To develop high−precision, high−timeliness and high−resolution relative soil moisture products, based on the daily relative soil moisture product from the China Meteorological Administration Land Data Assimilation System (CLDAS) and hourly relative soil moisture data from 309 automatic soil moisture observation stations in Henan from March to November during 2022−2024, the CLDAS relative soil moisture products for the 0−10cm, 0−20cm and 0−50cm layers in six agro−ecological zones of Henan were evaluated by using correlation coefficient, root mean square error（RMSE）, and bias at the site, regional and regional monthly scales. Regional correction models of three layers were developed by regression−based correction, and the models were validated through a comparative analysis of RMSE and the bias before and after correction. The results showed that the CLDAS simulated values of relative soil moisture in Henan were consistent with the observed values from March to November during 2022−2024. At the site scale, the CLDAS simulated values of relative soil moisture at depths of 0−10cm and 0−20cm were close to the observed values, while the CLDAS simulated values of relative soil moisture at a depth of 0−50cm were smaller than the observed values. At three depths, the correlation coefficient between the CLDAS simulated and observed values was generally greater than 0.50, with the RMSE below 30pp. At the regional scale, the correlation coefficient between the CLDAS simulated and observed values at a depth of 0−10cm ranged from 0.83 to 0.91, with the highest values in southwestern and southern Henan; the RMSE ranged from 5.72pp to 9.00pp, with the smallest value in eastern Henan; and the bias ranged from −6.20pp to 5.54pp, with the smallest absolute value of bias in eastern Henan. The correlation coefficient between the CLDAS simulated and observed values at a depth of 0−20cm ranged from 0.85 to 0.92, with the highest value in eastern Henan; the RMSE ranged from 4.13pp to 9.07pp, with the smallest value in eastern Henan; and the bias ranged from −6.36pp to 4.74pp, with the smallest absolute value of bias in western Henan. The correlation coefficient between the CLDAS simulated and observed values at a depth of 0−50 cm ranged from 0.80 to 0.91, with the highest value in eastern Henan; the RMSE ranged from 4.68pp to 12.51pp, with the smallest value in northern Henan; and the bias ranged from −11.05pp to 1.39pp, with the smallest absolute value of bias in northern Henan. At the regional monthly scale, the overall monthly correlation between the CLDAS simulated and observed values was good, with correlation coefficient exceeding 0.70 in most areas from May to October. The RMSE was generally less than 15%, with the smallest values in western and eastern Henan at three depths. After regional correction, the RMSE between the CLDAS simulated and observed values decreased to 3.47−7.25pp, with a reduction range of 0.04−7.20pp; and the bias decreased to −0.04−0.02pp, with a reduction range of 0.45−11.42pp. Additionally, the correction models for northern, eastern, and central−southern Henan demonstrated good generalization capabilities. Overall, the CLDAS relative soil moisture product has high simulation accuracy in northern, western, and eastern Henan, with higher simulation accuracy from May to October. After regional correction, the simulation accuracy of CLDAS relative soil moisture was further improved, providing important support for agricultural drought and flood monitoring, assessment, and early warning.

Crop intercropping and fragmented planting patterns are major factors limiting the accuracy of crop identification using remote sensing. Multi-source satellite time−series data can effectively distinguish target crops from other vegetation by capturing their unique growth characteristics during specific phenological stages. In this study, the spatial distribution of winter wheat was extracted by integrating the EVI derived from Sentinel-2 across various growth stages, along with NDBI, SAVI, FY-3D EVI time series data, and VV, VH and VH/VV time series data from Sentinel-1. Principal component analysis and the random forest algorithm were employed for feature selection and classification. The results showed that the EVI trends of winter wheat during the emergence to green−up stages differed significantly from those of other vegetation. Similarly, VV and VH/VV backscatter features showed clear distinctions after the green−up stage. The overall classification accuracies using time−series data from sowing to wintering, heading and maturity stages were 95.58%, 98.41%, and 98.65%, respectively. Data from the sowing−heading period achieved higher accuracy for field roads and boundaries compared to pre−wintering data. The addition of the FY-3D dataset improved the overall identification accuracy by 1.71pp to 4.10pp across different growth stages, while the inclusion of Sentinel-1 data increased accuracy by 0.21pp to 1.66pp.

Based on soil moisture data, meteorological data and soil texture data from 37 high−standard farmland stations in Henan province from 2020 to 2023‌, this study employed regularity analysis to investigate the variation patterns of soil gravimetric water content across 8 soil depth layers (10−100cm), characteristics of soil water storage changes and responses of different soil textures to varying precipitation intensities during the period from regreening to maturity of winter wheat in Henan. The objectives were to provide scientific references for optimizing soil water utilization efficiency and formulating targeted farmland management strategies in high−standard agricultural systems. The results showed that: (1) during regreening to maturity period of winter wheat in Henan high−standard farmland (2020–2023), the soil gravimetric water content of the 10−60cm layer increased with soil depth. In contrast, the 10−50cm layer exhibited a gradual decreased in gravimetric water content as the growth stage progressed, with the 10cm surface layer showing the most significant reduction (a decrease of 3.5pp). (2) During regreening to maturity period of winter wheat from 2020 to 2023 in Henan high−standard farmland, when the precipitation anomaly percentage was positive, soil water storage at various layers predominantly showed an increasing trend. Conversely, a negative precipitation anomaly percentage was associated with a general decrease in soil water storage at various layers. (3) Throughout the study period, the soil gravimetric water content across three soil textures followed the order: clay soil (17.7%–23.8%) > loam (16.4%–20.9%) > sandy soil (10.3%–16.2%). While the coefficient of variation ranked as sandy soil (0.32−0.53) > loam (0.25−0.38) > clay soil (0.22−0.34). Sandy soils exhibited the highest variability in soil gravimetric water content, while clay soils showed the lowest fluctuations. Across all three soil textures, the 10 cm depth layer exhibited the strongest moisture variability. (4) The response of soil gravimetric water content to precipitation events decreased with increasing soil depth across three soil textures, while the penetrable water depth increased with precipitation intensity. Loam and clay soil showed a more pronounced hysteresis effect in moisture response to precipitation compared to sandy soil. Under heavy rainfall conditions, sandy soil gravimetric water content reached peak on the day of precipitation and returned to pre−precipitation levels within 5 days. In contrast, loam and clay soil achieved maximum moisture increments 2 to 5 days after precipitation and failed to recover to pre−precipitation levels within 7 days. Overall, sandy soil responded rapidly to precipitation but retained water for a shorter duration and have poor water retention, whereas clay soil responded more slowly but retain higher moisture for longer periods, demonstrating superior water retention.

Apple leaf black rot is a common and destructive disease that severely affects apple quality and yield. To address the poor sensitivity to small targets, background clutter, and low efficiency of traditional identification methods, this study proposed an improved YOLOv11−based detector. A C3K2 module was introduced into the backbone to enhance multi−scale feature modeling; a C2PSA attention module was appended after the SPPF block; and the detection head adopted depthwise separable convolutions together with Distribution focal loss (DFL) and CIoU loss. The improved model achieved an mAP of 99.5%, a recall of 99.7%, and an F1−score of 99.6%, outperforming YOLOv8 by 3.2 percentage points and reaching 48 frames·s⁻¹. Ablation experiments showed that combining C3K2, C2PSA and depthwise separable convolutions raised mAP from 93.1% to 95.2%. The proposed method ensures high−precision detection of small black rot lesions on apple leaves while markedly improving real-time performance, and has strong practicality and deployment value.

In order to explore the impact of different separation methods on yield forecast accuracy, summer maize yield data and meteorological data from Xuchang city, the main grain producing area in central Henan province from 1985 to 2024 were used. The 3−year moving average, 5−year moving average, HP filtering, five point quadratic smoothing, quadratic exponential smoothing and ARIMA model were used to separate meteorological yield and construct a summer maize yield forecast model. The simulation effect was evaluated by calculating indicators such as trend forecast accuracy and yield forecast accuracy. The results showed that the 3−year moving average, 5−year moving average, 5−point quadratic smoothing and quadratic exponential smoothing had better separation effects on yield, while there were differences in the positive and negative relationship of separating meteorological yield among different methods. Among the key meteorological factors selected, precipitation during the jointing-silking stage, temperature and sunshine hours during the grain-filling stage, showed a significant positive correlation with meteorological yield(P<0.05). The temperature during the seedling stage and precipitation during the grain filling stage showed a significant negative correlation with meteorological yield (P<0.05), which was consistent with the growth and development characteristics of maize. The yield trend forecast accuracy of 6 models in backtesting was over 78.1%, the yield forecast accuracy exceeded 94.5%, and RMSE was less than 410.5kg×ha⁻¹. The combination weighted forecast model performed the best in forecast testing, with an accuracy of 97.2% for yield forecast, which was better than the performance of a single method model. It can provide reliable data reference for accurate grain yield forecasting and scientific decision−making in agricultural production.

This study employed bibliometric and knowledge mapping techniques to analyze meteorological themes in high−standard farmland research in China, based on literature retrieved from the CNKI database between January 2004 and November 2024. The aim was to clarify the research hotspots, development trends, interdisciplinary features, and their evolution in this field, and to refine the theoretical framework, provide scientific support for the planning, construction, cultivation of high−standard farmland, enhance agricultural meteorological services, and boost its capacity for stable and high yields. The results showed that 174 Chinese publications addressed meteorological aspects of high−standard farmland, with distinct phases: slow growth from 2004 to 2013, steady growth from 2014 to 2018, and a notable surge after 2022. "land use" "land consolidation" and "Food security" were identified as high−frequency keywords, with research primarily concentrated on the latter two. The meteorological research clustered around four main themes: climate adaptation in farmland planning, responses to climate change in water resource utilization, eco−meteorological risks in arable land use and precise monitoring of meteorological and secondary disasters. However, several core challenges remain: limited theoretical depth in understanding meteorological driving mechanisms, significant technical bottlenecks in multi−source data integration and intelligent decision−making, and delays in developing meteorological service technologies for the operational phase. To address these issues, future efforts should emphasize deeper theoretical exploration, innovations in data fusion, and the enhancement of service systems−ultimately fostering a closer integration between meteorological theory and practical application.

Based on MODIS NDVI data from 2000 to 2020, along with key climatic variables, including annual mean temperature, precipitation, drought index and soil moisture, obtained from the National Tibetan Plateau Data Center and the UK National Centre for Atmospheric Science, this study integrated trend analysis and First−difference multiple regression analysis to systematically examine the spatiotemporal variation in NDVI across major vegetation types (grassland, forest and cropland) in Inner Mongolia. The research further elucidated their differential responses to climate change. The results indicated that: (1) from 2000 to 2020, the  NDVI in Inner Mongolia showed a significant increasing trend across more than 90% of the region. (2) All major vegetation types exhibited significant NDVI increases, with cropland having the highest growth rate (5.7×10⁻³·y⁻¹) and the Gobi desert the lowest (8.2×10⁻⁴·y⁻¹). (3) The sensitivity of NDVI to climatic factors differed considerably among vegetation types. Increased precipitation and soil moisture promoted coverage in most vegetation types, whereas a rise in mean annual temperature exerted a suppressive effect. Notably, grassland NDVI was the most sensitive to changes in both soil moisture and drought. Integrated analysis confirmed that water availability was the key limiting factor controlling vegetation cover dynamics in Inner Mongolia. Despite the overall regional greening trend, the high sensitivity of grassland ecosystems to warming and drought underscores their underlying vulnerability. Hence, future efforts should prioritize adaptive management and conservation strategies for grasslands to enhance regional climate resilience and support the sustainable development of Inner Mongolia's ecological environment.

Using an optimized APSIM model and based on daily meteorological data from Fuxin, Liaoning province from 1961 to 2020, with sole maize (SM) and sole peanut (SP) as controls, eight maize−peanut row ratio configurations were established: narrow strip scenarios 2:2 (M2P2), 4:4 (M4P4), 6:6 (M6P6); medium strip scenarios 8:8 (M8P8), 10:10 (M10P10); and wide strip scenarios 12:12 (M12P12), 14:14 (M14P14) and 16:16 (M16P16). A comparative analysis was conducted to assess the effects of different maize−peanut row ratio configurations under climate change on rainfed yield, potential yield, land equivalent ratio, soil water content and soil organic carbon content. This study aimed to identify the optimal maize−peanut intercropping row ratio configuration in western Liaoning, thereby addressing the sustainable development of maize−peanut intercropping systems in the region and providing theoretical guidance for enhancing the economic and ecological benefits of grain production in western Liaoning. The results indicated that: (1) under rainfed conditions, maize yield in the maize−peanut intercropping system showed a decreasing trend across all 8 row ratio configurations from 1961 to 2020, declining from 3469kg·ha⁻¹ to 3418kg·ha⁻¹, while peanut yield increased from 746kg·ha⁻¹ to 926kg·ha⁻¹. Under fully irrigated conditions, maize yield in the maize−peanut intercropping system decreased from 4166kg·ha⁻¹ to 4156kg·ha⁻¹, while peanut yield increased from 751kg·ha⁻¹ to 1004kg·ha⁻¹ across the eight maize−peanut row ratio configurations from 1961 to 2020. Compared to the wide−strip scenario M16P16, the narrow−strip scenario M2P2 maize yield reduced by 51kg·ha⁻¹ under rainfed conditions and by 10kg·ha⁻¹ under fully irrigated conditions, while peanut yield increased by 180kg·ha⁻¹ and 253kg·ha⁻¹, respectively. (2) Under eight row ratio configurations in the maize−peanut intercropping system from 1961 to 2020, the rain−fed and potential land equivalence ratios for intercropped maize fluctuated stably around 0.56 and 0.62, respectively. The rain−fed land equivalent ratio for intercropped peanuts increased from 0.29 to 0.35, while the potential land equivalent ratio increased from 0.25 to 0.33. Both maize and peanut land equivalent ratios in intercropping systems remained <1.0. (3) From 1961 to 2020, soil organic carbon content in maize−peanut intercropping systems increased from 0.82% to 1.02%. Soil moisture content decreased from 0.26mm·mm⁻¹ to 0.14mm·mm⁻¹. From 1961 to 2020, soil moisture content in the maize−peanut intercropping system showed an interannual decreasing trend, while soil organic carbon content exhibited a significant interannual increasing trend (P<0.001). Based on the performance of total yield, land equivalent ratio, soil moisture content, narrow−strip intercropping of maize and peanuts (M2P2 and M4P4) could effectively enhance land productivity for the fragile ecosystems in western Liaoning province. This approach is economically and ecologically valuable under global warming conditions.

Photoregulated agricultural films, serving as key materials for optimizing the light environment in protected agriculture, significantly influence crop photosynthesis, growth, yield and quality by modulating light quality and intensity. This article systematically reviewed global research progress from 1991 to 2025, focusing on their mechanisms, material systems, manufacturing processes and impacted on crop seedling and reproductive growth, eventually summarizing the application effects on different crops. Results indicated that: (1) these films enhance photosynthetic efficiency and ultimately improved yield and quality indicators (e.g., single fruit weight, sugar and vitamin C content) by converting UV and yellow/green light into red/blue radiation or by improving light distribution through scattering. (2) Notable differences existed between Chinese and international preferences: China primarily employed light−converting films on high−value crops like tomatoes and sweet peppers, achieving an average yield increase of 16%−30%; international research emphasized light−selective films for leafy vegetables (e.g., lettuce) and UV−regulating films for enhancing fruit quality, demonstrating broad application potential and significant effects. Future development will focus on novel light−converting materials with high spectral matching efficiency and environmental friendliness, paving the way for these films to become more multifunctional, specialized and biodegradable for broader application in sustainable agriculture.

In this potted experiment, organic milk cabbage （Brassica campestris ssp. chinensis L.） was used as the test crop. Seven treatments were established, including five rotation treatments without conventional disease management: garlic−milk cabbage（A）, lettuce−milk cabbage（B）, capsicum−milk cabbage（C）, green bean−milk cabbage（D） and coriander−milk cabbage（E）, and two control treatments: continuous cropping of milk cabbage without （F） and with （G） conventional disease management. The effects of different rotation patterns on the yield and quality of organic milk cabbage, the structure of soil microbial community and the control of clubroot disease were investigated. This study sought to unravel the mechanisms underlying the control of clubroot disease by various preceding crops. The results showed that the garlic−milk cabbage rotation（A） and coriander−milk cabbage rotation（E） exhibited the most comprehensive performance in suppressing clubroot disease, markedly reducing disease incidence, with the garlic−milk cabbage rotation（A） resulting in the lowest disease index at only 2.31%. Both rotation treatments significantly increased the α−diversity of rhizosphere microorganisms（as indicated by Chao1 and Shannon indices） and improved soil physicochemical properties. The disease suppression mechanisms differed between the two rotations. When coriander was used as the preceding crop, it enhanced the activity of antioxidant enzymes（e.g., peroxidase, POD）, strengthening the defensive responses of milk cabbage. In contrast, when garlic was the preceding crop, it promoted the synergistic interaction of hormones, such as abscisic acid（ABA） and gibberellic acid（GA₃）, which in turn improved overall plant growth regulation and resistance. However, capsicum−milk cabbage rotation（C） and continuous cropping of milk cabbage without conventional disease management（F） led to reduced microbial diversity and increased disease pressure. Continuous cropping of milk cabbage with conventional disease management（G） did alleviate clubroot disease to a certain extent, but its control efficacy remained significantly inferior to that of the rotation treatments.

To clarify the thresholds of early frost damage for sunflower organs under different sowing dates in the Hetao irrigation district, this study used 'HZ2399' (the main local sunflower cultivar in the Hetao irrigation district) as experimental material and employed an MSX−2F artificial frost simulation chamber. During 2022 and 2023, artificial frost simulation tests with six temperature gradients (2℃ intervals) were conducted on seeds, disks, stems and leaves of sunflowers at three sowing dates: May 20^th (S1), May 31^st (S2) and June 10^th (S3). The study investigated the temperature ranges of the supercooling points and freezing points of each organ and analyzed the correlation between their freezing injury rates and the low temperatures. The results showed that: (1) the supercooling points of sunflower stems and leaves at sowing date S1 were −6.93±0.25℃ and −4.80±0.93℃, respectively, which were significantly lower (P<0.05) than those at sowing date S3 (−5.48±0.20℃ and −3.68±0.66℃). Overall, the supercooling points for different sowing dates followed the order with S1<S2<S3. Within the same sowing date, the supercooling points of the organs followed the order with disk<stem<leaf. (2) The critical temperature for irreversible freezing was generally 0.74–2.51℃ higher than the supercooling point. When the minimum air temperature did not reach the supercooling point, the crop freezing injury rate continued to increase with the prolongation of freezing duration. (3) The cold resistance of sunflowers in the Hetao irrigation district was in the order S1>S2>S3 from highest to lowest. For the same sowing date, the cold resistance of sunflower organs was ranked as disk>stem>leaf from highest to lowest.

In the context of global climate change leading to frequent extreme meteorological disasters, an accurate understanding of the development and evolution of agrometeorological disaster prevention and control technology is crucial for the construction of more resilient disaster prevention and mitigation systems. Based on 927 publicly available patents related to global agrometeorological disaster prevention and control technology from January 1997 to April 2025, this study employed methods such as technology life cycle analysis, social network analysis and topic modeling to reveal development trends, competition and cooperation patterns and innovation evolution path in this field. The results indicated that agrometeorological disaster prevention and control technologies had entered a phase of rapid growth phase since 2014 and are expected to maintain an upward trajectory in the future. China stood out as a leading research and development hub in this field, particularly excelling in the intersection of agrometeorological disaster prevention and control technologies with information and communication technologies. In contrast, the scale of patent applications in the United States, South Korea and Japan had slowed relatively in recent years. In terms of research and development collaboration, cross−institutional, cross−sectoral and cross−national cooperative efforts had yet to achieve significant scale. The research and development ecosystem was characterized by a typical "academia−dominant" structure, with universities and research institutes accounting for 53.61%, while corporate participation remained relatively low at 26.97%. This imbalance, to some extent, hindered the transformation and application of innovative outcomes in practical production. Though technological evolution, the field had evolved from early chemical regulation and basic monitoring to a comprehensive chain of technologies covering pre−disaster warning, in−disaster regulation and post−disaster assessment. Key highlights included meteorological disaster monitoring and warning systems, crop stress resistance improvement technologies and post−disaster assessment mechanisms. In conclusion, this field of technology is currently experiencing a period of active development. In the future, it is essential to further strengthen industry−academia research integration and build collaborative networks. Together, these efforts will drive the development of comprehensive smart agricultural management systems and innovation in agrometeorological disaster prevention and control technologies, thereby supporting global agricultural sustainable development.