Digitalization and Agricultural Green Total Factor Productivity: Evidence from China

Abstract

Based on panel data of 31 provinces (autonomous regions and municipalities) in China from 2011 to 2022, this paper empirically examines the impact of digitalization on the inputs and outputs of the agricultural production process, and thereby derives the effects and mechanisms by which digitalization empowers the growth of agricultural green total factor productivity. The study finds that agricultural and rural areas’ digitalization significantly improves agricultural green total factor productivity, and this promotion mainly comes from the improvement of technical efficiency. Further analysis shows that digitalization mainly reduces land input and labor input, increases expected output, and reduces undesired output during the agricultural production process to achieve an improvement in agricultural green total factor productivity, indicating that digitalization has altered the allocation of agricultural factors. Heterogeneity analysis finds that the effect of digitalization on the growth of agricultural green total factor productivity is more pronounced in the eastern regions, southern regions, and areas with higher levels of agricultural digitalization, indicating that the development of digitalization exacerbates the gap in agricultural green total factor productivity among regions.

1. Introduction

In the digital economy era, the digital transformation of agriculture and rural areas in various countries has entered the fast lane. With the deepening of the new round of technological revolution, the digital economy has become a new driving force for promoting high-quality economic development [1], and the development of agriculture and rural areas has also ushered in an important opportunity for transformation. In 2023, the United States Department of Agriculture released the “Fiscal Year 2024–2026 Data Strategy”, which stated that “The power of data in shaping the future of food, agriculture, natural resources, rural areas development, and nutrition cannot be overstated”. Australia has also undertaken a series of strategic plans and practices in digital agriculture and digital rural area development, applying digital technologies such as drones, remote sensors, and satellite imagery in agricultural production to provide information-based decision making for farms [2]. China also proposed the “Implementation of Digital Rural Areas Strategy” in 2018 and has included relevant strategic measures for digital rural areas and digital agricultural development in its “No. 1 Central Document” for five consecutive years. The “Digital Rural Areas Development Strategy Outline” released in 2019 proposed the creation of smart agriculture; the “14th Five-Year Plan” proposed “accelerating the development of smart agriculture, promoting the digital transformation of agricultural production, operation, and management services”; the 2023 No. 1 Central Document proposed “accelerating the application of big data in agriculture and rural areas and promoting the development of smart agriculture”; and the 2024 No. 1 Central Document proposed “continuing to implement the digital rural areas development initiative and developing smart agriculture”. Currently, the digital development of agriculture and rural areas in various countries has entered the fast lane, showing broad prospects [3].

As a major agricultural country in the world, China has made tremendous strides in agricultural development; however, the issues of resource and environmental constraints are also prominent. Therefore, it is crucial to actively promote the green transformation of agricultural production and comprehensively enhance agricultural green total factor productivity (AGTFP) [4]. In 2023, the added value of China’s primary industry reached CNY 8.98 trillion, an increase of 4.1% over the previous year. The total grain output was 695.41 million tons, an increase of 1.3% over the previous year, setting a new historical record. While achieving significant progress, China’s agricultural production also faces challenges such as low utilization rates of soil and water resources and excessive use of chemical fertilizers. Between 2000 and 2020, the average water use efficiency in China’s agriculture sector was 0.71 [5]. In 2023, China’s agricultural water use reached 367.24 billion cubic meters, accounting for 62.18% of total water use in China. Under the severe scarcity of water resources, ensuring national food security and effective supply of agricultural products requires a significant improvement in agricultural water use efficiency. The long-term overuse of fertilizers in China’s agricultural production has also caused problems such as non-point source pollution and a decline in agricultural output [6]. AGTFP, as a measure of the overall efficiency of the agricultural production system, indicates that the higher the level of AGTFP, the lower the reliance on resources such as fertilizers, pesticides, and labor, and the higher the technological content and sustainability of agricultural development. Therefore, accelerating the transformation of agricultural development and comprehensively improving AGTFP is imperative [7].

The application of digital technology in the field of agriculture and rural areas has profoundly transformed agricultural development and will influence agricultural production through various channels. According to statistics, in 2022, the informatization rate of China’s agricultural production reached 27.6%, an increase of 2.2% over the previous year. Digital technology now permeates all areas and stages of agricultural development [8]. The application of digital technology in agricultural production can assist agricultural operators in decision making and improve decision making efficiency. For example, technologies such as digital soil fertility testing and smart irrigation in farmland can reduce non-point source pollution and prevent waste of water resources, thereby increasing the efficiency of production factors like fertilizers and water resources.

In the context where the potential of traditional agricultural production factors has been nearly exhausted, digital technology is poised to profoundly change agricultural development. Can digitalization become a new driving force for improving AGTFP? What is its mechanism of action? What characteristics and patterns do its effects exhibit? Clarifying these issues is of great significance for accelerating the creation of new driving forces to promote the growth of China’s AGTFP.

2. Literature Review

Some scholars have studied the impact of Information and Communication Technology on agricultural production. Kaila and Tarp (2019), based on data from 2008–2012, studied the impact of internet use in rural areas of Vietnam on agricultural production. The results showed that internet use significantly increased total agricultural output by 6.8%, and the findings are stronger for younger households [9]. Bi et al. (2022) studied the impact of internet development on grain production in China from 1997 to 2018. The results showed that the development of the internet greatly increased China’s grain production [10]. Khan et al. (2022), based on a sample of 628 representative wheat farmers in Pakistan, studied the adoption of mobile internet technology (MIT) and its impact on sustainable agriculture. The study found that farmer’s age, farm size, farm location, and knowledge of internet technology (IT) were closely related to MIT adoption, with approximately 55% of farmers applying MIT to sustainable agriculture [11]. Lio and Liu (2006), based on production data from 81 countries between 1995 and 2000, studied the relationship between information and communication technology (ICT) adoption and agricultural productivity. The results showed that ICT adoption had a positive impact on agricultural productivity, and this effect was more pronounced in richer countries [12]. Chavula (2014), based on data from 34 African countries from 2000 to 2011, found that information technology played an important role in enhancing agricultural productivity in African countries [13]. Ogutu et al. (2014) used the Propensity Score Matching (PSM) technique to study the impact of Information and Communication Technology (ICT) platforms on agricultural input and production efficiency in Kenya. The results showed that ICT platforms significantly improved the productivity of fertilizers, land, and labor [14]. Issahaku et al. (2018), based on data from Ghana, studied the impact and mechanism of mobile phone use on maize production efficiency. The results showed that the use of mobile phones by farmers could increase maize output by 261.20 kg/ha, and the improvement in production efficiency was mainly achieved through enhanced extension services, adoption of modern technology, and market participation [15]. Rajkhowa and Baumüller (2024) used data from 86 countries for the period 2000–2019 to study, from a global perspective, the impact of information and communication technologies (ICTs) on land and labor productivity in agriculture. The results show a positive and significant association between ICT adoption and land and labor productivity in agriculture at the global level [16]. e Souza et al. (2020) studied the impact of agricultural technological advances on agricultural production in Brazil during the period 1976–2016 based on the Data Envelopment Analysis model, and the results indicated that agricultural technological advances in recent years have played an important role in the improvement of total factor productivity in agriculture [17].

In recent years, some scholars have discussed the impact of the digital economy and digitalization on agricultural TFP and AGTFP, but such studies are relatively few in number. Bocean (2024) studied the impact of digital technology adoption on agricultural productivity in EU countries, and the results showed that digital technology is a catalyst for enhancing agricultural productivity in EU countries [18]. The same conclusion was reached in the study of Rehman and Nunziante [19]. Shi (2024), based on provincial panel data from China between 2011 and 2022, empirically examined the impact of digital economy development on the growth of agricultural total factor productivity and its mechanisms. The results indicated that the digital economy significantly promoted the growth of agricultural total factor productivity, and the mechanism tests showed that the digital economy mainly facilitated TFP growth by improving capital and land misallocation [20]. Zhou et al. (2023), based on provincial panel data from China between 2011 and 2019, studied the impact of digital agriculture development on AGTFP. The results showed that the relationship between the growth of digital agriculture and AGTFP presents an inverted U-shaped curve [21]. Zhang et al. (2023) [22], Chen et al. (2023) [23], and Hong et al. (2023) [24] studied the impact of the development of the digital economy on AGTFP in China. The results showed that the digital economy enhanced AGTFP, with this effect varying across regions. However, some studies argue that although information technology is increasingly applied in agriculture, this has not been significantly reflected in agricultural productivity [25]. On the one hand, this is because production factors in the agricultural sector are not able to or cannot easily achieve digital integration in the short term [26]; on the other hand, this is due to the existence of a digital divide in certain aspects, such as the level of human capital [27].

In conclusion, the impact of the digitalization on agricultural production efficiency remains a subject of debate, and there is significant potential for further exploration of how the digitalization influences AGTFP. First, existing studies mainly focus on the initial stage of informatization in the digital economy. Although some studies have examined the impact of the digitalization on AGTFP, the direction of the impact of agricultural and rural digitalization on the components of AGTFP remains unclear. Second, the examination of the pathways through which agricultural and rural digitalization empowers the growth of AGTFP is still in its infancy, and the “black box” of mechanisms has yet to be opened. Third, existing studies have looked at the impact of digitalization on AGTFP from a more macro perspective, but the effects of agricultural and rural digitalization on AGTFP remain unclear.

AGTFP is key to building a strong agricultural nation and achieving high-quality, sustainable agricultural development. The key to improving AGTFP lies in continuously enhancing the optimal allocation of agricultural resources and improving the efficiency of factor allocation. The digital economy, with its penetration effect, substitution effect, and integration effect, can promote the optimal allocation of various factors. Therefore, from the perspective of factor allocation, this paper empirically examines the impact of agricultural and rural area digitalization on agricultural inputs and outputs, based on panel data from 31 provinces (autonomous regions, and municipalities) in China from 2011 to 2022, to derive the effects and mechanisms of agricultural and rural area digitalization in enabling the growth of AGTFP. Compared with previous studies, the marginal contributions of this paper are mainly threefold: First, this paper constructs an index system for agricultural and rural area digitalization, explores the impact of agricultural and rural area digitalization on AGTFP, and tests the effects of current agricultural and rural area digitalization on AGTFP. Second, from the perspective of factor allocation, this paper identifies the internal mechanisms by which agricultural and rural area digitalization affects AGTFP, helping to deepen the understanding of the relationship between the digitalization and AGTFP. Third, it explores the heterogeneity of digitalization’s enabling effect on AGTFP at the regional and provincial levels, revealing the characteristic fact that the digital divide between regions ultimately leads to a gap in AGTFP.

3. Data and Methods

3.1. Variable Selection

3.1.1. Dependent Variable

This paper examines the impact of agricultural and rural area digitalization on AGTFP; thus, the dependent variable in this study is AGTFP. Due to the long agricultural production cycle and the nature of the production process, improvements in digitalization over the long term can also enhance productivity levels. The Malmquist index is well-suited to reflect changes in production efficiency [28]. Therefore, following the research by Guo (2021, 2024) [29,30], this paper uses the SBM-ML index based on a non-radial directional distance function to measure AGTFP. Inputs include labor input, land input, and capital input. Labor input is represented by the number of employees in the primary industry, land input is represented by the total sown area of crops, and capital input is the agricultural capital stock of each province over the years, calculated using 1978 as the base year [20]. The output variable is the added value of the primary industry. The undesired output is agricultural carbon emissions, with specific calculations detailed in Appendix A.

3.1.2. Core Explanatory Variable

The core explanatory variable in this study is the level of agricultural and rural area digitalization. To accurately measure the level of agricultural and rural area digitalization, this paper selects seven indicators to construct the agricultural and rural area digitalization index, based on the existing literature [20,31] and considering data availability. Specific details can be found in

Table 1. Evaluation index system for the development of agricultural and rural area digitalization.

Criterion Layer	Indicator Layer	Indicator Calculation Method	Weight
Digital Infrastructure Construction	Rural Area Internet Broadband Penetration Rate	Number of rural area broadband access users/rural area permanent population, households per 100 people	0.1470
Digital Infrastructure Construction	Rural Area Computer Penetration Rate	Average number of computers per 100 rural area households at the end of the year	0.1512
Digital Infrastructure Construction	Rural Area Mobile Phone Penetration Rate	Total length of rural area investment roads/rural area permanent population, kilometers per 10,000 people	0.1495
Digital Technology Application	Agricultural Meteorology Application	Number of agricultural meteorological observation stations, units	0.1493
Digital Technology Application	Rural Area E-commerce Development	Number of Taobao villages, units	0.1031
Digital Technology Application	Digital Financial Development	Peking University Digital Inclusive Finance Index	0.1504

Source: Shi, 2023 [12].

. Regarding the method of index construction, this paper uses the entropy method, an objective weighting method, to calculate the indicator weights. The agricultural and rural area digitalization index ranges between 0 and 1, with higher values indicating a higher level of agricultural digitalization.

3.1.3. Mechanism Variables

The impact of agricultural and rural area digitalization on AGTFP may be realized by altering the allocation of production factors. First, as a special type of production factor, digital technology penetrates various aspects of agricultural production, coordinating land, labor, and capital elements throughout the agricultural production process. This leads to disruptive innovations in traditional agricultural production, promoting agricultural technological advancements [32]. Second, the integration of digital technology with traditional agriculture allows producers to access production data in real time and make adjustments based on these data, enabling scientific planting practices such as precise fertilization and pesticide application. This precise management of agricultural input factors reduces agricultural carbon emissions [27], thereby improving AGTFP. Therefore, the mechanism variables in this paper are the allocation of agricultural production factors. Labor input, land input, capital input, agricultural output, and undesired output are selected to verify the mechanism of agricultural and rural area digitalization on AGTFP.

3.1.4. Other Control Variables

To mitigate omitted variable bias and based on existing research, this paper introduces the following variables as control variables. (1) Rural Area Human Capital (RUEDU): measured by the average years of education in the rural area population. The specific calculation method is detailed in

Table 2. Description of variables.

Variable	Full Name	Description
AGTFP	Agriculture green total factor productivity change	Based on SBM-ML model calculations
TECH	Technical efficiency change	Based on SBM-ML model calculations
TECCH	Techological change	Based on SBM-ML model calculations
DIGIT	Agricultural Digitalization Index	Based on SBM-ML model calculations
RUEDU	Average Years of Education for Rural Area Population	(Number of people with no education × 1 + number of people with primary education × 6 + number of people with junior high school education × 9 + number of people with high school education × 12 + number of people with college education or above × 16)/Total population aged 6 and above
PLSTR	Planting structure	Grain Crop Sown Area/Total Sown Area
INDSTR	Industrial structure	(Value added by secondary industry + value added by tertiary industry)/GDP
GDPCAP	Economic Development Level	Per Capita GDP (CNY 10,000/person)
AGOP	Agricultural Openness	Agricultural Import and Export Value/Total Output Value of Agriculture, Forestry, Animal Husbandry, and Fisheries
AGSUB	Level of Fiscal Support for Agricultural Development	Local Government Expenditures on Agriculture, Forestry, Animal Husbandry, and Fisheries/Local General Public Budget Expenditures
IRRT	Irrigation Rate of Arable Land	Irrigated Arable Land Area/Total Sown Area of Crops
DISRT	Natural Disaster Rate	Area Affected by Natural Disasters/Total Sown Area of Crops

. Improving human capital can not only effectively enhance the efficiency of physical capital use but also promote the dissemination and application of agricultural technology, which positively contributes to the growth of AGTFP. (2) Industrial Structure (INDSTR): represented by the proportion of the added value of the secondary and tertiary industries to the regional GDP. On the one hand, developed secondary and tertiary industries can provide many non-agricultural employment opportunities for surplus rural area labor, which, given China’s population density and limited land, helps to alleviate land–labor conflicts and mitigate the over-intensification of agricultural production. However, on the other hand, the competition between industry and agriculture may lead to a significant outflow of high-quality labor from agriculture, potentially negatively impacting local agricultural production. (3) Agricultural Planting Structure (PLSTR): represented by the proportion of grain crop sown area to the total sown area of crops. The structure of agricultural production is a key factor influencing agricultural productivity growth. Optimizing the agricultural production structure can help improve the efficiency of resource allocation and enhance AGTFP. (4) Regional Economic Development Level (GDPCAP): represented by per capita GDP. The level of regional economic development affects agricultural development and agricultural technology levels, thereby influencing AGTFP. (5) Agricultural Openness (AGOP): measured by the ratio of the total import and export value of agricultural products to the total output value of agriculture, forestry, animal husbandry, and fisheries. According to the new trade growth theory, foreign trade can promote domestic agricultural growth through learning effects and technology spillover effects. However, the influx of large quantities of foreign agricultural products may also lead to a contraction of domestic agricultural production. (6) Fiscal Support for Agriculture (AGSUB): measured by the ratio of agricultural, forestry, and water expenditures to local public budget expenditures. Government fiscal support for agriculture is not only an important source of funding for agricultural technology investment and infrastructure construction but also a crucial guarantee for agricultural development. However, excessive government intervention may also distort market signals and lead to inefficient resource allocation. (7) Irrigation Rate of Arable Land (IRRT): measured by the ratio of irrigated land area to the total sown area of crops. Drought and water scarcity have long been significant constraints on China’s agricultural production. Improving farmland water conservancy facilities and increasing the effective irrigation rate of arable land can partially mitigate or compensate for the adverse effects of drought on AGTFP. (8) Natural Disaster Rate (DISRT): measured by the ratio of the area affected by natural disasters to the total sown area of crops. Agriculture is inherently a vulnerable industry, and changes in the external natural environment have a significant impact on AGTFP.

3.2. Data Source

Considering the availability and completeness of the data, this paper selects panel data from 31 provinces in China from 2011 to 2022. The data source includes the China Statistical Yearbook, China Agricultural Statistical Yearbook, Historical Data on China’s Gross Domestic Product Accounting (1952–1995), Historical Data on China’s Gross Domestic Product Accounting (1996–2002), Monthly Statistics Report on China’s Agricultural Product Imports and Exports (December), China Population and Employment Statistical Yearbook, Peking University Digital Inclusive Finance Index (2011–2022), China Taobao Village Research Report, and the National Bureau of Statistics database. For missing data, this paper uses linear interpolation to fill in the blanks. Descriptive statistics for all the data are shown in Table 2.

We focus on the most important variables in this paper, AGTFP and DIGIT. As can be seen in

Table 3. Data descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
AGTFP	341	0.0039	0.0116	−0.0944	0.1006
TECH	341	−0.0071	0.0224	−0.1572	0.1276
TECCH	341	0.0127	0.0144	−0.0206	0.0741
DIGIT	372	0.2985	0.1175	0.0899	0.6253
RUEDU	372	7.8386	0.7777	4.3038	10.1474
PLSTR	372	0.6497	0.1414	0.3551	0.9708
INDSTR	372	0.9439	0.1254	0.7526	1.5392
GDPCAP	372	3.6868	1.7531	1.5908	10.0109
AGOP	372	0.4345	1.3246	0.0036	9.0743
AGSUB	372	0.1151	0.0341	0.0404	0.2038
IRRT	372	0.4517	0.1968	0.1720	1.2337
DISRT	372	0.1346	0.1114	0.0000	0.6183

, the mean value of AGTFP in China is 0.0039, which indicates that AGTFP in China has shown an increasing trend from 2011 to 2022. The mean value of DIGIT, the core explanatory variable of this paper, is 0.2985, and the maximum value is 0.6253, which indicates that the digitization level of agriculture and rural areas in China is still relatively low, and the highest level is only 0.6253.

3.3. Method

Based on the previous analysis, this paper uses panel data from 31 provinces (autonomous regions and municipalities) in China from 2011 to 2022 and employs a fixed-effects model to examine the impact of agricultural and rural area digitalization on AGTFP. The model is specified as follows:

$${\mathrm{A}\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}}_{it}=constant+\beta {\mathrm{D}\mathrm{I}\mathrm{G}\mathrm{I}\mathrm{T}}_{it}+\gamma C_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(1)

where ${\mathrm{A}\mathrm{G}\mathrm{T}\mathrm{F}\mathrm{P}}_{it}$ represents the growth of AGTFP in province $i$during year $t$, ${\mathrm{D}\mathrm{I}\mathrm{G}\mathrm{I}\mathrm{T}}_{it}$ is the agricultural and rural area digitalization index of province $i$ during year $t$, and $C_{it}$ includes other control variables for province $i$during year $t$. These control variables include the average years of education of the rural area population (RUEDU), planting structure (PLSTR), industrial structure (INDSTR), economic development level (GDPCAP), agricultural openness (AGOP), fiscal support for agricultural development (AGSUB), irrigation rate of arable land (IRRT), and natural disaster rate (DISRT). This study also controls for provincial fixed effects ${\mu }_i$ and time fixed effects ${\delta }_t$, with ${\epsilon }_{it}$ representing the error term. The standard errors of the estimated coefficients are calculated using cluster-robust standard errors at the provincial level.

4. Results and Analysis

4.1. Main Results

Table 4. Main results.

	(1)	(2)	(3)	(4)	(5)
	AGTFP	AGTFP	AGTFP	TECH	TECCH
DIGIT	0.0200 **	0.0280 ***	0.0290 *	0.0699 **	−0.0032
	(0.008)	(0.010)	(0.015)	(0.027)	(0.014)
Control Variable	Yes	Yes	Yes	Yes	Yes
Province FE	No	No	Yes	Yes	Yes
Year FE	No	Yes	Yes	Yes	Yes
Observations	341	341	341	341	341
R-squared	0.063	0.124	0.280	0.335	0.885

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

presents the main regression results regarding the effect of agricultural and rural area digitalization on AGTFP. In column (1), control variables such as the average years of education of the rural area population (RUEDU), planting structure (PLSTR), industrial structure (INDSTR), economic development level (GDPCAP), agricultural openness (AGOP), fiscal support for agricultural development (AGSUB), irrigation rate of arable land (IRRT), and natural disaster rate (DISRT) are included. In column (2), year fixed effects are included. In column (3), provincial fixed effects are further included, resulting in a two-way fixed effects estimation. The estimation results of all regression equations consistently show that digitalization has a significant and robust positive effect on the growth of AGTFP. The results of this study are similar to the findings of Shi (2024) [20], Zhang et al. (2023) [22], Chen et al. (2023) [23], and Hong et al. (2023) [24]. The digitization of agriculture and rural areas has led to the mechanization and high-tech development of agricultural production, which has resulted in the achievement of higher agricultural output and lower environmental pollution with the existing level of inputs.

The change in AGTFP is further decomposed into two parts: Technical Efficiency Change (TECH) and Technological Change (TECCH). The results show that the coefficients of agricultural and rural area digitalization on Technical Efficiency Change (TECH) are positively significant at the 5% level, while the effect on Technological Change (TECCH) does not pass the significance test. This suggests that agricultural and rural area digitalization primarily promotes the overall growth of AGTFP by improving agricultural technical efficiency.

4.2. Robustness Checks

To ensure the robustness of the results, this paper validates its conclusions by replacing the method for calculating AGTFP, substituting the agricultural digitalization index, lagging the explanatory variable by one period, and using the instrumental variable method.

Replacing the method for calculating AGTFP: To ensure robustness, this paper also uses the SBM-ML index based on the radial directional distance function to calculate AGTFP. The regression results reported in column (1) of

Table 5. Robustness checks.

	(1)	(2)	(3)	(4)
DIGIT	0.0262 **			0.5562 **
	(0.012)			(0.224)
FREQU		0.0024 *
		(0.001)
L. DIGIT			0.0363 *
			(0.018)
Control Variable	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	341	341	310	341
R-squared	0.051	0.470	0.324	0.536

Robust standard errors in parentheses: ** p < 0.05, * p < 0.1.

show that, even after changing the method for calculating AGTFP, digitalization still has a positive impact on AGTFP at the 1% significance level.

Replacing the agricultural and rural area digitalization index: In the previous sections, the agricultural and rural area digitalization level of each region was depicted through a multidimensional evaluation index system. Here, it is replaced with the proportion of agricultural digital economy policy word frequency for robustness analysis. Using Python 3.8 web scraping and text analysis methods, this paper calculates the frequency of keywords related to agricultural informatization, smart agriculture, digital agriculture, and digital rural area development in provincial government work reports. The ratio of these keyword frequencies to the total number of policy words (FREQU) is used as a proxy for agricultural and rural area digitalization. The results in column (2) of Table 5 show that the increase in the proportion of digital economy policy word frequency improves AGTFP.

Lagging the explanatory variable by one period: Considering the lag effect of the digital economy and to mitigate the potential endogeneity problem caused by reverse causality, this paper lags the core explanatory variable, the agricultural and rural area digitalization index, by one period. The results, presented in column (3) of Table 5, demonstrate that digitalization still has a significant positive effect in enhancing AGTFP.

Instrumental variable method: The reliability of the baseline regression results may be affected by issues such as bidirectional causality, omitted variable bias, and measurement errors, leading to endogeneity problems. This paper adopts the instrumental variable method to address endogeneity issues. According to the studies of Shi [20] and Wang et al. (2023) [33], this paper uses the 1998 rural area fixed-line telephone penetration rate (the number of rural area fixed-line telephone users per 100 people) as an instrumental variable for digitalization. Digital technology is an extension and development of traditional communication technology. Areas with high historical rural area fixed-line telephone penetration are likely to have had an earlier development of the digitalization, meeting the relevance requirement of the instrumental variable. However, with the advancement of communication technologies, traditional fixed-line telephones have largely been phased out, and their influence on current economic activities is diminishing. Therefore, after controlling for other variables, this instrumental variable also satisfies the exogeneity requirement. Since the 1998 rural area fixed-line telephone penetration rate is an exogenous and relatively fixed variable, and the panel regression model used in this paper is a fixed-effects model, to enhance the heterogeneity of the instrumental variable across time, this paper uses the interaction of the 1998 rural area fixed-line telephone penetration rate with time as an instrumental variable. Column (4) of Table 5 reports the results of the instrumental variable estimation. The first-stage F-statistic is 12.63, which is greater than the empirical threshold of 10, indicating that the selected instrumental variable is a strong and effective instrument. The relationship between digitalization and AGTFP remains significantly positive.

4.3. Heterogeneity Tests

China has a vast territory, with significant differences in economic development levels, resource endowments, and agricultural digitalization levels across regions. These differences may lead to regional variations in the impact of agricultural and rural area digitalization on AGTFP. This paper examines the differences in the effect of digitalization on AGTFP from two dimensions: geographical location and digitalization. Following the classification standards of China’s National Bureau of Statistics, the entire sample is divided into eastern and central-western regions, as well as southern and northern regions, and grouped regressions are conducted. Columns (1) and (2) of

Table 6. Heterogeneity tests.

	(1)	(2)	(3)	(4)	(5)	(6)
DIGIT	0.0273	0.1045 **	0.0988 *	0.0315	0.0433 *	0.0104
	(0.025)	(0.043)	(0.050)	(0.026)	(0.024)	(0.050)
Control Variable	Yes	Yes	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Observations	176	165	110	231	186	155
R-squared	0.480	0.340	0.329	0.722	0.677	0.582

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

represent the southern and northern regions, respectively, while columns (3) and (4) represent the eastern and central-western regions, respectively. The results show that the impact of digitalization on AGTFP exhibits strong regional differentiation. In the southern and eastern regions, digitalization has a highly significant positive effect on AGTFP growth, while in the northern and central-western regions, this effect is not significant.

According to Metcalfe’s Law (Metcalfe, 2013 [34]), the impact of digitalization on AGTFP may exhibit nonlinear characteristics with increasing marginal effects. Therefore, this paper divides the sample into two groups based on the median of the agricultural and rural area digitalization index, including a high digitalization development group and low digitalization development group. Columns (5) and (6) of Table 6 represent the high digitalization development group and the low digitalization development group, respectively. The regression results show that the effect of digitalization on AGTFP growth varies across different levels of digitalization. In regions with high levels of agricultural and rural area digitalization, digitalization has a significant positive effect on AGTFP growth, whereas in regions with low levels of digitalization, the effect is not significant. This implies that the empowering effect of digitalization on AGTFP growth adheres to “Metcalfe’s Law”.

4.4. Mechanism Checks

To further discuss the mechanism by which agricultural and rural area digitalization affects the growth of AGTFP, this section examines the impact of digitalization on labor input, land input, capital input, agricultural output, and undesired output. Columns (1) to (5) of

Table 7. Mechanism checks.

	(1)	(2)	(3)	(4)	(5)
	Labor	Land	Capital	Output	Undesirable-Output
DIGIT	−0.9483 ***	−0.2616 **	−0.5272	1.0411 ***	−0.5266 ***
	(0.299)	(0.108)	(0.613)	(0.341)	(0.192)
Control Variable	Yes	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Observations	372	372	372	372	372
R-squared	0.994	0.998	0.918	0.999	0.996

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

respectively report the effects of digitalization on labor, land, capital, agricultural expected output, and undesired output. The results indicate that agricultural digitalization improves AGTFP by reducing labor input and land input, increasing expected output, and reducing undesired output.

5. Conclusions

Based on the panel data of 31 provinces (autonomous regions and municipalities) in China from 2011 to 2022, this paper empirically examines the impact of agricultural and rural area digitalization on inputs and outputs in the agricultural production process and thereby derives the effects and mechanisms by which digitalization empowers the growth of AGTFP. To ensure the robustness of the results, this paper also conducts robustness tests using various methods, including replacing the measure of AGTFP, the agricultural and rural area digitalization index, and using instrumental variables. The study finds that agricultural and rural area digitalization significantly improves AGTFP, with this enhancement primarily stemming from improvements in technical efficiency. Further analysis shows that agricultural and rural area digitalization primarily reduces land input and labor input, increases expected output, and reduces undesired output during the agricultural production process, thus improving AGTFP. This indicates that digitalization has altered the allocation of agricultural factors. Heterogeneity analysis finds that the effect of digitalization on AGTFP growth is more pronounced in the eastern regions, southern regions, and areas with higher levels of agricultural digitalization, indicating that the development of the digital economy exacerbates the gap in AGTFP among regions.

Based on the research findings, this paper puts forward the following policy recommendations. First, improving AGTFP is key to high-quality agricultural development, and digitalization plays an important role in this process. Therefore, efforts should be made to further strengthen digital infrastructure construction, accelerate the integration of the digital economy with modern agriculture, and promote the digital transformation of agriculture. Second, attention should be paid to the fact that the impact of agricultural and rural area digitalization on AGTFP exhibits regional heterogeneity, and the digital divide may exacerbate differences in AGTFP. Therefore, the government needs to focus on the balanced development of agricultural digitalization from an institutional and policy perspective, speeding up the elimination of the shortcomings in agricultural and rural area digitalization in the central-western and northern regions, so that digitalization can become a new engine driving AGTFP growth. Third, efforts should be made to enhance the promotion of agricultural technologies with the support of digital technologies, fully leveraging the role of technology in improving AGTFP.

However, there are some limitations to this study. First, this paper uses province panel data; the observation is limited, which may not fully capture the relationship between AGTFP and the digitization of agriculture and rural areas. Second, this article does not explore spatial effects. As we know, the spatial effect is likely to exist in the field of digital economy. In a future study, we may use county-level data to find the relationship between AGTFP and the digitization of agriculture and rural areas to try to find the spatial effects of the digitization of agriculture and rural areas.