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Characteristics of Climatic Seasonal Variation in Northeast China under the New Standard SHAO Qi-duo, FENG Xi-yuan, REN Hang, TU Gang, LI Shang-feng, LIU Gang, YANG Xu, WU Di Chinese Journal of Agrometeorology    2025, 46 (6): 741-752.   DOI: 10.3969/j.issn.1000-6362.2025.06.001 Abstract （1138）      PDF（pc） （16028KB）（210）       Knowledge map    Save According to the national standard of Climate Seasonal Division (GB\T 42074−2022), the characteristics of seasonal variation in Northeast China (NEC) for the period 1961–2020 were analyzed using CN05.1 gridded data, and the changes caused by the shift of the standards and climatological baselines were investigated. The results showed that the climatic season of NEC was divided into regions with four seasons and non−summer zone regions, and the non−summer regions were mainly located in the northern part of the NEC, high−altitude regions and their surroundings. Spring and summer started from the south to the northeast, from the central plains to the high altitude mountains, and vice versa in autumn and winter. Compared with the 1981–2010 baseline period, parts of the Sanjiang plain and Hulun lake changed from non−summer regions to four−season regions. The starting dates showed a significant advance of 1d·10y−1 in spring over most of the NEC region, and a significant advance of 2−3d·10y−1 in summer over the central and western parts of the Northeastern plains. The starting dates were significantly delayed in autumn over the four-season regions, and in winter over the non−summer regions and the central of Northeastern plains. The summer and winter duration were significantly prolonged and delayed, respectively. Compared to the original standard, there were more areas with significant changes in spring and summer starting dates and summer and winter duration under the new standard. The areas up to the summer standard showed a significant upward trend of 3.9PP·10y−1 and had a significant positive correlation with the area−mean June−July−August NEC temperature. The rating of starting date of seasons obeys the normal distribution law, with a slight advance in summer. Related Articles | Metrics | Comments（0）

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Changes of Main Crops Growth Periods in China and Their Influencing Factors in the Context of Climate Warming GAO Jing, YANG Bing-yu, LIAO Jie, LIU Yuan-yuan Chinese Journal of Agrometeorology    2025, 46 (9): 1261-1276.   DOI: 10.3969/j.issn.1000-6362.2025.09.004 Abstract （507）      PDF（pc） （11434KB）（236）       Knowledge map    Save Based on phenological data of wheat, rice, and maize from 1981 to 2022 collected at 653 agrometeorological stations across China, as well as concurrent meteorological data, trend analysis and correlation analysis methods were used to analyze the characteristics of changes in the growth periods of these three crops. The study also explored the main meteorological factors influencing these changes, aiming to provide a basis for adapting agricultural production to climate change. The results indicated that the mean temperature and growing degree days (except for late double−cropping rice) during the whole growth periods of all three crops showed significant increasing trends (P<0.05). Precipitation during the whole growth period of winter wheat significantly decreased, while it significantly increased for spring wheat and maize. The sunshine duration during the whole growth period of maize and late double−cropping rice significantly decreased. From 1981 to 2022, the whole growth periods of spring wheat, winter wheat and late double−cropping rice mainly shortened, with average decreases of 1.6d per decade, 2.5d per decade, and 2.2d per decade, respectively. In contrast, the whole growth periods of single−season rice, early double−cropping rice, and maize mainly extended, with average increases of 1.9d per decade, 0.01d per decade, and 0.6d per decade, respectively. Compared with the sowing dates in the 1980s, the sowing dates of spring wheat, winter wheat, maize, and late double−cropping rice in the 2010s were delayed by an average of 1.0d, 4.0d, 4.0d and 9.0d, respectively. In contrast, the sowing dates of single−season rice and early double−cropping rice advanced by an average of 2.0d. For spring wheat, winter wheat, and maize, more than 82%, 76% and 85% of the observation stations, respectively, showed a significant positive correlation between the length of each growth stage and sunshine duration. The mean temperature and sunshine duration were key factors influencing the phenological changes of spring wheat, with mean temperature having a particularly significant impact on the duration of the sowing−to−emergence stage of spring wheat. The active accumulated temperature ≥0℃ was the primary factor responsible for the changes in the whole growth period and the vegetative growth period of winter wheat. The mean temperature had the greatest impact on the duration of the sowing−to−tillering stage, while the overwintering period was mainly influenced by sunshine duration.The effective accumulated temperature ≥10℃ was the main factor influencing the changes in the whole growth period of rice (including single−season rice, early double−cropping rice, and late double−cropping rice). The duration from the three−leaf stage to transplanting had the highest correlation coefficient with sunshine duration, while during this stage, the correlation coefficient between precipitation and both early double−cropping rice and late double−cropping rice was the highest.The mean temperature was the primary factor determining the changes in the whole growth period of maize, and the effective accumulated temperature ≥10℃ had the highest correlation coefficient with the duration from the seven−leaf stage to the silking stage. Related Articles | Metrics | Comments（0）

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Characteristics and Causes of Spatiotemporal Variation of Dry-wet Climate in Jilin Province REN Jing-quan, LIU Yu-xi, WU Yu-jie, MU Jia, LIU Cong, GAO Yan, WANG Dong-ni, YU Qing-bo Chinese Journal of Agrometeorology    2025, 46 (10): 1383-1394.   DOI: 10.3969/j.issn.1000-6362.2025.10.001 Abstract （479）      PDF（pc） （6056KB）（198）       Knowledge map    Save In order to study the characteristics and causes of dry−wet climate change in Jilin province, the aridity index (AI) was calculated based on daily meteorological data from 46 meteorological stations in Jilin from 1961 to 2021. Linear tendency estimation and inverse distance weighted spatial interpolation based on ArcGIS 10.2 were used to analyze the spatiotemporal variation characteristics of AI, and contribution rate analysis was used to analyze the cause of AI. The results indicated that the AI values in Jilin province and its western, central and eastern regions exhibited negative trends from 1961 to 2021. There was a significant spatial difference in the average AI values in Jilin province, with a spatial distribution pattern of ‘high−low−high’ from west to east. It was divided into sub arid, sub humid and humid regions in Jilin province. The sub humid region had been expanding over the years, reaching its maximum area in the 2010s. ET0 showed a downward trend, while the precipitation showed an upward trend in Jilin from 1961 to 2021, but the changes trend were not significant. The water vapor pressure and average temperature both showed a significant upward trend, with the climate tendency rate of 0.008kPa·10y−1 (P<0.01) and 0.32℃·10y−1 (P<0.01), respectively. The net solar radiation and wind speed both showed a significant downward trend, with the climate tendency rate of −0.077MJ·m−2·10y−1(P<0.01) and −0.14m·s−1·10y−1(P<0.01), respectively. ET0, net solar radiation, average temperature and wind speed gradually decreased from west to east, while the precipitation showed a gradual increase from west to east and the vapor pressure mainly exhibited a spatial distribution characteristic of ‘low−high−low’. The meteorological factors of vast majority of station had a negative contribution to AI. Precipitation was the dominant factor for the variations in AI values in Jilin province and its west and east, followed by wind speed and ET0, but in the central of Jilin province, wind speed was the dominant factor for the AI change, followed by precipitation and ET0. The research results can provide support for the formulation of strategies to cope with dry−wet climate change and the rational utilization of climate resources in Jilin province.  Related Articles | Metrics | Comments（0）

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Progress on Monitoring, Forecasting and Service of Airborne Allergenic Pollen LIU Su-qin, LI Jian-qiang, CHENG Wen-xiu, ZHAO Lin-na, XU Xi, YE Cai-hua Chinese Journal of Agrometeorology    2025, 46 (8): 1095-1110.   DOI: 10.3969/j.issn.1000-6362.2025.08.003 Abstract （455）      PDF（pc） （1951KB）（1708）       Knowledge map    Save The prevention and management of allergy risks caused by airborne allergenic pollen has become a critical concern in safeguarding public health during urban greening. The prevention and management framework covers three main strands from bottom to top: pollen monitoring, pollen forecasting and pollen service. To gain a deeper understanding of this framework, a comprehensive literature review was conducted in this paper. The principles, equipment and station layout of pollen monitoring were analyzed. The development of pollen forecasting methods, spanning from statistical regression to machine learning and deep learning was summarized. Current manifestations and applications of user−friendly pollen service products were summarized. In addition, the challenges faced by each link were discussed, and future research directions were prospected. The results indicated that domestic pollen monitoring equipment primarily relied on gravity settling, which was cost−effective and easy to operate but heavily depended on manual daily monitoring. Pollen forecasting was still mainly based on statistical regression, and future advancements should focus on integrating cutting−edge technologies, such as Artificial Intelligence Large Models, to develop multi−modal factor−driven forecasting methods and support more refined forecasting. Pollen service was launched via WeChat mini−programs, mobile applications, platforms and other products that provide diverse information, including total and classified pollen concentration and medical guidelines. Future developments should prioritize addressing the specific needs of different user groups through personalized and customized solutions. Related Articles | Metrics | Comments（0）

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Temporal and Spatial Variations of Drought in the Yellow River Basin from 1980 to 2020 GU Yang-yang, ZHAO Wen-ji, WU Shu-qi Chinese Journal of Agrometeorology    2025, 46 (8): 1192-1205.   DOI: 10.3969/j.issn.1000-6362.2025.08.011 Abstract （403）      PDF（pc） （8259KB）（179）       Knowledge map    Save The Yellow river basin, as an ecological barrier zone in northern China, experiences frequent drought events with significant spatiotemporal differentiation. However, its large−scale evolution pattern and atmospheric oceanic driving mechanisms are not yet clear, which hinders the optimal allocation of regional water resources and drought risk management. This study was based on monthly precipitation and temperature data from 340 meteorological stations in and around the Yellow river basin from 1980 to 2020. The Thornthwaite model was used to calculate the Standardized Precipitation Evapotranspiration Index (SPEI), and linear trend estimation, Mann−Kendall trend/mutation test, continuous wavelet transform and wavelet coherence analysis were combined to reveal the spatiotemporal evolution of drought in the Yellow river basin and its multi−scale coupling mechanism with the Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), El Niño−Southern Oscillation (ENSO) and East Asian Summer Monsoon (EASM), in order to provide scientific basis for regional basin drought warning and adaptive regulation. The results showed that from 1980 to 2020, the spring SPEI−3 in the Yellow river basin significantly decreased at a rate of 0.021·y−1 (P<0.01), and mild or above drought occurred 30% of the time in spring. In autumn of 1997, the detection of SPEI−3 mutation identified a significant jump point, with a 20% increase of droughts within five years thereafter. There was no significant sustained trend of drought in the Yellow river basin during summer and winter, which manifested as short−term random fluctuations. From 1980 to 2020, the annual scale SPEI−12 in the Yellow river basin showed a slight downward trend of 0.005·y−1 (P=0.06). From 1997 to 2002, there were a total of eight occurrences of mild to moderate drought in the Yellow river basin, accounting for 65% of the total annual drought events. In 1986, it suddenly changed to a sustained drought. Spatially, from 1980 to 2020, the high−frequency drought zone in the Yellow river basin migrated from the middle and lower reaches of the northeast to the southwest, forming a radiation belt of "frequent occurrence in the northeast and weakening in the southwest" covering 35.23% of the middle and lower reaches. The Arctic Oscillation (AO) caused a 2−4 month lag in drought in the Yellow river basin through a 3−60 month resonance. The bimodal effect of Pacific Decadal Oscillation (PDO) caused drought to advance or lag by 3−8 months. ENSO multi−cycle alternation caused drought to lag by 3−9 months or advance by 12−18 months. The coupling of short period negative phase and medium to long period positive phase in the East Asian Summer Monsoon (EASM) caused drought to lag by 1−15 months. In summary, the periodic fluctuations of multi−scale weather and climate events were the key driving mechanism for the meteorological drought evolution in the Yellow river basin from 1980 to 2020 through the cross scale synergistic effects of AO suppressing soil moisture, PDO weakening water vapor transport and EASM driving changes. This study can provide reference for regional drought warning and adaptive regulation. Related Articles | Metrics | Comments（0）

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Report on Weather Impacts to Agricultural Production in Spring 2025 LI Xuan, ZHANG Yan-hong, WU Men-xin, TAN Fang-ying, ZHAO Yun-cheng Chinese Journal of Agrometeorology    2025, 46 (8): 1221-1224.   DOI: 10.3969/j.issn.1000-6362.2025.08.013 Abstract （368）      PDF（pc） （560KB）（1159）       Knowledge map    Save During the spring of 2025 (March –May), China’s national average temperature was 11.7°C, 1.0°C above the long−term average (1991–2020, hereinafter referred to as norm), which was the fourth highest during the same period since 1961. Total precipitation averaged 137.4mm nationwide, which was close to the norm. In particular, the average spring precipitation in Shaanxi was the five−lowest the same period since 1961. The national average sunshine duration reached 629.4h, less than the norm. In the summer grain and oil crop regions, favorable light and temperature conditions generally benefited the growth and yield formation of winter wheat and rapeseed. However, severe spring droughts occurred in Henan, Shaanxi and Shanxi, with some wheat−growing areas additionally affected by dry−hot winds, hindering the grain filling of winter wheat. Rapeseed−producing areas experienced periodic low temperatures, overcast rains and insufficient sunlight, adversely affecting rapeseed growth. In most spring−sown regions, suitable hydrothermal conditions and good soil moisture facilitated crop sowing and seedling emergence, with overall smooth progress. However, agricultural droughts persisted in Shaanxi and other areas, while frequent rainfall in eastern northeast China led to prolonged waterlogging in some farmlands. Additionally, regions like Jiangnan encountered periodic heavy rainfall and cold, wet weather, disrupting spring sowing operations. Related Articles | Metrics | Comments（0）

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Identification Technology of Rice Development Stages Based on Real Pictures via Deep Learning WANG Yang-yang, OU Xiao-feng, HUANG Wan-hua, YUAN Yu-rong, LIU Fan, PANG Xin-wei, SHUAI Zi-ang, SHUAI Xi-qiang Chinese Journal of Agrometeorology    2025, 46 (7): 1050-1062.   DOI: 10.3969/j.issn.1000-6362.2025.07.013 Abstract （363）      PDF（pc） （3822KB）（1092）       Knowledge map    Save Based on photos of rice collected hourly during the daytime and manually observed developmental stage data from Qiaoshang village, Luyang town, Zhongfang county, Huaihua city, Hunan province in 2023, a picture dataset containing developmental stages of rice which were seeding period, greening period, tillering period, jointing period, booting period, heading period, grain filling period as well as pre-seeding and the harvested period in a total of 10 periods had been established and processed using cutting、preprocessing and data enhancement techniques. Six representative deep learning network models, namely 18-layers residual network（ResNet18）, 50-layers residual network variant（ResNet50_vd）, lightweight convolutional network（MobileNetV3_large）, lightweight convolutional network（PPLCNet）, deep convolutional residual network（Xception41）, and densely connected convolutional network（DenseNet121）were selected, which were used as pre-training models to recognize the developmental stages of rice based on real pictures, the performances of the six models were compared in the training and test sets to evaluate their accuracy and loss rate to verify the feasibility of the deep learning model for the intelligent recognition of rice developmental stage, analyze its differences, and screen out the optimal rice developmental stage recognition model to promote its application in the business service. The results indicated that all models achieved a recognition accuracy of 92% or higher on the test set, among these models, Xception41 exhibited the highest recognition accuracy of 96.19%. The best model for recognition of pre-seeding, seeding, tillering, booting, grain filling and maturity periods of rice was Xception41, the best model for recognition of greening period of rice was ResNet50_vd, the best models for recognition of jointing and heading periods of rice were DenseNet121 and Xception41, and the best model for recognition of harvested period of rice were ResNet50_vd and DenseNet121 for test set. The study provided a new idea for the intelligent recognition of rice developmental stage, demonstrating the feasibility of deep learning models in recognition of rice live pictures and their potential to meet the demand of agricultural meteorological business services.  Related Articles | Metrics | Comments（0）

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Spatiotemporal Variation Characteristics of Meteorological Drought in Xinjiang Based on Pixel−scale SPEI YI Ke-fan, LIN Hai-xia, QIN Guo-peng, YAO Ning, GAO Xue-hui, LU Wei-juan, LU Yu-hang, LIU Jian Chinese Journal of Agrometeorology    2025, 46 (7): 1026-1038.   DOI: 10.3969/j.issn.1000-6362.2025.07.011 Abstract （356）      PDF（pc） （13267KB）（98）       Knowledge map    Save Based on the 1981−2018 China meteorological forcing dataset, the standardized precipitation evapotranspiration index (SPEI) was calculated for different time scales. The Mann−kendall (M−K) test and Sen's slope method were used to analyze the spatial and temporal characteristics of meteorological drought in Xinjiang, including trends of change, frequency of occurrence and duration. This study endeavored to provide a scientific basis for the formulation of strategies for drought prevention and mitigation in Xinjiang. Results showed that: (1) from 1981 to 2018, the climate of Xinjiang exhibited a non−significant trend of aridification, with the proportion of drought−affected area decreasing at a rate of 0.845 percentage points per decade in a non−significant manner. Both spring and summer climates showed a non−significant trend of aridification, while autumn meteorological droughts significantly intensified after 2005 and winter had tended to become wetter after 1997. (2) The spatial distribution of seasonal drought based on the SPEI−3 in Xinjiang was regionally distinct. Significant drought−affected areas were observed in spring, summer and autumn, with the intensified drought concentrated in the Tarim basin and a few eastern regions. During the winter, 57.82% of the area in Xinjiang exhibited a non−significant trend of wetting, 29.23% showed a significant trend of wetting, and only 0.03% showed a significant drought trend. The remaining areas showed a non−significant drought trend. (3) The spatial distribution of drought frequency at monthly, seasonal, and annual scales was relatively consistent, with the eastern region being a high−frequency drought zone. The average annual frequency of drought was 36.05%, with Turpan reaching 44.97%. Interdecadal differences in drought duration across Xinjiang were minimal, with the longest average drought duration observed in 2000−2009, which lasted 3.6 months. Generally, trends of aridification have intensified in the southern and eastern regions of Xinjiang over the past 38 years, with a high frequency of droughts, there is an urgent need for measures to mitigate the adverse impacts of drought. Related Articles | Metrics | Comments（0）

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Distribution of Potential Suitable Areas for Flue-cured Tobacco in Hubei Province under the Climate Change Scenario XU Hao-yuan, LI Wen-feng, REN Yong-jian, LI Jin-jian Chinese Journal of Agrometeorology    2025, 46 (7): 999-1011.   DOI: 10.3969/j.issn.1000-6362.2025.07.009 Abstract （354）      PDF（pc） （11886KB）（166）       Knowledge map    Save Using the Maximum Entropy Model (MaxEnt), 80 flue−cured tobacco planting sites in Hubei province and 11 environmental variables from 1970 to 2000 were selected. Based on the future climate scenario data of CMIP6, the potential suitable area for flue−cured tobacco in the 2030s (2021−2040), 2050s (2041−2060), 2070s (2061−2080) and 2090s (2081−2100) was predicted to provide a scientific reference for the planning and layout of flue−cured tobacco planting in Hubei province. The results showed that the dominant environmental variables affecting flue−cured tobacco were elevation, precipitation of wettest month, slope and temperature seasonality, with a cumulative contribution rate of 86.8%. Under the current climate from 1970 to 2000, the total area of the potential suitable area for flue−cured tobacco was 4.52×104km2, mainly distributed in Enshi, the western and northern parts of Yichang, the southwestern part of Xiangyang and the central and southern parts of Shiyan. The area of high, moderate and low suitable area was 1.02×104km2, 1.45×104km2 and 2.05×104km2 respectively. Under the SSP1−2.6 and SSP2−4.5 climate scenarios, the potential suitable area for flue−cured tobacco in Hubei was projected to decrease in the 2030s, 2050s 2070s and 2090s and compare to the current climate, indicating a decline in overall suitability. Conversely, under the SSP3−7.0 and SSP5−8.5 climate scenarios, the potential suitable area was projected to increase in the 2030s, 2050s and 2090s, but decreased in the 2070s, relative to the current climate. The centroid of the potential suitable area for flue−cured tobacco in Hubei generally exhibited a westward shift from 2030s to 2090s under future climate change scenarios. To adapt to future climate change, it is recommended to promote the cultivation of flue-cured tobacco in Enshi, where the potential suitable area remains relatively stable and experiences some expansion, thereby capitalizing on the region's existing and emerging planting potential. Related Articles | Metrics | Comments（0）

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Advances in Remote Sensing Estimation of Crop Yield Based on Hybrid Modeling XU Jing-yuan, DU Xin, LI Qiang-zi, DONG Tai-feng, ZHANG Yuan, WANG Hong-yan, XIAO Jing, ZHANG Jia-shu Chinese Journal of Agrometeorology    2025, 46 (10): 1472-1486.   DOI: 10.3969/j.issn.1000-6362.2025.10.009 Abstract （353）      PDF（pc） （476KB）（216）       Knowledge map    Save  Timely and accurate access to crop yield information is critical for decision−making in national food policy and safety assessments. Remote sensing technology, with low cost and high efficiency, provides an effective means for large−scale crop yield estimation. Strengthening the integration of agronomic knowledge into crop yield estimation models and addressing the scarcity of training samples are key challenges in current research. In this paper, authors synthesized existing literature, summarized data− and knowledge−driven methods for crop yield estimation, and systematically discussed the methods for constructing multi−scenario simulation datasets and modelling techniques for crop yield estimation based on hybrid modeling approaches. The paper also provided an overview of commonly used models and algorithms, and summarized the application of remote sensing technology to hybrid modeling methods. Finally, it comprehensively discussed the uncertainties in hybrid modeling and outlined the future trends and challenged in crop yield estimation studies. The results showed that hybrid modeling approaches, driven by both data and knowledge had made significant progress in crop yield estimation. By combining the advantages of data−driven and knowledge−driven models, these approaches reduced the reliance on ground−truth samples while enhancing the mechanistic support for the predictions. Limiting factors for improving the accuracy of crop yield estimation included uncertainties in remote sensing data sources, the uncertainties in knowledge−driven models when simulating crop physiological processes, and the uncertainties in the predictions of data−driven models. Future trends will focus on improving the quality and availability of input data, strengthening the theoretical foundation of knowledge−driven models, and advancing algorithm improvements in data−driven models. Related Articles | Metrics | Comments（0）

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Spatial-temporal Variation Characteristics of Spring Maize Drought in Northeast China Based on Dynamic Thresholds of SIF Index CHEN Yu-ye, WANG Pei-juan, ZHANG Yuan-da, LI Yang, Wang Qi, AN Xiao-ying Chinese Journal of Agrometeorology    2025, 46 (7): 1012-1025.   DOI: 10.3969/j.issn.1000-6362.2025.07.010 Abstract （350）      PDF（pc） （6167KB）（723）       Knowledge map    Save Drought has become a problem faced by terrestrial ecosystems globally, with its development exhibiting distinct regional characteristics. Revealing the temporal and spatial distribution characteristics of regional drought is urgently required to address climate change and ensure the safety of agricultural production. Based on the drought dynamic threshold constructed using the Solar−induced chlorophyll fluorescence (SIF) index during the entire growth period of spring maize in Northeast China, drought frequency and affected areas of spring maize in this region from 2000 to 2020 had been calculated using pixel−by−pixel analysis method. Linear trend estimation and other methods were used to analyze the drought trends and spatial distribution characteristics of spring maize in Northeast China. The research findings were as follows: (1) from 2000 to 2020, the Solar−Induced Chlorophyll Fluorescence (SIF) values of spring maize in Northeast China exhibited a fluctuating upward trend on both the interannual scale and across different developmental stages in time series. Spatially, regions with an increasing trend in SIF values accounted for more than 50%, indicating that the study area had experienced a gradual reduction in drought impacts and a decreased risk of drought over the past 20 years. (2) From 2000 to 2020, the drought−affected areas in the spring maize cultivation regions of Northeast China showed a fluctuating decreasing trend. For different drought levels, the extent of drought−affected areas was ranked as follows: mild drought > moderate drought > severe drought. For different developmental stages of spring maize, the extent of drought−affected areas was ranked with: seedling stage > jointing to booting stage > filling to maturity stage > heading to flowering stage.(3) Most of the spring maize planting areas in northeast China experienced droughts, with a frequency exceeding 40%. The frequency of droughts was higher in the west than in the east. Among different drought severity levels, light drought occurs most frequently, while moderate and severe droughts had lower frequencies. In terms of different growth stages, most of the study area experienced drought frequencies exceeding 25% during the seedling stage. In particular, the central and western parts of Jilin province had frequencies higher than 45%, indicating that the seedling stage remained a period of high drought incidence for spring maize in northeast China. Related Articles | Metrics | Comments（0）

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Suitable Regional Distribution of Blueberry in Liaoning Province Under Current and Future Climate Scenarios Based on MaxEnt Model DONG Hai-tao, XING Hong-bin , LI Ru-nan, FANG Yi-he Chinese Journal of Agrometeorology    2025, 46 (9): 1338-1349.   DOI: 10.3969/j.issn.1000-6362.2025.09.011 Abstract （344）      PDF（pc） （8118KB）（88）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Modeling of Tea Plants Carbon Stock Estimation Based on Aboveground Biomass by Age of Trees MA Chun-yan , WEI Xiang-hua, HU Jun-ming, ZHENG Fu-hai, ZHANG Jun-hui, WEI Bin-bin Chinese Journal of Agrometeorology    2025, 46 (10): 1395-1404.   DOI: 10.3969/j.issn.1000-6362.2025.10.002 Abstract （337）      PDF（pc） （687KB）（305）       Knowledge map    Save Under the national strategy of "carbon peak and carbon neutrality", systematic assessment of the carbon sequestration potential of tea plants holds significant importance for realizing the value of ecological products in tea plantations. There is a huge difference in the growth rate of young tea plants under 2 years old and mature tea plants over 3 years old. By collecting the relevant research literature and fieldwork data of tea plant growth in domestic and international tea plantations from 1950 to 2023, this study constructed the model of tea plant biomass and carbon stock growth dynamics based on aboveground and belowground biomass data of young tea plants (0−2y) and mature tea plants (3−25y) to calculate and evaluate the carbon sequestration capacity of tea plants. The results showed that: (1) age−specific belowground biomass models of tea plants were established. Nonlinear model for mature tea plants (Bb=0.013Ba²−0.087Ba+3.269, R²=0.959, P<0.001) and linear model for young tea plants (Bb=0.665Ba−0.217, R²=0.933, P<0.001) were constructed based on the relationship between aboveground biomass (Ba) and belowground biomass(Bb). (2) The models for accounting tea plants carbon stock based on tea plants aboveground biomass were formed. Using the internationally recognized plant carbon conversion factor (0.5) provided by the Intergovernmental Panel on Climate Change(IPCC), carbon stock models for mature tea plants (C=0.006Ba²+0.492Ba+1.536, R²=0.995, P<0.001) and young tea plants(C=0.833Ba−0.108, R²=0.989, P<0.001) were developed based on total biomass data. (3) The tea plant carbon stock estimation model demonstrated simplicity and accuracy. Traditional methods relied on destructive whole−plant excavation to measure biomass, whereas the non−destructive model, based solely on aboveground biomass, enhanced both the efficiency and precision of carbon stock quantification. This approach offers distinct advantages for carbon stock quantification in tea plantations. Related Articles | Metrics | Comments（0）

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Impact Mechanism and Moderating Effects of Agricultural Insurance on Food Security Under Climate Change AN Min, MA Quan, WEI Ya-qian Chinese Journal of Agrometeorology    2025, 46 (8): 1206-1220.   DOI: 10.3969/j.issn.1000-6362.2025.08.012 Abstract （328）      PDF（pc） （6778KB）（4271）       Knowledge map    Save Based on agricultural statistics data and meteorological data from 31 provinces in 2001−2021, a food security indicator system was constructed from four dimensions: food supply capacity, availability, stability and sustainability. Climate change was characterized by the fluctuation of monthly average temperature and precipitation. The impact mechanism of climate change on food security and the moderating effects of agricultural insurance were analyzed by using fixed−effects bidirectional model and moderation effect model, aiming to provide references for exploring the relationship of agricultural insurance, climate change and food security, optimizing climate-adaptive agricultural insurance policies, and formulating regionally differentiated food security strategies. The results indicated that: (1) the fluctuation of temperature and precipitation from 2001 to 2021 had a significant impact on China's food security. Specifically, temperature fluctuation had a negative effect on food security, while precipitation fluctuation had a highly significant positive impact on food security (P<0.01). (2) The moderating effect of agricultural insurance was reflected in significantly weakening the negative impact of temperature volatility on food security (P<0.01) and significantly enhancing the positive impact of precipitation volatility on food security (P<0.01). (3) There were regional differences in the impact of climate change on food security and the role of agricultural insurance. The fluctuation of temperature had a negative impact on food security in the south, while the fluctuation of precipitation had a significant promoting effect on food security in the north (P<0.05). The moderating effect of agricultural insurance could significantly weaken the negative impact of temperature fluctuations on food security (P<0.01). From the perspective of grain functional areas, the regression coefficients of the impact of temperature volatility on food security in main grain production areas, main sales areas and production sales balance areas were −0.0085, −0.0012 and −0.0421, respectively. The negative impact on the grain production sales balance area was the greatest (P<0.05). Agricultural insurance had the strongest moderating effect in main grain producing areas, which could simultaneously weaken the adverse effects of temperature (P<0.01) and precipitation volatility (P<0.05) on food security. Related Articles | Metrics | Comments（0）

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Research Progress on the Extraction Methods of Agricultural Irrigation Information Based on Optical Remote Sensing Data LI Dong-yu, WANG Pei-juan, LI Yang, WANG Qi, MA Yu-ping Chinese Journal of Agrometeorology    2025, 46 (6): 895-906.   DOI: 10.3969/j.issn.1000-6362.2025.06.014 Abstract （317）      PDF（pc） （360KB）（313）       Knowledge map    Save  Irrigation plays an important role in farmland management and timely and accurate access to irrigation information is important for modern agricultural production. With the continuous development of remote sensing technologies, optical remote sensing has surpassed traditional field measurement methods, which are known for their low quality and efficiency, and has found wide application. In this paper, the principles of inversing agricultural irrigation information using the vegetation indeices, soil moisture, and evapotranspiration methods based on optical remote sensing data were outlined, and the advantages and disadvantages of each method and their development trends were summarized. The results indicated that as research expands from small irrigation districts to larger provincial and national regions, the types of irrigation information required become more diverse, researchers were continuously optimizing existing methods to address their shortcomings, leading to the maturation of research techniques. Integration of multi-parameter inversion methodologies and machine learning algorithms could effectively improve the precision of agricultural irrigation information extraction, which was also the leading trend in optical remote sensing data extraction. This approach representd two major trends in the extraction of agricultural irrigation information based on optical remote sensing data. However, challenges such as time lags and limited penetration still remain. Future research should focus on developing models suitable for different spatial and temporal scales by utilizing the integration of multi-parameter inversion methodologies and machine learning algorithms, and should aim to deepen the understanding of underlying mechanisms and continuously improve the precision of agricultural irrigation information extraction from optical remote sensing data. Related Articles | Metrics | Comments（0）

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A Dataset of Pest Lists for Major Grain, Cotton and Oil Crops in China ZHANG Quan-jun, KONG Xiang-sheng, WU Ze-xin, WU Dong-li Chinese Journal of Agrometeorology    2025, 46 (12): 1826-1835.   DOI: 10.3969/j.issn.1000-6362.2025.12.013 Abstract （316）      PDF（pc） （459KB）（395）       Knowledge map    Save Crop insect pests pose a major biological threat to global food security and agricultural sustainability. In China, insect pests affecting key grain, cotton and oil crops (including rice, wheat, maize, cotton, rapeseed and soybean) were characterized by their high species diversity, wide geographical distribution, severe damage potential and complex control requirements. However, existing research had predominantly focused on single crops or localized monitoring and there was a lack of systematic electronic data resources on crop pests on a national scale. To clarify the current status of pests in China's major grain, cotton and oil crops, this study employed a literature review approach, compiling and integrating pest records from plant protection publications and authoritative works spanning 1979 to 2024. A comprehensive data set of crop pests was created through multiple source data screening, standardization and quality control, encompassing 512 pest species: 196 on rice, 51 on wheat, 54 on maize, 103 on cotton, 39 on canola and 69 on soybeans. The dataset included 16 attributes for each pest species, such as Chinese/common name, Latin scientific name, geographic distribution, host crops, damage symptoms, and control methods, reflecting species diversity, spatial patterns and management strategies. The dataset was stored in excel format (file size: 316 KB) for accessibility and practical use. This electronic resource provided fundamental data for pest monitoring, early warning systems and resistance management analysis. It also supported training of pest image recognition models, optimization of integrated pest management strategies, and policy-making for agricultural sustainability, thereby bridging scientific research and field-level pest control practices. The URL for obtaining the entity dataset was https://doi.org/10.57760/sciencedb.20018. Related Articles | Metrics | Comments（0）

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Assessment of Agricultural Precipitation and Heat Resources in China Based on CMIP6 Climate Models HOU Wei, HUANG Ming-xia, ZHANG Liu-hong, CHEN Xiao-min, LI Wei-guang, ZOU Hai-ping Chinese Journal of Agrometeorology    2025, 46 (12): 1683-1696.   DOI: 10.3969/j.issn.1000-6362.2025.12.001 Abstract （313）      PDF（pc） （12116KB）（115）       Knowledge map    Save Based on five CMIP6 climate models and using the SSP126 and SSP245 scenarios, this study employed the high−resolution meteorological grid data for China (CN05.1) from 1970 to 2014 (historical period) as the baseline dataset. Delta bias correction, Taylor diagram and Bayesian model averaging (BMA) were used to assess the spatiotemporal changes in agricultural precipitation and heat resources in China from 2015 to 2050. The aim was to provide scientific evidence for optimizing planting systems, adjusting agricultural layout and adapting to climate change. The results showed that: (1) the five CMIP6 climate models and BMA ensemble demonstrate good performance in simulating temperature and precipitation, effectively capturing regional climate characteristics, with better accuracy in temperature simulations. BMA could effectively balance the performance of multiple models in simulating temperature and precipitation. (2) Under the SSP126 and SSP245 scenarios, the average temperature increase rate from 1970 to 2050 was 0.37°C·10y−1 and 0.40°C·10y−1, respectively. With the most significant temperature increasing in the Tibetan plateau, northwest, north and northeast regions, generally exceeding 0.4°C·10y−1. Annual precipitation showed a slight increasing trend, with rates of 5.6mm·10y−1 and 4.8mm·10y−1. Significant increases (≥10mm·10y−1) were observed in south, east and northeast regions, while the southwest region showed a varying degrees of decrease. (3) Compared with the historical period (1970−2014), the regions with an average temperature ≤0°C during 2015−2050 showed significant warming, with the area gradually shrinking. The isotherms for 5°C, 10°C, 15°C and 20°C moved northward by 2.1°, 2.9°, 4.2° and 2.2°, respectively. The region in south China with annual precipitation ≥1500mm slightly expands. (4) Under the SSP245 scenario during From 1970 to 2050, the trend rates of effective accumulated temperature for thresholds ≥0°C, ≥5°C, ≥10°C, and ≥15°C were 10.4°C·d·y−1, 9.3°C·d·y−1, 7.6°C·d·y−1 and 5.9°C·d·y−1, respectively, showing a pattern in which higher temperature thresholds correspond to smaller increases in effective accumulated temperatures. (5) From the distribution of effective accumulated temperature at different thresholds, the low value areas in high−altitude and mid−latitude regions shrinked in the future, while the high value areas in south China expanded to varying degrees.  From 1970 to 2050, the area with an increase in effective accumulated temperature ≥10℃·d·y−1 for thresholds of ≥0℃, ≥5℃, ≥10℃ and ≥15℃ gradually decreased as the temperature threshold rises, while the area with an increase in the range of 0−10℃·d·y−1 continued to expand. Climate change has led to an extended growing season, a northward expansion of planting boundaries and an increase in the cropping index. However, it also presented new challenges for the growth of cool−season crops such as winter wheat, pest control and disease prevention. Strengthening agricultural adaptation strategies to cope with the uncertainties of future climate change is crucial. Related Articles | Metrics | Comments（0）

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Future Changes of Precipitation and Temperature over Tailan River Basin Based on CMIP6 GCMs CELIGEER, DONG Wen-ming, HAO Zhe, XU Jing-dong, PENG Liang Chinese Journal of Agrometeorology    2025, 46 (7): 918-931.   DOI: 10.3969/j.issn.1000-6362.2025.07.002 Abstract （304）      PDF（pc） （22661KB）（174）       Knowledge map    Save Mountainous alpine areas are important water−producing areas in inland river basins that are highly sensitive to climate change. This study explored the projected changes in precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) in the Tailan river basin (TRB) based on 14 Global Climate Models (GCMs) from the coupled model intercomparison project phase 6 (CMIP6) and the multi−model ensemble (MME) of the bias−corrected dataset by Delta method during 2081−2100, with reference to the baseline period 1995−2014, under three integrated scenarios (SSP1−2.6, SSP2−4.5, and SSP5−8.5) of Shared Socioeconomic Pathways (SSPs) in order to understand precipitation and temperature changes over TRB and provide references for local strategies against climate change. The results showed that: (1) the biases between CMIP6 GCMs and observation value could be effectively corrected by DCM. Meanwhile, it was found that the MME was better than all other individual models for each climatic variable. (2) Precipitation,Tmax,Tmin over the TRB from 2025 to 2100 was projected to increase under SSP1−2.6, SSP2−4.5 and SSP5−8.5. Precipitation was projected to increase at the rate of 6.51mm·10y−1, 8.81mm·10y−1 and 9.01mm·10y−1 under SSP1−2.6, SSP2−4.5, and SSP5−8.5, respectively. Tmax was projected to increase at the rate of 0.09℃·10y−1, 0.16℃·10y−1 and 0.42℃·10y−1 under SSP1−2.6, SSP2−4.5 and SSP5−8.5, respectively. Tmin was projected to increase at the rate of 0.09℃·10y−1, 0.17℃·10y−1 and 0.43℃·10y−1 under SSP1−2.6, SSP2−4.5 and SSP5−8.5, respectively. (3) During 2081−2100, both precipitation and temperature over TRB increased under three integrated scenarios (SSP1−2.6, SSP2−4.5 and SSP5−8.5). The annual precipitation was projected to increase by 7.84−11.45pp, 18.45−21.35pp and 20.20−24.02pp under SSP1−2.6, SSP2−4.5 and SSP5−8.5, respectively. The annual mean Tmax over the TRB was projected to increase by 2.03−2.07℃, 2.95−3.02℃ and 5.07−5.12℃ under SSP1−2.6, SSP2−4.5 and SSP5−8.5. The annual mean Tmin over the TRB was projected to increase by 1.85−1.92℃, 2.99−3.04℃ and 6.09−6.13℃ under SSP1−2.6, SSP2−4.5 and SSP5−8.5. Related Articles | Metrics | Comments（0）

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Effects of Different Proportions of Organic Fertilizer Instead of Chemical Fertilizer on N2O Emission and Sunflower Yield HUANG Jing, HU Guo-zheng, HASBAGAN Ganjurjav, ZHAO Fen, LI Zheng, YU Pei-dong, HAN Dong-xun, LIU Han-jiang, HAO Ya-ru, GAO Qing-zhu Chinese Journal of Agrometeorology    2025, 46 (10): 1460-1471.   DOI: 10.3969/j.issn.1000-6362.2025.10.008 Abstract （303）      PDF（pc） （2060KB）（328）       Knowledge map    Save In agricultural production, the combined application of organic fertilizer and chemical fertilizer is often used to reduce the negative effects of single application of chemical fertilizer, but the unreasonable proportion of organic fertilizer instead of chemical fertilizer can easily lead to crop yield reduction, and the effect of organic fertilizer instead of chemical fertilizer on farmland N2O emission is not uniform. To explore the impact of organic fertilizers instead of chemical fertilizers on N2O emissions and crop yields in farmland, the sunflower fields in the Hetao irrigation district of Inner Mongolia were used as the study site. Five fertilization treatments with chemical fertilizer (T0), organic fertilizer replacing 25% (T25), 50% (T50), 75% (T75) and 100% (T100) were set up in the experimental base of Ganzhao temple of Bayannur Academy of Agricultural and Animal Husbandry Sciences in 2023. The N2O emission was determined by static chamber−gas chromatography. Combined with quantitative analysis of soil carbon and nitrogen indicators, enzyme activity and sunflower yield, the N2O emission law and its relationship with soil indicators were clarified. Explore the appropriate proportion of organic fertilizer substitution in local sunflower production. The results showed that after additional fertilizer (July 13−August 10) was the peak stage of N2O emissions. Compared with the T0 treatment, the T25, T50, T75 and T100 treatments significantly reduced the cumulative N2O emissions from farmland during the growing season by 30%, 45%, 52% and 64% respectively. Compared with T0 treatment, the contents of soil soluble carbon and nitrogen (DOC and DON) were significantly increased by 11%−30% and 38%−53%, respectively. Nitrous oxide reductase (NOS) activity was significantly increased by 11%−32%; nitric oxide reductase (NOR) activity was significantly reduced by 14%−30%. The contents of DOC and DON were significantly correlated with NOS activity, and the content of DON was significantly correlated with NOR activity. The increase in NOS activity and the decrease in NOR activity resulted in a decrease in the cumulative N2O emission from the farmland. Compared with T0 treatment, the sunflower yield of T25 treatment was significantly increased by 30%, and the sunflower yield of T50 treatment was not significantly reduced, and the N2O emission intensity of T25 and T50 treatments was lower, which was suitable for the local stable production and emission reduction of organic fertilizer instead of chemical fertilizer fertilization scheme. Related Articles | Metrics | Comments（0）

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A Review of Rice-fishery Co-culture System Effects on Crops and the Environment FAN Hai-dan, LV Wei-guang, PEI Ya-nan, MA Meng-qian, BAI Na-ling, ZHANG Juan-qin, ZHANG Hai-yun, LI Shuang-xi, ZHANG Han-lin Chinese Journal of Agrometeorology    2025, 46 (7): 967-976.   DOI: 10.3969/j.issn.1000-6362.2025.07.006 Abstract （293）      PDF（pc） （362KB）（1694）       Knowledge map    Save The co−culture system of rice and aquatic animals is one of the main models of modern ecologically sustainable agriculture. The rice−fishery aquaculture model, which combines rice and aquatic animals, can not only protect the biodiversity of paddy fields, but also maintain soil fertility and stabilize crop yields, with the potential of controlling water pollution, reducing greenhouse gas emissions, and increasing soil microbial activity in paddy fields. However, there are still controversies about the production performance and eco−environmental benefits of the rice−fishery co−culture system. In this paper, based on the latest research results of the impact of rice−fishery co−culture system on crops and the environment durning 2014 to 2023 at home and abroad, authors synthesized the key effects of rice−fishery co−culture system on crop yields, soil quality, water and gas, combined with the actual situation of rice−fishery co−culture system in China, and made outlooks and suggestions on the future research direction, with the aim of providing scientific support for the sustainable development of rice−fishery co−culture system. The results showed that the rice−fishery co−culture system could increase rice yield and quality, significantly improved soil physicochemical properties and soil microbial activity, and enhanced water quality and reduced CH4 emission. However, the mechanism of influence on N2O was not clear, which was mainly due to multiple factors such as water management, fertilizer management and aquatic animal species.  Related Articles | Metrics | Comments（0）