Accurate, scalable estimation of rice planting dates is essential for climate-adaptive management in multi-cropping regions, yet most models rely on static calendars, which fail to capture climate-driven shifts and bias simulated yield responses. This study aims to develop a climate-driven, spatially explicit framework to simulate dynamic transplanting dates across diverse multi-cropping systems in monsoon Asia. Utilizing daily AgERA5 reanalysis and Monsoon Asia Rice Calendar (MARC) data from 2019 to 2020, we present Geo-ROCKET. The framework integrates an automated K-means clustering workflow to delineate bimodal planting windows and employs random convolutional kernel transforms with adaptive geographic neighborhoods to capture local climate heterogeneity. Evaluated by area-weighted mean absolute error (MAE), the model achieves high accuracy across six seasons (MAE 6.53-12.50 days), outperforming six traditional ROCKET and ensemble baselines while preserving smooth spatial error fields. Sensitivity experiments reveal that a 15-day bias in the previous harvest date can increase transplanting error to 10.8-17.8 days, emphasizing the importance of sequential consistency. By providing dynamic, climate-sensitive inputs, Geo-ROCKET improves the accuracy of crop modeling for climate impact projections. This framework offers a flexible tool for characterizing human management decisions and evaluating adaptation strategies in intensive agricultural systems.

Global pre-harvest crop yield forecasting is challenged by diverse data characteristics and environmental conditions across different countries, crop types, and lead times. The datasets used in crop yield predictive modeling differ in coverage, sample size, and signal noise. To address this challenge, we propose a diagnostic spatiotemporal multimodal fusion network that selects model structure based on dataset characteristics to determine whether temporal trend coupling and spatial module activation are warranted. Guided by these diagnostic outcomes, we develop a spatiotemporal multimodal fusion deep learning model that conditionally predicts detrended residuals and activates geolocation encoding only when spatial autocorrelation is detected. We evaluate the approach on a crop yield benchmark (CY-Bench), covering maize across 38 countries and wheat across 29 countries, under three lead times (early, mid, and late season) against widely used baselines. Our proposed approach achieves the lowest pooled NRMSE for both crops at all lead times, and it remains in the leading group for complementary metrics including MAPE and KGE. The diagnosis shows that different crops need different model structures. Wheat always relies on the full spatiotemporal model. However, maize mostly sticks to the temporal trend-only structure (without spatial inputs) at mid-season. Ablation studies further show that diagnostic module selection provides the primary performance lift, while fusion strategies offer secondary improvements. Variance decomposition shows that performance varies more across countries than across models. This research highlights that automated data diagnostics offers a strategic advantage in managing dataset heterogeneity, serving as a critical prerequisite to model configuration in global, large-scale crop yield forecasting.

Blue foods are indispensable for strengthening global food supplies, and addressing nutritional and food security worldwide. Despite the significant growth and structural transformation of the mariculture sector in China over recent decades, it faces pressing challenges from weather fluctuations and deteriorating water quality. Strategic adaptations to meteorological risks are imperative for maintaining resilient coastal ecosystems and ensuring the sustainable development of related economic sectors. In this study, we employ a Fractional Logit model to examine the relationship between weather fluctuations, water quality, and the structural composition of mariculture in Zhejiang Province, from 2006 to 2018. Our analysis focuses on three economically significant species: Litopenaeus vannamei (Penaeu), Scylla paramamosain (Scylla), and Sinonovacula constricta (Sinonovacula). The results reveal that variations in temperature and precipitation are crucial determinants of structural shifts within the mariculture sector, demonstrating significant interspecies differences. Furthermore, our findings indicate that large-scale mariculture operations can mitigate the degree of structural adjustments driven by climatic factors. Mechanistic analysis uncovers that elevated concentrations of inorganic nitrogen, ammonia nitrogen, nitrite, and nitrate substantially influence the structural adjustment processes for Litopenaeus vannamei. These findings provide critical insights into the adaptive responses of the mariculture industry and offer a robust basis for the formulation of targeted strategies to enhance resilience and sustainability.

Against the backdrop of global commitments to sustainable development and carbon neutrality objectives, the agricultural sector faces compelling imperatives to transition toward environmentally sustainable and resource-efficient production systems. Focusing on the critical role of agricultural inputs, this study investigates how China’s Zero Growth Policy for Fertilizer and Pesticide Use (ZGP), implemented in 2015, influences green transformation in the agricultural inputs sector through a quasi-natural experiment framework. Employing a staggered difference-in-differences (DID) design with comprehensive nationwide firm registration data from 2013 to 2020, we provide novel micro-level evidence on environmental regulation’s market-shaping effects. Our findings demonstrate that the ZGP significantly enhances green market selection, stimulating entry of environmentally certified firms, with effect heterogeneity revealing policy impacts are attenuated in manufacturing-intensive regions due to green entry barriers, while being amplified in major grain-producing areas and more market-oriented regions. Mechanism analyses identify three key transmission channels: intensified regulatory oversight, heightened public environmental awareness, and growing market demand for sustainable inputs. Furthermore, the policy has induced structural transformation within the industry, progressively increasing green enterprises’ market share. These results offer valuable insights for designing targeted environmental governance mechanisms to facilitate sustainable transitions in agricultural input markets.

Rural households engage in labor allocation to tackle agricultural loss stemming from meteorological shocks, whereas the intra-household allocation strategy remains unclear in this process. This study examines the impact of meteorological disasters on the allocation of labor within rural households from the perspective of gender heterogeneity. We construct a meteorological disaster index and investigate its impact on rural labor markets in China by using 15 years of longitudinal data from the China Health and Nutrition Survey. Our analysis reveals significant gender disparities in rural labor allocation in response to meteorological disasters, with one standard deviation increase in the meteorological disaster index from the last year elevating the proportion of male nonfarm employment by 3.00 % and posing no effect for females. This structural shift results in a reduction in females' investment in non-farm activities and an increase in their engagement in farm work and household chores. We identify three critical factors that contribute to this gender-differentiated impact: industry affiliation differences, variations in expected returns, and gendered social norms. Our findings highlight the unequal impact of meteorological disasters on rural labor markets and provide novel insights for the formulation of targeted policies aimed at enhancing women's resilience and adaptability in job markets affected by natural disasters.

This paper examines the impact of China's Specialty Agricultural Products Advantageous Zones (AZs), a prominent place-based policy, on the development of new agricultural operating entities (NAOEs). Using detailed registration data of NAOEs, we find that the establishment of AZs increases the operating revenue of NAOEs by 4.4% in the leading industry. However, the magnitude of this effect varies by industry, market, and geographic characteristics, with greater benefits for entities located downstream of leading industrial chains, those involved in industrial integration, and those operating in areas with higher market competition or potential. These effects are primarily driven by increased land availability, industry agglomeration, market competition, and innovation within AZs. Additionally, our analysis reveals spillover effects on leading industry entities outside AZs. Within four years of AZ establishment, we also observe a significant and positive structural transformation in local agriculture.

Using data from the 2014 China Family Panel Studies, this study investigates whether diversifying agricultural production can enhance dietary diversity, particularly by increasing the consumption of animal-based foods. We introduced refined measures of dietary diversity and categorized them into animal-based household dietary diversity scores (A-HDDS) and plant-based household dietary diversity scores (P-HDDS). Additionally, we categorize production diversity into crop production diversity (CPD), livestock and aquaculture production diversity (LAPD), and mixed production diversity (MPD). Our results show that most households exhibited high dietary diversity, except for low-income and hilly households. Specifically, CPD positively influenced dietary diversity, and all three types of production diversity promoted A-HDDS. Moreover, CPD primarily serves as a substitute for enhancing household self-sufficiency, affecting all three dietary diversity measures, especially among hilly households. Increasing LAPD and MPD can improve A-HDDS through self-sufficiency and agricultural commercialization income pathways, benefiting farmers in hilly and mountainous regions.

The COVID-19 pandemic has had a profound impact on people’s lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the conventional infectious disease dynamics model (SEIR model), which is approximately 9% higher, and reflects the spatial and temporal heterogeneity well. In scenario simulations, this study established five scenarios for epidemic prevention measures, and the results indicate that masks are the most influential single preventive measure, reducing deaths by 5.38%, followed by vaccination at 3.59%, and social distancing mandates at 2.69%. However, implementing single stringent preventive measures does not guarantee effectiveness across all states and months, such as California in January 2025, Florida in August 2024, and March–April 2024 in the continental U.S. On the other hand, the combined implementation of preventive measures proves 5 to-10-fold more effective than any single stringent measure, reducing deaths by 27.2%. The deaths under combined implementation measures never exceed that of standard preventive measures in any month. The research found that the combined implementation of measures in mask wearing, vaccination, and social distancing during winter can reduce the deaths by approximately 45%, which is approximately 1.5–3-fold higher than in the other seasons. This study provides valuable insights for COVID-19 epidemic prevention and control in America.

Purpose The paper proposes a research method to verify the perception bias of consumers on the freshness preservation effects of vacuum packaging (VP) and modified atmosphere packaging (MAP) chilled pork packages, the influence of “sensory experience” on correcting consumers' perception bias of packaging performance and willingness-to-pay (WTP) enhancement channels. Design/methodology/approach Using data from 458 and 188 participants who completed the contingent valuation method (CVM) and auction experiment, respectively, the study aimed to uncover consumers' packing quality perception bias and WTP, and investigated the societal factors that contribute to variations in WTP. Findings The CVM experiment revealed that although consumers' high perception bias rate toward MAP to maintain freshness, as compared to lab test results, came along with low WTP premium to cost rate with sensory experience in the auction experiment, the proportion of consumers with quality perception bias decreased from 49.85% to 34.46%, while the WTP premium to cost rate for MAP increased largely by 36.7%. Perceptive embedding has a positive effect on chilled pork packaging WTP, while normative embedding decreases WTP. Originality/value The findings emphasize the need of public policies to promote positive consumption attitudes, while whittling the negative consumption norms, to increase the WTP for packaged child pork and promote the chilled pork market formation.

As concerns over African swine fever (ASF) in China continue, measures have been taken to regulate the inter-provincial transport of live hogs, yet entrenched non-chilled fresh pork consumption habits make it challenging to expand the chilled pork in the market. To address this issue, our study employed multiple rounds of random Nth-price auction experiments to measure participants' quality perceptions of, and willingness to pay (WTP) for chilled pork under different packaging, storage temperature and duration labels. By comparing the results with physicochemical lab testing outcomes, we confirmed the existence of a quality perception bias among the participants. Notably, consumers who regularly consume chilled meat demonstrated a higher average WTP, while consumers born in the 1980s exhibited significantly higher bids. Furthermore, access to emerging purchase channels positively influences consumer WTP for chilled meat, particularly through branded product purchasing experiences. Consequently, it is recommended that the public sector takes steps to balance the distribution of pork production capacity and supports the development of cold chain transportation technology to meet the growing demand for high-quality chilled pork, especially from younger consumers.

Food security has been one of the greatest global concerns facing the current complicated situation. Among these, the impact of climate change on agricultural production is dynamic over time and space, making it a major challenge to food security. Taking the U.S. Corn Belt as an example, we introduce a geographically and temporally weighted regression (GTWR) model that can handle both temporal and spatial non-stationarity in the relationship between corn yield and meteorological variables. With a high fitting performance (adjusted R2 at 0.79), the GTWR model generates spatiotemporally varying coefficients to effectively capture the spatiotemporal heterogeneity without requiring completion of the unbalanced data. This model makes it possible to retain original data to the maximum possible extent and to estimate the results more reliably and realistically. Our regression results showed that climate change had a positive effect on corn yield over the past 40 years, from 1981 to 2020, with temperature having a stronger effect than precipitation. Furthermore, a fuzzy c-means algorithm was used to cluster regions based on spatiotemporally changing trends. We found that the production potential of regions at high latitudes was higher than that of regions at low latitudes, suggesting that the center of productive regions may migrate northward in the future.

Revealing the spatiotemporal variations of nutrients in coastal waters is crucial to the understanding and evaluation of coastal environment, thereby providing efficient guidance for the aquatic environmental treatment. This study proposed a spatiotemporal-incorporated deep learning model, which is easily applicable to establish the quantitative relationships between measured environmental factors and large-scale satellite maps, and can reduce estimation errors by more than 40% compared with non-spatiotemporal-incorporated deep learning model. The spatiotemporal distributions of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphate (DIP) over 44400 km2 of the East China Sea on 8-day scale from 2010 to 2018 were obtained. Based on the spatiotemporal variations, the water quality patterns were depicted, and the fluctuation variations of the two essential nutrients were found in the harbors with complex anthropogenic influences, in the typical estuaries with multiple river inputs, and in the open seas with important fisheries. Although the concentration of DIN and DIP decreased by 24% and 19% in 9 years, respectively, the water quality level in the inshore sea has not been significantly improved, especially in autumn and winter. Further, we quantitatively analyzed the main factors of deteriorated water and provided scientific suggestions for targeted monitoring and regional cooperative governances.

This study aimed to measure Chinese consumer preferences and Willingness-to-Pay (WTP) for different aquatic product safety information attributes considering stated attribute non-attendance (ANA). Taking white shrimp (Penaeus vannamei) as an example, we designed and conducted two rounds of choice experiments to determine the preferences of 556 consumers for the safety properties of various aquatic animal products in Shandong Province, China. The first round of choice experiment included 5 selected safety attributes, whereas the second round excluded one consumer's stated ANA. Results showed that in the decision-making process, consumers merely give less importance to their stated ANA attribute instead of completely ignoring this attribute. Different safety attributes bring different premiums of Total WTP. Specifically, the premium will be brought in organic certification (14.20%–30.61%), traceability information (12.85%–36.61%), brands (8.41%–26.46%), geographical indications of aquaculture products (8.01%–28.63%) and mariculture (5.26%–16.18%). A better understanding of consumers' ANA preference in reality empowers aquatic producers and marketers to carry on better aquatic food safety information conveying practices.

Freshness is an essential attribute of agricultural products. To meet consumers' demand, suppliers take multiple methods to provide fresher products. But sometimes the effort does not get paid because of quality perception bias. In this study, we investigated the existence of quality perception bias and the effect of reference points. Our results confirmed the existence of quality perception bias. Our results also show that information accuracy significantly influenced consumers' quality perception and WTP. At last, a low reference point had a significant positive effect on consumers’ WTP. Results of this paper provide important insight into the approaches to alleviate perception bias, therefore to motivate suppliers to provide high-quality products in the market.

Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m3, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 μg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.

要实现共同富裕,关键要补足乡村建设与发展短板.未来乡村建设作为乡村振兴进程中的一种乡建新探索,是美丽乡村建设升级的新体现,也是推动乡村高质量振兴和农村居民高品质生活的新需要.在未来乡村建设中嵌入共创共富机制,对于加快补足乡村发展短板,推进城乡区域协调发展、融合发展和推动共同富裕发展,具有重要的理论和现实意义.

Points-of-interest (POIs) are an important carriers of location text information in smart cities and have been widely used to extract and identify urban functional regions. However, it is difficult to model the relationship between POIs and urban functional types using existing methods due to insufficient POIs information mining. In this study, we propose a Global Vectors (GloVe)-based, POI type embedding model (GPTEM) to extract and identify urban functional regions at the scale of traffic analysis zones (TAZs) by integrating the co-occurrence information and spatial context of POIs. This method has three main steps. First, we utilize buffer zones centered on each POI to construct the urban functional corpus. Second, we use the constructed corpus and GPTEM to train POI type vectors. Third, we cluster the TAZs and annotate the urban functional types in clustered regions by calculating enrichment factors. The results are evaluated by comparing them against manual annotations and food takeout delivery data, showing that the overall identification accuracy of the proposed method (78.44%) is significantly higher than that of a baseline method based on word2vec. Our work can assist urban planners to efficiently evaluate the development of and changes in the functions of various urban regions.

Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, and per capita GDP) on the Chinese mainland, we established a geographically weighted regression (GWR) model to analyze their impacts in different seasons and provinces. The GWR model successfully describes the spatial changes of the influence of potential risks, and shows greatly improved estimation performance compared with the ordinary linear regression (OLR) method. Our findings help to understand the seasonally and spatially relevant effects of natural environmental and socioeconomic factors on the I-HFMD, and can provide information to be used to develop effective prevention strategies against HFMD at different locations and in different seasons.