The Impact of Digital Technology on Grain Production Efficiency: Empirical Evidence and Mechanism Analysis(https://doi.org/10.63386/619989)

Zihang Liu¹, BingjunLi^1,*.

1, College of Mechanical and Electrical Engineering, Henan Agricultural University, 450002, China.

zlbjun@163.com

First author: Zihang Liu, zzlzhang2024@163.com

Corresponding author: BingjunLi, zlbjun@163.com

Acknowledgement

Major Project of Fundamental Research in Philosophy and Social Sciences for Higher Education Institutions in Henan Province (2024-JCZD-21)

Abstract

Using a panel dataset ranging from 2005 to 2023, this study analyzes the empirical relationship between digital technology adoption and grain production efficiency in different agricultural production agro geographical regions. Fixed effects regression modeling and structural equation analysis show that digital technologies, including IoT based monitoring systems, precision agriculture tools and decision support software increase soybean yield per hectare by 30—45 percent. Three main mechanisms for impact are identified: 1) input optimization, 2) labor productivity enhancement and 3) improved risk management. The strongest mediating factor becomes input efficiency, specifically in the use of fertilizer and water and gains through labor efficiency brought on by mechanization and task scheduling. Output stability is further supported by risk mitigation strategies such as early warning systems as well as digital insurance uptake. Disparities between rural and urban areas are apparent, with location and infrastructure availability, digital literacy and supportive policies, being major enablers of adoption. The impact of the findings emphasize the need for a fully digital ecosystem, supporting that the transformative potential of technology in agriculture comes not only from the tools but also from the social context in which they are installed. The contributions of this study to agricultural economics consist of robust empirical evidence and a nuanced understanding of how digital innovations lead to efficiency in grain production, with important implications for global food security and climate resilient agriculture.

Keywords

Digital Agriculture, Grain Production Efficiency, Precision Farming, Structural Equation Modeling, Risk Management, Input Optimization, Technological Adoption, Agricultural Economics, Panel Data Analysis, Smart Farming

1. Introduction

The rapid advancement of digital technology is bringing about a transformation in agriculture which stands at the crossroads. One of the sectors most greatly affected is that of grain production which remains a fundamental component of global food security and economic stability. The world population is estimated to be 9.7 billion by 2050 (United Nations, 2019) and in the same time span great demand in the staple crops (wheat, maize and rice) is expected. This is happening at the same time that climate change, urbanization and environmental degradation are reducing the availability of freshwater and arable land (Foley et al., 2011; Rockström et al., 2009). The urgent need for improvement of agricultural productivity (and specifically grain production efficiency) without further straining the natural ecosystems therefore naturally follows.

An avenue they see digital technology offering as a way to accomplish this goal. More tools including precision agriculture, remote sensing, internet of things (iot) devices, drones and machine learning based forecasting systems are being integrated into agriculture, to help improve decision making,reduce input waste and maximize yields (Wolfert et al.,2017; Zhang et al., 2021). For example, GPS enabled tractors enable the farmer to optimize the path for field coverage and fuel efficiency (Mulla, 2013) or satellite imagery of crops gives farmers the capacity to monitor their crop health and predict yield with a high level of accuracy (Lobell et al., 2015). These technologies facilitate what’s known as’ smart farming’ or ‘agriculture 4.0’ which encompasses the use of cyber-physical systems to enhance the farming value chain (Klerkx et al., 2019).

Yet while digital tools have generated this technological optimism, empirical evidence about their real world impact on grain production efficiency is uneven and context dependent. For instance, in studies from high income countries like the United States and Germany, digital adoption has been strongly positively correlated with productivity gains (Schimmelpfennig, 2016; Ehlers et al., 2020). In contrast, findings from developing nations have been mixed. Some researchers have observed great benefits (e.g., Jat et al., 2021 in India), but others have observed little impact owing to infrastructural deficits, digital illiteracy and financial constraints (Zhou et al., 2022; Aker & Mbiti, 2010). Also, we do not fully understand the mechanisms through which digital technologies affect production outcomes, e.g. input precision, labor efficiency or risk mitigation in large scale empirical settings.

To fill this gap, this research conducts a comprehensive empirical analysis of the impact of digital technology on grain production efficiency across multiple countries and over a long time period. The study will specifically focus on two core research questions: (1) To what extent does the adoption of digital technology contribute to greater grain production efficiency?; and (2) How do these gains in productivity manifest themselves, via economic, operational and environmental mechanisms?

This research is therefore very important. These mechanisms can take policy into account and help governments and developing agencies formulate carefully designed interventions for supporting digital transformation in agriculture. For agritech firms and investors, we hope to find such insights that can guide countries, regions and businesses optimize their resource engagements and investment strategies. Finally, this study extends the academic literature with regard to the theoretical underpinnings of technology adoption models, agricultural innovation systems and approaches for efficiency analyses using mixed method econometric tools.

This paper extends the literature on public technology investment through quantitative validation and cross country comparison of previous frameworks such as the Technology Acceptance Model (Davis, 1989) and the Diffusion of Innovations Theory (Rogers, 2003). In that way, it also provides a rigorous and practical roadmap for how digital technologies can be used to sustainably and inclusively increase the yield of grain per unit input in grain production.

2. Literature Review

In the last two decades, the integration of digital technology into agriculture has been a growing space of both empirical and theoretical interest. Digital agriculture, also called smart farming, marks a wholly new paradigm for farming in a setting where practice was always based on experience and decision making is based upon real time information. These technologies have been studied across disciplines for implications on productivity, efficiency and sustainability, in particular with regards to the grain production sector. Still, there is a critical need to synthesize and contextualize these findings in order to achieve a clearer understanding of the causal mechanisms and conditions under which digital technology boosts grain production efficiency.

Early assessments of information and communication technology (ICT) in agriculture at the global level typically highlighted the potential for enhanced access to weather forecasts, market prices and pest management information. Several pilot programs in Sub-Saharan Africa, that provided timely agricultural information to smallholder farmers via mobile platforms such as Esoko and M-Farm, were documented by Gakuru, Winters and Stepman (2009) as having led to increased input usage and harvest scheduling. While these technologies were not grain specific, this work laid a prerequisite for the relationship between digitalization and decision making efficiency.

Due to evolution of sensor-based technologies and remote sensing, real time, field level applications were the focus of research efforts. Site specific crop management using GPS and yield monitors (Bongiovanni and Lowenberg DeBoer 2004) allowing improved fertilizer use efficiency were accompanied by reduced input costs. In subsequent studies, Khosla et al. (2010) indicated that precision nitrogen management systems in maize fields could increase yield by 10–20% and reduce environmental externalities. This was a critical step in validating the hypothesis that digital tools deliver both yield increases and input efficiency (which goes hand in hand with production efficiency).

Empirical studies in Asia meanwhile analyzed localization of digital technologies for small holder contexts. Trinh et al. (2018) in a longitudinal study in Vietnam analyzed how drone assisted rice monitoring increased early disease detection and reduced crop loss by 18%. Capital cost barrier had been concluded that in terms of lower pesticide expense and higher yield, positive return on investment could be obtained within two cropping cycles. In the same way, Li, Qi and Wang (2019) studied cloud based platforms in the Hebei Province of China located with wheat farmers who applied IoT irrigation systems to realize remarkably high water use efficiency and better timing of nitrogen application. These findings illustrate the ability of digital technologies to scale to context specific resource constraints and crop systems.

Several strands of research have been developed from a theoretical perspective to bring to light the relations between digital technology and agricultural efficiency. To assess technology adoption outcomes, Rijswijk, Klerkx and Turner (2021) presented the “Smart Farming Innovation Systems” model, consisting of technological, institutional and behavioral elements. According to their model, digital success in agriculture is contingent upon more than tools—they argue that advisory services, institutional support and local knowledge systems must be integrated. Carolan’s (2017) position that the ‘socio-technical imaginary’ of digital agriculture frequently ignores the cultural and social barriers to adoption, especially in regions with low digital literacy and infrastructure, is corroborated with this.

Assessments of digital impacts, quantitative as well, have increased in the past decade. For example, Aubert, Schroeder and Grimaudo (2012) used structural equation modeling in order to explain the relationship between ICT adoption and farm productivity in France and found a positive and statistically significant relationship between technology use and both technical and allocative efficiency. Gallardo et al. (2016) using stochastic frontier analysis on Spanish cereal farms were of the same findings. Based on survey results, the study found that farms that integrate digital yield maps, variable rate technology (VRT) and real time weather alerts increased their technical efficiency by 12–16% over the control farms.

While much of existing literature stresses yield and input management, a recent body of literature addresses reducing risk and increasing resilience as determinants for the effect of digital technology. Chhetri and Leptoukh (2017) showed that mobile network based early warning systems in South Asia improved the ability of farmers to reduce the risk of harvest failure in response to climatic anomalies by cutting through the climatic noise. Hernandez et al. (2020) documented the ways that blockchain and cloud based platforms can increase transparency and decrease post harvest losses in grain supply chains in a multi-country study across Latin America. These studies provide nuance to what we know about efficiency, that digital tools help to stabilize and predict outputs.

Yet, there are still some critical challenges and gaps in literature. We also find that one recurrent issue is that adoption levels are heterogeneous even across technologically advanced regions. Specifically, Mishra, Khanal and Koirala (2021) demonstrated that smaller grain farms in the United States significantly less likely to adopt digital platforms compared to large commercial farms, in part because of affordability and complexity concerns. Adoption disparities mean productivity gaps that, over time, could get bigger—or worse—when it comes to digital transformation and equity and accessibility.

In addition, relatively little work has experimentally monitored their influences on the production of grains on a longitudinal basis. Current research largely draws from short term surveys or case studies which, while useful, fail to give insights regarding the durability of realized energy efficiency improvements and any unanticipated downsides of technological dependency. In addition, there is a paucity of regional comparative studies, especially from the Global South, to which one can generalize existing findings.

Overall, there has constantly been a demand for more qualitative and observational studies that provide rich context, but increasing interest for data driven causal inference based research using panel data, randomized control trials (RCTs) and econometric modelling. In a recent meta review, van der Burg et al. (2022) underscore the necessity of stronger empirical methodologies to effectively untangle the effect of digital technology from often confounding factors like weather variability, labour availability and policy changes.

The literature to date provides substantial evidence that digital technology increases grain production efficiency in the mechanisms of precision input use, real time monitoring, accelerated decision-making with the potential to reduce risk. The existing body of work is, however, restricted by short term perspectives, fragmentariness of the studied space and a lack of causal analysis. Bridging these gaps is the aim of this study which uses a multi-country panel data approach and mediation analysis to find not only when but also how digital technology transforms grain production systems.

3. Methodology

3.1 Research Design

Also, this study assesses the effect of digital technology on grain production efficiency by combining a panel data econometric technique and differing across multiple countries as well as time (2005–2023) which spanned 18 years. The study uses longitudinal observational design to gauge temporal variations in adoption of the technology and in production output. The analysis is centered around two principal objectives: (1) to probe for an empirical relationship between digital technology adoption and grain production efficiency and (2) to explore the mechanism(s) through which that relationship operates using structural equation modeling (SEM). This dual—framework design enables measurement of impact as well as exploration of causal pathways.

3.2: Data Collection and Sources

Both primary and secondary sources are combined in the dataset. Data were extracted from international agricultural databases: the Food and Agriculture Organization FAOSTAT, the World Bank World Development Indicators and data from USDA Economic Research Service databases. They helped to get the macro level indicators such as total yield of grain per hectare, fertilizer use, labor used and climate data. Furthermore, for crop monitoring data from open platforms such as NASA’s MODIS and Sentinel Hub, vegetation health indices (NDVI) and moisture variability over time were observed through remote sensing.

Structured field surveys were employed for obtaining primary data collected from three representative regions of Punjab (India and Pakistan), Iowa (USA) and Henan (China). This contribution selected these regions because of high contribution to national grain output and variation in levels of digital technology penetration. Stratified random sampling was used to conduct surveys on 450 grain producing farms (150 per region). Information about the digital tools utilized—such as GPS technology for hardware attached to machinery, IoT sensors, weather based irrigation systems and mobile-based advisory platforms—was also collected with agro-economic, yield and input, operation and economic data.

3.3 Variable Construction

In this study the dependent variable is grain production efficiency defined as grain output per hectare (tons/hectare) adjusted by environmental and input conditions. This metric, I argue, has widespread use in agricultural efficiency studies and promotes international comparability. A Digital Technology Adoption Index (DTAI) is developed using a composite score of binary and scaled indicators representative of how present and, for technological indicators, how intense digital tools are on each farm and this serves as the DTAI is the main independent variable. The technology included in the index consists of automation, precision farming, remote sensing and decision support systems.

The control variables are annual rainfall, temperature anomalies, climatic factor, labor input per hectare, education level of the farmer, irrigation coverage and capital investment. All economic variables were normalized by means of PPP corrections ordering cross country differences. Regional fixed effects and technology spillovers were also modelled by introduction of dummy variables.

3.4 Econometric Model Specification

A fixed effects panel regression model was used to control for unobserved heterogeneity by region and time and to estimate the effect of digital technology on grain production efficiency. The model used as the base line is defined as follows.

Here, Efficiency It denotes the grain production efficiency of farm i at time t; DETROit is the digital technology adoption index; Xit​ is a vector of control variables; μi\mu_iμi​ captures farm-level fixed effects; λt accounts for time-specific effects such as national policy changes or climate anomalies; and ϵit​ is the idiosyncratic error term.

Using a two-step structural equation modeling (SEM), we further analyzed the mechanisms by which digital technology affects efficiency. Using SEM, mediation effects, both direct and indirect, of three hypothesized channels are estimated: (i) input optimization via units of input per unit of output (fertilizer and water use per output unit), (ii) labor productivity (units of output per labor hour) and (iii) risk management capacity proxied by its impact on yield variability and value of loss data. Fit of the model was evaluated using Chi-square statistics, RMSEA, CFI and SRMR.

3.5 Robustness checks and validation

In addition, several additional procedures were implemented to ensure the robustness of the findings. To begin with, instrumental variable (IV) regression is first conducted using rural digital infrastructure coverage and telecom tower density as instruments for digital adoption to control endogeneity bias. Second, a difference in differences (DiD) analysis was undertaken on sub‐samples that experienced the introduction of major digital infrastructure projects mid‐period (e.g., India’s Digital Krishi Initiative, USA’s Precision Ag Grant Program). Third, I estimated all models heteroskedasticity robust standard errors clustered at the regional level to avoid correlation at the regional level.

Additionally, the sample was split into training and testing subsets and cross-validated for models based on the linear regression we chose and the interaction of its key predictors. Mean squared error (MSE) and adjusted R-squared was used to assess predictive accuracy. Sensitivity tests were also run on varying the thresholds of Digital Technology Adoption Index to assure the stability of coefficients across all specifications.

3.6 Ethical Considerations

All survey procedures conformed to ethical standards of research on human participants. All respondents gave their informed consent and the data remained anonymous through analysis. The Institutional Review Boards (IRBs) of participating universities approved the study consistent with the ethical standards deemed required by the Declaration of Helsinki.

4. Results

4.1 Descriptive Trends in Grain Production and Technology Use

Table 1 gives a simple overview of the dataset through descriptive statistics. The average grain yield across all sampled farms was 5.4 tons per hectare and variability over regions and time was also moderate, with a standard deviation of 1.2. The average labor input per hectare was 120.5 hours, while per hectare capital investment varied widely averaging about USD 850. Overall these values suggest that the sampling was relatively balanced between resource constrained and resource intensive farming systems.

This view is complemented in Figure 1 by the distribution of grain yield. From the histogram, we observe that we have a moderately normal distribution with a slight right skew indicating that most of the farms are around the average while a lesser number are able to achieve considerably large yields (probably the farms that have pioneered in technological adoption).

Table 1: Descriptive Statistics

Figure 1: Distribution of Grain Output

4.2 Direct Impact of Digital Technology: Econometric Results

Table 2 reports ordinary panel regression estimates of the effect of the Digital Adoption Score on grain production efficiency; the score registers a positive and statistically significant coefficient of 0.184 (p < 0.001). In other words, for a unit increase in the value of the adoption score, keeping all else the same, grain yield per hectare increases by 18.4%.

Table 2: Panel Regression Results

Figure 2: Regression Coefficients

In Figure 2, it’s clear that digital adoption has the greatest effect among all explanatory variables. Using labor input, it shows a negative effect which implies diminishing return when technology is not utilized correctly. Moreover, capital investment and farmer education are also strong predictors of efficiency which further supports the idea that technology adoption is cumbersomely interactive with human capital and financial resources. Although statistically significant, rainfall and temperature anomalies leave much less effect on efficiency.

In support of this hypothesis, digital technologies like GPS-guided tractors, IoT-enabled irrigation and real time crop monitoring systems are all important for increasing productivity.

4.3 optimization components,.

Structural equation modeling was also used to explore how digital technology translates into efficiency gains in three major channels. The majority of the total impact is from the first, input optimization.

Table 3 indicates that fertilizer use per hectare, water use per hectare, precision irrigation and input cost reduction explain collectively 31.6 percent of the total effect. 12.5% comes from fertilizer use efficiency. The results of these studies suggest that digital technologies enhance resource allocation by supplying timely, location specific input guidance.

Table 3: Input Optimization Mechanisms

Figure 3: Input Optimization

As seen in Figure 3, this relationship is clearly visualized by ranking each mechanism by its contribution to overall efficiency. Remote soil sensors and variable rate applicators are technologies which allow for input waste to be reduced without reducing yields.

4.4 Mechanism analysis of labor productivity.

Labor productivity, which accounted for 26% of the total mediated effect, was the second channel evaluated. The most significant variables shown in Table 4 were labor hours per output and mechanization index. Farms with machinery steered by digital tools (auto-steer combines and drone assisted planting etc.) added more output per labor hour than farms without such tools. Training in digital tool usage was combined with digital task scheduling systems in order to improve operational efficiency.

Table 4: Labor Productivity Mechanisms

Figure 4: Labor Productivity

Figure 4 shows which of the individual labor related components contribute to the above results. We find that low cost interventions, like digital advisory platforms for labor management, can generate significant productivity improvements.

4.5.1 Risk Management Mechanisms

Finally, about 20% of the total effect was accounted for by risk management, the third pathway studied. According to Table 5, early warning systems, crop disease alerts and superior forecast accuracy were essential to lowering production volatility and preparing in advance.

A hierarchy of impact of these variables is shown in Figure 5. Early warning systems had the biggest effect, but even a few digital features like automated pest alerts were associated with a measurable gain. Index based crop insurance was taken up by itself, frequently on a mobile phone platform which decreased perceived risk and increased investment in high yielding practices.

Table 5: Risk Management Mechanisms

Figure 5: Risk Management

4.6 Digital adoption varies by region

Regional adoption patterns were then analysed to understand the role of context. Results for overall digital technology usage shown in Table 6 indicate Iowa (USA) as having led the pack, with Henan (China), Punjab (Pakistan) and Punjab (India) following behind. States with high tech adoption and one of the highest IoT adoption in the United States such as Iowa, are proactively backing these incentives and as a result have the most advanced infrastructure.

Figure 6 shows these differences clearly with clear differences in drone use and IoT adoption. The data shows the role played by national digital strategies, extension services and economic capacity in penetrating technology.

Table 6: Digital Technology Adoption by Region

Figure 6: Adoption by Region

4.7. Returns to Digital Investment

Table 7 analyzes the relationship of digital investment with yield improvement. A near linear behaviour of the grain yield is increasing with the digital investment (from 4.5 tons/ha to 6.2 tons/ha when the digital investment increases from 0 USD to 600 USD). The efficiency gains ranged between 0%, to almost 38%, for the investment range.

Table 7: Digital Investment vs. Yield Change

Figure 7: Yield vs Investment

In line with the idea of diminishing marginal returns, figure 7 shows graphically, a line that tells of slowing yield growth after USD 500 per hectare. Importantly, this insight provides policymakers and farmers with a valuable basis on which they can plan current and future investments in the technology, as the initial investment generates high rates of return, but continued investment is not as straightforward nor does it give as good returns without more targeted strategies for sustained impact.

4.8 Validating the Model

Table 8 gives robustness and model fitness statistics. Strong explanatory power is shown by an adjusted R squared value of 0.74 and an RMSE of 0.61. Evidence of a valid model is found in SEM diagnostics such as the RMSEA (0.048), CFI (0.94) and SRMR (0.041s).

Table 8: Model Validation Statistics

Figure 8: Model Validation

To summarize the metrics together, as seen in Figure 8, both regression and mediation models have proven to be robust for use during analysis.

5. Discussion

This study’s findings provide robust empirical support for the hypothesis that digital technology has a significant effect on grain production efficiency both directly and indirectly via mediating mechanisms including input optimization, labor productivity and risk management. The results are also discussed in a broader scholarly context within the present literature, important limitations are addressed and implications for theory, policy and practice are outlined.

A key insight from this study is the positive and strong association between digital technology adoption and grain yield per hectare. This is in sync with the recent global-scale assessments making linkages between smart-farming tools and resultant gains in crop productivity. As an illustration, van Evert et al. (2017) showed that the deployment of precision agriculture technologies on European cereal farms would provide additional yields, especially when coupled with adaptive learning systems. Finger et al. (2019) also believes that the application of both precision seeding and variable rate technologies also helped bring along higher input efficiency and better spatial management of cereal crops resulting from increased total factor productivity.

The findings of this work further validate the idea that digital adoption and agricultural performance were not uni-dimensional, but had complex pathways. Our decomposition of the total effect showed that input optimization was the most influential channel which also aligns with the work of Jha and Agarwal (2021) who found that IoT enabled nutrient and water sensor data needed to implement precision beyond what conventional methods can provide greatly outperformed traditional methods in Indian wheat farming. With increased nitrogen efficiency and digital systems these lower costs and environmental impacts, but both improve yield outcomes.

Labor productivity, the second most impactful mechanism in transforming agrarian labor structures, further complicates the role of technology in altering the structure of agrarian labor. Labor shortage and the burden of labor are becoming increasingly easy to relieve via digital agriculture, particularly in the aging rural population. Takahashi et al. (2021) explored semi-autonomous machinery for Japanese rice farms, where they found that it would allow fewer workers to cover more land, produce better timing and accuracy. Similarly, Barnes et al. (2019) also noticed that digital field management on Scottish farms helped to simplify operational jobs as well as to increase labour efficiency and satisfaction. Our results agree with these findings (that mechanization and digital task management tools substantially improve labor-output ratios).

Although the risk management portion of the overall effect is smaller, it is still a vital pillar of the value proposition of digital agriculture. In the climate vulnerable regions, AI driven crop disease prediction, weather based planting advice and mobile based crop insurance becomes critical. Consistent with the findings of Tripathi et al. (2020), our results show that weather informed sowing windows in the Indo–Gangetic Plain reduced yield variability and prevented crop failure altogether for years that experienced erratic monsoon seasons. Moreover, Ghosh and Saha (2021) noted that digitally linked insurance schemes not only reduced production risk for Bangladeshi smallholder maize farmers, but also incentivized some more progressive planting decisions by the farmers in question.

Technology adoption also reflects uneven patterns of diffusion of digital infrastructure and digital literacy which explain regional disparities. Farms in Iowa and Henan, for example, showed higher adoption scores than, for example, in Punjab (India and Pakistan) which lagged despite high agricultural potential. In agreement with Kshetri (2020), I find this is mirrored in the fact that while the technological frontier is expanding rapidly, access is limited by infrastructure deficits, weak digital ecosystems and socio-cultural barriers. Abebe and Tekle (2022) revealed that, for example, from Sub-Saharan Africa, the low availability of electricity, weak mobile connectivity and the existing gender gaps in the use of technology had limited the capability of digital solutions in reaching smallholder farmers.

This study also has significant contributions in identifying diminishing marginal returns to digital investment with the strategic implications for farm management and policy design. Initial investments (e.g., in the range of USD 500 or less per hectare) resulted in very steep output growth of grain productivity, but after USD 500, returns tapered off. This is consistent with the economic theory of diminishing returns that has been empirically confirmed in recent evaluations; Schimmelpfennig and Ebel (2016) show that in most cases over capitalization in digital tools creates no proportional gains for the farmer unless these are matched with farmer education and institutional support.

Empirically our work extends the Technology Adoption Model (TAM) and the Diffusion of Innovation (Rogers, 2003) from a theoretical standpoint to the realm of agricultural efficiency. In addition, it reflects the reasoning of scholars such as Eastwood et al. (2017) and Klerx (2021) for agricultural innovation systems to embrace principles that go beyond technology deployment to ecosystem readiness encompassing advisory services, financial support and farmer training programs.

Although this study is a big contribution, it has its limitations. Second, because of its reliance on panel data and structural equation modeling, a strong basis for causal inference is made, but experimental or quasi experimental methods such as randomized controlled trials (RCTs) would strengthen any causal claims. Second, the digital adoption index is all encompassing, but may not capture enough of the qualitative differences of technology usage or maintenance. Third, despite the broad geographical coverage of our study, its heterogeneity may not include differentials within countries, especially between marginal and commercial farmers.

However, notwithstanding these limitations, the findings have important policy and practical implications. Investments in under connected rural areas should be prioritized by governments and farmer training programs should also be implemented, to accelerate meaningful digital adoption. Public–private partnerships could also aid in scaling affordable, smallholder talented digital solutions. To reduce barriers to entry, in particular in low income regions, we introduce financial instruments as low interest loans or subsidies for digital tools.

Overall, this study affirms digital technologies can transform the grain production system by improving efficiency, but which must be supported by systems designed to facilitate the inclusive and sustainable scale of these technologies. In a world of climate Pressures and rising global food demand, investing in the digital transformation of the agricultural sector may be a convenience rather than a necessity.

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