As an important carrier for the application of digital technologies, digital infrastructure plays a crucial role in promoting the digital transformation of the grain system and ensuring food security in the current era. This study utilizes panel data from 31 provinces (municipalities) in China, spanning the years from 2006 to 2022, and constructs a comprehensive evaluation index system for grain system resilience, grounded in its core components of resistance, recovery, and transformation. The grain system resilience index is measured using the entropy method. A spatial Durbin model is employed to estimate the impact of digital infrastructure on grain system resilience, and a panel threshold model is used to analyze the nonlinear effects of digital infrastructure on grain system resilience. The research findings are as follows: (1) Both the direct and spatial spillover effects of digital infrastructure on grain system resilience are significantly positive, but considerable regional heterogeneity is observed. Due to differences in economic development levels, digital infrastructure investments, and policy priorities, the indirect and total effects of digital infrastructure on food system resilience are more pronounced in the southeast region, whereas the direct effects are more significant in the northwest region. (2) The threshold regression results show that when market integration is below the threshold value, the estimated coefficient of digital infrastructure is 0.2242, which is significant at the 1% significance level. When market integration is above the threshold value, the estimated coefficient of digital infrastructure is 0.0790, which is also significant at the 1% significance level. However, its regression coefficient significantly decreases, indicating that the impact of digital infrastructure on food system resilience will weaken as the degree of market integration increases. (3) The analysis of the attenuation boundary of spatial spillover effects shows that within a distance of 225 km, the estimated coefficients of the indirect effects of digital infrastructure on grain system resilience are positive and statistically significant at least at the 10% significance level. However, beyond 225 km, the regression coefficients become negative and insignificant, indicating that the effective boundary of the spillover effect of digital infrastructure on grain system resilience is 225 km, after which the spillover effect gradually diminishes. Based on these findings, it is recommended that the southeast region further strengthen regional digital governance collaboration to maximize spillover effects, whereas the northwest region should prioritize improving digital infrastructure and introduce digital technologies through models such as an “enclave economy” to bridge the digital divide. This study reveals the impact of digital infrastructure on grain system resilience and provides a new perspective for scientifically evaluating the spatial spillover effects of digital infrastructure.

In the context of increasingly severe resource and environmental constraints, examining the impact of citrus industry agglomeration on the green total factor productivity (GTFP) of citrus is of great importance for the sustainable development of the citrus industry and is crucial for promoting the green, high-quality growth of China’s agricultural sector. In this study, the global Malmquist–Luenberger productivity index (GMLPI) model was used to measure the GTFP of mandarins and tangerines based on inter-provincial panel data from China’s major citrus-producing regions between 2007 and 2022. The dynamic spatial Durbin model was employed to empirically analyze the effects of citrus industry agglomeration on the GTFP of mandarins and tangerines, including the disaggregation of its spatial spillover effects. The results indicate that, in terms of temporal dynamics, the GTFP, technical progress index (GTC), and technical efficiency index (GEC) of mandarins and tangerines significantly fluctuated, especially during the period from 2007 to 2015. Regional disparities in GTFP and the GTC are more pronounced for mandarins than for tangerines, while the GEC shows greater regional disparities for tangerines than for mandarins. The intensification of citrus industry agglomeration has had a significant positive impact on the GTFP of mandarins and tangerines, both locally and in neighboring regions. The spatial correlation of the green total factor productivity of mandarins and tangerines fluctuated; mandarins showed significant spatial aggregation in some years, while tangerines showed significant spatial dispersion in several years. The local Moran scatterplot further reveals the significant negative spatial autocorrelation of mandarin and tangerine green total factor productivity from 2007 to 2022. The direct, indirect, and total effects of citrus industry agglomeration on the GTFP of mandarins and tangerines are significant and positive in both the short- and long-term, with short-term benefits exceeding long-term effects. Consequently, enhancing regional cooperation and exchange while advancing citrus industry agglomeration is essential for sustained productivity growth.

With the deepening implementation of agricultural full-cost insurance and crop income insurance, agricultural insurance has gradually become a significant force in promoting agricultural and rural modernization and achieving the strategic goal of building a strong agricultural nation. Based on data from 30 provinces in China from 2011 to 2023, a comprehensive evaluation index system for agricultural economic resilience was constructed, and the impact of agricultural insurance on agricultural economic resilience, along with its underlying mechanisms, was systematically analyzed. The findings reveal that: (1) There exists a nonlinear “U-shaped” relationship between agricultural insurance and agricultural economic resilience, a conclusion that remains robust after a series of tests; (2) Agricultural insurance can positively influence agricultural economic resilience by promoting agricultural technological progress; (3) When the level of industrial structure exceeds 7.108, agricultural insurance has a significant effect on agricultural economic resilience, and as the industrial structure level improves, the promoting effect of agricultural insurance becomes more pronounced; (4) The “U-shaped” impact of agricultural insurance on agricultural economic resilience is more prominent in the eastern, central, and northeastern regions, while it is not significant in the western region.

This article constructs an evaluation index system for the development of nutrition-sensitive agriculture in China and measures the development level of nutrition-sensitive agriculture using the entropy method, based on the panel data of 31 provinces from 2000 to 2022. The Dagum Gini coefficient is employed to analyze the magnitude and sources of regional differences in the development level between the whole country and the four major regions. The Kernel density estimation method is applied to describe the dynamic evolutionary characteristics of the development level in different regions. Furthermore, this study delves into the σ convergence and β convergence characteristics of the development level. The results show the following: (a) The level of nutrition-sensitive agricultural development at the national level and in the four major regions has been rising year by year, with a clear spatial pattern of “high in the east and low in the west”. (b) There are significant regional differences at the national level and in the four major regions, which tend to widen. (c) The dynamic evolution characteristics of the development level of nutrition-sensitive agriculture in various regions differ greatly, with polarization in the national, eastern, western and northeastern regions. (d) There is no σ convergence in the development level of nutrition-sensitive agriculture in the country or in the four major regions, but there is absolute β convergence and conditional β convergence in all of them, with the northeastern and central regions having faster convergence speeds; the “catching-up effect” is obvious. The report concludes by outlining the policy ramifications for implementing a methodical and comprehensive strategy approach to support regionally coordinated development plans for leapfrogging and upgrading.

Advertising images constitute an important factor influencing consumer purchase intentions in commercial settings. Drawing on the perspective of self-conscious emotions, this study examines the impact of advertising image types for fresh agricultural products on consumer purchase intentions and explores the underlying mechanisms. Advertising images are classified into three categories: meat-typical, animal-typical, and composite. Evidence from two randomized experiments reveals the following findings: (a) The effectiveness of the three advertising image types in promoting purchase intentions follows the order of meat-typical > animal-typical > composite; (b) guilt mediates the relationship between advertising image types and purchase intentions, such that composite images evoke greater guilt than meat-typical and animal-typical images, thereby reducing consumer willingness to purchase; and (c) self-construal partially moderates the mediating effect of guilt, in that interdependent self-construal consumers exposed to composite advertising images are more likely to experience heightened guilt and consequently exhibit lower purchase intentions. This study extends the application of animal-related classifications in advertising and marketing research and provides new empirical evidence and practical insights for the design of advertising strategies for fresh agricultural products.

As a strategic material second only to grain, cotton serves both as a vital agricultural commodity and a key industrial crop. With the increasing frequency of global shocks and the deepening financialization of commodity markets, price linkages among major international cotton futures markets have strengthened. Consequently, in addition to fundamental supply and demand factors, cross-border price transmission has become a significant determinant of cotton pricing. This study employs daily closing prices of China’s cotton futures, cotton yarn futures, and U.S. cotton futures from 1 September 2017 to 31 March 2025 to examine the spillover effects among these three futures markets using time series models. The results reveal that U.S. cotton futures have dominated the Chinese cotton-related futures markets even prior to the onset of trade tensions, with strong domestic market comovements. However, both the U.S.-China trade war and the COVID-19 pandemic significantly weakened price co-movements while intensifying volatility spillovers. Although these external shocks enhanced the relative independence of China’s cotton yarn futures and modestly increased China’s pricing influence, U.S. cotton futures have consistently maintained their central role in price discovery.