate causes an increase in the level of agricultural agglomeration. Although this coefficient is not significant, this also implies that for relevant decision-makers, in order to further promote the concentration of agricultural production, reducing the proportion of crop diseases and pests will play a certain role.

5.2. Mechanism Regression Analysis

After confirming that digital technology can drive agricultural agglomeration, it is necessary to further explore and examine the pathways through which digital technology operates. By elucidating the mechanism of their interaction, an indirect effect test will offer a theoretical foundation for the advancement of agricultural agglomeration through digital technology. The findings of the digital technology indirect effect test on agricultural agglomeration are shown in

Table 5. The mechanism results of digital technology influencing agricultural production agglomeration.

Variables	Model (1)	Model (2)	Model (3)
	Agg	Lt	Rhc
Dig	0.273 **	1.614 *	0.561 **
	(2.45)	(2.03)	(2.11)
Control variables	Yes	Yes	Yes
${\mathsf{\mu }}_{\mathrm{i}}$	Yes	Yes	Yes
${\mathrm{v}}_{\mathrm{t}}$	Yes	Yes	Yes
N	319	319	319
R2	0.688	0.623	0.642

Note: Standard errors in parentheses: * p < 0.1, ** p < 0.05.

.

The overall impact of digital technology on agricultural agglomeration is depicted in Model (1). Models (2) and (3) investigate the indirect mechanisms of land transfer and rural human capital, respectively. Model (2) examines how digital technology affects land transfers; at the 10% confidence level, its regression coefficient of 1.614 is significant. This indicates that digital technology significantly promotes land transfer to promote agricultural production agglomeration, validating Hypothesis H2a. The impact of digital technology on rural human capital is examined in Model (3), which displays a regression coefficient of 0.561, which is significant at the 5% confidence level. This supports Hypothesis H2b by indicating that digital technology promotes rural human capital to encourage agricultural production agglomeration.

5.3. Spatial Effect Analysis

5.3.1. Spatial Autocorrelation Test

A spatial autocorrelation test on digital technology and agricultural agglomeration must be carried out prior to performing a spatial econometric regression analysis. From 2012 to 2022, we compute the geographical effects of digital technology on agricultural agglomeration using Moran’s I index.

Table 6. Results of spatial autocorrelation test.

Year	Digital Technology		Agricultural Production Agglomeration
	Moran’I Value	Z Value	Moran’I Value	Z Value
2012	0.320 ***	2.875	0.336 ***	2.960
2013	0.298 ***	2.716	0.312 ***	2.783
2014	0.277 **	2.567	0.287 ***	2.608
2015	0.238 **	2.270	0.244 ***	2.281
2016	0.212 **	2.111	0.218 **	2.127
2017	0.187 *	1.918	0.191 **	1.919
2018	0.212 **	2.085	0.201 **	1.957
2019	0.222 **	2.172	0.229 **	2.185
2020	0.239 **	2.291	0.226 **	2.141
2021	0.273 **	2.557	0.242 **	2.250
2022	0.355 ***	3.176	0.261 **	2.367

Note: Standard errors in parentheses: * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the detailed findings. From 2012 to 2022, the Moran’s I index for each region’s levels of agricultural agglomeration and digital technology development is significantly favorable. This suggests a strong spatial relationship between the degrees of digital technology development and agricultural agglomeration in different geographical areas. Therefore, a spatial econometric regression analysis is warranted.

We must use the LM, LR, and Hausman tests to choose the best spatial econometric model for this investigation after performing Moran’s I test.

Table 7. Test results for the selection of spatial econometric models.

Model Test	Specific Type	Spatial Weight Matrix
LM Test	LM-lag	16.197 ***
	LM-err	5.283 **
SDM Test	SAR&SDM	91.80 ***
	SEM&SDM	91.70 ***
Hausman Test	SDM	45.75 ***
Fixed Effects Type Test	Ind&Both	15.48
	Time&Both	633.55 ***

Note: Standard errors in parentheses: ** p < 0.05, *** p < 0.01.

displays the outcomes of these examinations. Firstly, the LM test is performed. The spatial weight matrix’s LM-lag and LM-err statistics both pass the test at least at the 5% significance level, indicating that spatial econometric models can be used for regression in this research. Moreover, the t-statistic of LM-lag is greater than that of LM-err. This implies that the spatial error model (SEM model) is inferior than the spatial autoregressive model (SAR model). Second, to ascertain whether the spatial Durbin model (SDM model) is better than the SAR and SEM models, the LR test is performed. The LR statistic is significant at the 1% level, as shown in Table 7, suggesting that the SDM model is a better fit. Finally, the model type is determined using the Hausman test, which suggests choosing the fixed effects model. Further examination suggests selecting the time-fixed effects. In summary, the spatial Durbin model (SDM model) with temporal fixed effects is the suitable spatial econometric model for this investigation.

5.3.2. Spatial Effect Result Analysis

Table 8. Spatial effect of digital technology on agricultural production agglomeration.

Variables	Model (1)	Model (2)	Model (3)	Model (4)
	SDM	Direct Effects	Indirect Effects	Total Effect
	Agg	Agg	Agg	Agg
Dig	1.007 ***	1.014 ***	0.587 ***	1.601 ***
	(12.95)	(12.23)	(4.18)	(10.51)
Control variables	Yes	Yes	Yes	Yes
${\mathsf{\mu }}_{\mathrm{i}}$	Yes	Yes	Yes	Yes
${\mathrm{v}}_{\mathrm{t}}$	Yes	Yes	Yes	Yes
N	319	319	319	319
R2	0.759	0.759	0.759	0.759

Note: Standard errors in parentheses: *** p < 0.01.

displays the findings of the analysis carried out with the time-fixed effects spatial Durbin model. According to Model (1), the spatial weight matrix’s coefficient of digital technology development level is 1.007, which is significant at the 1% level. This finding confirms the positive spatial spillover effect of digital technology on the agglomeration of agricultural production, thereby validating Hypothesis H3.

Direct, indirect, and total impacts are the three categories into which the spatial effects are broken down. While the indirect effect shows how the development of digital technology in nearby regions affects the local agglomeration of agricultural outputs, the direct effect shows how local digital technology affects the agglomeration of agricultural production within the same area. Models (2)–(4) demonstrate that the direct, indirect, and total effects of digital technology are all significant at the 1% level. This suggests that the advancement of digital technology in nearby regions is just as important to the growth of agricultural production agglomerations as the improvement of local digital technology. Furthermore, the direct effect’s coefficient (1.014) is higher than the indirect effect’s coefficient (0.587), suggesting that local digital technology development has a greater impact on the agglomeration of agricultural production than it does in nearby regions.

5.4. Robustness Test

To ensure the robustness of the baseline regression results, potential endogeneity is addressed, which could occur from missing variables or reverse causality. Instrumental variable (IV) estimation is used to tackle this concern. The number of telephones per 10,000 persons in each province in 1984 is used as an instrument, in accordance with Huang et al. (2019) [68]. Two-stage least squares (2SLS) estimation is performed. Given that the instrument is cross-sectional and cannot be directly regressed in a panel setting, an approach inspired by Tao, Z et al. (2022) [69] is applied. Specifically, the interaction between the previous year’s national Internet users and the cross-sectional instrument is constructed as a time-varying variable. This serves as the IV for digital technology. The logic behind this choice is twofold: firstly, the first-order lag of national Internet users at the national level has minimal direct influence on the current agricultural production agglomeration in a specific region; secondly, the variation in Internet user data is exogenous to regional agglomeration dynamics.

The findings are reported for Model (1) in

Table 9. The robustness results analysis.

Variables	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)
	2SLS	Replace Explanatory Variables	Replace the Dependent Variable	Tail Reduction Processing	Control Fixed Effects
Dig	1.026 ***		1.647 *	0.310 **	1.424 ***
	(3.43)		(1.65)	(2.28)	(4.99)
Dig-new		0.292 ***
		(3.24)
Control variables	Yes	Yes	Yes	Yes	Yes
${\mathsf{\mu }}_{\mathrm{i}}$	Yes	Yes	Yes	Yes	Yes
${\mathrm{v}}_{\mathrm{t}}$	Yes	Yes	Yes	Yes	Yes
First-stage F Value	135.10
Kleibergen-Paap rk Wald F	130.66
$\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\times \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{e}$					Yes
N	319	319	319	319	319
R2	0.794	0.696	0.559	0.690	0.648

Note: Standard errors in parentheses: * p < 0.1, ** p < 0.05, *** p < 0.01.

. The instrumental variable passes the test, since the first-stage F-statistic is 135.10 and the Kleibergen–Paap rk Wald F-statistic is 130.66, both of which are above the crucial value of 16.38 at the 10% significance level. The results of Model (1) show that the influence of digital technology on the agricultural production agglomeration is still substantially positive, even when endogeneity is taken into account.

(1)

Replacement of explanatory and dependent variables

Replacement of explanatory variable

In this research, the entropy weighting approach was used to quantify the degree of digital technology. The digital technology level was remeasured and included in the regression using a principal component analysis (PCA), another widely used measuring technique. The findings are displayed in Table 9’s Model (2), which confirms the model’s resilience by showing that the digital technology coefficient is still significantly positive.

Replacement of core dependent variable

The indicator of agglomeration in agricultural production was recalculated. Following the approach of most scholars, the location quotient index of the output was adopted to re-measure the agglomeration in agricultural production and conduct a regression analysis. The formula for the location quotient is as follows:

$${Agg}_{it}={\displaystyle \frac{q_{it}/Q_{it}}{q_t/Q_t}}$$

(5)

In this case, $q_{it}$ stands for the total agricultural output value for the i area and t year, $Q_{it}$ represents the total output value of the i region and t year, $q_t$ represents the total output value of agriculture nationwide, and $Q_{t }$ represents the total output value nationwide. Table 9’s Model (3) displays the findings, with the digital technology regression coefficient continuing to be significantly positive at the 10% level. This supports the validity of the finding that digital technology promotes the improvement of agglomeration in agricultural production.

(2)

Truncation of samples

To guarantee the baseline regression’s dependability, this study conducted truncation on potential outliers in the sample. The core variable, digital technology, and agricultural production agglomeration results were subjected to truncation, eliminating outliers that fell between the first and 99th percentiles, and then re-entered into the regression analysis. The findings, which are displayed in Table 9’s Model 4, reveal that the digital technology coefficient is still strongly positive, confirming the model’s resilience.

(3)

Controlling for fixed effects

Rural areas with high levels of agglomeration in agricultural production and economic development tend to prioritize the development of the Internet. This poses an endogeneity issue in the empirical section of this research. Therefore, in order to lessen the macro-systemic changes brought about by the advancement of digital technology, this research added interaction effects between the year and region. The results for Table 9’s Model (5) show that even after accounting for systemic changes in macro variables, the conclusion that digital technology drives agglomeration in agricultural production remains robust.

5.5. Heterogeneity Analysis

This research explores the regional variations in how digital technology influences agricultural production agglomeration, considering variations in economic development, digital progress, and natural resource endowments such as climate and geography. For the sake of a heterogeneity analysis, the nation is separated into four regions: northeast, central, western, and eastern.

Table 10. Heterogeneity test results for the regions.

Variables	East	Central	West	Northeast
	Agg	Agg	Agg	Agg
Dig	1.077 *	0.367 **	0.907	0.664 *
	(0.96)	(5.68)	(7.85)	(2.91)
Control variables	Yes	Yes	Yes	Yes
μi	Yes	Yes	Yes	Yes
${\mathrm{v}}_{\mathrm{t}}$	Yes	Yes	Yes	Yes
N	99	66	121	33
R2	0.671	0.643	0.714	0.776

Note: Standard errors in parentheses: * p < 0.1, ** p < 0.05.

’s findings indicate that digital technology’s impact in the western region is not statistically significant and it greatly encourages agricultural production agglomeration in the eastern, central, and northeastern regions. The limited effectiveness in Western China primarily reflects the region’s unique developmental constraints, where inadequate digital infrastructure, lower farmer digital literacy, and challenging natural conditions collectively hinder the adoption and impact of agricultural technologies. Unlike more developed regions where digital tools are readily integrated into existing production systems, the western provinces face fundamental barriers including unreliable electricity and Internet access that limit basic technology functionality, coupled with fragmented landholdings in mountainous areas that physically prevent the formation of the contiguous production zones needed to realize scale benefits from digital coordination. The eastern region exhibits the greatest influence among the regions, with the northeastern and central regions following closely behind. In the eastern area, with its advanced economy and well-established digital infrastructure, digital technology is more effectively integrated into agriculture, enhancing the production efficiency and promoting intensive development. This leads to a higher agglomeration level of agricultural production. Favorable natural features such as the soil, climate, and terrain in the central and northeastern regions—both significant grain-producing regions—allow digital technology adoption and promote agricultural agglomeration. Compared to the eastern region, where digital technology supports the entire agricultural process—from pre-production to post-production—the impact is marginally lower. In contrast, in the northeastern and central areas, it is more focused on in-production activities such as irrigation and plant protection. The western region, with slower economic development and less favorable natural conditions for agriculture, shows a less significant effect of digital technology on agricultural agglomeration.

The impact of digital technology on the agglomeration of agricultural outputs in both grain-producing and non-grain-producing regions is shown in

Table 11. Heterogeneity test results for producing areas.

Variables	Non-Grain-Producing Areas	Grain-Producing Areas	Grain Crops	Economic Crops
	Agg	Agg	Agg	Agg
Dig	0.285 **	0.090 *	0.315 **	0.350 ***
	(2.24)	(−0.59)	(2.46)	(2.79)
Control variables	Yes	Yes	Yes	Yes
${\mathsf{\mu }}_{\mathrm{i}}$	Yes	Yes	Yes	Yes
${\mathrm{v}}_{\mathrm{t}}$	Yes	Yes	Yes	Yes
N	176	143	319	319
R2	0.656	0.628	0.742	0.738

Note: Standard errors in parentheses: * p < 0.1, ** p < 0.05, *** p < 0.01.

. Digital technology significantly promotes agglomeration in both areas, although its impact is smaller in grain-producing regions, with a coefficient of 0.367, compared to 1.077 in non-grain-producing regions. This difference may stem from the greater mechanization and path dependence in grain production, limiting the marginal benefits of digital technology. In contrast, non-grain regions, focusing on cash crops, experience greater benefits from digital technologies due to their role in labor diffusion, technical processes, and market integration. To confirm further how digital technology affects agglomeration across several crop sectors, the average agglomeration rates of grain crops and economic crops in each province were measured and analyzed through group regression. According to Table 11’s findings, at the 1% and 5% significance levels, respectively, digital technology has a substantial impact on the agglomeration of both economic crops and grain crops. This suggests that digital technology contributes to higher agglomeration levels in both crop types. However, when comparing the coefficients, the grain crops stand at 0.315, compared to 0.350 for economic crops, suggesting a higher contribution of digital technology to the production and coordination processes of cash crops.

5.6. Discussion

First, both the benchmark regression results and the heterogeneity analysis results revealed the positive role of digital technology in agricultural production agglomeration in various regions. This indicates that for the relevant stakeholders, actively promoting digital transformation in the agricultural sector and among farmers is conducive to improving agricultural production efficiency [70], and to a certain extent, helping ensure regional food security. Additionally, with the in-depth advancement of the new round of technological revolution and industrial transformation, the development of digital technology is comprehensively penetrating and applied to all links of the agricultural industrial chain [71], which will become a new engine and driving force for promoting the construction of a strong agricultural country. Investigating the reason for this, digital technology can accelerate the transformation of traditional agriculture, organically integrating data elements with agriculture in all aspects and throughout the entire chain, achieving labor substitution, decision-making assistance, environmental control, and personalized services [72,73]. This can effectively reduce the labor and time costs and promote the leapfrog development of modern agricultural productivity. Furthermore, digital technology also has significant functions of resource integration, collaborative connection, and efficiency improvement. It can effectively expand the possibility boundaries and functional extensions of agricultural production, thereby enhancing the resilience and stability of the agricultural industry value chain and promoting an increase in agricultural production agglomeration levels. Of course, when effectively implementing and promoting the application of digital technology in agricultural production in various regions, all relevant stakeholders also need to focus on continuously consolidating the new infrastructure for the digital transformation and development of agriculture, promoting the digital integration and development of the entire agricultural industrial chain and strengthening the support of digital technology talents for the strategy of building a strong agricultural country, so as to deeply play the role of digital technology during the regional agricultural economic development.

Second, looking at the mechanism analysis, this paper found that the influence of digital technology on regional agricultural production agglomeration was accomplished through rural human capital and land transfer. Specifically, regarding rural human capital, optimizing the environment for digital technology innovation and entrepreneurship, improving the guarantee and incentive mechanism for digital talents in rural areas, smoothing the channels for the integration of digital talents in urban and rural areas, and precisely cultivating a group of high-quality farmers who master modern information technology will provide an important talent foundation for comprehensively promoting rural revitalization and accelerating the building of a strong agricultural country. While focusing on land transfer, our research also implied that based on the fundamental characteristics of the development of digital technology, implementing the digital reform of rural land transfer, digitally registering the land contracted by households, investing the land management rights into village collective (shareholding) economic cooperatives, and leasing them to new types of agricultural business entities through online bidding at the district rural property rights trading center will be conducive to promoting the agglomeration of regional agricultural production [74,75]. This will, thus, strongly promote the high-quality development of the regional agricultural economy. Moreover, the analysis of the spatial effects revealed that to enhance the agglomeration of regional agricultural production, more attention should be paid to the spillover effects of digital technologies among regions. This requires the relevant decision-makers to rely on policy coordination to further leverage the spillover effects of digital technologies.

6. Conclusions, Policy Implications, and Limitations

6.1. Conclusions

China’s significant grain production capacity makes it a key player in the current global food crisis and the UN Sustainable Development Goal (SDG) of achieving “zero hunger”. Increasing agricultural production agglomeration is a vital tactic for raising agricultural productivity and encouraging superior industry development. As China modernizes its agricultural industry, one of the main forces behind improving the manufacturing agglomeration is digital technology. This research investigated the ways in which digital technology affects agricultural production agglomeration using panel data from 29 provincial-level cities between 2012 and 2022. The following is a summary of the findings. First, digital technology significantly promotes agricultural production agglomeration, a finding that holds up well to multiple tests. Second, it facilitates this process by enhancing land transfer and improving rural human capital. Moreover, digital technology demonstrates spatial spillover effects, with its direct impacts being stronger than its indirect effects. According to the heterogeneity analysis, digital technology has a greater positive impact on non-grain areas and in the eastern, central, and northeastern regions than it does in the western regions.

6.2. Policy Implications

In light of this, we make the following policy recommendations: First, governments and international organizations should prioritize targeted investments in rural broadband networks, smart agriculture sensors, and modern data management systems. This includes working with the private sector to install affordable digital farming tools and platforms that support data-driven crop management, real-time weather forecasts, and precision farming applications. Pilot programs can be set up to test these technologies in different rural environments, ensuring scalability and adaptability to local needs. Second, governments should establish training programs that promote digital literacy among farmers, agricultural workers, and rural entrepreneurs. This can be achieved through partnerships with local institutions, agricultural cooperatives, and international agencies to create accessible, mobile-based learning platforms. These platforms should focus on practical skills such as using digital tools for crop monitoring, market access, and financial management, ensuring that rural populations can fully leverage digital technologies to boost productivity and income. Third, policy implementation processes must account for substantial regional variations in digital readiness and agricultural characteristics. The eastern region, characterized by an advanced economy, efficient logistics, and strong consumer demand, should harness digital technologies to advance facility agriculture, precision farming, and organic food production. At the same time, it should aim to upgrade agro-processing and logistics systems to promote value-added outputs. Conversely, the central and northeastern regions, which are the nation’s primary grain production areas, have fertile land but are still rooted in traditional practices. Therefore, targeted government interventions, such as large-scale farming demonstrations and the adoption of smart agricultural equipment, are crucial for driving intensive production. Meanwhile, the diverse climates and low population density of the western region make it suitable for cultivating niche crops, such as specialty fruits and medicinal herbs. Here, digital tools can be employed to strengthen branding, and e-commerce platforms can extend value chains, ultimately enhancing productivity, increasing farmers’ incomes, and fostering regional agricultural clustering.

6.3. Limitations and Future Research Directions

This research has a number of limitations that should be noted despite the insightful results. The measurement of digital technology’s impact on agricultural production agglomeration is constrained by the availability of accurate and comprehensive data. Since no direct or standardized metrics are available to fully capture the growth and integration of digital technology in agriculture, the study relies on proxy indicators such as digital infrastructure or general technological development. These proxies might not accurately represent how digital technology is actually adopted and used in rural regions, which could add errors to the analysis. To ensure a more accurate assessment of the influence of digital technology in agriculture, future research should work to create more sector-specific and nuanced metrics that directly evaluate its scope. Additionally, this research utilizes provincial-level panel data, which might obscure micro-level dynamics within individual regions or communities. Aggregated data may mask intra-regional disparities in digital technology adoption and the varying effects on agricultural production. Future research could benefit from incorporating farm-level or household-level data through surveys or interviews, allowing for a more granular analysis of how digital technology influences production decisions and agglomeration at the local level.