Balancing productivity and sustainability: evaluating harmful agricultural practices and regulatory responses on climate resilience in the Yellow River Basin, China

Agricultural production in the Yellow River Basin (YRB) faces challenges including inadequate extension services, outdated technologies, environmental degradation from excessive use of fertilizers and pesticides, and poor waste management. This study employs stochastic frontier analysis to investigate the effects of environmental regulation on technical efficiency, offering new empirical evidence on how environmental regulations influence agricultural production choices and farm-level performance. Stochastic production frontier analysis is used to estimate technical efficiency and inefficiency while evaluating the trade-offs associated with regulatory policy interventions. Data was collected from a stratified random sample of 957 farmers in Gansu, Shaanxi, and Shanxi provinces at the county level through in-person interviews conducted in August 2022 using a questionnaire tool. Regulatory policy interventions, like penalties on crop residue burning, improve efficiency, while excessive use of hazardous chemicals negatively impacts efficiency at the 1% significance level. This study highlights the need for farmer training, with even a 0.10% efficiency gain from demonstration or theory classes. Sustainable farming practices and fertilizers ensure ecological sustainability and long-term agricultural productivity. In conclusion, urgent and robust regulatory oversight is essential to prevent the overuse of chemicals and residue burning, promoting environmental and sustainable agriculture.

1 Introduction

Agriculture is a critical sector for food security and economic stability, yet it is increasingly threatened by climate change and unsustainable practices (IPCC, 2023; Suresh et al., 2021). Enhancing technical efficiency in agricultural output is essential to ensuring food security and promoting sustainable development (Zhong et al., 2025). In several developing countries, the agricultural sector continues to face significant challenges due to low productivity and technical inefficiency. These issues are compounded by the persistent threat of climate change, which particularly affects smallholder farmers (Benjamin et al., 2025). In China, the Yellow River Basin is a vital agricultural area facing numerous challenges, including ecological problems such as farmland degradation, freshwater resource depletion, soil pollution, soil and water desertification, and climate variability (IPCC, 2023; Lee et al., 2024; Suresh et al., 2021; Wang et al., 2025).

The Yellow River Basin is a key agricultural region in China, supporting a large population and contributing significantly to the national economy (Wang et al., 2025). It spans nine provinces and autonomous regions, stretching from northern China's arid and semi-arid zones to the Bohai Sea. It covers an area of 752,443 square kilometers and supports around 420 million people, about 30% of the country's population, and comprises roughly 26% of China's gross domestic product (Cardascia and Panella, 2023; Zhu et al., 2004; Zhang et al., 2025).

Despite the importance of agriculture in the Yellow River Basin, several critical problems persist (Niu and Mao, 2025). China's agriculture has traditionally been characterized by small, fragmented, and scattered landholdings, leading to low specialization, inefficient use of agricultural labor, and overall productivity challenges (Zhong et al., 2025). The region is dominated by smallholder farmers (Zhou et al., 2024), who often operate below their potential technical efficiency levels, resulting in suboptimal resource utilization and reduced yields (Liu J. et al., 2020). In the context of China's agricultural sector, the challenges posed by intensified non-agricultural labor transfer and an aging agricultural workforce have necessitated a search for solutions to enhance technical efficiency (Zhong et al., 2025). Improving agricultural technical efficiency is an important source of agricultural growth (Huang et al., 2021). Therefore, improving the allocation and utilization efficiency of agricultural production inputs is vital for attaining low-carbon agricultural development (Guo and Zhang, 2025).

Additionally, while China's soil quality is improving, it remains concerning; 16.1% of samples exceed standards, with cultivated land at 19.4%. Heavy metals, pesticides, and fertilizer residues are key management issues (Feng et al., 2025). Agriculture in China, predominantly managed by smallholders, encounters excessive use of chemical fertilizers and pesticides, as well as unsustainable irrigation practices, resulting in significant food safety concerns, ecological deterioration, environmental externalities, and a decline in long-term productivity (Li et al., 2024; Xu et al., 2025). Zheng et al. (2020) claim that the household responsibility system constitutes the fundamental agricultural framework in China, significantly fostering the nation's agricultural development. However, it also results in the phenomenon of land fragmentation, which diminishes farming efficiency. To maximize yields, farmers are compelled to increase the use of fertilizers and pesticides.

While the IPCC (2007) stated that even slight temperature increases can reduce agricultural productivity, with a two-degree rise leading to sharp declines, short-term extreme temperature fluctuations negatively impact China's agricultural total factor productivity and input utilization, resulting in lower farm output. The increasingly severe frequency of weather events, altered precipitation patterns, and rising global temperatures are accelerating the effects of climate change (IPCC, 2023).

Addressing these agricultural challenges in the Yellow River Basin carries significant economic implications. China spends about 2.1 trillion RMB per year on agriculture, with the Yellow River expected to require 114.673 billion RMB in ecological development by 2020 (Zhou et al., 2022; Wang et al., 2024). Environmental degradation costs continue to rise, with flood damages alone costing an average of 134.95 billion RMB per year across the affected regions (Song et al., 2022). Since the Yellow River Basin contributes 26% of China's GDP, optimizing agricultural efficiency is not only an environmental development goal but also a crucial economic objective that affects national economic stability.

Agricultural inputs such as fertilizers and pesticides play crucial roles in modern agriculture, providing crops with nutrients and protecting them from disease and pests (Mondal, 2018). However, the widespread usage of these chemicals has generated concerns about their potential harm to human health and the environment, prompting considerable inquiries regarding the hazards of these chemicals. Moreover, these harmful agricultural practices contribute significantly to global greenhouse gas emissions through ammonia manufacturing, which accounts for 1–2% of worldwide carbon dioxide emissions, while fertilizer application releases nitrous oxide, which has approximately 300 times the global warming potential (Erisman et al., 2008; IPCC, 2022).

Yellow River Basin provinces exhibit distinct chemical fertilizer consumption patterns, requiring differentiated regulatory intervention (National Bureau of Statistics of China, 2024). Shanxi demonstrates the highest fertilizer intensity with 86.3 thousand tons of compound fertilizers, 71.8 thousand tons of nitrogen fertilizers, and 19.3 thousand tons of potassic fertilizers across 1181.2 thousand hectares of irrigated land, representing unsustainable input levels per hectare. Shaanxi and Gansu show identical compound fertilizer consumption at 25.4 thousand tons each and similar nitrogen fertilizer usage at 29.8 thousand tons, distributed across 1490.9 and 1423.7 thousand hectares, respectively, indicating a more balanced but still concerning chemical dependency (National Bureau of Statistics of China, 2024; Niu and Mao, 2025).

These practices influence efficiency, thereby affecting the safety, quality, and cost of agricultural products (Jiang et al., 2025). Despite the implementation of numerous policies, their effectiveness remains uncertain (Xiong et al., 2025). Existing policies and regulatory responses may not adequately address the challenges, hindering the adoption of sustainable practices (Feng et al., 2025). China's agricultural sector remains vulnerable to climate change impacts, threatening food security and farmers' livelihoods (Guo and Zhang, 2025). Improving efficiency and mitigating harmful practices are essential for climate resilience and sustainable development (Lee et al., 2024; Liu Z. et al., 2020; Liu, 2023). These practices, though adopted to cope with structural constraints, insufficient extension services, limited capital resources, and weak market linkages, have precipitated a destructive feedback loop that sacrifices long-term climate resilience and agricultural sustainability for short-term productivity gains (He et al., 2024; Movilla-Pateiro et al., 2021).

Previous studies on agricultural efficiency and climate resilience in China have several limitations. The existing research primarily focuses on specific aspects of agricultural output efficiency or regions without considering the broader context of the Yellow River Basin in China and lacks systematic theoretical elucidation and empirical validation concerning their role in advancing low-carbon agricultural development (Ai et al., 2018; Zhang et al., 2025, 2023; Zhou et al., 2024). Moreover, previous studies have not comprehensively integrated the analysis of agricultural output efficiency, harmful practices, regulatory responses, and climate resilience in the Yellow River region. Local governments significantly impact farmer behavior, and their environmental regulatory status directly impacts agricultural pollution control. Consequently, the implementation of environmental regulations by township-level governments has the most immediate effect on agricultural pollution management (Xiong et al., 2025).

This multi-level governance structure poses particular challenges for policy optimization, as environmental regulations must be designed at the national level but implemented through local administrative systems with varying capacities and incentive structures. Technical efficiency analysis becomes essential in this context because it enables the evaluation of how local implementation variations affect both environmental compliance and economic outcomes, providing evidence for optimal policy design across administrative levels. Previous research has predominantly focused on single provinces or crops without integrated regional analysis. Zhang et al. (2023) examined fertilizer efficiency in the Bohai Rim region but excluded regulatory compliance variables, while Zhou et al. (2024) analyzed land fragmentation effects in Chinese agriculture without considering environmental policy impacts. Zhong et al. (2025) investigated technical efficiency improvements through outsourcing but did not evaluate the trade-offs associated with environmental compliance. Most critically, no existing study in China has quantified the trade-off between environmental compliance and technical efficiency at the farm level using comprehensive 2022 data that captures recent policy implementation effects. Previous research exhibits three critical gaps: first, Benedetti et al. (2019) used SFA for input efficiency analysis in Italian agriculture but excluded regulatory compliance measures, while Ali et al. (2019) focused on hybrid maize efficiency without environmental considerations. Second, existing studies fail to distinguish between different environmental regulatory mechanisms and their differential efficiency impacts. Tang et al. (2022) examined general environmental regulation effects on ecological efficiency but did not quantify specific trade-offs from penalties, supervision levels, or harmful practice restrictions. Third, most research overlooks heterogeneous regulatory supervision and the effectiveness of information sources, treating policy implementation as uniform across regions despite evidence of significant local variations (Xia et al., 2024).

Therefore, there is a need for more rigorous empirical evidence and an integrated approach to understand and optimize agricultural practices in this critical region. While SFA is a powerful tool for measuring technical efficiency, its application in assessing the impact of climate change and policy interventions on agricultural productivity in the Yellow River region is limited (Ai et al., 2018).

This study addresses the critical need to evaluate how environmental regulatory measures respond to harmful agricultural practices and farm-level technical efficiency in the Yellow River Basin. This research specifically analyzes the effectiveness of penalties for toxic pesticide use, crop residue burning, chemical fertilizers, agricultural inputs, and climate adaptation information sources on farm-level technical efficiency. Hence, this research contributes to existing literature by integrating the analysis of agricultural efficiency, identifying harmful practices, and assessing the effectiveness of regulatory responses in promoting climate resilience among small farmers in the Yellow River Basin. It also contributes to understanding the economic trade-offs between environmental compliance and farm-level output by using 2022 data from the Yellow River Basin and using stochastic production frontier analysis (SFA).

2 Literature review

A growing body of empirical evidence demonstrates that climate-smart agricultural practices significantly enhance production efficiency across diverse agro-ecological contexts. Sedebo et al. (2021) developed a comprehensive climate-smart agricultural index within a Cobb–Douglas stochastic frontier framework, revealing substantial efficiency gains in wheat and teff production systems in southern Ethiopia as adoption rates increased. These findings are corroborated by (Pangapanga-Phiri and Mungatana 2021), who documented that balanced nutrient management practices substantially enhance maize production efficiency among drought-affected households in similar environments.

The methodological sophistication in efficiency estimation has proven critical for accurate assessment. Liu Z. et al. (2020) demonstrated that conventional stochastic frontier models systematically underestimate adaptation benefits in irrigated rice systems when spatial spillover effects are not properly accounted for. Collectively, this evidence suggests that climate-smart agricultural practices fundamentally shift the production frontier upward, enabling farmers to achieve higher output levels from given input combinations.

Conversely, substantial evidence documents severe efficiency losses attributable to harmful agricultural practices, creating counterproductive cycles that diminish both productivity and profitability. Xiong and Zhao (2024) quantified these effects, finding that a 10% reduction in fertilizer intensity paradoxically increased net farm income by 8–12% through improved technical efficiency. This apparent contradiction is explained by the diminishing marginal returns to excessive input use and the negative externalities of over-fertilization on soil health.

The environmental and economic costs of crop residue burning exemplify these inefficiencies. Prateep Na Talang et al. (2024) demonstrated that residue burning results in complete carbon loss compared to alternative management practices, which not only significantly reduce global warming potential but also generate positive cash flows through improved soil organic matter. Lan et al. (2022) extended this analysis by documenting substantial premature mortality associated with agricultural burning, while longitudinal studies reveal that residue burning reduces long-term productivity by 25–30% through accelerated soil degradation. This creates a vicious cycle wherein farmers must progressively increase input applications to compensate for declining soil fertility, further eroding profitability.

Recent research on regulatory enforcement provides nuanced insights into the effectiveness of penalty structures in modifying agricultural behavior. Analysis of the U.S. Environmental Protection Agency's enforcement data reveals that civil penalties ranging from $1,000 to $25,000 for pesticide violations have differential impacts on compliance behavior, with commercial operations facing higher penalties demonstrating significantly greater efficiency improvements compared to private applicators (Pilvere et al., 2024). However, enforcement capacity remains severely limited, with annual inspections covering only 1% of agricultural operations, nearly half of which are found in violation (Donley and Bullard, 2024).

Contemporary regulatory approaches increasingly emphasize integrated policy frameworks that simultaneously address harmful practices while supporting technical efficiency improvements. Leng et al. (2023) identified an optimal regulatory intensity threshold that maximizes productivity gains while achieving environmental objectives, suggesting that well-designed regulations can catalyze rather than constrain agricultural development. Supporting this view, Luo et al. (2024) demonstrated that regulatory measures effectively encourage green technology adoption when incentive structures properly align stakeholder interests. The European Union's Farm to Fork Strategy exemplifies this integrated approach, showing positive efficiency impacts where comprehensive support systems accompany regulatory restrictions (Tarasova, 2022). Nevertheless, enforcement coordination remains a persistent challenge globally, particularly in developing country contexts where institutional capacity is limited.

The Gansu, Shaanxi, and Shanxi provinces within China's Yellow River Basin exemplify the complex interplay between agricultural intensification and environmental degradation. These regions face severe and interconnected challenges of agricultural non-point source pollution and soil erosion, driven by harmful agricultural practices that collectively result in low resource use efficiency and diminished land productivity (Asian Development Bank, 2022). The heterogeneous agro-ecological conditions across these provinces necessitate region-specific efficiency analyses that account for varying production technologies and environmental constraints.

The stochastic frontier production function, grounded in microeconomic production theory, provides a robust econometric framework for estimating the maximum potential output achievable with available resources (Greene, 2008). This approach distinguishes between three complementary dimensions of efficiency. Technical efficiency represents the ability to produce maximum output from given inputs, ensuring optimal production with the existing technology. Allocative efficiency is achieved when production factors are selected and combined in cost-minimizing proportions based on their relative prices. Economic efficiency integrates both technical and allocative components, enabling farmers to maximize output while minimizing costs within the constraints of available technology (Bravo-Ureta and Pinheiro, 1993).

The frontier function framework facilitates the understanding and decomposition of technical change in production processes (Førsund and Hjalmarsson, 1979). Recent applications have demonstrated the superiority of stochastic frontier analysis in regulatory contexts. Sultana et al. (2023) evaluated policy impacts on potato farming efficiency in Bangladesh, finding that regulatory compliance variables significantly influenced technical efficiency outcomes with greater precision than alternative methodologies. Their comparative analysis with data envelopment analysis confirmed that SFA's explicit treatment of stochastic noise makes it particularly suitable for policy evaluation in agricultural sectors, where regulatory compliance involves inherently uncertain outcomes.

This study adopts a two-stage analytical approach consistent with contemporary efficiency analysis. The first stage estimates the standard production frontier incorporating conventional inputs—land, labor, capital, and agricultural inputs—that directly contribute to output. The second stage models technical inefficiency as a function of external factors, including regulatory supervision, penalty enforcement mechanisms, agricultural input management practices, and climate information sources. This approach also incorporates negative factors representing harmful practices and demographic characteristics that influence farmers' ability to achieve frontier production levels, following the methodological framework established by Caudill et al. (1995) and refined by Mango et al. (2015).

3 Material and methods

3.1 Study locations

The Yellow River is the second largest river in the PRC, spanning 5,464 kilometers from its headwaters on the Tibetan Plateau to its Yellow Sea estuary. It is ecologically and economically vital, containing key grain crop-producing areas and resource-rich regions (Cardascia and Panella, 2023; Shi et al., 2020). The river plays a crucial role in China's development, but rapid industrialization and population growth have led to severe water scarcity and pollution (Cai and Rosegrant, 2004). Soil erosion, particularly in the Loess Plateau, is one of the basin's most pressing environmental challenges. This region, the most erosion-prone in China, suffers from soil degradation, poverty, and desertification, which threaten the ecological stability of the entire basin (Zhang et al., 2016).

The Yellow River Basin (YRB) is crucial for China's ecological balance and economic growth. In 2023, the combined population of provinces and regions along the Yellow River Basin was approximately 410 million, representing 28.5% of China's total population, whereas the gross regional product was approximately 31 trillion yuan, constituting 25.3% of China's GDP (Xu and Li, 2024; Hasnain et al., 2015; YRCC, 2018).

Historically, the management of this river represents a significant example from China where the focus remained on a few issues, such as floods, irrigation, soil erosion, and sediment management (Baosheng et al., 2004; Zhu et al., 2004; Shi et al., 2020; Singh et al., 2021). Figure 1 shows the river's flow through western, central, and eastern China, highlighting counties in Gansu, Shaanxi, and Shanxi, which represent diverse ecological zones. Gansu's terrain, including deserts, mountains, and plateaus, is highly vulnerable to environmental change (Wang et al., 2023). The landscape of Shanxi is dominated by mountains and hills, while Shaanxi features a mix of geographical and climatic conditions.

Figure 1

Figure 1. Map of the study area. Source: ARC GIS 10.8.

3.2 Data

A household survey of 957 respondents was conducted through face-to-face interviews across Gansu, Shanxi, and Shaanxi provinces using a stratified random sampling method, as depicted in Figure 1. The survey focused on environmental regulatory measures, agricultural inputs, climatic adaptation information sources, agricultural efficiency, and socioeconomic factors. Key policy variables included supervision levels, the use of highly toxic pesticides, pesticide application, burning of crop residues, overuse of chemical fertilizers, and penalties for these practices. This study analyzes multiple agricultural practices, including planting systems, open field, greenhouse, and combined methods, as well as the application frequencies of chemical and organic fertilizers and both modern and traditional irrigation methods.

3.3 Theoretical framework

Technical efficiency, defined as the ability of farmers to produce the maximum possible output from a given set of inputs, is a critical factor influencing agricultural productivity and economic outcomes. The conceptual foundation for assessing technical efficiency in agriculture is rooted in the production frontier framework first introduced by Farrell (1957). This framework postulates the existence of an optimal production frontier that represents the highest attainable output under given technological conditions and input levels. Deviations from this frontier indicate inefficiencies.

The efficiency analysis literature identifies two broad methodological paradigms: parametric and non-parametric. The parametric method, represented by SFA, employs econometric techniques, assumes a specific functional form for the production function, and explicitly models random errors and inefficiency. Conversely, the non-parametric data envelopment analysis (DEA) utilizes mathematical programming techniques without presupposing a specific functional form, thus offering flexibility in handling multiple inputs and outputs.

In agricultural contexts, production outcomes are affected by factors such as measurement errors, environmental variations, and random shocks. Therefore, a key theoretical consideration is the need to distinguish between inefficiency and random noise. By grounding this study in Farrell's efficiency frontier concept, SFA is particularly suitable because it explicitly separates inefficiency from stochastic influences, thereby ensuring robustness when dealing with data uncertainty and external disturbances common in agriculture.

Figure 2 illustrates the two-stage analytical approach incorporating production factors—capital, technology, labor resources, land resources—and inefficiency model factors, including demographic characteristics, regulatory frameworks, enforcement measures, agricultural production input methods, climate adaptation information sources, and training services that constrain farmers from achieving frontier production. This integrated framework enables the simultaneous estimation of production elasticities and inefficiency determinants, revealing how regulatory interventions and information access affect agricultural productivity in the Yellow River Basin.

Figure 2

Figure 2. Theoretical framework for agricultural technical efficiency and inefficiency.

3.4 Empirical framework

3.4.1 Stochastic frontier model technical efficiency estimation

The stochastic frontier approach (SFA), introduced by Aigner et al. (1977) and (Meeusen and van Den Broeck 1977), is widely used to estimate technical efficiency (TE) in agricultural production (Kompas and Che, 2006; Rahman et al., 2012). TE for a single unit is calculated as the ratio of observed output to frontier output, given the same input levels (Battese and Coelli, 1995). Estimating TE involves determining the parameters of the stochastic frontier model through maximum likelihood estimation, which quantifies the impact of input factors on farm output value. The stochastic frontier model is a robust analytical framework for estimating technical efficiency by accounting for inefficiencies beyond the control of farmers. The Cobb–Douglas production function, selected for its simplicity and estimation advantages, is ideal for TE in this context. This model has been frequently used in recent studies (Cabrera et al., 2010; Rabbany et al., 2022; Dong et al., 2016). Using a generalized production function and cross-sectional data, this method can be depicted as follows:

$\begin{array}{ll}{Y}_i=f(X_{ij};\beta )exp({\epsilon }_i)& (1)\end{array}$

where y_i represents the output value of farm i, f (X_i; β) denotes the production frontier, with X_i as the vector of agricultural input values used by farm i and β as the vector of unknown parameters to be estimated, and ε_i is the error term.

$\begin{array}{ll}{\epsilon }_i=v_i-u_i& (2)\end{array}$

The error term is composed of two independent factors ε_i = v_i − u_i, where v_i ~ N (0, σv²) is a two-sided error term expressing statistical noise due to measurement error or unobserved factors beyond the operator's control. The second component is ui, a non-negative stochastic one-sided error term truncated at 0, with E [ui²] = σu², following a half distribution (Battese and Coelli, 1995; Kumbhakar and Lovell, 2000). Both random variables v_i and u_i are distributed independently of the input variables and are uncorrelated with each other. Technical efficiency is defined as the ratio of observed output to the corresponding stochastic frontier output y^* (when u_i = 0).

$\begin{array}{ll}T{E}_i=\frac{y_i}{y_i^{*}}=\frac{f(X_{ij};\beta .exp(v_i-u_i)}{f(X_{ij};\beta .exp(v_i)}=exp(-u_i)& (3)\end{array}$

$\begin{array}{l}{\sigma }^2={\sigma }^{2}u+{\sigma }^{2}v\\ \lambda =\frac{\sigma u}{\sigma v}\end{array}$

The empirical analysis is based on the estimation of a Cobb–Douglas production function in which both the output and inputs are expressed in logarithmic form, based on Rahman et al. (2012). Hence, the estimated coefficients reflect the output elasticities (Kumbhakar and Lovell, 2000). It is important to indicate that preliminary comparisons led to the rejection of the Translog functional form. Although both Cobb–Douglas and Translog methods were tested to determine the appropriate functional form, while the likelihood ratio (LR) test suggests the Translog specification (p = 0.023), the Bayesian Information Criterion strongly favors the Cobb–Douglas form (BIC = 47.9), which is lower than that of the Translog function. Given that BIC appropriately penalizes model complexity and is preferred for model selection with large samples, the Cobb–Douglas function was adopted. In addition, a sensitivity analysis was conducted by estimating the model under alternative distributions μ_i (half-normal and truncated normal) and by varying the functional form (Cobb–Douglas vs. Translog). The results were consistent, supporting the robustness of the efficiency estimates following (Arsad and Isa, 2020; Qin and Wang, 2024)

$\begin{array}{ll}ln{Y}_i=f(X_i;\beta )+(v_i-u_i),2\dots & (4)\end{array}$

3.5 Technical inefficiency estimation

$\begin{array}{ll}T{I}_i=1-T{E}_i& (5)\end{array}$

$\begin{array}{ll}{U}_i=z_i\delta +e_i& (6)\end{array}$

U_i is the inefficiency of farm i, z_i represents the (agricultural inputs, socioeconomics characteristics and climatic adaptation information sources and training characteristics) those have impact on the inefficiency of a farm i, δ the vector represents the unknown parameters that need to be estimated, and e_it are error terms as studied (Rahman et al., 2012). Environmental penalties represent predetermined policy decisions made by regulatory authorities, independent of individual farm efficiency levels, satisfying the temporal precedence requirement for exogeneity. Following established methodology in agricultural efficiency studies (Mango et al., 2015; Battese and Coelli, 1995), such policy variables are appropriately treated as exogenous determinants of technical inefficiency. In this approach, the parameters for the production frontier and the inefficiency model are estimated jointly using the maximum likelihood technique (Caudill et al., 1995).

H₁: The hypothesis argues that environmental regulatory measures do not significantly influence farm technical efficiency.

H₂: The hypothesis posits that there is no statistically significant impact of climatic adaptation information sources on farm technical efficiency.

To standardize the structural variables (inputs and total value of crop output), we divided each variable by its sample mean before applying the natural logarithm transformation. This procedure allows the first-order coefficients to be interpreted as output elasticities, calculated at the sample means. Supplementary Table S1 presents the descriptive statistics for the variables used in this study.

4 Results

Supplementary Table S2 presents the cost structure analysis of the agricultural inputs across the farms. Farmers demonstrated substantial financial investment in productivity-enhancing inputs, with mean expenditures of 4551.84 RMB for chemical fertilizers and 2612.22 RMB for other agricultural resources. Mechanical costs represented the largest expenditure category, with irrigation costs at 1008.116 RMB, plowing costs at 375.58 RMB, organic fertilizer application costs at 479.04 RMB, and other artificial machinery usage costs at 595.37 RMB, constituting the primary cost. These mechanical investments reflect farmers' strategic focus on soil fertility enhancement and production optimization through capital-intensive management practices. Secondary cost categories comprised crop preparation and protection inputs, with sowing labor at 882.71 RMB, pesticide application at 523.077 RMB, and seedling costs at 165.77 RMB, representing substantial financial commitments. Conversely, labor-intensive practices demonstrated relatively lower cost expenditures.

Figure 3 demonstrates systematic right-skewed distributional patterns across all agricultural input and output variables, revealing the fundamental structural heterogeneity that drives technical efficiency variations in Yellow River Basin agriculture. Moreover, input cost distributions for fertilizers, organic fertilizers, pesticides, seedlings, irrigation, lamination, plowing, plant protection, artificial machinery, harvesting, and other agricultural resources consistently demonstrate modal clustering at modest expenditure levels, with extended tails toward high-investment, empirically supporting our econometric results where moderate input application enhances productivity, while excessive use by intensive commercial operations generates technical inefficiency.

Figure 3

Figure 3. Frequency graph of farmer's agricultural input and output. Source: Graph pad.

Figure 4 demonstrates socioeconomic distribution consistent with econometric results. Age distribution peaks at 50–65 years, educational attainment averages 8 years, farming experience clusters around 30–35 years, and family size concentrates at 3–4 members. These patterns validate the training coefficient and support targeted extension interventions for this demographic profile.

Figure 4

Figure 4. Relationship between the farmers and the socioeconomic variables. Source: GraphPad.

Figure 5 provides a comprehensive visualization of the socioeconomic stratification within Yellow River Basin agriculture, revealing critical demographic characteristics that fundamentally shape technical efficiency patterns, policy intervention effectiveness, and address the percentage of farmers' gender, health, and training.

Figure 5

Figure 5. Demographic distribution analysis of the farmers. Source: R Studio.

Supplementary Table S3 presents descriptive statistics for environmental regulatory measures and agricultural production inputs, harmful practices, and climate adaptation information sources including training sessions. Descriptive statistics revealed different levels of environmental practices among the farmers. Environmental regulatory supervision showed a mean score of 11.37, while harmful agricultural practices, including the use of toxic pesticides, crop residue burning, and overuse of chemical fertilizers, demonstrated adoption rates with means ranging from 0.64 to 0.79. Farmers showed distinct preferences across information sources for climate adaptation. For weather disaster information, social networks, including neighbors, relatives, and agricultural cooperatives, were most utilized, scoring a mean value of 0.89, while technical information demonstrated the highest ICT adoption at 0.97. Generally, agricultural information showed moderate reliance on mass media sources, with a mean of 0.67, and ICT mean of 0.47, with minimal use of conventional methods, with a mean of 0.020. Training participation rates varied considerably, with technology training sources' access mean at 0.75, higher than actual participation at 0.21.

Figure 6 shows that the regulatory compliance patterns reveal widespread environmentally harmful practices, with over 600 farmers engaging in highly toxic pesticide use, approximately 750 farmers practicing crop residue burning, and over 650 farmers applying excessive chemical fertilizer practices that significantly reduce technical efficiency while generating substantial environmental externalities. The supervision distribution indicates systematic regulatory failure, with 400 farmers reporting very inadequate supervision and another 400 indicating that supervision is not in place. This further demonstrates that the planting method and cropping pattern empirically validate our finding that inadequate supervision correlates with reduced efficiency and highlight the urgent need for enhanced regulatory enforcement, including the implementation of effective penalty mechanisms for highly toxic pesticide restrictions, while simultaneously improving environmental outcomes and technical efficiency across agricultural farming practices.

Figure 6

Figure 6. Relationship of the farmers with the environmental regulatory measures, cultivation practices, and techniques. Source: Graph Pad.

Figure 7 demonstrates the agricultural input application patterns that characterize Yellow River Basin agriculture, with frequency distributions revealing both optimization opportunities and efficiency constraints that directly correspond to our econometric findings.

Figure 7

Figure 7. Relationship of the farmers with the environmental regulatory measures, cultivation practices, and techniques. Source: Graph Pad.

Figure 8 shows the agricultural practice adoption patterns that fundamentally influence technical efficiency outcomes, demonstrating widespread utilization of both conventional and sustainable agricultural inputs.

Figure 8

Figure 8. Relationship of the farmers with the environmental regulatory measures, cultivation practices, and techniques. Source: Graph Pad.

Figure 9 depicts climate adaptation information sources in Yellow River Basin agriculture, where farmers demonstrate extensive use of various information sources, particularly mass media, ICT platforms, and meteorological services. This empirically validates our econometric finding that the effectiveness of information channels varies significantly.

Figure 9

Figure 9. Shows response of the farmers toward climatic adaptation information sources. Source: Graph Pad.

The maximum likelihood estimation results for the stochastic production frontier are presented in Table 1. The model demonstrates a good statistical fit, indicated by the chi-square value, and a robust model specification. The production elasticities reveal significant heterogeneity across the inputs and provide insights into resource allocation efficiency in the Yellow River Basin. Farm labor is the most important factor in enhancing smallholder farmers' efficiency, showing that a 1% increase in labor input improves efficiency by 0.279%. This is consistent with previous studies in developing countries, where labor still plays a significant role (Rahman et al., 2012; Ali et al., 2019). Sowing labor costs also positively influence farm efficiency, as found by Benedetti et al. (2019), who emphasized the importance of field preparation practices for Mediterranean agriculture. While sown area has no significant effect on yield, this also validates results presented by Zhou et al. (2024), as land fragmentation limits the productivity of land expansion in Chinese agriculture.

Table 1

Table 1. Maximum likelihood estimates of the Cobb–Douglas of the stochastic frontier model.

These findings emphasize that intensive management practices, rather than extensive cultivation, represent the primary pathway to sustainable agricultural productivity improvements, partially aligning with previous research by Ali et al. (2019). Labor and seed costs significantly influence production value. Specifically, a one-unit increase in labor and seed costs results in a 0.08% and 0.07% increase in output value, respectively, which aligns with Ali et al. (2019). The application of chemical fertilizer significantly improves farm output, with a 1% increase in fertilizer applications raising farm output by 0.165%, as reported by Zhang et al. (2023) in the Bohai Rim region with fertilizer coefficients of 0.18. The use of organic fertilizer on crop yield is estimated to have a smaller but still significant and positive impact on efficiency, consistent with Li et al. (2024), who reported that Chinese organic amendments enhance productivity and soil health in the Chinese agricultural farming system. In contrast, chemical fertilizer labor had a negative effect on output, while organic fertilizer labor was non-significant. This trend implies inefficiency in the practices of chemical fertilizer application, likely due to excessive or untimely applications, consistent with Xu et al. (2025) in their study of Chinese agricultural farm management.

Pesticide costs demonstrated a negative effect associated with efficiency, as increased pesticide expenditures reduce output by 0.051% per unit increase. This finding aligns with Zheng et al. (2020), who reported diminishing returns from excessive pesticide use. Conversely, pesticide labor showed a positive effect on efficiency, suggesting that skilled application practices can enhance pest control effectiveness, as found by Cao et al. (2022). Plant protection practices, including insecticides and fungicides, also negatively affected efficiency, as shown in Table 1, supporting the notion that current pest management practices exceed optimal application thresholds, as suggested by (Khatri-Chhetri et al. 2023) in their efficiency analysis. Machinery costs demonstrated a strong positive effect, indicating that mechanical investment enhances efficiency by 0.1128% per unit increase, which aligns with Zhong et al. (2025), who found that agricultural outsourcing and mechanical investment significantly improve technical efficiency among Chinese farms of various scales. However, irrigation and its labor costs reduce efficiency; these results contradict conventional expectations and align with Ali et al. (2019), who reported similar findings in hybrid maize production, suggesting that potential over-irrigation represents inefficient water management practices.

Lamination costs negatively impacted production, while labor showed a positive effect. This pattern emphasizes that the cost of plastic material reduces efficiency, while labor may enhance efficiency in controlling weeds and retaining moisture. Plowing costs demonstrated negative effects, consistent with Hasnain et al. (2015), who reported similar findings in Bangladeshi rice production. This may reflect excessive tillage practices that increase costs without proportional productivity gains. Harvesting costs showed negligible effects, while other agricultural resources negatively impacted output, as shown in Table 1, supporting Benedetti et al. (2019)'s findings on input use efficiency in agriculture. Collectively, these results suggest that Yellow River Basin agriculture exhibits characteristics typical of transition economies, where traditional extensive practices coexist with emerging intensive management approaches, creating both opportunities and inefficiencies in resource allocation patterns.

Table 2 presents the maximum likelihood estimation results for factors influencing technical inefficiency in agricultural production. The model demonstrates adequate explanatory power (R² = 0.216, F = 6.323, p < 0.001), indicating that the selected variables collectively explain 21.6% of the variation in technical inefficiency across farmers. Table 2 presents that years of formal schooling significantly increase technical inefficiency, suggesting that farmers with higher formal education may be less technically efficient. However, agricultural training demonstrated strong effect on efficiency, indicating that 10.8% reduce inefficiency accompanies training participation. These findings support the study by Hoang-Khac et al. (2022), which identified agricultural training as a key determinant of technical efficiency enhancement among smallholder farmers. Gender showed no significant effect on technical inefficiency, consistent with the results of the study by Wang et al. (2012) and Benjamin et al. (2025), who found similar technical performance between male and female farmers in Rwandan agriculture. Age and farming experience exhibited negligible impacts; furthermore, health status demonstrated minimal influence, suggesting that physical capacity constraints do not significantly limit technical efficiency in the study region. These findings align with the study by Ji et al. (2023), who also investigated inefficiency using the framework of Battese and Coelli (1995).

Table 2

Table 2. Estimated coefficients of socioeconomic characteristics, environmental regulatory measures and farmer's agricultural harmful input in the inefficiency model.

Inadequate supervision marginally increased inefficiency, supporting the hypothesis that regulatory oversight enhances technical performance. Harmful agricultural practices, such as the application of highly toxic pesticides, increased agricultural inefficiency; however, penalties for such practices reduced inefficiency. This suggests that the regulatory imposition of penalties can achieve both goals of environmental protection and productivity improvement, supporting the findings of Hou et al. (2019), Cao et al. (2022), and Downing et al. (2022) on the effectiveness of pesticide regulation. The overuse of chemical fertilizers increased inefficiency, which aligns with the findings of Xu et al. (2025), who noted a decline in efficiency due to excessive chemical input in the Chinese agriculture system. More surprisingly, the overall use of pesticides reduced inefficiency. This may imply that effective pest control improves productivity when implemented properly (Ji et al., 2023; Naramo and Tafesse, 2022).

All planting systems—open field, greenhouse, and combined methods—are increasing inefficiency relative to basic practices, as shown in Table 2. These findings suggest suboptimal implementation of the intensive cultivation system, potentially due to inadequate technical knowledge and improper technology adoption. Fortin (2022) also noted that knowledge gaps, driven by limited capacity at lower administrative levels, hinder the adoption of sustainable, climate-resilient agricultural practices. Both chemical and organic fertilizers increased inefficiency, indicating excessive application rates across inputs. This pattern supports the findings of Zhang et al. (2025), which state that balanced nutrient management, rather than intensive input use, optimizes technical efficiency.

Traditional irrigation methods significantly reduced inefficiency, while modern irrigation showed no significant effect, as shown in Table 2. These findings align with Khatri-Chhetri et al. (2023), who reported that context-appropriate technology often outperforms modern alternatives in smallholder farming systems. Irrigation frequency marginally increased inefficiency, suggesting potential for improvement in irrigation practices.

Information acquisition through conventional sources (family, friends, and neighbors) and ICT platforms increases inefficiency, while mass media (TV, radio, and newspapers) reduces inefficiency. This differential effectiveness pattern suggests that mass media provides more standardized, actionable information compared to informal networks, consistent with the findings of Salam and Phimister (2017) on information access effects in Ugandan agriculture, which also align with Roco et al. (2017).

Farmers receiving weather disaster information demonstrated increased inefficiency; this finding aligns with Roco et al. (2017), who noted that information availability does not automatically improve technical efficiency without adequate organizational and management capacity. Sources providing meteorological hazard information increased inefficiency, while measures and techniques communicated through mass media showed marginal significance. These results suggest that the current climate information system is not optimally designed for the farmer decision-making process. Most critically, the predominant reliance on single-crop systems, despite favorable conditions for diversification, represents the largest untapped potential for sustainable intensification. This affirms our finding that different cropping positively affects efficiency, as presented in Table 2, while highlighting that structural constraints hinder the adoption of optimal agricultural practices. Collectively, this demonstrates that Yellow River Basin farmers possess a robust adaptive capacity and effective information utilization skills but require targeted policy interventions to address access barriers to training and support for crop diversification to achieve substantial efficiency improvements and advance environmental sustainability objectives through climate-smart agricultural practices.

Figure 10 presents the technical efficiency distribution across 957 farmers, demonstrating a right-skewed pattern with a mean efficiency of 0.5. The distribution reveals that 283 farmers (29.6%) operate within the model efficiency range of 0.6–0.7, while 187 farmers (19.5%) and 208 farmers (21.7%) perform within the 0.4–0.5 and 0.5–0.6 ranges, respectively. Notably, 111 farmers (11.6%) operate at an efficiency level below 0.4, indicating substantial underperformance, while only 109 farmers (11.4%) achieve an efficiency level above 0.7. This distribution pattern indicates considerable heterogeneity in farmers' performance and significant potential for productivity enhancement, with approximately 60% of farmers operating below the sample mean efficiency level.

Figure 10

Figure 10. Technical efficiency range score. Source: RStudio.

Trade-off or Allowance = Y^* δ_r

In this context, Y represents the mean value of farm-level income, while δ_r denotes the coefficient of the respective regulatory variable. A negative coefficient value for the regulatory measure in the inefficiency model implies a positive effect on technical efficiency, resulting in a gain for the farmer. Conversely, a positive coefficient indicates a negative impact on technical efficiency, leading to a trade-off scenario.

Table 3 specifically addresses penalties for the use of pesticides, the application of highly toxic pesticides, and the burning of crop residues, yielding average gains of 20.294, 223.23, and 101.47 RMB per farmer, respectively. These gains can be regarded as allowances. In contrast, imposing penalties for the excessive application of chemical fertilizers results in an average trade-off of 101.47 RMB per farmer, reflecting a decline in production and income. This suggests that there remains a deficit in the quantities of fertilizers applied. The trade-off value represents the cost of abatement that farmers incur to mitigate environmental degradation associated with the excessive use of chemical fertilizers.

Table 3

Table 3. The trade-off and allowance of the farmers' average value of the output by imposing regulatory policies.

The regulatory impact of allowing or penalizing certain practices benefits both farmers and the environment. Policymakers must weigh the trade-offs and benefits for farmers when enacting such measures. According to Adolph et al. (2021), small-scale farmers in sub-Saharan Africa struggle to balance social, economic, and environmental goals due to resource constraints and regulations that prioritize short-term productivity over long-term sustainability. Similarly, while environmentally sustainable farms may show lower technical efficiency, improvements in social factors could boost efficiency, as indicated by Ait et al. (2022).

4.1 VIF estimation

The literature has many suggestions regarding what constitutes an acceptable value of VIF. A maximum level of VIF of 10 has been suggested (Hair et al., 1995). The assumption is fully satisfied with a maximum VIF value of 5.09 in the current investigation. There are several published recommendations regarding reasonable tolerance levels; however, the lowest threshold value is 10 (Tabachnick and Fidell, 2001). The assumption is that the present study meets the acceptable threshold value.

5 Discussion

5.1 Agricultural production inputs and farm efficiency

Research has demonstrated that crop efficiency in the Yellow River Basin is significantly increased by various agricultural inputs, with a strong positive relationship observed at the 1% statistical significance level. Increased utilization of inputs, including seeds, water, fertilizers, and pesticides, improved efficiency. To maximize technical efficiency, policy makers should prioritize improve the farmers' capabilities through enhancing the marketing strategies, management skills, and extension services as suggested by Ashrit (2023). Existing literature confirms that technical efficiency is fundamentally linked to enhanced agricultural and livestock production (Dong et al., 2018; Henderson et al., 2016). However, certain input factors have counterproductive effects on efficiency; for instance, excessive labor allocation for fertilizers and pesticides, suboptimal irrigation practices, and inefficient plowing and plant protection can negatively impact efficiency, corroborating findings by Lu et al. (2018), who emphasized the detrimental effects of inefficient labor and input management on farm efficiency. The evidence indicates that strategic agricultural inputs not only improve technical efficiency but also contribute to more sustainable and productive farming.

5.2 Socioeconomic factors and farm efficiency

Training programs significantly improve crop efficiency among farmers. Targeted training interventions increase efficiency by an estimated 0.10%. Beyond training, several socioeconomic factors influence technical efficiency. Hoang-Khac et al. (2022) identified key drivers, including human capital, farmers' beliefs, trust in institutions, and land accumulation. These findings highlight that agricultural efficiency depends not only on technical efficiency factors but also on broader social and economic conditions. The study also highlights the positive impact of extension programs on farm efficiency, with trained farmers achieving an efficiency rate of 95.6%, compared to 88.1% for untrained farmers.

5.3 Environmental regulatory measures, harmful practices and farm efficiency

Pesticide use demonstrates a complex relationship with farm efficiency. While pesticide application contributes to a modest 0.09% efficiency gain, highly toxic pesticides and crop residue burning substantially reduce productivity. This contradiction highlights the critical importance of pesticide selection and application methods in sustainable agriculture. Strict enforcement correlates positively with farm efficiency, suggesting that regulatory pressure may drive farmers toward more efficient, environmentally sound practices, which contradicts the hypothesis that regulatory measures negatively affect efficiency and supports Porter's (1991) hypothesis that framing regulatory measures positively can enhance efficiency. Tang et al. (2022) highlight that direct command regulations and financial incentives have significantly improved ecological efficiency in China's agriculture sector, aligning with our findings; both studies confirm that environmental and economic objectives can be aligned under a balanced policy framework. Moreover, chemical fertilizer overuse has a strong negative effect, leading to a 0.03% decrease in efficiency with each additional unit, supporting the diminishing returns theory in agricultural inputs. As He et al. (2020) stated, stated, China must reduce chemical fertilizer use by at least 21.8 million tons to achieve sustainability targets, emphasizing the economic rationale for balanced nutrient management. van Wesenbeeck et al. (2021) discussed how China can reduce nitrate surpluses by more than 50% and phosphate surpluses by more than 75% without significantly impacting food self-sufficiency, given appropriate policy combinations.

5.4 Climatic adaptation information sources and farm efficiency

Information source utilization reveals a striking hierarchy that challenges conventional assumptions about the agricultural extension system. Mass media emerged as an effective information channel, with a 0.028% efficiency gain per unit increase in usage, contradicting the traditional emphasis on interpersonal extension services. These preferences align with Khan et al. (2025) comprehensive review across the Global South, documenting how ICT platforms surpass traditional face-to-face methods. Our findings contradict Linh et al. (2016) assumption of equal effectiveness between mass media and personal sources, suggesting that content quality and technical accuracy in mass media may matter more than the frequency of contact and social relationships in extension services. Advanced meteorological information and training services demonstrate a consistent positive impact across diverse contexts. Gong et al. (2024) analyzed Chinese farmers and found that digital agricultural technology services significantly enhance farmers' willingness to adopt digital production technologies, with a mediating effect through digital literacy. This aligns with our findings on the effectiveness of meteorological hazard information, leading to systematic information reforms. Salam and Phimister (2017) stated that increased access to information improves farm efficiency. Multiple cropping also shows a positive relationship with efficiency; Terefe Kena (2023) documented that adaptive strategies like crop diversification, improved varieties, and better irrigation methods can enhance technical efficiency. Similarly, this relationship extends globally, with Emran et al. (2022) conducting a multi-criteria analysis in Bangladesh that shows that diversification enhances both cropping system intensity and resource use efficiency among smallholder farmers. Contrary to our hypothesis that climatic adaptation information sources have no significant impact on farm efficiency, we found robust positive effects across multiple information channels. A sustainable management system is crucial for ecological development and farm efficiency.

6 Conclusion

This study examines the complex relationships between environmental regulations, harmful agricultural practices, and technical efficiency in China's Yellow River Basin. Our analysis reveals that the widespread use of toxic pesticides and crop residue burning significantly reduces technical efficiency, while regulatory penalties for these practices paradoxically improve both environmental outcomes and farm productivity. This challenges the conventional trade-off narrative between environmental protection and agricultural output. The variance decomposition confirms that inefficiency, rather than random factors, drives most production variation, indicating substantial scope for improvement through better management and regulatory enforcement. Inefficiency analysis reveals critical heterogeneity in information channel effectiveness. Farmers accessing climate information through mass media demonstrated significantly higher technical efficiency than those relying on ICT or conventional peer-to-peer networks. Training participation emerged as the strongest efficiency enhancer, while advanced meteorological information from institutional sources outperformed fragmented informal networks. These findings suggest that information quality and source credibility matter more than technological sophistication or frequency of contact in agricultural extension systems. Our results indicate that proper environmental enforcement can achieve ecological and agricultural sustainability. Policy intervention should prioritize strengthening penalty systems at the village level and organizing a regular monthly monitoring system. Reorganizing the extension services, which serve as a bridge between farmers and information providers, should emphasize mass media and training sessions rather than farmers' dependency on their own information sources like ICT. Additionally, a subsidized monitoring system should be introduced to ensure funds are used for relevant purposes and provide performance-based incentives based on bidirectional ecological and production efficiency.

6.1 Limitations

This study is based on cross-sectional data from 2022, which has several limitations. Given that climate resilience and agricultural efficiency are dynamic phenomena that evolve over time, incorporating multiple years of temperature, perception, drought, and frost data, including pesticides and fertilization, into panel data would provide more robust insights for developing climate resilience policies, regulatory measures, and agricultural efficiency initiatives. Future research should prioritize the use of panel data to better inform policy recommendations and improve the reliability of findings on climate adaptation in agriculture.

6.2 Recommendations

First, in the short term, reforms strengthening laws and enforcement measures to impose penalties for harmful practices like excessive pesticide use, crop residue burning, and improper chemical fertilizer application are essential. Secondly, promoting sustainable management systems and incentivizing the use of organic fertilizers, integrated pest management, and conservation agriculture is crucial for long-term environmental sustainability and improved agricultural productivity. Thirdly, training initiatives are essential to educate farmers on sustainable techniques, climate change mitigation, and efficient resource use. Additionally, structural reforms such as strengthening communication channels will ensure farmers receive timely updates on climate change, weather alerts, and best practices through mass media, ICT, and local organizations. Furthermore, a regular monitoring and evaluation system will be crucial to assess the long-term effectiveness of these efforts and ensure continuous development.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

HK: Conceptualization, Investigation, Methodology, Software, Visualization, Writing – original draft. BM: Data curation, Writing – review & editing. SK: Writing – review & editing. SL: Data curation, Writing – review & editing. AD: Writing – review & editing. MM: Writing – review & editing. SR: Formal analysis, Writing – review & editing. LH: Funding acquisition, Resources, Supervision, Validation, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research supported by the National Key Research and Development Program of China during the 14th Five-Year Plan Period (Grant No. 2023YFF1305104), under the project “Comprehensive Management of Mountains, Rivers, Forests, Farmlands, Lakes, Grasslands, and Deserts in Small Watersheds of the Loess Plateau and Synergistic Enhancement of Ecosystem Services,” and specifically by the sub-project “Technologies for Soil Conservation, Water Retention, Carbon Sequestration Enhancement, and Comprehensive Management in Small Watersheds Affected by Wind-Water Compound Erosion in the Beiluo River Basin.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2025.1669386/full#supplementary-material

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