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20 April 2026, Volume 47 Issue 4

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Atmospheric Circulation Characteristics of the Coupling between the East Asian Summer Subtropical Westerly Jet and the Northeast Low Pressure and Impact on Regional Summer Precipitation

SHI Chuan-miao, JIAO Cheng-cheng, XIE Wen-jin, LI Fan, WANG Xiang-ping, Zhao Jie

2026, 47(4):  483-494.  doi:10.3969/j.issn.1000-6362.2026.04.001

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Using global atmospheric reanalysis data from the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) and the Global Precipitation Climatology Project (GPCP) for the period 1979–2021. By integrating the East Asian Summer Subtropical Westerly Jet Index (EAJI) and the Northeast Low Pressure index (NEALI), this study analyzed the characteristics of their distinct configurations on the interannual scale as well as the associated atmospheric anomalous conditions,further explored the synergistic effects of different circulation systems of the east Asian monsoon on climate change in east Asia and summer precipitation in China. The results showed that the variability in the intensity of Northeast Low Pressure and the displacement of the East Asian Westerly Jet were not independent. When both systems were in phase (either positive or negative), significant anomalous wave patterns appeared in the middle troposphere over the mid–to–high latitudes of Eurasia. Geopotential height anomalies were observed at 850hPa, with positive (negative) anomalies near the Ural mountains and negative (positive) anomalies east of Baikal lake. At 500hPa, geopotential height anomalies showed a positive–negative–positive (negative–positive–negative) pattern from the Ural mountains to Baikal lake and Japan. The jet shifted northward (southward), causing a slight northward (southward) movement of the subtropical high, leading to below–average (above–average) summer precipitation in southern China and along the Yangtze river, with above–average (below–average) precipitation in coastal southern China and northeast China. When the two systems were in an out–of–phase configuration, the spatial distribution of atmospheric circulation and precipitation anomalies was similar to that of the in–phase years, but the intensity of the climate anomalies over east Asia was significantly weaker.

Characteristics of Low−temperature during the Jointing-flowering Stage of Winter Wheat in Henan Province Based on Hourly Temperature Data

YU Wei-dong, HU Li-ting, MA Mei-juan, GUO Yan-ling

2026, 47(4):  495-507.  doi:10.3969/j.issn.1000-6362.2026.04.002

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Low−temperature disasters during the jointing−flowering stage severely impact wheat growth and yield formation in the Huang−Huai plain. The identification of the frequently−occurring and severely−affected areas can enhance the monitoring and defense capabilities against such disasters. To more accurately identify the low−temperature risk regions and assess low−temperature disaster severity in Henan province, authors utilized hourly temperature data of Stevenson screen from national meteorological stations in 118 counties of Henan province from 2006 to 2024. Besides, hourly wheat fields (10−70cm above ground) temperature data from four agrometeorological stations from 2023 to 2024 were used to analyze the temperature differences between wheat fields and Stevenson screen and establish the temperature indicators of low−temperature disaster. Based on dual temperature indicators of ≤4℃ and ≤2℃ from Stevenson screen, the spatiotemporal characteristics of low−temperature events during the jointing−flowering stage of winter wheat were analyzed at different severities. The results showed that: (1) temperatures at different heights within the field exhibited a highly significant correlation with Stevenson screen temperatures (P=0.001) during the jointing−flowering period. When the wheat field temperatures dropped below 0°C, the Stevenson screen temperatures were on average 3.7°C higher than the field temperatures measured at the height of 10−50cm. (2) The Stevenson screen temperatures ≤4℃, ≤2℃ and daily minimum temperatures occurred most frequently at 06:00, with corresponding frequencies of 13.1%, 15.5% and 35.0%, respectively. For most stations, the latest termination dates for ≤4℃ and ≤2℃ events were April 20−25 and April 16−20, respectively. (3) The most prolonged low−temperature durations (Stevenson screen temperatures ≤4℃and ≤2℃) and highest accumulated chilling injury indices were concentrated in central−southern area (Zhumadian and Luohe) and eastern area (Shangqiu). In the two high−risk zones, the low−temperature durations exceeded 200h (≤4℃) and 120h (≤2℃) respectively, while the accumulated chilling surpassed 50.0℃·h (≤4℃) and 10.0℃·h (≤2℃) respectively. Moreover, low−temperature duration exhibited a highly significant correlation with both daily minimum temperature and daily accumulated chilling. However, the correlation between low−temperature duration and the decrease of daily minimum temperature was comparatively weak. (4) During the jointing−flowering stage of winter wheat in Henan province, the maximum decreases of daily minimum temperature ranged from 5.8 to 9.2℃·d⁻¹, with the maximum decrease rates of temperature ranging from 1.0 to 2.0℃·h⁻¹. Based on the accumulated low−temperature frequency and perennial jointing dates, northern and western regions exhibit lower spring low−temperature frequency, while central and southwestern regions‌ show lower frequency of severe low temperatures but higher frequency of mild events. In contrast, eastern and central−southern regions experience relatively high frequencies of both mild and severe low−temperature events, which require prioritized preventive measures against low−temperature disasters.

Spatial Evolution and Driving Factors of Watermelon and Melon Production in China

WANG Fu-hong, XIA Yong, DING Ning, LV Yi, ZHAO Lan-lan, WANG Zi-yang

2026, 47(4):  508-520.  doi:10.3969/j.issn.1000-6362.2026.04.003

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Based on production data of watermelon and melon in China from 2001 to 2022, this study analyzed the spatial evolution characteristics and key factors of watermelon and melon production in China using panel econometric models, the regional Gini coefficient and the spatial shift coefficient. The results indicated that:（1）watermelon production in China stayed relatively steady between 2001 and 2022, reaching about 6.00×10⁷t. Melon production had a steady upward trend, increasing by an average annual growth rate of 3.13% (P<0.01). Over the same time, watermelon and melon yields per unit area in China exhibited a consistent upward trend, with average annual growth rates of 1.50% and 2.37%, respectively (P<0.01).（2）The spatial concentration of melon production in China was higher than that of watermelon production, although the see−saw pattern of change was evident in the spatial concentration of melons and watermelons. The production center for watermelons moved 277.68km southwest from the area near Ningling county, Henan province, to the area near Lushan county, Henan province, and the production center for melons moved 623.38km southwest from the area near Hengshui city, Hebei province, to the area near Hancheng city, Shaanxi province.（3）Between 2001 and 2022, number of provincial regions with stable region for watermelons and melons gradually increased, reaching 6 and 9 respectively. The number of provincial regions with transfer−in region for watermelons and melons steadily declined, reaching 16 and 7, respectively. In China, the pattern of watermelon and melon production's spatial distribution has tended to stabilize.（4）Spatial evolution of watermelon and melon production was influenced by a number of significant elements, including sunshine hours, policy support, output value change, urbanization level, technological advancement, effective irrigation area and competition from similar crops. The future development of watermelon and melon industry in China will require further acceleration of related cultivation technology innovation and promotion, fortification of industry−related infrastructure construction, and improvement of the industry's resilience to risks.

Research Progress on Rhizosphere Temperature Control Technologies for Facility Crops

BAI Yu-run, YAN Shi-feng, WANG Fa-xiang, LI Ling-zhi, HAI Yun-rui, GUO Wen-zhong

2026, 47(4):  521-529.  doi:10.3969/j.issn.1000-6362.2026.04.004

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In protected agricultural systems, fluctuations in crop rhizosphere temperature exert significant impacts on key physiological processes, including water uptake, nutrient translocation and photosynthetic activity. Although contemporary environmental control technologies focus primarily on air temperature optimization, while precise management of rhizosphere temperature, they remain challenged by dual constraints: inadequate adaptability to extreme climatic conditions and subpar energy efficiency. This study systematically evaluated the research advancements and development trajectories of rhizosphere temperature regulation technologies through an integrative bibliometric analysis and technical characteristic comparison, with the aim of clarifying the application frontiers and efficiency enhancement strategies for passive and active regulation approaches. The core objective was to address the conflict between the risk of rhizosphere temperature runaway under extreme climatic conditions and the high energy costs inherent in temperature control systems. Findings indicated that passive regulation techniques, including plastic mulching, ridge cultivation and phase−change materials, mitigated root temperature fluctuations by 40%−60% via physical barrier effects, delivering significant energy−saving benefits under conventional climatic conditions (reducing greenhouse energy consumption by 60%−80%). However, during low−temperature or high−temperature events, the rhizosphere faced an elevated risk of temperature control failure, with the probability of such failure positively correlated with the extremity of climatic conditions. Conversely, active regulation systems, comprising heat pump technologies, active thermal energy storage, release devices and nutrient solution circulation temperature control, enabled precise temperature management within a range of±1°C, enhancing greenhouse crop yields by 17%−55%. These systems were constrained by technical bottlenecks, including a low coefficient of performance (COP 1.5−1.9) and high energy consumption costs (0.7−5.0kWh·m⁻²) per unit area. Future research should prioritize the deep integration of IoT sensing with multi−energy complementary technologies, the development of composite energy storage materials with tunable phase−transition temperatures (15−25°C), and the construction of a full−chain technical framework that integrates climate warning, dynamic compensation, and intelligent control. Such efforts will facilitate a paradigm shift in protected−crop rhizosphere temperature management, enabling a transition from rudimentary buffering strategies to adaptive smart regulation systems.

    

Identification Algorithm of Precipitation Echoes, Insect Echoes and Bird Echoes Based on Fuzzy Logic

XIANG Xiao-tong, BAO Yun-xuan, JIAO Sheng-ming, TAO Li, ZHANG Yi-yang, ZENG Juan

2026, 47(4):  530-545.  doi:10.3969/j.issn.1000-6362.2026.04.005

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Weather radar plays an important role in meteorological services such as nowcast and quantitative precipitation estimation due to its high spatiotemporal resolution, large detection range and wide network coverage. Radar echoes included the precipitation, insect and bird echoes, but non-meteorological echoes such as insect and bird echoes were usually removed as noise in the current meteorological operations, resulting in wasted information. In this paper, the authors constructed the identification algorithm of precipitation, insect and bird echoes based on fuzzy logic algorithm using the dual polarization weather radar data from Lianyungang, evaluated the identification effect of the algorithm, and analyzed the identification results of precipitation echoes, insect echoes, bird echoes as well as their mixed scenarios, aiming to provide a foundation for aerial biological monitoring. The results showed as follows: (1) the accuracy (ACC) of precipitation echoes and biological echoes were improved from 0.90 and 0.88 to 0.95 and 0.94, respectively, after adding post-processing to the identification results of the fuzzy logic algorithm. (2) In the results of classifying biological echoes into insect echoes and bird echoes by fuzzy logic algorithm, the ACC was 0.96, the true positive rate (TPR) was 0.94, the true negative rate (TNR) was 0.98, and the heidke skill score (HSS) achieved 0.92. The radar echo identification algorithm constructed in this paper is able to identify precipitation echoes, insect echoes, bird echoes and their differences very well.

Climate Quality Evaluation Method for Chili Peppers in Guizhou

LONG Yu-yun, YANG Shi-qiong, CUI Lei, GAO Hong-mei, HU Jia-min, HE Jian-wen, ZUO Jin

2026, 47(4):  546-557.  doi:10.3969/j.issn.1000-6362.2026.04.006

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Based on the quality data of chili pepper dry matter, crude fiber, reducing sugar, capsaicin and meteorological observation data of 15 sampling points in the main chili pepper producing areas of Guizhou from 2020 to 2024, the correlation analysis, regression analysis, weighted summation and other methods were used to construct and verify the evaluation model of chili pepper climate quality. Based on the model, the temporal and spatial distribution characteristics of chili pepper climate quality in Guizhou were evaluated to provide a reference for the rational utilization of climate resources for high−quality chili pepper production. The results showed that: (1) the key meteorological factors affecting the quality of chili pepper in Guizhou were the cumulative sunshine hours from mid−May to mid−June, the cumulative sunshine hours from mid−August to late September, the average daily range from mid−April to early June and the average daily range from early July to late September. (2) The climate quality index (I_CQ) of chili pepper was constructed by normalized dry matter, crude fiber, reducing sugar and capsaicin, which could better characterize the comprehensive quality level of chili pepper, and the climate quality of chili pepper could be classified into four grades. I_CQ<0.39 was the general grade, 0.39≤I_CQ<0.46 was the good grade, 0.46≤I_CQ<0.53 was the excellent grade, and ICQ≥0.53 was the special excellent grade. (3) Based on the data of 2024, the determination coefficient R² of the climate quality of chili pepper evaluation model was 0.88, with residuals all within 0.07, root mean square error (RMSE) of 0.05, and relative error of 10.12%, indicating that the model had high simulation accuracy and good consistency. (4) The climate quality index of chili peppers fluctuated within the range of [0.26,0.69] from 2005 to 2024, and its spatial distribution gradually decreased from east to west. Among them, the climate quality of chili peppers in Jinping, Yinjiang, Taijiang, Yanhe, Yuqing and other places was better.

Methods for Prediction Rice Initial Panicle Date Based on Machine Learning Algorithm

REN Yi-fang, ZHU Feng, CHEN Si-ning

2026, 47(4):  558-571.  doi:10.3969/j.issn.1000-6362.2026.04.007

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The initial panicle date of rice is a critical period during which disease−causing bacteria, such as rice smut, rice blast disease and sheath blight, invade organs like grains, leading to reduce the rice quality and lower yield In order to improve the ability of rice disease control and better meet the requirements of "prevention first, comprehensive control, green damage control, drug reduction and efficiency", taking Jiangsu as an example, using historical meteorological data and rice growth period observation data, based on the analysis of the characteristics and key influencing factors of the beginning panicle period, four groups of simulation schemes were set up by using principal component analysis, error back propagation neural network algorithm and random forest algorithm to establish the prediction model of the beginning panicle period of rice. In addition, the coefficient of determination and root mean square error were taken as the evaluation indices to analyze and evaluate the accuracy and compatibility of the model. The results showed that: the initial panicle stages of rice in northern Jiangsu, central Jiangsu and southern Jiangsu were concentrated from August 4 to 31, August 9 to September 18 and August 16 to September 20. The average standard deviation of each region was 4d, 6d and 5d respectively. The key factors affecting the initial panicle stage of rice in Jiangsu were basically the same. The days sequence of the three growth stages before the initial panicle stage of rice was the most critical. The temperature factors was significantly more important than the precipitation and sunshine hours factors during three growth stages, from sowing to tillering, from tillering to jointing and from jointing to booting. Compared with the model based on RF algorithm, the model based on BP algorithm had higher simulation accuracy, and had better "acceptability" for the predictors with weakened correlations after PCA treatment. In addition, based on the proposed method, the simulation prediction error of rice initial panicle stage of rice epidemic in all regions of Jiangsu was within 2d, with a prediction advance of about 10d, which can provide a reference basis for accurately capturing the key period of rice disease control. 

Hail Probability and Size Identification Algorithm Based on Fuzzy Logic and Semi-supervised Learning

LI Heng-sheng, LIU Zhong-yang, ZHANG Jing-yi, ZHANG Li, WANG Qian-qian, FENG Dan

2026, 47(4):  572-580.  doi:10.3969/j.issn.1000-6362.2026.04.008

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The development of a classification product for hail occurrence probability and hail size can improve the accuracy of hail identification. Based on 92 hail observation records from Henan Province in 2022, together with radar data and sounding data, nine characteristic parameters were selected: composite reflectivity（CR）, height difference between 55dBZ base reflectivity and 0°C level （H₀）, height difference between 45dBZ base reflectivity and −20°C level （H₋₂₀）, vertical integrated liquid （VIL）, vertical integrated liquid density（VILD）, echo top height （ET）, differential reflectivity （ZDR）, specific differential phase（KDP） and correlation coefficient （CC）. By integrating fuzzy logic with a Semi−supervised Learning algorithm based on a weak K−nearest neighbor classifier (referred to as the FL−ST−KNN model), the probability of hail occurrence and hail size grades were identified, thereby further reducing the impacts of hail on buildings, agricultural production, and human safety. The results show that the FL−ST−KNN model achieved an accuracy of 83% on the test set (20% of the dataset). Its precision was 80% and its recall was 83%, indicating high reliability in identifying majority−class samples. Moreover, the F1−score approached the excellent threshold of 80%, demonstrating that the proposed model performs well in identifying both hail occurrence probability and hail size.

Monitoring Method of Flood Disaster of Summer Maize Based on Multi-temporal Sentinel-1 SAR Images: A Case Study of Xun County, Henan Province

WU Wen-tao, Xue Chang-ying

2026, 47(4):  581-591.  doi:10.3969/j.issn.1000-6362.2026.04.009

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Based on the Synthetic Aperture Radar (SAR) remote sensing image data from the Sentinel−1 earth observation satellite, meteorological, disaster, crop and other multi−source data, the regional monitoring method of flood disaster of summer maize based on Sentinel−1 Dual Polarized Water Index (SDWI) was established. Taking county−level administrative units as the research object, the inundation information of flood disasters of summer maize was extracted. The inundation area of summer maize caused by flood in each town of each county−level unit was determined. The dynamic monitoring and disaster assessment during the jointing－maturity stage of summer maize in 2021 in Xun county, Henan province were carried out. The results indicated that: (1) the regional monitoring method of flood disaster of summer maize based on SDWI index could effectively monitor the occurrence and development process of flood disaster of summer maize in Xun county. The monitoring results showed that during the jointing−maturity stage of summer maize in 2021, the flood disasters mainly occurred in the central and southern parts of Xun county, which had significant impacts on summer maize in Baisi town and Xiaohe town. (2) The flood disaster had significant impacts on summer maize, with severe damage observed throughout the study area. The maximum affected area reached 4043.60×10⁴m². Compared to the normal period (July 15^th), the submerged area of summer maize on July 27th during the flood period accounted for 12.10% of planting area of summer maize in Xun county. By August 8th, this percentage had increased to 19.28%. (3) There were significant differences in the disasters of summer maize among county−level administrative units. The summer maize in Baisi town was the most severely affected by the flood disaster, followed by Xiaohe town. Compared to the flood period on July 27^th, the percentage of increased submerged area in each town to the total increased submerged area of summer maize in Xun county, the percentage of submerged area to the total submerged area of summer maize in Xun county, and the percentage of submerged area in each town to the total planting area of summer maize in Xun county on August 8^th, were found to be relatively high in Baisi town, with the percentage of 54.01%, 52.74%, and 7.37%, respectively. In Xiaohe town, their percentages were 20.45%, 24.01%, and 6.13%, respectively.

Evaluating Ozone−induced Rice Yield Losses and Associated Economic Impacts in the Chengdu Plain Economic Zone, China

ZHANG Jing-wen, WANG Ming-tian, ZHAO Huan

2026, 47(4):  592-602.  doi:10.3969/j.issn.1000-6362.2026.04.010

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Based on the hourly ground−level ozone (O₃) concentration data from national environmental air quality monitoring stations during the rice−growing season in eight cities of the Chengdu plain economic zone (CPEZ) from 2015 to 2022, this study analyzed the temporal and spatial variation characteristics of O₃ hourly concentration and the dose−based metric (AOT40) on different time scales. The impact of O₃ pollution on rice yield and associated economic losses in the CPEZ from 2015 to 2022 was evaluated, revealing the characteristics of O₃ pollution during the rice−growing season and providing a scientific basis for pollution control strategies. The results indicated that the daily average O₃ hourly concentration in the CPEZ from 2015 to 2022 exhibited a distinct unimodal pattern, with the highest peak value in Chengdu reaching 145.1μg·m⁻³.During the same period, the annual average O₃ hourly concentration displayed a fluctuating upward trend, with a regional increase of 21.6μg·m⁻³. Spatially, the annual average O₃ hourly concentration decreased in the order of Zone I (Chengdu, Deyang, Meishan)>Zone II (Mianyang, Suining, Ziyang)>Zone III (Leshan, Ya’an). Ziyang recorded the highest annual average O₃ concentration of 79.5μg·m⁻³, while Ya’an had the lowest at 67.5μg·m⁻³. From 2015 to 2022, the the annual trend of AOT40 in the CPEZ generally followed a pattern consistent with that of the O₃ hourly concentration, although the AOT40 peak occurred earlier and its trough lagged behind those of O₃ concentration. In 2022, the regional average AOT40 increased by 2.7×10⁻³mL·L⁻¹·h compared to that in 2015. Among these eight cities, Chengdu exhibited the highest annual average AOT40 at 8.0×10⁻³mL·L⁻¹·h, whereas Ya’an had the lowest at 1.4×10⁻³mL·L⁻¹·h. O₃ pollution caused a cumulative reduction of 2.313 million metric tons of rice yield in the CPEZ, accounting for 4.9% of the actual total production, with an average annual economic loss reaching 762 million yuan. In summary, the findings reveal that O₃ pollution in the CPEZ has already posed a significant threat to regional food security. In future, it is urgent to take effective emission−reduction measures to mitigate the negative impact of O₃ pollution on agriculture and safeguard regional food security. 

Spatiotemporal Evolution Characteristics and Influencing Factors of Crop Disaster Rates by Meteorological Disaster in Gansu Province

LI Xiao-peng, JIA Fu-gui, LI Kang, LEI Shuang, HU Wei-tong, Zhang Yong-kai

2026, 47(4):  603-615.  doi:10.3969/j.issn.1000-6362.2026.04.011

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Based on the crop disaster rate data of 14 cities (prefectures) in the four sub−regions (Hexi, Longzhong, Longdong and southern Gansu) from 2009 to 2023, this study comprehensively adopted methods including cold and hot spot analysis, gravity center migration model, kernel density estimation and Dagum Gini coefficient to analyze the spatiotemporal differentiation characteristics of the crop disaster rate in the four sub-regions of Gansu province. The geodetector was used to quantify the impacts of natural and anthropogenic factors on the disaster pattern and their interaction, aiming to reveal the spatiotemporal evolution patterns and key influencing factors of the crop disaster rate in Gansu province from 2009 to 2023. The research was intended to provide references for optimizing regional agricultural layout and preventing agricultural meteorological disaster risks. The results showed that: (1) from 2009 to 2023, the overall crop disaster rate in the four sub−regions of Gansu province showed a shrinking trend, with drought being the dominant agricultural meteorological disaster. (2) The cold spots of the crop disaster rate in Gansu province showed a shrinking trend from 2009 to 2023 and completely disappeared by 2021. Since then, only hot spots existed, and by 2023, the hot spots were concentrated in Zhangye, Jinchang and Wuwei city in the Hexi region. The gravity center of the crop disaster rate in Gansu province generally migrated from Baiyin city in the Longzhong region to Wuwei city in the Hexi region in a northwest direction from 2009 to 2023, with a migration distance of 238.7km. (3) Kernel density estimation indicates that the crop disaster rate in Gansu province showed a tended to ease trend from 2009 to 2023, but spatial disparities in disaster distribution in Hexi region intensified. The disaster disparity within the Longzhong region decreased, while the Longdong and southern regions exhibited polarization with significant differences among cities (prefectures) within these sub−regions. (4) The Gini coefficient of the crop disaster rate in Gansu province fluctuated between 0.389 and 0.604, showing an overall increasing trend from 2009 to 2023, indicating relatively large overall disparities in the crop disaster rate among the regions of Gansu province. (5) The effective irrigated area (0.230), slope (0.153), and total agricultural machinery power (0.143) in Gansu province were the main factors affecting the crop disaster rate. The interaction between altitude and total agricultural machinery power (0.447) had the highest impact on the crop disaster rate in Gansu province. In conclusion, the crop disaster pattern in Gansu province is jointly constrained by both topographic conditions and the level of agricultural mechanization. In the future, emphasis should be placed on strengthening water conservancy facilities in light of local conditions, optimizing water resource allocation, and enhancing the level of agricultural mechanization to improve disaster resistance and address the bottleneck of coexisting water scarcity and inefficient utilization.

Risk Assessment of Cold Dew Wind Disaster on Double-cropping Late Rice in the Middle and Lower Reaches of the Yangtze River

LI Yi-zhi, TIAN Hong-wei, HUANG Wan-hua, ZHENG Chang-ling, DENG Jian-bo, XIE Ao

2026, 47(4):  616-626.  doi:10.3969/j.issn.1000-6362.2026.04.012

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The middle and lower reaches of the Yangtze river (MLYR) are the main areas for double cropping rice in China, where cold dew wind is a significant agricultural meteorological hazard. Scientific assessment of its risk is of great significance for guiding agricultural production. Based on daily average temperature and sunshine hours data from 220 national meteorological stations in the late rice planting areas of the middle and lower reaches of the Yangtze river from 1961 to 2023 during the late rice heading and flowering period, growth stage data from 31 agro−meteorological observation stations , alongside yield per unit area and planting area data of late rice in major producing counties (cities, districts), the intensity index of cold dew wind process was established by comprehensively considering the equivalent cold accumulated temperature, duration of low temperature, and sunshine hours during the process. The cumulative effect of multiple cold dew wind events within a year was further incorporated to construct an annual cold dew wind intensity index. On this basis, high−resolution land use information was integrated, and a risk assessment model was established based on the hazardousness of disaster−inducing factors , exposure, and vulnerability of the affected system. The results showed that: (1) the process intensity index effectively quantified the severity of cold dew wind events. Higher average index values were observed in most areas of Hunan, central Hubei and western and southern Zhejiang, while lower values were noted in other regions. (2) Annual cold dew wind intensity levels were classified using the kernel density estimation method. Annual cold dew wind intensity index values ≤3 were defined as light cold dew wind years, values from 3 to 10 as moderate years, and values >10 as severe years. This classification was found to be highly consistent with historical disaster records, verifying the reliability of the index. (3) The natural breaks (Jenks) method was applied to determine comprehensive risk levels of cold dew wind in the middle and lower reaches of the Yangtze river. High and moderately high−risk zones were identified in northern and central Hunan, as well as southwestern Hubei. Low and moderately low−risk zones were located in southern Hunan, eastern Hubei, northern and southern Jiangxi, southern Anhui, and most parts of Zhejiang. Other areas were categorized as medium−risk zones.

Identification of Rice Potassium Nutrient Stress Severity Based on SAE-ResNet34 Model

YANG He, YANG Hong-yun, SUN Ai-zhen, LIAO Xuan-ying, LIU Zhi-yang

2026, 47(4):  627-637.  doi:10.3969/j.issn.1000-6362.2026.04.013

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To achieve accurate and fast identification of potassium nutrient stress levels in rice, a rice potassium identification method based on SAE-ResNet34 was constructed with ResNet34 as the core. Taking the late rice ‘Huanghuazhan’ as the research object, six potassium application gradient field trails were set up, while the total fertilization amounts were 0 (K1), 3.78g·m⁻² (K1), 9.45g·m⁻² (K2), 14.17g·m⁻² (K3), 18.90g·m⁻² (K4) and 28.35g·m⁻² (K5), respectively. Based on the high−resolution leaf images data obtained from scanning the fully expanded upper three leaves from tillers during rice tillering and jointing stages. Enhanced super−resolution generative adversarial network (ESRGAN) was incorporated at the data pre−processing stage. Within each residual block, the ReLU activation function was replaced by the Swish activation function, a feature fusion structure based on Addition was designed, and the Efficient local attention (ELA) mechanism module was introduced, with a view to solving the problems of partial feature value loss and low classification accuracy of the network model caused by the reduction of resolution after image resize. The results showed that the rapid recognition method based on SAE−ResNet34 achieved the average recognition accuracy of 82.87% and 84.58% for the six stress levels at the tillering and jointing stages, respectively, on the validation set of the self−constructed rice dataset, which were 7.1 percentage points and 6.7 percentage points higher than that of the original ResNet34 network. The results of confusion matrix showed that the best recognition accuracy for the stress levels at the tillering and jointing stages, were 83.67% for the K3 treatment and 89.11% for the K4 treatment, respectively. Compared with image classification networks such as VGG16, ResNet50 and Swin Transformer, the SAE−ResNet34 network was only slightly behind VGG16 in terms of precision, recall and F1 score, and had the shortest time consumed for 250 rounds of training iterations, with model size of 97.49 MB, which was 7.43MB larger than that of ResNet50 and had the best overall performance. In summary, the identification method based on SAE−ResNet34 network model is able to quickly and accurately identify the degree of potassium nutritional stress during the tillering and jointing stages of rice, which can be used as a scientific reference for nutritional diagnosis and identification of rice and other crops.