This research by Lio and Liu (2006) analyzed 81 countries worldwide and demonstrated that ICT enhances agricultural productivity, with the effect stronger in high-income countries. This research established initial evidence that supported ICT's positive effects at the macroeconomic level. Research conducted in developing economies has established that ICT has a positive effect on agricultural productivity. This research by Sethi et al. (2024) analyzed 27 developing nations and found that their ICT index, which combined internet and mobile phone subscriptions, was associated with higher agricultural productivity. Oyelami et al. (2022) conducted a panel ARDL model analysis to demonstrate that ICT creates positive long-term effects on agricultural output in Sub-Saharan Africa. This research by Rehman et al. (2024) on South Asian countries established a positive link between ICT and food security, and found that ICT directly affects food security through their causal analysis. Research indicates that ICT functions as a vital driver of agricultural growth worldwide because it enhances information access and reduces costs, and enables knowledge sharing in developing countries. Overall, the studies reviewed above describe multiple ways in which ICT development can be associated with agricultural performance. This motivates the province-level analysis, which distinguishes between internet connectivity and mobile phone penetration when linking ICT diffusion to grain production.

Research conducted in China focuses on provincial and farm household levels to understand how ICT affects agricultural production. This research by Chen et al. (2022) demonstrated that Yangtze River Basin rice farmers who used the internet achieved better technical performance in rice farming. This research by Fu and Zhu (2023) demonstrated that internet usage enhances technical efficiency in grain production. This research by Ma et al. (2020b, 2023) demonstrated that ICT adoption leads to growth in rural household income through improved credit access. Multiple studies have shown that ICT adoption creates an indirect path to boosting rural household income by enhancing credit access. This research by Yu et al. (2022) demonstrated an inverted U-shaped relationship between internet usage and agricultural green production efficiency which indicates a maximum benefit point exists. The existing research investigates technical efficiency and income and particular crops but lacks studies that use provincial panel data to evaluate total grain output while analyzing internet and mobile phone effects. In summary, while the preceding discussion of China provides essential context for understanding ICT diffusion in agriculture, it also highlights a gap in understanding long-run, province-level trends in total grain production. This study addresses this gap by employing provincial panel data spanning an extended period and by formulating hypotheses that directly link specific ICT measures to key production inputs within the empirical model.

Classical production theory views agricultural output as relying on land, labor, capital, and intermediate inputs. The agricultural sector can benefit from government investment (GIN), which can support agricultural growth by improving infrastructure, fostering technological progress, and easing financing constraints (Salim and Islam, 2010; Zhang et al., 2022). This research by Pickson et al. (2022) demonstrates that proper fertilizer application increases crop yields in contemporary agricultural systems. The fundamental land component of cultivated area (CUA) remains a primary predictor of agricultural output while maintaining stability in its effects. The marginal value of agricultural labor (AGL) has become a complex factor due to technological advancements in farming practices. The essential nature of labor as a production factor persists, but modern mechanization and digitalization technologies reduce its marginal contribution to the point that it becomes insignificant or even negative (Baig et al., 2024). These considerations motivate the hypotheses on the expected signs of government investment, fertilizer use, cultivated area, and agricultural labor in the grain production function.

This study focuses on two observable ICT channels: internet connectivity (INT) and mobile phone penetration (MOBT). This research examines their association with grain output across China's key grain-producing provinces. Grounded in the conceptual framework outlined in previous sections, this research first summarizes the principal mechanisms through which INT and MOBT may influence agricultural performance. As discussed, ICT access can affect grain production through multiple practical pathways. These include improving access to timely market information and agricultural knowledge, supporting production and management decisions, and facilitating communication and service delivery in agricultural activities. Based on these mechanisms, this research then formulates distinct testable hypotheses for the effects of both internet connectivity and mobile phone penetration. The empirical model also accounts for the role of conventional production factors. This research hypotheses include the following statements:

This study presents its framework in Figure 5, which demonstrates the proposed connections between grain production and ICT technologies and control variables.

Ensuring food production is fundamental to national governance and serves as a cornerstone of national security. ICT adoption has become a key component of China's agricultural transformation and is increasingly important for improving production structure and efficiency. Accordingly, the analysis examines how ICT adoption relates to sustainability outcomes in the grain sector, using a province-year panel covering 18 of China's key grain provinces over 2001–2022, which covers the country's principal grain-producing areas, but differs from the conventional set of 13 central grain-producing provinces, this study found that one or more key series required were not consistent, so this study excluded some options. This study focuses on the 2001–2022 period because it is the time window for which this research compiled a province-level panel with consistent coverage of all variables used in the empirical specification across the selected provinces. At the same time, to ensure sufficient and comparable research samples, this study also included some provinces in this study. The dataset is compiled based on series reported in the China Statistical Yearbook and the China Rural Statistical Yearbook. Variables were selected in line with the 2030 Sustainable Development Goals (SDGs). Additional information on variable definitions and measurement is reported in Table 1. In line with the focus on farmers, INT and MOBT are compiled for the population employed in agriculture rather than the total provincial population.

Following Lio and Liu (2006), Oyelami et al. (2022), Rahaman et al. (2024), this research specifies the empirical model below to assess how digital-technology adoption affects food grain productivity, while controlling for agricultural investment, fertilizer use, cultivated land, and labor. The econometric connection among the studied variables is developed as:

where, FDP denotes the food grain production, INT refers to internet technology use, MOBT indicates mobile phone technology use, GIN is government investment, FRT is fertilizer use, CUA is cultivation land, and AGL is agricultural labor. Following Sethi et al. (2024), this research applies a natural-log transformation to the panel variables. This transformation helps mitigate heteroscedasticity and yields estimates that are more consistent and statistically efficient, thereby improving the reliability of the results relative to a simple linear specification. Accordingly, this study specifies a log-linear model as shown in Equations (2), (3).

Model 1:

Model 2:

The term β₀ is the constant, while β₁−β₅ captures the long-run elasticities. This study uses i to label the 18 provinces and t to denote the annual observations over 2001–2022.

In this section, this research follows a stepwise econometric workflow: this study first tests for cross-sectional dependence (CSD), then apply second-generation panel unit-root procedures, conduct cointegration analysis using Westerlund (2007) and Kao (1999), estimate long-run elasticities, and finally perform the Dumitrescu and Hurlin (2012) panel Granger-causality test.

This section provides an overview of the panel data used in the analysis, which examines how ICT adoption relates to grain output in China's key grain-producing provinces over 2001–2022. Table 2 summarizes the descriptive statistics for the variables, and Figures 6–8 display their pairwise correlation patterns. Overall, grain production is positively associated with both ICT indicators and the main input variables, suggesting that digital adoption and conventional inputs are relevant correlates of higher grain output.

The present study relies on the 18 major grain-producing regions of China for the empirical analysis. Before estimating the empirical model, this research conducts CSD tests to assess the panel data (Table 3). The test statistics indicate that, at the 1% significance threshold, this study can reject the null of no CSD for every variable in the panel, implying the presence of CSD across the series. Given this evidence, this study then proceeds with second-generation panel unit-root testing.

Because the panel exhibits CSD, conventional first-generation unit-root procedures may yield unreliable inferences. This research, therefore, applies the second-generation CADF panel test. The corresponding statistics are reported in Table 4. At levels, the series are generally non-stationary, with CUA as the exception. After taking first differences, FDP, INT, MOBT, GIN, FRT, and AGL become stationary.

To examine whether the variables in the specification move together in the long run, this study applies panel cointegration tests based on Westerlund (2007) and Kao (1999). It is worth noting that the Westerlund ECM co-integration method is robust to cross-sectional dependence in the dataset. Tables 5, 6 report the outcomes of the cointegration tests. The cointegration results indicate that, at the 1% significance threshold, this research rejects the null of no long-run cointegration for the variables considered. This provides evidence of a stable long-run linkage across the series included in the analysis.

After establishing long-run cointegration among the variables, this study estimates long-run effects using the D-K estimator technique (see Table 7). Under the D-K estimates, Internet connectivity is associated with a statistically significant 0.066% increase in long-run grain production in China's major grain-producing regions. Related empirical work reports comparable evidence in China (Chandio et al., 2023; Chen et al., 2022; Fu and Zhu, 2023). Currently, internet connectivity has improved agricultural production systems by integrating digital technologies, transforming traditional practices into digitized agriculture (Fu and Zhu, 2023). Internet access can be obtained through various devices, including smartphones, computers, tablets, digital televisions, and similar technologies (Kaila and Tarp, 2019). Adopting digital technologies, such as GPS-guided systems, drones, soil moisture sensors, and farm management software, significantly enhances efficiency and farm productivity (Hasan et al., 2023). Moreover, internet access allows farmers to revolutionize their farming methods by enabling real-time field monitoring and precise resource management (Zou and Mishra, 2022). Since 2023, China has made significant progress in digitalization, particularly with 5G technology, which is now widely accessible. A large portion of the population is connected to 5G networks, which have extensive coverage across all regions. This advanced and widespread use of 5G technology has a significant impact on the food production process (Ma et al., 2023).

Table 7 shows that government investment enters the long-run specification with a positive and statistically significant coefficient. The implied elasticity is 0.0130, meaning that a 1% increase in government investment corresponds to a 0.0130% increase in grain output. This pattern highlights the importance of public spending for agricultural performance: well-designed policies at both central and provincial levels, together with sustained resource allocation, can strengthen production capacity and ultimately support food security. This aligns with prior evidence reported for Australia (Salim and Islam, 2010), Nepal (Pickson et al., 2025a,b), and China (Zhang et al., 2022).

Production factors, such as cultivated land and fertilizer application, contribute significantly to increasing grain food production, while the negative impact of labor is observed. In the long-run estimates, a 1% expansion in cultivated land corresponds to a 1.0263% rise in grain output, while a 1% increase in fertilizer use is associated with a 0.0895% gain. Both inputs, fertilizer and land, significantly influence crop yields and total agricultural output. Fertilizers supply vital nutrients that stimulate plant growth, enhance soil fertility, and improve crop quality. The quantity and variety of fertilizer utilized can profoundly influence the efficacy of cereal production. Similar patterns have been documented in prior work, including evidence for China (Pickson et al., 2022), broader developing-country samples (Sethi et al., 2024), and Pakistan (Farooq et al., 2023), all of which emphasize that cultivated land and fertilizer application make meaningful contributions to agricultural productivity.

This study explores how fast Internet connectivity and Mobile phone access influence grain output across China's central grain-producing provinces. As a robustness exercise for the D-K long-run estimates, this study also employs the FGLS, MG, and FMOLS methods. These estimation techniques provide a similar sign for all estimated coefficients. The findings from Table 8 show that Internet access, government investment, fertilizer application, and cultivated land positively contribute to grain food production.

This study used food grain production as the dependent variable, focusing on food production, enabling a clear assessment of how technological innovations contribute to the agricultural industry. Moreover, this industry is highly sensitive to innovations in farming practices, such as adopting precision agriculture facilitated by digital tools. The D-K estimates are reported in Table 9. The long-run coefficient on mobile phone use is positive and statistically significant. The estimated elasticity is 0.0530, so a 1% increase in mobile-phone use corresponds to about a 0.0530% increase in food production. Ma et al. (2020a) emphasized that smartphones are powerful tools for progressing agricultural practices. Aker and Mbiti (2010) and Khan et al. (2022) stated that increased smartphone penetration indicates farmers' potential access to digital tools. The results are consistent with existing evidence reported in the literature (LoPiccalo, 2022; Rehman et al., 2024; Sethi et al., 2024; Zheng et al., 2021). The main input variables enter with positive and statistically significant long-run effects on food output. A 1% increase in government investment, fertilizer use, and cultivated land corresponds to 0.003%, 0.089%, and 1.041% higher grain production, respectively. This indicates that in grain-producing regions, adequate investment in the agricultural industry, improved agricultural land, and appropriate fertilizer use can boost the food industry and promote food security. Behera et al. (2024) reported that the quantity and variety of fertilizer utilized can profoundly enhance the efficiency of food production. Land use for crop cultivation is considered a vital input factor in agricultural production systems (Shi et al., 2021). Research shows that adequate fertilizer supply and the availability of agricultural land can raise cereal output and strengthen food security. This interpretation is consistent with prior evidence (Ahsan et al., 2020; Behera et al., 2024; Huynh, 2024; Shi et al., 2021).

This research uses the FGLS, MG, and FMOLS estimators to assess the robustness of long-run estimates of the D-K model. Table 10 displays the estimated outcomes. The FGLS, MG, and FMOLS methods validate that MOBT significantly enhanced food production. Therefore, this research concludes that Mobile phone technology use, government investment, and fertilizer application are also positively associated with food production across China's major grain-producing regions. Additionally, the FGLS results validated the food production-enhancing effects of MOBT, GIN, FRT, and CUA at the 1% level of significance for all variables except AGL. The robustness of the D-K results is verified.

To investigate directional causal linkages among grain food production, Internet access, Mobile phone use, government investment, fertilizer application, cultivated land, and agricultural labor, this study employs the Dumitrescu and Hurlin (2012) panel causality test. Table 11 reports the panel causality results, indicating a one-way causal link running from Internet access to grain food production, government investment to grain food production, Mobile phone use to Internet access, Internet access to fertilizer application, Mobile phone use to fertilizer application, Mobile phone use to agricultural labor, government investment to fertilizer application, cultivated land to agricultural labor, and a bidirectional causality connection between Mobile phone use to grain food production, fertilizer application to grain food production, cultivated land to grain food production, agricultural labor to grain food production, government investment to Internet access, agricultural labor to Internet access, government investment to Mobile phone use, cultivated land to Mobile phone use, cultivated land to government investment, agricultural labor to government investment, cultivated land to fertilizer application, agricultural labor to fertilizer application, respectively. Table 11 also indicates a two-way causal relationship between mobile-phone technology (MOBT) and food production (FDP), implying that broader mobile use in agricultural practices is associated with higher grain output. Therefore, policy initiatives aimed at boosting food production should enhance funding for sustainable agricultural development and improve ICT infrastructure in rural areas. This will help farmers adopt better farming methods and promote the food production industry. In addition, the unidirectional causality from INT to FDP indicates that changes in Internet access precede changes in grain food production in the panel. This result aligns with the long-run estimates reported earlier.

The baseline Driscoll-Kraay estimates, together with the robustness checks based on alternative long-run estimators, provide a coherent picture of how ICT adoption and conventional production factors relate to long-run grain output in China's major grain-producing regions. Figure 9 summarizes the findings.

H1 proposes that Internet connectivity (INT) has a positive long-run effect on grain production (FDP). The results support H1: INT is positive and statistically significant in the baseline long-run estimates (Table 7), and the robustness checks confirm the same sign (Table 8). Moreover, the Dumitrescu and Hurlin causality test indicates a unidirectional causal link running from INT to FDP (Table 11), which reinforces the interpretation that changes in Internet access precede changes in grain output in the panel. This evidence is consistent with macro-level findings reported in the broader literature that ICT access is associated with higher agricultural productivity and output (Lio and Liu, 2006; Oyelami et al., 2022; Sethi et al., 2024), and it aligns with China-focused evidence showing that Internet use improves agricultural technical performance and efficiency (Chen et al., 2022; Fu and Zhu, 2023).

H2 posits that mobile phone penetration (MOBT) has a positive long-run effect on grain production. The results also support H2: MOBT is positive and statistically significant in the baseline long-run estimates (Table 9), and the robustness checks confirm a positive sign across alternative estimators, with statistical significance in FGLS and FMOLS and marginal significance under MG (Table 10). The causality results further suggest a bidirectional relationship between MOBT and FDP (Table 11), implying that mobile adoption and production outcomes may co-evolve over time. This pattern aligns with earlier findings that mobile technologies can facilitate communication and information access in agricultural activities and are associated with improved agricultural outcomes in developing-country settings (Aker and Mbiti, 2010; Khan et al., 2022; Sethi et al., 2024). This study's conceptual discussion is also consistent with the finding that mobile devices can provide practical, accessible digital tools for agricultural decision-making and service delivery (Ma et al., 2020a).

H3 concerns conventional production factors and predicts positive long-run effects of government investment (H3a), fertilizer use (H3b), and cultivated area (H3c), and a negative effect of agricultural labor (H3d). The estimates are broadly consistent with H3, but the strength of statistical support differs across specifications. Government investment is positive in both models (Tables 7, 9), and it is statistically significant in Model 1 but not in Model 2, indicating that its long-run association with grain output is sensitive to the ICT specification. This finding aligns with previous studies that emphasize the role of public investment in improving agricultural performance (Salim and Islam, 2010; Zhang et al., 2022). Fertilizer use and cultivated area are positive in both models, with cultivated area showing a consistently strong and highly significant association with grain output (Tables 7, 9). These results are consistent with the literature, which shows that fertilizer and land area are key determinants of agricultural production (Pickson et al., 2022). By contrast, agricultural labor shows a negative long-run association in both models. It is statistically significant in the baseline estimates, consistent with structural change and the increasing role of mechanization and technology in modern agricultural systems. This is consistent with previous research that argues that technology adoption and mechanization can reduce the marginal contribution of labor in agriculture (Baig et al., 2024). Taken together, the results suggest that ICT development complements rather than replaces the roles of conventional inputs and public support in sustaining grain production.

Finally, the findings also speak to the broader debate noted in Section 2, namely that ICT effects can be context-dependent in their magnitude. In this study, in the baseline D–K long-run estimates, both ICT channels display positive, statistically significant associations with grain output, and the causality results indicate a clear directionality for Internet access. These patterns are consistent with earlier work emphasizing that ICT can enhance agricultural performance by improving information access and reducing transaction costs (Lio and Liu, 2006; Oyelami et al., 2022), while the province-level long-horizon setting provides additional macro-empirical evidence for China's major grain-producing regions.

Using province-level data for China's major grain-producing areas over 2001–2022, this study assesses the long-run relationship between digital adoption, captured by internet connectivity and mobile-phone use, and grain production. This research employed several estimation methods, including the Pesaran CD test to detect CSD, the CADF test to verify variable stationarity, the Westerlund ECM cointegration test to validate long-run equilibrium relationships among variables, and the DKSE regression to determine the long-term impact of digital technologies on grain production. The results from the DKSE model indicate that internet connectivity has a strong, positive impact on long-run grain production. Similarly, mobile phone use in this digitalization period significantly increases grain production in the long run. This suggests that the adoption of digital technologies is a valuable tool for improving grain production and promoting food security in China's major grain-producing regions. The FGLS model findings further confirm the consistency and reliability of the DKSE regression results. Finally, the Dumitrescu and Hurlin causality test results reveal a bidirectional causal connection between Mobile phone use, fertilizer application, cultivated land, and agricultural labor to grain food production. Moreover, the analysis reveals a unidirectional causality running from Internet access and government investment to grain food production.

Based on the conclusions, the following policy implications are suggested:

While the province-level analysis identifies a long-run association between ICT diffusion and grain production, future work can examine the underlying mechanisms more directly by incorporating additional variables that capture how farmers obtain information and agricultural knowledge and how production decisions respond to digital access. Future research may adopt stronger identification strategies to address potential endogeneity between ICT expansion and agricultural development. It would be valuable to explore heterogeneity across provinces, since the benefits of internet connectivity and mobile technologies may differ by regional conditions and stages of development. Combining macro-level panel data with more granular data would strengthen causal inference and improve the design of targeted policies for food security.