To promote high-quality agricultural development and implement the “storing grain in the land” strategy, the construction of Well-Facilitated Farmland (WFF) plays a critical role in enhancing grain production capacity and optimizing the spatial distribution of food supply, thereby contributing to national food security. However, accurately assessing the potential impact of WFF construction on China’s grain production and regional self-sufficiency by 2030 remains a significant challenge. Existing studies predominantly focus on the provincial level, while fine-grained analyses at the city level are still lacking. This study quantifies the potential increase in grain production in China under the 2030 WFF construction target by employing effect size analysis, multi-weight prediction, and Monte Carlo simulation across multiple spatial scales (national, provincial, and city levels), thereby addressing the research gap at finer spatial resolutions. By integrating 2030 population projections and applying a grain self-sufficiency calculation formula, it further evaluates the contribution of WFF to regional grain self-sufficiency: (1) WFF could generate an additional 31–48 million tons of grain, representing a 5.26–8.25% increase; (2) grain supply in major crop-producing regions would expand, while the supply–demand gap in balanced regions would narrow; and (3) the number of cities with grain self-sufficiency ratios below 50% would decrease by 11.1%, while those exceeding 200% would increase by 25.5%. These findings indicate that WFF construction not only enhances overall grain production potential but also facilitates a transition from “overall supply-demand balance” to “structural security” within China’s food system. This study provides critical data support and policy insights for building a more resilient and regionally adaptive agricultural system.

Against rapid urbanization in China, food security challenges including shrinking arable land and weak agri‐food supply chain resilience have become increasingly prominent, which elevates the strategic value of peri‐urban agriculture in localizing supply chains and safeguarding urban food security. Taking Guangdong Province, China, as a study area, this study uses municipal‐level statistical data and multidimensional quantitative analysis to establish a comprehensive food security assessment framework integrated with sustainable development orientation via the incorporation of future scenario prediction and resource sustainability assessment, aiming to ensure long‐term, stable and resource‐efficient food security, and systematically examines the contribution of agricultural production in highly urbanized municipalities to food security. The results show that agricultural production in highly and non‐highly urbanized regions presents significant and persistent spatial differentiation: the grain, vegetable and melon yields in highly urbanized municipalities are significantly lower, with the grain self‐sufficiency rate nearly zero and basic self‐sufficiency of vegetables and melons but limited surplus. The non‐grain proportion of cropland is more prominent in highly urbanized municipalities. Despite steady gains in agricultural water use efficiency across both region types, combined disparities in cropland structure, water utilization and well‐facilitated farmland (WFF) construction sustain a long‐term structural gap in grain self‐sufficiency. The proposed suggestions including differentiated peri‐urban agriculture development strategies and a benefit compensation mechanism for grain production and consumption areas can provide empirical and policy references for highly urbanized municipalities in China to implement the “Rice Bag” and “Vegetable Basket” policies and offer Guangdong's experience for improving the regional food security guarantee system amid rapid urbanization.

With the continuous advancement of sustainable agriculture, drone technology has become a focus of attention. Current research primarily relies on classical models for questionnaire surveys and analyses within specific regions, rather than implementing macro-level investigations that incorporate innovative algorithms. This study designed a survey questionnaire to investigate Chinese farmers’ preferences for agricultural drones and their technology adoption mechanisms under sustainable agriculture context. The Ant Colony Optimization-Decision Tree (ACO-DT) model and SHAP (Shapley Additive exPlanations) value analysis are applied to analyze the contribution of different indicators to technology adoption. The ACO-DT model outperformed traditional machine learning models with approximate accuracy 0.85, recall 0.98, and F1 Score 0.90, effectively identifying potential drone users compared to other traditional machine learning models. The SHAP analysis showed “Time Required for Promotion” (average SHAP value exceeds 1.25) and “Understanding of UAV Agriculture” (average SHAP value is about 1.0) were core influencing factors. Specifically, high-cognition farmers preferred shorter promotion cycles, while low-cognition group favored longer cycles to reduce decision-making uncertainty. Practically, the study enriches agricultural technology adoption research methodologically and offers references for advancing smart agriculture and optimizing rural production factors.

Farmland fragmentation is a critical structural constraint hindering the improvement of farmers' chemical fertilizer use efficiency. However, crop‐specific micro‐level evidence focusing on maize growers, along with systematic empirical verification of the moderating roles of mechanized fertilization and labor input, remains lacking. Based on micro‐survey data of 1213 maize‐growing households across seven major maize‐producing provinces from the 2020 China Rural Revitalization Survey, this paper applies Stochastic Frontier Analysis to measure chemical fertilizer use efficiency, combined with Ordinary Least Squares estimation and ‐based Shapley value decomposition for empirical verification. The results show that the average fertilizer use efficiency of the sample households is 0.420, representing a potential model‐based fertilizer reduction space of approximately 57.97%. Each additional plot per hectare decreases efficiency by approximately 11.9% on average. Mechanized fertilization exacerbates efficiency loss under conditions of high fragmentation, whereas labor input dedicated to fertilization acts as a buffering mechanism. Farmland fragmentation explains 25.60% of the explained variation in fertilizer use efficiency, making it the second most critical explanatory factor after geographic and environmental characteristics. Accordingly, on the premise of safeguarding farmers' rights and balancing ecological trade‐offs, priority should be given to promoting differentiated land consolidation and contiguous management, complemented by scale‐appropriate machinery, agricultural service provision, and precision nutrient management tailored to small parcels.

Against the backdrop of global climate change and the “dual carbon” goals, the issue of agricultural greenhouse gas emissions has garnered increasing attention. As a major grain and oilseed crop in China, carbon emissions from soybean production have a significant impact on the green and low-carbon development of agriculture. Although research on agricultural carbon footprints has grown in recent years, existing studies have largely focused on single regions or specific stages of crop production, and analyses of the carbon footprint of production systems in China’s major soybean-producing regions remain relatively limited. This study employs the Life Cycle Assessment (LCA) methodology to calculate and analyze the carbon footprint of soybean production systems across China’s 10 major soybean-producing provinces, utilizing agricultural input data from 2014 to 2023. The study establishes a carbon footprint accounting system based on two key aspects: carbon emissions from agricultural inputs and soil N₂O emissions. It further analyzes the temporal trends, regional variations, and contribution characteristics of each component within the carbon footprint. The results indicate that the average carbon footprint of soybean production in China is approximately 528 kg CO₂eq/ha (ranging from 273 to 855) and 0.25 CO₂eq/kg of soybean (ranging from 0.13 to 0.46). Specifically, the carbon footprint per unit of area and yield declined simultaneously, indicating a continuous improvement in the low-carbon efficiency of soybean production. Spatially, there are significant regional differences in the carbon footprint of soybean production. Henan, Anhui, and Inner Mongolia have relatively low carbon footprints, while Shaanxi and Shanxi have relatively high levels. In terms of composition, chemical fertilizer inputs and soil N₂O emissions are the primary sources of the carbon footprint in soybean production, with chemical fertilizer inputs being the largest source, accounting for approximately 40–60%, and soil N₂O emissions being the second major source. Overall, differences among regions in natural conditions, agricultural input structures, and production methods result in distinct regional characteristics in the carbon footprint composition. The findings of this study provide a scientific basis for the low-carbon transition of China’s soybean production system and serve as a reference for the formulation of policies related to green agricultural development.

The complex interdependencies among agricultural systems, urbanization, and ecosystem vulnerability represent critical yet underexplored determinants of carbon emissions. To move beyond single-factor analyses, this study constructs a multidimensional framework to quantify the synergistic effects of these factors on U.S. carbon emissions from 1995 to 2022. By integrating data from The Notre Dame Global Adaptation Initiative (ND-GAIN) with national emission inventories, we adopt a multi-method analytical approach: Gaussian fitting validates the Environmental Kuznets Curve (EKC) hypothesis, through which an inflection point is identified at a per capita GDP of about $40,000, while the results also reveal the model’s limitations in capturing policy-induced nonlinearities. Partial least squares regression (PLSR) exhibits superior explanatory power with an R² value of 0.869 compared to ridge regression, and its latent component analysis further uncovers significant nonlinear interactions. Key findings show that food vulnerability and urban concentration act as significant suppressors of carbon emissions, whereas agricultural production capacity and water vulnerability function as major driving factors. Scenario simulations based on the Stochastic Impacts by Regression on Population, Affluence and Technology model further confirm the emission-reducing effect of ecological restoration, which leads to an approximate 1.29% decrease in carbon emissions, while also indicating that adaptive agriculture development may result in a short-term increase of about 3.44%. This study underscores the necessity of multi-system governance, proposes a “natural restoration-technological innovation-institutional synergy” policy framework, and provides a data-driven pathway for U.S. carbon neutrality as well as a novel paradigm for multi-scale climate governance analysis.