Accurate knowledge of the precipitation estimates with high quality and fine spatiotemporal resolutions is crucial to the precipitation science communities, especially over poorly gauged regions. In this study, we apply various validation metrics to comprehensively evaluate three latest model‐based and satellite‐based precipitation products, namely the fifth‐generation global atmospheric analysis data set of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), ERA5‐Land and Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG‐Final), against hourly rain gauge observations over Jiangxi province, south central China, in 2019. The main conclusions of this study include but not limited to: (a) considering the precipitation amount, both reanalysis precipitation data and satellite‐based precipitation products have similar spatial patterns and show overestimations; (b) IMERG‐Final generally outperforms ERA5 and ERA5‐Land at multiple temporal scales, especially in terms of CC, FAR and MFI; (c) ERA5 and ERA5‐Land precipitation products have similar spatiotemporal error characteristics, and ERA5‐Land performs better than ERA5; (d) in the respect of the capabilities of capturing precipitation events, the spatial characteristics of IMERG‐Final are the closest to those of rain gauges, while ERA5 and ERA5‐Land significantly overestimate the event duration and underestimate the mean event precipitation rate. These findings could help the state‐of‐the‐art model‐based and satellite‐based precipitation products improve the data quality in the future generations.

A deeper understanding of the factors that influence the ecological farming adoption strategies of farmers can help in developing effective adaptation measures to mitigate the degradation of cultivated land quality and ecological degradation. This study used a stratified random sampling technique to select 1488 households in Jiangxi Province, while the double-hurdle model was used to analyze the influence of labor endowments (LEs) and cultivated land fragmentation (CLF) on ecological farming decision-making. The results indicate that LEs and CLF play important roles in farmers’ decision-making with regard to ecological farming. To be more specific, the total household population and CLF have negative effects on adoption willingness, while the number of laborers has a positive effect. For the degree of adoption, the total household population has a negative effect, while the number of laborers and part-time employment have positive effects. In addition, farmers’ ecological farming behavior is also influenced by factors such as education, agricultural income, cooperatives, and agricultural subsidies. Overall, farmers have a high willingness to adopt ecological farming, but the degree of actual adoption is low. This is not conducive to the promotion of ecological farming techniques and the sustainable development of agriculture in China. The study proposes that the government should further improve financial investment and provide relevant technical services. In addition, a temporary work platform should be set up for farmers to encourage them to find other work during the agricultural low season.

Introduction: Rice production is the core component of the food security strategy in China, but it is also a major source of methane and nitrous oxide. Promoting low-carbon rice farming (LCRF) to increase the carbon sink, decrease carbon emissions, and achieve low-carbon, high-yield production is an inevitable “win-win” choice for achieving “double carbon” targets and guaranteeing national food security. This study contributes to the advancement of research on farmers’ adoption decisions and fills gaps in LCRF’s technical research on farmers’ decision-making behavior. The results also provide a basis for formulating policies to encourage LCRF and protect cultivated land.Methods: This study conducted field research on 2,173 farming households in Jiangxi Province, a traditional agricultural province in China, and examined the effects of personal forces, local forces, and cultural forces on LCRF adoption behavior by introducing the distributed cognition theoretical framework based on a status quo analysis and employing the multivariate ordinal logistic model.Results and discussion: The results of the study showed that: 1) the overall acceptance of LCRF behavior is currently not very high. In the 2173 questionnaires, the mean number of LCRF behavior items accepted by farmers was 3.10 items; 153 farmers did not adopt any LCRF behavior, and only 77 farmers adopted all LCRF behaviors. Most farmers (n = 535) adopted three LCRF behaviors. 2) In distributed cognition affecting LCRF behavior, acceptance was primarily affected by cultural forces, followed by local forces, while the effects of personal forces were limited. Therefore, it is recommended that training and promotion should be increased, policy subsidies should be increased, the land market should be improved, and LCRF demonstrations should be carried out to increase the acceptance of LCRF behavior among rice farmers.

Accurate knowledge of the precipitation estimates with high quality and fine spatiotemporal resolutions is crucial to the precipitation science communities. Reanalysis and satellite-based precipitation products are two primary sources of precipitation estimates for various applications. In this study, three latest reanalysis and satellite-based precipitation products, namely ERA5 (released for public in 2018 and 2020), ERA5-Land (released for public in 2019) and IMERG-Final (released for public as V06B in 2019), are selected in order to figure out their error characteristics against rain gauge observations at multiple scales over Jiangxi province, south central China, in 2019. The main conclusions of this study include but not limited to: (1) considering the accumulated yearly precipitation amount, both reanalysis precipitation data and satellite-based precipitation products have similar spatial patterns and show overestimations; (2) except for the performance revealed by POD, IMERG-Final generally outperforms ERA5 and ERA5-Land at multiple temporal scales, especially in terms of CC, FAR and MFI; (3) ERA5 and ERA5-Land precipitation products have similar spatiotemporal error characteristics, and ERA5-Land, which has finer spatial resolution, performs better than ERA5; (4) in the respect of the capabilities of capturing precipitation events, the spatial characteristics of IMERG-Final are the closest to those of rain gauges, while ERA5 and ERA5-Land significantly overestimate the event duration and underestimate the mean event precipitation rate. These findings could help the state-of-the-art reanalysis and satellite-based precipitation products improve the data quality in the future generations.

Elucidating the multi-scale driving mechanisms of the spatiotemporal distribution of soil pH is critical for regional soil ecological regulation and the preservation of cultivated land fertility. However, systematic elucidation of the multi-scale driving mechanisms of the spatiotemporal distribution of soil pH remains limited. Here, we integrated random forest, XGBoost, and geographically interpretable SHAP (GeoShapley) to systematically investigate the spatiotemporal changes in soil pH and their driving mechanisms across three analytical scales (global, raster, and point-level) before and after high-standard farmland construction, based on 149 paired soil sampling points collected in 2008 and 2023 in Taihe County, Jiangxi Province. The mean soil pH in the study area increased from 5.12 in 2008 to 5.53 in 2023, suggesting mitigation of soil acidification. XGBoost outperformed random forest in both periods, with R² values of 0.71 and 0.67 for the two periods, respectively, and the spatial predictions indicated that soil pH in the western and southeastern regions increased following high-standard farmland construction. At the global scale, feature importance based on XGBoost indicated that climate contributed most to soil pH variation in both periods, followed by anthropogenic activities. At the raster scale, GeoShapley revealed a shift from a combination of anthropogenic activities and climate in 2008 to an interplay among climate, vegetation, and topography in 2023, suggesting a decreasing influence of anthropogenic disturbance. At the point level, the factors primarily influencing soil pH shifted from topographic and anthropogenic ones in 2008 to topographic and climatic ones in 2023, regardless of whether the sampling points exhibited low or high pH values, further corroborating the diminishing role of anthropogenic influence. Interaction analyses indicated that topography and vegetation consistently modulated the effects of anthropogenic and climatic factors throughout the study period, suggesting that these two factors were consistently associated with soil pH dynamics across the study area. In summary, this study provides a scientific basis for the targeted improvement of cultivated land fertility and the formulation of differentiated soil management strategies for high-standard farmland in the hilly regions of southern China.