Global food systems face unprecedented challenges from climate change, resource scarcity, and growing demand. Agricultural water use accounts for over 70% of global freshwater consumption, yet water availability is increasingly constrained by climatic variability and competing demands. In this context, resource-efficient technologies represent critical pathways for reconciling food security with environmental sustainability. Smart irrigation systems offer potential to enhance water productivity while maintaining agricultural output, yet adoption rates remain limited, particularly in developing country contexts. Understanding the structural and institutional factors that enable or constrain technology diffusion is therefore essential for designing effective policies to support sustainable food systems transformation. This study examines how land consolidation and operational scale influence smart irrigation adoption among maize farmers in northern China, a region where water scarcity poses acute constraints on agricultural development. By analyzing adoption patterns across diverse institutional and environmental contexts, this research contributes insights for promoting resource-efficient technologies in water-stressed agricultural regions.

The urgency of this challenge is underscored by projections that global food demand will increase by 50% by 2050, placing ever greater pressure on agricultural production and resource utilization (Grafton et al., 2015). Agricultural activities currently account for more than 70% of global freshwater consumption (FAO, 2022), yet water availability is increasingly threatened by climate change. Rising temperatures and growing frequency of extreme weather events have resulted in unstable climate patterns and increasingly uneven spatial and temporal distribution of water resources (Yildiz et al., 2022; Intergovernmental Panel On Climate Change (Ipcc), 2023; Saleem et al., 2025), complicating global water management and intensifying tensions between food production and environmental sustainability (Godfray et al., 2010). The international community has recognized these interconnected challenges through the Sustainable Development Goals, particularly SDG 2 (Zero Hunger) and SDG 6 (Clean Water and Sanitation), which call for agricultural innovation that jointly addresses food security and resource conservation (Springmann et al., 2018). Smart irrigation technologies provide a potential means of reconciling these objectives by improving resource-use efficiency while sustaining agricultural productivity, making the adoption and diffusion of such technologies essential for designing sustainable food systems capable of supporting population growth within planetary boundaries (Rockstróm et al., 2017). It is imperative to underscore the importance of precision agriculture, delivered through the convergence of technology, digitization, and innovation, for safeguarding the food security of future generations (Cook et al., 2025; Song and Sun, 2025). The transition to the digital era has catalyzed a profound transformation in farming practices, unlocking unprecedented levels of efficiency and productivity (Baiming and Voskerichyan, 2024). The integration of advanced technologies—artificial intelligence (AI), the Internet of Things (IoT), big-data analytics, robotics, and blockchain—has propelled precision agriculture into a new epoch commonly termed Agriculture 4.0 or smart farming (Kumar Apat et al., 2023).

These global pressures manifest with particular intensity in northern China, a region that illustrates both the promise and complexity of agricultural technology adoption. China plays an essential role in global food security, with a particularly high share of agricultural water use, yet faces mounting pressure to balance food supply with severe constraints of limited water resources and environmental stress (Wang et al., 2017; Altemus Cullen et al., 2025; Lu et al., 2015). The challenges are particularly acute in northern China, where the continued growth of food production has been accompanied by escalating resource depletion and environmental stress, including land degradation and soil erosion (Ash and Edmonds, 1998; Jiao, 2010). Despite significant water limitations, the center of China's agricultural production has been gradually shifting toward this northern region (Jiao, 2010), creating a pressing need for water-efficient farming practices. Northern China is characterized by a semi-arid to arid climate with limited precipitation and strong evaporation; traditional irrigation methods remain inefficient, resulting in significant water loss (Lakhiar et al., 2024) and posing serious challenges to sustainable agricultural development (Zhu et al., 2022). Agricultural systems in this region typically follow a mixed farming and pastoral model with high water demand. Maize, the principal crop in northern China, covers extensive areas and depends heavily on water availability. Under reduced precipitation and intensified evaporation linked to climate change (Su et al., 2022; Wan et al., 2022), improving irrigation practices for maize has become essential to enhance yields and maintain food security (Cao et al., 2021). Increasing irrigation efficiency and reducing water consumption are therefore central to achieving sustainable agricultural development in this region.

Smart irrigation technology has emerged as a modern agricultural innovation capable of addressing these challenges. In response to the interconnected water-food-climate crisis (Behnassi et al., 2024), smart irrigation systems (Gamal et al., 2025; Ndunagu et al., 2022; Khan et al., 2021) enhance water-use efficiency, reduce irrigation losses, and improve agricultural productivity through precision management enabled by sensors, data acquisition, and automated control systems (Ali et al., 2025). These systems continuously monitor soil moisture, climatic variables, and related data to adjust irrigation volumes in real time according to crop requirements, preventing both over-irrigation and under-irrigation and thereby substantially improving the efficiency of water resource utilization (Barkunan et al., 2019; Ismail et al., 2019). Beyond water savings, smart irrigation systems can optimize irrigation scheduling through data-driven analysis, minimize water waste, and reduce dependence on manual labor, thereby lowering labor costs and improving overall agricultural efficiency (Kavianand et al., 2016; Morchid et al., 2025). For large-scale farming operations, such technologies not only decrease labor intensity but also increase irrigation accuracy, contributing to higher yields and improved crop quality (Touil et al., 2022; Ahmed et al., 2023; Jaafar and Kharroubi, 2021). In contrast, traditional irrigation typically relies on manual control or fixed irrigation schedules, which rarely align with dynamic crop water demands and often result in resource waste. The adoption of smart irrigation enables greater precision and automation in agricultural production, particularly in arid and semi-arid areas, thereby enhancing water-use efficiency and crop productivity (Morchid et al., 2024). Smart irrigation systems prioritize sustainability, resilience, and regenerative practices, thereby ushering in a new era of agricultural innovation.

More broadly, the adoption of resource-efficient agricultural technologies represents a critical pathway for sustainable food systems development (Pretty et al., 2018). Smart irrigation systems contribute to sustainability across multiple dimensions: environmentally through water conservation and reduced nutrient leaching, economically through improved crop yields and reduced input costs, and socially through enhanced food security in water-stressed regions (Birkenholtz, 2017). Beyond its technical advantages, smart irrigation aligns with the broader framework of circular economy in agriculture, which emphasizes resource efficiency and waste minimization (Jurgilevich et al., 2016). In water-limited environments, precision irrigation contributes to circular water management by optimizing application rates, reducing runoff, and enhancing nutrient utilization, situating smart irrigation not only as a technological innovation but also as a key element in the transition toward sustainable and resilient food systems (Kalmykova et al., 2018).

Despite these compelling advantages and alignment with sustainable development goals, the practical promotion of smart irrigation remains limited and faces several constraints (Qian et al., 2024; Pagano et al., 2023; Idoje et al., 2021). Despite documented efficiency gains, precision and smart irrigation remain underused in practice, with uptake limited by extension and capability gaps (Timpanaro et al., 2023). Foremost among adoption barriers is the high initial investment cost. Smart irrigation systems require sensors, automated control units, and related infrastructure, all of which involve considerable installation and maintenance expenses; for small and medium-sized farmers with limited capital, the long payback period makes it difficult to afford such investments (Ahmed et al., 2018; Garc-a et al., 2020). Furthermore, although smart irrigation offers clear advantages in water conservation and operational efficiency, many farmers have only limited understanding of its functions and benefits. In the absence of sufficient technical training and support, willingness to adopt the technology remains low (Genius et al., 2014; Koundouri et al., 2006). The dissemination of smart irrigation is also influenced by broader contextual factors, including infrastructure, policy incentives, and market conditions. In remote areas, inadequate communication and power facilities hinder system operation and maintenance, while weak policy frameworks and insufficient subsidies reduce farmers' motivation to invest in these technologies. Due to these factors, the adoption of smart irrigation technology in northern China remains limited (Han et al., 2020; Yan et al., 2020), and the overall diffusion rate has not yet met expectations (Tao et al., 2024; Pivoto et al., 2019). With roughly 84% of the world's farmers being smallholders, the challenge is even more daunting for those operating on a micro scale (Zeng et al., 2025).

These adoption barriers raise a critical question: what structural and institutional conditions might help farmers overcome the cost and information constraints that impede smart irrigation uptake? Land consolidation and large-scale farming have become key trends in agricultural modernization that may provide an answer. Supported by land transfer policies and rural land system reforms, intensive land management is increasingly viewed as a central pathway for improving production efficiency and facilitating technology adoption. Through land consolidation, farmers can address challenges such as fragmented landholdings, limited labor, and capital shortages, thereby enhancing productivity and creating favorable conditions for adopting high-cost technologies such as smart irrigation (Ngango and Hong, 2021). Recent evidence shows that land fragmentation generally constrains uptake of technology-intensive green practices, while resource endowments and organizational integration mitigate these barriers (Das et al., 2025).

Empirical studies indicate that large-scale operations promote the application of new agricultural technologies by providing greater financial capacity, optimizing resource allocation, and strengthening technical support—factors that are particularly relevant for technologies requiring substantial upfront investment (Xie et al., 2017). In large-scale systems, economies of scale help distribute technology costs more efficiently, improving the economic feasibility of smart irrigation. In contrast, small-scale farms, constrained by financial and managerial limitations, often find it difficult to implement such technologies (Dhillon and Moncur, 2023; Kendall et al., 2022). Large-scale operations not only alter production structures but also facilitate the broader adoption of modern agricultural technologies such as soil testing, formula fertilization, and integrated pest management, all of which depend on contiguous land management.

Research on technology adoption in agriculture has increasingly recognized the importance of these institutional arrangements and socioeconomic contexts (Shikuku et al., 2017). Land consolidation and farm scale affect not only the economic viability of technology investments but also farmers' access to information, credit, and technical support. These factors are particularly salient in developing country contexts where fragmented landholdings and limited institutional support constrain sustainable intensification efforts (Lowder et al., 2016). Understanding how land tenure arrangements facilitate or hinder adoption of resource-efficient technologies provides insights for designing policies that promote sustainable agricultural transformation. At the same time, the relationship between farm scale and technology adoption intersects with debates about inclusive development and smallholder participation in sustainable food systems (Wiggins et al., 2010). While large-scale operations may more readily adopt capital-intensive technologies, ensuring smallholder access to resource-efficient innovations remains crucial for equitable sustainability transitions (Maertens and Barrett, 2013). Examining scale effects therefore contributes to understanding pathways for inclusive sustainable intensification.

However, while existing studies have examined the relationship between agricultural scale and technology adoption (Hu et al., 2022), most have focused on regions with concentrated agricultural activity, leaving northern China relatively understudied. This region's fragmented land resources, diverse production systems, and uneven economic development suggest that farmers' technology adoption behaviors may differ substantially from those observed elsewhere. Moreover, the mechanisms linking land consolidation and large-scale operation to farmers' adoption of smart irrigation are complex. Beyond scale effects, factors such as policy support, technological awareness, capital availability, and market demand may also play decisive roles. Although prior research has emphasized the relationship between large-scale farming and technology adoption, few studies have explored in depth how land consolidation and farm scale influence farmers' acceptance of smart irrigation technology across diverse institutional and environmental contexts—precisely the conditions that characterize northern China's maize-producing regions.

This study addresses these gaps by investigating the adoption of smart irrigation systems among maize farmers in northern China, examining the roles of land consolidation and large-scale operation within the broader context of sustainable agricultural development. Specifically, this research aims to: (1) analyze the impact of land consolidation and operational scale on smart irrigation technology adoption using household survey data from 803 maize farmers in Inner Mongolia; (2) examine how these effects vary by institutional support mechanisms, regional suitability, and water resource endowment through heterogeneity analysis; (3) contribute to literature on sustainable food systems by revealing how land tenure arrangements and farm scale interact with resource efficiency technologies; and (4) provide empirical evidence for policy design aimed at improving water efficiency, promoting agricultural modernization, and advancing circular economy approaches in food production systems.

Under the dual pressures of food security and water scarcity, understanding how maize growers in northern China adopt smart irrigation technology and how this process is shaped by land consolidation and large-scale operation constitutes the central focus of this study. Addressing this issue will clarify the relationship between institutional innovation, scale expansion, and technological advancement. It will also provide micro-level evidence to inform policies aimed at achieving integrated goals of food security, water conservation, and sustainable resource management in semi-arid agricultural regions, while offering empirical insights for agricultural technology promotion in comparable settings worldwide.

This study develops a theoretical model based on expected profit maximization to examine the impact of land consolidation and large-scale operation on the adoption of smart irrigation technology (Foster and Rosenzweig, 2010). The framework posits that land consolidation improves the average comprehensive output rate of land, though it may have negative effects on returns to scale in grain production.

We assume that, given resource constraints, a farmer's decision to adopt smart irrigation technology results from a dynamic comparison of costs and benefits, with the objective of maximizing profits. The farmer's technology decision can be expressed as the following dynamic optimization model:

Here, β represents the discount rate, and x denotes the farmer's decision on whether to adopt smart irrigation technology. When x = 0, the farmer continues to use the traditional irrigation method, with the profit obtained being π_t = π_u. When x = 1, the farmer abandons the previous irrigation method in favor of adopting smart irrigation technology, with the profit obtained being π_t = π_c.

The value function V_πG that satisfies the Bellman equation can be expressed as:

Here, C represents the total cost of technology transition, that is, the corresponding investment made in adopting smart irrigation technology. Define π_GE as a critical value, which represents an equilibrium profit level after adopting smart irrigation technology. At this profit level, the profits corresponding to the two different decisions of adopting and not adopting smart irrigation technology are equal. Therefore, the value of π_GE can be calculated as follows:

At this stage, the farmer's decision regarding the adoption of smart irrigation technology can be expressed as follows:

If the profit π_G obtained after adopting smart irrigation technology is less than or equal to the critical value π_GE, the farmer will choose to maintain the status quo and not adopt the technology. Conversely, if π_G exceeds π_GE, the farmer will adopt smart irrigation technology.

Further, the total cost of technology transition C can be defined as:

where c(L) represents the unit cost of technology transition, and L denotes the scale of operation. Based on the derivation above, the relationship between the critical profit level and the operational scale can be expressed as:

The sign of ∂πGE∂L is thus the same as that of ∂C∂L. If the unit cost of adopting smart irrigation technology decreases as the operational scale increases, and the rate of reduction in unit cost exceeds the rate of scale expansion, then the total cost of technology transition will also decline. Consequently, the equilibrium value π_GE decreases with increasing L, expressed as:

Following the expansion of land operation scale, farmers can achieve economies of scale when purchasing smart irrigation equipment, which reduces unit costs (Figure 1). For instance, large-scale farmers can purchase equipment in bulk and benefit from more favorable prices. In addition, land consolidation reduces plot fragmentation, thereby lowering installation costs per unit area as consolidation increases. This relationship can be expressed as ∂c(L)∂L<0.

On this basis, since the unit cost decreases with the increase in the scale of operation, and the degree of unit cost reduction is greater than the degree of scale expansion, the total cost of technology transition will also decrease. That is, ∂C/∂L < 0.

Given that the unit cost decreases with the scale of operation and that the reduction rate surpasses the expansion rate, the total technology transition cost declines (∂C∂L<0). According to the model derivation, a reduction in C with increasing L results in a lower critical profit level (∂πGE∂L<0). A lower π_GE raises the probability that π_G>π_GE, meaning that farmers are more likely to find the adoption of smart irrigation technology economically feasible (Blasch et al., 2022).

Therefore, as land consolidation and large-scale operation expand, the adoption threshold for smart irrigation technology decreases. Farmers perceive adoption as more cost-effective and are thus more inclined to implement it, leading to a higher probability of adoption.

Based on this theoretical framework, the following hypotheses are proposed:

In studying farmers' production technology adoption behavior, whether a household adopts smart irrigation technology is treated as a binary variable. Accordingly, this study employs a binary Logit regression model for empirical analysis. The basic regression model is specified as follows:

In Equation 8, Adoption is the unobservable latent variable; consolidation denotes land consolidation; farmsize represents large-scale operation; X is a vector of control variables listed in Table 1; α, β, and δ are parameters to be estimated; and ϵ is the disturbance term, which follows a standard normal distribution.

The empirical analysis proceeded through three main stages. First, a baseline Logit regression was conducted to evaluate the effects of land consolidation and large-scale operation on the adoption of smart irrigation technology. The Logit model was selected because the dependent variable is binary (adoption versus non-adoption). This baseline estimation provided an initial understanding of the key relationships among the variables.

Second, a series of robustness checks were performed to validate the stability of the results. To address potential information loss from the binary outcome and to capture the intensity of adoption, the dependent variable was replaced with the actual number of smart irrigation devices owned by each household. The model was then re-estimated using Ordinary Least Squares (OLS) regression, allowing for a more detailed assessment of how land consolidation and large-scale operation influence the degree of adoption. To control for intra-village correlation, robust standard errors were clustered at the village level, ensuring that standard errors were not underestimated and that the significance tests remained reliable. To reduce the effect of extreme values, the core explanatory variables-corn planting area, per capita corn planting area, and average corn planting area per plot-were subjected to 1% Winsorization. This process truncated the top and bottom 1% of data, minimizing the influence of outliers. The Logit model was then re-estimated using these adjusted variables to confirm the robustness of the findings.

Third, heterogeneity analysis was performed to examine differential effects of land consolidation and large-scale operation across varying conditions.

This study examined the role of land consolidation and large-scale operation in promoting smart irrigation technology adoption among maize farmers in northern China. Positioned within the broader context of sustainable food systems transformation, our findings provide insights into how structural changes in agriculture can facilitate resource-efficient technology diffusion and contribute to integrated sustainability objectives.

The analysis confirms that land consolidation and large-scale operation significantly promote smart irrigation adoption. Whether measured by total planting area, per capita planting area, or Average corn planting area per plot, operational scale demonstrates positive effects on technology uptake. These effects persist across multiple robustness checks and align with theoretical expectations regarding economies of scale and investment thresholds. Importantly, the magnitude of effects varies by institutional context, resource endowment, and regional suitability, highlighting the contingent nature of scale-technology relationships.

The heterogeneity analysis reveals critical insights for sustainable food systems policy. Government promotion amplifies scale effects, particularly for larger operators, suggesting that public support can catalyze technology diffusion but may require complementary measures to ensure inclusive access. The stronger effects observed in medium-low suitability areas and non-Yellow River irrigation zones indicate that resource-efficient technologies may offer greatest value where environmental constraints are most binding. This pattern suggests strategic opportunities for targeting technology promotion to enhance both productivity and sustainability in marginal agricultural areas.