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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
    Abstract1082)      PDF(pc) (16028KB)(203)       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 nonsummer zone regions, and the nonsummer 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 nonsummer regions to fourseason regions. The starting dates showed a significant advance of 1d·10y1 in spring over most of the NEC region, and a significant advance of 2−3d·10y1 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 nonsummer 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·10y1 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.

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    Report on Meteorological Condition Impact to Agricultural Production in Winter of 2024/2025
    HE Yan-bo, WU Men-xin, ZHAO Xiao-feng, GUO An-hong, LI Sen, HOU Ying-yu
    Chinese Journal of Agrometeorology    2025, 46 (5): 737-740.   DOI: 10.3969/j.issn.1000-6362.2025.05.014
    Abstract995)      PDF(pc) (310KB)(253)       Save

    During the winter of 2024/2025 (December 2024–February 2025), China’s national average temperature was −3.3°C, 0.3°C above the long-term average (1991–2020, hereinafter referred to as perennial). Spatially averaged total precipitation was 23.7mm across the nation, 39.4% less than the perennial , while average sunshine duration reached 519h, 6.1% more than the perennial. In most agricultural regions, favorable light and heat conditions prevailed during the winter, and suitable soil moisture supported the safe overwintering of winter wheat in northern China and the vigorous growth of rapeseed and other crops in southern China. However, persistent snow cover in parts of northern China had posed challenges for livestock farming and conservation protected agriculture. In additionally, the lateseason snowmelt and freezethaw cycle disrupted grain storage and transportation in northeast China. In eastcentral of south China, a prolonged precipitation deficits had led to mild to moderate drought, negatively affecting field crops and economic fruit trees. In the latter part of winter, periodic cold and rainy weather in eastern of southwest China, southern Yangtze, and western of south China hampered the steady growth of rapeseed and openfield vegetables.

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    Development of a Growth Conditions Dataset of Major Crops in China (V2.0)
    GAO Jing, LIAO Jie, YANG Bing-yu, LIU Yuan-yuan
    Chinese Journal of Agrometeorology    2025, 46 (5): 725-736.   DOI: 10.3969/j.issn.1000-6362.2025.05.013
    Abstract720)      PDF(pc) (6104KB)(482)       Save

    A dataset of the growth conditions of major crops in China was mainly constructed from paper-based annual records before 2012 and electronic annual records after 2013. However, there were problems such as inconsistencies in the observed items and data unitsthe quality of some data had not been evaluated. To improve the consistency and accuracy of agricultural meteorological data, based on these two types data, a high-quality dataset of the growth conditions China's major crops (including wheat, rice, maize, cotton, oil-seed rape, soybean and peanut) from 1981 to 2022 was developed by using the observation items standardization, integrity checks, cross-year value checks, observation time checks, value range checks, internal consistency checks element limit value check and manual verification. The dataset promoted effective application in agricultural research and decision-making. The results showed that the valid rate of crop common stage from 1981 to 2022 was over 96.0% of the expected observations, while the valid rate for growth status, crop height, stem count and effective stem count were all over 86.0%. The accuracy rate of the above five mentioned elements were above 99.3%. The distribution of observation stations for the seven major crops had obvious spatial and temporal distribution characteristics, with dense stations, uniform spatial distribution and long observation years in eastern China, but sparse and short observation years in northwest China. There were also obvious differences in the number of observation stations between different crops, and the number of observation stations for cotton and oil crops were less than that for staple crops. The valid data was relatively low in the 1980s, but improved significantly after 1994. After quality control and data verification, the valid rate of crop common stage increased from 94.7% to 96.2%, the crop height increased from 88.2% to 92.0%, the stem count increased from 77.1% to 86.7%. The accuracy rate of the common stage data increased from 99.3% to 99.6%. Compared to the "China Major Crops Growth and Development Dataset V1.0"the overall quality of this dataset has been improved, with the addition of element boundary value checks. This dataset can provide critical fundamental information for studying the impact of climate change on the growth and development of major crops in China.

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    Microbial Seed Coating Promote Wheat Seed Germination and Seedling Growth under Drought Stress Condition
    JIANG Ya-wen, XIE Wen-yan, HE Jiu-xing, GONG Min, HUO Qiu-yan, YANG Xi, HAN Wei, LV Guo-hua
    Chinese Journal of Agrometeorology    2025, 46 (5): 609-618.   DOI: 10.3969/j.issn.1000-6362.2025.05.002
    Abstract511)      PDF(pc) (1365KB)(328)       Save

    Drought stress at the germination and seedling stages is a key factor in reducing crop yields in arid and semiarid areas. Seeded surrounding microenvironment regulation is one of the important technical measures to improve crop drought resistance. In this paper, wheat (Jimai 22) was selected as the experimental variety, while Bacillus subtilis ACCC 19742 and Bacillus magaterium ACCC 04296 were chosen as the experimental strains. The bacteria were encapsulated using microencapsulation. Wheat seeds coated with bacterial microcapsulation were investigated for seed germination and seedling growth under drought stress. Four treatments were established: Bacillus subtilis coatied (M), Bacillus megaterium coatied (B), Bacillus subtilis and Bacillus megaterium compound coatied (MB), and uncoated treatment (CK). The results showed that the compound bacterial coating had the best effect than the single-strain coatings. The ratio of emergency, above ground dry weight and root-shoot ratio were significantly improved, increased by 12.8 percentage points, 17.8% and 5.3% compared with M treatment, while 15.3 percentage points, 14.7% and 5.7% compared with B treatment. Compared with CK treatment, ratio of emergency was increased by 25.9 percentage points, above ground dry weight increased by 21.8%, root-shoot ratio increased by 9.8%, and total root length, surface area and total volume increased by 37.5%, 34.7% and 84.3% respectively. The activity of superoxide dismutase (SOD), peroxidase (POD), catalase from micrococcus lysodeikticus (CAT) significantly increased, and the content of malondialdehyde (MDA) decreased, but the content of proline (PRO) obviously increased. Fluorescence parameters Fv/Fm and ΦPSⅡ were both larger than that of CK treatment, which showed that coating with composite bacteria could improve the drought resistance significantly. In summary, seed coatied with bacterial microencapsulation can promote seed emergence, root growth, and stress-tolerant enzyme activity in drought stress conditions to increase drought tolerance. Moreover, a compound microbial coating is the optimal method.

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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
    Abstract449)      PDF(pc) (6056KB)(191)       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. 

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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
    Abstract442)      PDF(pc) (11434KB)(220)       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 doublecropping 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 doublecropping rice significantly decreased. From 1981 to 2022, the whole growth periods of spring wheat, winter wheat and late doublecropping 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 singleseason rice, early doublecropping 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 doublecropping 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 singleseason rice and early doublecropping 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 sowingtoemergence 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 sowingtotillering 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 singleseason rice, early doublecropping rice, and late doublecropping rice). The duration from the threeleaf stage to transplanting had the highest correlation coefficient with sunshine duration, while during this stage, the correlation coefficient between precipitation and both early doublecropping rice and late doublecropping 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 sevenleaf stage to the silking stage.

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    Characteristics of Drought Changes and Risk Analysis in Zhaotong Apple
    ZENG Ting-yu, LU Xing-kai, GAO Ying-ming, YAO Mu-chen, HE Juan, ZHOU Yong-sheng, ZHANG Xiu-ying, SUN Dong-han
    Chinese Journal of Agrometeorology    2025, 46 (5): 628-639.   DOI: 10.3969/j.issn.1000-6362.2025.05.004
    Abstract435)      PDF(pc) (5302KB)(878)       Save

    This study is based on the phenological data of Zhaotong apple from 2010 to 2023, the meteorological data and drought disaster data of Zhaotong from 1960 to 2023, and divided the phenologicals into six growth stages, including dormancy, bud, anthesis, fruitlet, expansion and harvest stage, and used the crop water deficit abnormal index (CWDIa) to analyze the drought frequency and duration of each growth stage, used the cumulative drought intensity (CDI) to identify the drought risk intensity, used the Morlet complex continuous wavelet transform(CCWT) to explore the timefrequency evolution characteristics of cumulative drought intensity, so as to analyze the occurrence law of drought risk in Zhaotong apple. The results showed that drought in the apple growing region of Zhaotong was characterized by a pattern of frequency, seasonality, suddenness, severity and subseasonality. The drought in each development stage followed as: there was a large difference in the frequency of occurrence, fruitlet (55y)>harvest and dormancy (49y)> anthesis (20y)> bud(19y)> expansion(8y), and the extreme drought were most likely to occur at the end of the fruiting period and during late harvests. The duration of drought was between 14 tendays, and there were significant differences in CDI, with the mean of fruitlet(335.0%)>dormancy (172.7%)>bud(137.7%)>harvest(137.1%)>anthesis(68.1%)>expansion(8.0%). The thresholds for the classification and identification of drought risk intensity were different, sensitivity to drought stress of stage was expansion>anthesis>harvest> fruitlet>bud>dormancy. The risk of severe drought disasters was seasonal and the fruitlet>bud>harvest>expansion>anthesis>dormancy. Drought risk had shown multi-scale periodic, phased and abrupt changes, with a general trend of increasing drought risk since 1991. 

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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
    Abstract407)      PDF(pc) (1951KB)(1392)       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.

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    Temporal and Spatial Evolution Analysis of Net Carbon Sink from Cultivated Land Utilization at the County Level in Henan Province
    MA Wen-bo, ZENG Li-yuan
    Chinese Journal of Agrometeorology    2025, 46 (5): 593-608.   DOI: 10.3969/j.issn.1000-6362.2025.05.001
    Abstract383)      PDF(pc) (12345KB)(165)       Save

    Drawing on statistical data spanning from 2006 to 2020, four key aspects were analyzed in this study: inputs of agricultural production materials, soil conditions, carbon emissions from wheat and rice cultivation, and carbon sequestration by crops. The carbon emission coefficient method was used to calculate the net carbon sink of cultivated land use across 104 counties in Henan province, and its spatiotemporal distribution characteristics were examined. The findings offer scientific insights for the low-carbon transformation of cultivated land use and the pursuit of carbon peaking and neutrality goals in Henan province. The results indicate that: (1) carbon emissions from cultivated land use in Henan province initially rose and then declined, while carbon absorption increased steadily, leading to a fluctuating increase in net carbon sinks. Notably, chemical fertilizers emerged as the primary carbon source. Compared to 2006, by 2020, most counties in Henan province experienced growth in carbon emissions, carbon sinks, and net carbon sinks, with respective county proportions of 65.4%, 78.9%, and 77.9%. In eastern Henan led in increments of carbon emissions, carbon sinks, and net carbon sinks, while northern Henan showed a faster growth rate in carbon emissions. In southern Henan, on the other hand, exhibited significant growth rates in carbon sinks and net carbon sinks. (2) In terms of spatial distribution, the net carbon sink of cultivated land use in Henan province displayed a pattern of “higher in the east and lower in the west.” Spatial agglomeration was evident, with notable regional differences. However, low−value areas of net carbon sinks were gradually transitioning towards high−value areas, indicating a trend of narrowing regional disparities. Most counties fell into the category of moderate net carbon sink areas. Counties exhibiting homogeneity in net carbon sink values accounted for over 95% of the aggregated counties. The center of gravity for net carbon sinks was situated in Yanling county, with a tendency to shift eastward. (3) Natural conditions, including climate, soil, and terrain, as well as national policies, influenced the crop planting structure, the level of agricultural mechanization, and the input of agricultural materials, thereby impacting carbon emissions and carbon absorption from cultivated land use. In the future, crop carbon sinks should be integrated into the decision−making framework for crop planting structure adjustment in Henan province. Efforts should also continue to reduce and enhance the efficiency of chemical fertilizers. Additionally, increasing investment in agricultural machinery technology innovation in Henan province and fostering inter−regional agricultural technology exchange and cooperation will fully harness its potential for emission reduction and carbon sequestration enhancement, ultimately promoting green agricultural development.

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    Research on Weather Index Insurance Pricing of Apple in Qingyang, Gansu Province Based on XGBoost Method
    LI Jin-rong, XIAO Hong-min
    Chinese Journal of Agrometeorology    2025, 46 (5): 715-724.   DOI: 10.3969/j.issn.1000-6362.2025.05.012
    Abstract381)      PDF(pc) (1017KB)(339)       Save

    Weather index insurance based on machine learning algorithms represents a significant innovation in agricultural insurance research. Since crop yields are primarily influenced by weather-related disasters, developing a robust data analysis model that accurately captures the relationship between yield losses and adverse weather conditions is crucial for pricing crop weather index insurance. This paper focuses on Qingyang apples in Gansu province, utilizing daily precipitation and temperature data during the growing season (April-October) and apple yield data from five counties (or districts) in Qingyang city spanning 1996–2020. Indices of low−temperature freezing, drought and continuous cloudy rainfall were constructed, and a regression model linking these indices to the meteorological yield reduction rate of apples was established using the XGBoost algorithm. The kernel density estimation method was applied to determine the pure rate of weather index insurance for apples in Qingyang. The findings of the study were as follows: (1) meteorological disasters caused significant fluctuations in the apple cimate yield reduction rates across counties (or districts) in Qingyang city. A nonlinear relationship was observed between the cimate yield reduction rate and seven types of apple disaster weather indices. (2) Regression models for the climate yield reduction rate-weather indices in Ning county, Qingcheng county, Zhengning county, Huan county, and Xifeng district (1996–2020) were constructed using the XGBoost algorithm. These models demonstrated superior fitting accuracy compared to multivariate stepwise regression models, with coefficients of determination (R²) improving by 0.157, 0.125, 0.190, 0.115 and 0.117, uhile root mean square errors (RMSE) decreasing by 0.045, 0.026, 0.335, 0.126, and 0.039 percentage points, respectively. (3) The climate yield reduction rate payout triggers for apple weather index insurance were 11.88%, 3.37%, 4.33%, 9.21%, and 17.70% in Ning county, Qingcheng county, Zhengning county, Huan county, and Xifeng district, respectively. The corresponding pure insurance rates were 4.00%, 3.64%, 4.91%, 1.94% and 4.98%.  

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    Research Progress of Crop Water and Fertilizer Management Decision-making Models Based on Bibliometrics
    CAI Meng-ting, SU Zhen-juan, ZHANG Peng, LIU Xue-zhi , XU Li-gang, YANG Hao, WANG Jiao, LIU Wei-lun
    Chinese Journal of Agrometeorology    2025, 46 (5): 704-714.   DOI: 10.3969/j.issn.1000-6362.2025.05.011
    Abstract377)      PDF(pc) (4342KB)(741)       Save

    Based on the Web of Science (WoS) core database and the China National Knowledge Infrastructure (CNKI) database, this paper retrieved the literature related to crop water and fertilizer management decision-making models, and analyzed the changes in the number of publications, publishing country and keywords of the papers in this field from 2003 to 2023, as well as the research progress of crop water and fertilizer management decision- making models at home and abroad, with the help of CiteSpace and VOSviewer visualization software, and understood the research status and trend of crop water and fertilizer management decision-making models. The results indicated that the overall publication volume of crop water and fertilizer management decision-making model research had shown an upward trend from 2003 to 2023. The main research focus on water and fertilizer management, model optimization, etc., and the United States and China dominated the research in this field. The main keywords in publications from 2003 to 2023 concentrated on nitrogen, water, management as well as water−fertilizer coupling, yield, water and fertilizer integration, respectively. Currently, the widely used crop water and fertilizer management decision−making models included APSIM, DSSAT, RZWQM, and AquaCrop. With the advantages of modularity and soil−centered design, APSIM model could accurately assess the dynamic changes in soil moisture and nutrients in agricultural land by setting up different scenarios of crops, soil types and climatic conditions, etc., thus providing important support for the development of more rational agricultural water and fertilizer management strategies. Integrating the current research progress in crop water and fertilizer management decision making, future research should pay more attention to agro-ecological effects and combine with new remote sensing technologies to establish, optimize or couple crop models to provide technical support for the assessment of agricultural water and fertilizer dynamics and the efficient use of resources.

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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
    Abstract368)      PDF(pc) (8259KB)(166)       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·y1 (P<0.01), and mild or above drought occurred 30% of the time in spring. In autumn of 1997, the detection of SPEI3 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·y1 (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 24 month lag in drought in the Yellow river basin through a 360 month resonance. The bimodal effect of Pacific Decadal Oscillation (PDO) caused drought to advance or lag by 38 months. ENSO multi−cycle alternation caused drought to lag by 39 months or advance by 1218 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 115 months. In summary, the periodic fluctuations of multiscale 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.

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    Meteorological Conditions Impact on Tobacco Target Spot Disease in Tianzhu County and Simulation Model Construction
    TANG Pi-ru, SUN Si-si, ZENG Xiao-shan, LIU Qiang, CUI Lei, YANG Yan
    Chinese Journal of Agrometeorology    2025, 46 (5): 652-659.   DOI: 10.3969/j.issn.1000-6362.2025.05.006
    Abstract361)      PDF(pc) (561KB)(182)       Save

     In recent years, climate change has had a significant impact on agricultural ecosystems, particularly on crop diseases. To further understand the effects of early meteorological factors on tobacco target spot disease, this study collected data of tobacco target spot disease index and diseased plant rate in the Tianzhu county Pingpu city tobacco region from 2022 to 2023, collected early meteorological data to analyze correlations between the tobacco target spot disease index, diseased plant rate and the meteorological factors affecting them, the key factors were screened. The support vector machine (SVM) model and multiple regression models was established to simulation model of the tobacco target spot disease and validate, respectively. The results showed that: (1) the initial outbreak of tobacco target spot disease in the tobacco-growing area of Pingfu village, Tianzhu county, Guizhou province was from the end of May to the first ten days of June. This was followed by a fluctuating increase in both disease index and disease incidence, culminating in a peak period of incidence in midJuly. (2)The key meteorological factors influencing tobacco target spot disease include the average ground temperature 15 days prior to the disease survey date, the cumulative precipitation 30 days prior, and the average relative humidity 15 days prior. These factors showed a significant positive correlation with both the disease index and disease incidence rate of tobaccotargeted endemic diseases.. Specifically, higher soil temperatures, greater precipitation, and increased relative humidity 1530 days prior to the date of disease investigation were associated with more severe outbreaks of tobacco target spot disease and a faster field transmission rate. (3) Based on the aforementioned key meteorological factors, a multiple linear regression model and an SVM model for tobacco target spot disease were established. The average fitting degrees (R2) of the two models were 0.95 and 0.93, respectively, indicating good simulation results. Upon testing, it was found that in the simulation of disease index, the average accuracy of the multiple linear regression model was 87%, higher than that of the SVM model, which was 75%. In the simulation of disease plant rate, the average accuracy of the multiple linear regression model was 80%, higher than that of the SVM model, which was 73%. The simulation results of the multi linear regression model outperform those of the nonlinear SVM model, indicating that the multilinear regression model is better suited to model the occurrence and development of tobaccotargeted scrofula. 

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    Advance in Operational Technology of Yield Forecasting in National Meteorological Centre in Recent 10 Years
    LIU Wei, ZHENG Chang-ling, SUN Shao-jie, QIAN Yong-lan, SONG Ying-bo
    Chinese Journal of Agrometeorology    2025, 46 (5): 694-703.   DOI: 10.3969/j.issn.1000-6362.2025.05.010
    Abstract350)      PDF(pc) (630KB)(437)       Save

    In recent 10 years, the dynamic and refined yield forecast have been promoted accompanied with the development of the agrometeorological observation technology, the remote sensing monitoring technology, crop model simulation technology, and the application of intelligent grid meteorological. All these have improved the accuracy of yield forecast and played an important role to ensure national food security. In this paper, from the perspective of the technical progress of crop yield forecasting and the test of forecast results in National Meteorological Center over the past decade, the statistical models based on key meteorological factors, meteorological influence index, climatic suitability index, multi model integrated forecasting, as well as the crop dynamic yield forecasting technology based on crop model simulation and multi-source data fusion, are systematically introduced. The forecast results of early rice in the main producing provinces in 2020 and in different periods in Fujian province showed that the accuracy of different mathematical statistical forecasting models was generally quite close to each other, ranging between 90.8% and 99.8%, and the climatic suitability index  outperformed the other two methods. The results of the forecast of the main single rice-producing counties in Jiangsu province indicate that the county scale yield forecasting accuracy based on the climate suitability index method was generally high. Specifically, the July 20 forecasts exhibited accuracy rates between 73.9% and 98.1%, while the August 20 forecasts showed rates between 80.4% and 98.3%. The impact index based on daily meteorological data, to a certain extent, can quantitatively assess the effect of meteorological conditions on crop yields at different time scales. Crop Growth Simulating and Monitoring System in China constructed by using different crop models could carry out county-level and provincial-level yield forecasting of different crops, and the forecast accuracy was relatively stable. The accuracy rates for different initial forecast dates were consistently maintained between 88.4% and 97.4%, while Shandong and Hebei province exhibited higher rates than those in other provinces. It is feasible to carry out yield forecast at national level based on the observed yield series and the new yield series could provide new data support for yield forecast in National Meteorological Centre. The county-level yield forecast based on remote sensing data and machine learning has good prediction accuracy, which can improve the technical of yield forecasting. The adoption of suitable yield prediction methodologies can significantly enhance forecast accuracy for diverse crops in various provincial regions.

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    Prediction of Climatical Suitable Areas for Rape in China Based on Optimized MaxEnt Model
    ZHAO Chen-yu, ZHANG Fang-min, YIN Si-yi, CAO Wen
    Chinese Journal of Agrometeorology    2025, 46 (5): 660-668.   DOI: 10.3969/j.issn.1000-6362.2025.05.007
    Abstract344)      PDF(pc) (7044KB)(163)       Save

    This study optimized the parameter settings of the MaxEnt model by using the ENMeval package in R, selected dominant climatic factors based on the data of 255 rape sample points of China and 19 climatic factors, and further predicted the distribution and change characteristics of climatical suitable areas for rape in China under the climate change scenarios for historical period (1970−2000) and future period (2041−2060) by optimized MaxEnt model. Results indicated that: (1) the optimal parameter setting of MaxEnt for rape in China was a linear combination of Linear, Quadratic, Hinge, Product and Threshold functions with a regularization multiplier of 4.0. This setting achieved the highest simulation accuracy. (2) The dominant climatic factors affecting the distribution of rape climatical suitable areas were minimum temperature of the coldest month, mean temperature of the wettest quarter, and precipitation of the driest month. (3) During the historical period, the low climatical suitable areas were mainly located in the western regions, central Inner Mongolia and Liaoning. The medium and high climatically suitable areas were primarily distributed in the central and eastern regions of China. Compared to the historical period, the future changes in climatical suitable areas were mainly reflected in the transition of unsuitable areas to low suitable areas, low suitable areas to medium suitable areas, and medium suitable areas to high suitable areas. The climatical unsuitable areas will decrease, the climatically suitable areas will increase, and the low, medium, and high climatical suitable areas will expand northward.

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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
    Abstract339)      PDF(pc) (11886KB)(163)       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.
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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
    Abstract324)      PDF(pc) (13267KB)(87)       Save

    Based on the 19812018 China meteorological forcing dataset, the standardized precipitation evapotranspiration index (SPEI) was calculated for different time scales. The Mann−kendall (MK) 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 nonsignificant trend of aridification, with the proportion of droughtaffected area decreasing at a rate of 0.845 percentage points per decade in a non−significant manner. Both spring and summer climates showed a nonsignificant 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 SPEI3 in Xinjiang was regionally distinct. Significant droughtaffected 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 nonsignificant 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 nonsignificant drought trend. (3) The spatial distribution of drought frequency at monthly, seasonal, and annual scales was relatively consistent, with the eastern region being a highfrequency 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 20002009, 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.

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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
    Abstract323)      PDF(pc) (3822KB)(915)       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 cuttingpreprocessing and data enhancement techniques. Six representative deep learning network models, namely 18-layers residual networkResNet18, 50-layers residual network variantResNet50_vd, lightweight convolutional networkMobileNetV3_large, lightweight convolutional networkPPLCNet, deep convolutional residual networkXception41, and densely connected convolutional networkDenseNet121were 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

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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
    Abstract321)      PDF(pc) (6167KB)(579)       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.

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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
    Abstract316)      PDF(pc) (560KB)(919)       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.

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