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20 August 2025, Volume 46 Issue 8

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Research Hotspots and Trends of Agricultural Carbon Emission in China Based on CiteSpace

TAN Hua, YANG Yue

2025, 46(8):  1077-1084.  doi:10.3969/j.issn.1000-6362.2025.08.001

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Based on the China Knowledge Network Literature Retrieval System (CNKI) database, 262 Chinese core literatures related to the field of agricultural carbon emissions were retrieved from 2019−2023, and the research status, hotspots and development trends in the field of agricultural carbon emissions in China were analyzed by using the CiteSpace visualization and analysis software, knowledge mapping. The results showed that the research results in the field of agricultural carbon emissions in China continued to increase. The keywords of carbon emission, low carbon agriculture, carbon neutralization and carbon peaking appeared 43 times, 20 times, 14 times and 13 times respectively, which were the hot spots in the field of agricultural carbon emission in China at present. The total amount of agricultural carbon emissions showed a downward or upward trend, which was mainly affected by the study area as well as different influencing factors, such as agricultural factor inputs, cultivation and livestock development. Under China's “carbon peaking and carbon neutrality” goal, future carbon emission reduction, agricultural science and technology innovation, and multidisciplinary cross−fertilization to promote the optimization of agricultural industrial structure are the future research directions in the field of agricultural carbon emission.

Water Vapor Inversion and Rainfall Prediction Method Based on Harbin CORS Station Network

WANG Hong, LIU Wei-long , YU Bin, XIE Hui-ling, ZHANG Jia-liang

2025, 46(8):  1085-1094.  doi:10.3969/j.issn.1000-6362.2025.08.002

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Three precipitation events in August 2022 in Harbin were selected as the research objects, and the atmospheric precipitable water (PWV) was calculated and retrieved based on the original GNSS observation data provided by the city's continuous operation reference station (CORS). The atmospheric precipitable water of ERA5 reanalysis dataset was calibrated using IGRA2 sounding dataset. The calibration ERA5 PWV was used to verify the inversion accuracy of GNSS PWV. The LSTM model was used to train PWV and analyze the temporal variability of PWV during the precipitation process to provide a reference for improving the monitoring ability of extreme precipitation events in Harbin and formulating countermeasures before disaster. The results showed that: (1) based on the calibration data of Harbin sounding station, the correlation coefficient of IGRA2 PWV and ERA5 PWV was 0.99, the mean deviation of IGRA2 PWV and ERA5 PWV was −0.91mm, the standard deviation was 2.01mm, and the RMSE was 2.20mm. It showed that ERA5 reanalysis data set had higher accuracy in Harbin. (2) The difference between GNSS PWV and ERA5 PWV was generally between ±4.00mm, the maximum mean deviation was −0.19mm, the maximum standard deviation was 1.51mm, and the maximum root mean square error was 1.52mm, indicating that GNSS had high accuracy in PWV inversion. (3) Based on the PWV value of GNSS inversion as the training data source, the RMSE of the training set of the constructed LSTM model was 0.73mm, indicating that GNSS PWV could train the LSTM model effectively. (4) The difference between LSTM PWV and GNSS PWV was generally maintained at ±5.00mm 12h before the precipitation event, and increased from 12h to 24h with the forecast step, and the difference between the two gradually increased to ±20mm. The variation trend of PWV was predicted by LSTM model and conducting short−term predictions of precipitation events within 12 hours was feasible.

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

2025, 46(8):  1095-1110.  doi:10.3969/j.issn.1000-6362.2025.08.003

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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.

Modeling Prediction of Potential Geographical Distribution of Litchi chinensis under the Background of Climate Change

HOU Wei, LI Huan-ling, LI Wei-guang, CHEN Xiao-min, WANG Xiang-he

2025, 46(8):  1111-1123.  doi:10.3969/j.issn.1000-6362.2025.08.004

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Based on global litchi distribution records and environmental variables from different climate scenarios during the base period (1970−2000) and future periods (2050s and 2090s), this study utilized the optimized MaxEnt maximum entropy model to analyze the key environmental variables influencing litchi distribution and predict its potential geographic distribution. The aim was to provide a theoretical basis for the sustainable development of the litchi industry by predicting the impact of climate change on the global distribution pattern of litchi. The results indicated the following: (1) the most important environmental variable affecting litchi distribution was the mean temperature of the coldest quarter (bio11), followed by precipitation of warmest quarter (bio18) and precipitation of wettest quarter (bio16). The values of bio11, bio18, and bio16 at a distribution probability of Q ≥ 50% ranged from 11−22°C, 487−1875mm and 554−2052mm, respectively. (2) Compared to the base period, the main areas of change in litchi’s potential distribution were in China and India. The highly and moderately suitable areas for litchi in different periods were primarily located in the southern regions of China, followed by India and Vietnam, with southern China having the largest highly suitable zone in the world. (3) In the future period, the suitable areas in southern China were projected to expand to higher latitudes. In particular, under the SSP5−8.5 scenario for the 2090s (2081−2100), the high, moderate and slight suitable areas would shift northward by 2° 4° and 6°, respectively, while the suitable area in India will expand westward by 15°. (4) Under the SSP2−4.5 scenario, the total suitable area was expected to increase significantly, especially in the slightly suitable zones. In contrast, under the SSP5−8.5 scenario, both moderate and slight suitable areas were predicted to decrease, with the slightly suitable area reaching its smallest size in the 2090s. However, the area of highly suitable land was projected to peak at 154.6×10⁴ km². (5) The distribution center of litchi was expected to shift toward higher latitudes, with the largest offset and the widest latitude span occurring under SSP5−8.5 in the 2090s. A comprehensive analysis suggests that global warming will lead to the northward expansion of suitable areas for litchi cultivation. However, the increasing frequency of extreme weather events poses significant challenges. The selection and breeding of litchi varieties with broad climatic suitability and high resistance are crucial for the sustainable development of the industry.

Comparison in Traits between Superior Wheat Dryland and Irrigated Varieties under Different Water Conditions

WANG Zhen-zhao, ZHANG Xin-ying, LIU En-ke, GUO Rui, GU Feng-xue, LI Shu-ying, ZHONG Xiu-li

2025, 46(8):  1124-1133.  doi:10.3969/j.issn.1000-6362.2025.08.005

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In this study, the superior dryland and the irrigated varieties were analyzed in term of population yield performance in field under rain−fed and supplemental irrigation conditions, biomass accumulation and physiological features during drought stress, aimed at instruction for genetically improving drought resistance of irrigated varieties. Superior dryland variety Jinmai 47 and superior irrigated variety Shijiazhuang 8 were selected as testing materials. Field population experiment was carried out in three consecutive growing years under two water treatments of rain−fed and supplemental irrigation conditions to compare their yield performance in field. Pot cultivation experiment protected by rain−out shelter was also carried out, with wheat plants subjected to drought stress at jointing stage. Individual plant biomass variation during the period of drought stress and individual plant grain yield after harvest were investigated, and net photosynthetic rate, stomatal regulation, osmotic regulation, and membrane stability regulation, as well as antioxidant system were also determined with stress aggravating. The results showed that: (1) Jinmai 47 obtained higher yield than Shijiazhuang 8 under rain−fed condition, and corresponded to Shijiazhuang 8 in most cases under supplemental irrigation condition, except suffering yield loss due to lodging in wet years. (2) Jinmai 47 accumulated lower biomass during 20d of drought stress than Shijiazhuang 8, while gained higher grain yield than Shijiazhuang 8. The net photosynthetic rate was lower on the 8^th day of stress, but higher after re−watering 3d in Jinmai 47 than in Shijiazhuang 8, indicating the stronger restoring ability of Jinmai 47. (3) Measurements on the 5^th, 10^th and 15^th day of stress showed that stomatal conductance and osmotic potential in the two varieties decreased, but no significant difference between the two varieties. Measurement on the 15^th day of stress showed the membrane ion leakage increased in the two varieties, with the increase magnitude being rather larger in Shijiazhuang 8 than in Jinmai 47, indicating the higher membrane stability in Jinmai 47. (4) The two varieties both enhanced the activity of antioxidant enzymes SOD, POD and CAT under drought stress, but only SOD activity was significantly higher in Jinmai 47 than in Shijiazhuang 8, with no significant difference in POD and CAT activities between the two varieties, indicating that SOD plays an important role in regulating membrane stability of Jinmai 47. In conclusion, under long−last severe drought stress, the superior dryland variety Jinmai 47 reduced photosynthetic rate, while increased antioxidant capacity to maintain cellular membrane stability and obtained higher recovering ability after released from stress. As a result, although the variety accumulated smaller biomass during drought stress at jointing stage, it gained higher grain yield at final harvest, thus demonstrating stronger drought resistance.

Prediction of Apple Climate Yield in Shaanxi under Future Climate Scenarios Using CanESM5 Model

GUO Hua, WANG Xi, LI Liang, WANG Da-fei, WU Xi

2025, 46(8):  1134-1142.  doi:10.3969/j.issn.1000-6362.2025.08.006

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Authors focused on 30 apple producing counties in Shaanxi province, utilizing historical meteorological data and apple yield data (2000−2022) to predict apple climate yields under future climate scenarios (2023−2050, SSP2−RCP4.5 and SSP5−RCP8.5 scenario of the CanESM5 model) using machine learning models. The HP filter method was used to isolate the climate yield of apples. After variable screening through Spearman correlation analysis and grey relational analysis, four machine learning models were trained (i.e., support vector machine model, linear regression model, BP neural network model, and random forest mode). Following model evaluation, the best−performing model was selected for prediction. The results showed that: (1) the annual variation in apple climate yields from 2023 to 2050 was significant, with minor spatial distribution differences. (2) Under the future SSP5−RCP8.5 scenario, apple climate yields were higher, with more significant regional disparities and more drastic trends. Given the impact of future climate change on apple climate yields in different regions of Shaanxi, strategic planning for apple cultivation should be systematically organized, and a scientific and reasonable agricultural policy system should be established to ensure the sustainable, stable, and efficient development of Shaanxi's apple industry in the context of climate change.

Construction and Selection of Flowering Prediction Model for Rape in Jiujiang Area

ZHOU Yan, WU Qiong, YIN Gui-lan , HE Qing, KONG Xiang-sheng

2025, 46(8):  1143-1152.  doi:10.3969/j.issn.1000-6362.2025.08.007

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In order to explore and find a suitable flowering prediction model for rape in Jiujiang area, the observational data of rape and meteorological data from 1995 to 2023, which were obtained from the Hukou County Meteorological Bureau, were used to construct the flowering prediction model for rape based on four methods: the grey correlation analysis method in pre−flowering phenology, the effective accumulated temperature method, the BP neural network and random forest regression. The simulation results of regression (1995−2018) and the prediction verification (2019−2023) for four models were compared. The results indicated that: (1) the grey correlation analysis method in the pre−flowering phenology of flowering prediction model for rape could be established based on the number of days from sowing to flowering, transplanting, budding and bolting, the model using BP neural network and random forest regression could be constructed based on seven meteorological factors, such as the average temperature in February, the sunlight situation in November, etc. (2)Among the effective accumulated temperature models established with different base temperatures and starting times, the model with a base temperature of 5℃ and reporting from the actual bolting had the best fitting effect. (3)Summarized the simulation results of the four models, the effective accumulated temperature method had the best applicability and high stability, which could be applied to the actual meteorological services for the rapeseed flowering period in JiuJiang area. 

Characteristics of Climate Resources Changes at Different Growth Stages of Millet in Shanxi Province and Its Effect on Millet Quality

LI Yan, HUO Zhi-guo, CHANG Qing, YANG Chao, MI Xiao-nan, LI Hai-tao, XU Yun

2025, 46(8):  1153-1164.  doi:10.3969/j.issn.1000-6362.2025.08.008

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Based on meteorological data of 108 national meteorological observation stations in Shanxi province from 1981 to 2021, phenological data of 21 sampling points in the typical planting area, and the quality data from 2019 to 2021, the growth periods and arable land area of millet throughout the province were determined by phenological calculation method and inverse distance weighting method. The distribution characteristics of climatic resources during sowing−jointing, jointing−heading and heading−maturity stage of millet were analyzed, and the influence of meteorological conditions on the quality of millet druing different growing stages were discussed. The results showed that: (1) the suitable area for millet of the heat resources were located in the northeastern part of northern planting area, the western part of Lvliang mountain in the central planting area and the southeastern part of Taihang mountain in the southern planting area. The suitable area for precipitation resources was mainly located in the western Lvliang mountain region of the central planting area. The suitable area for sunshine resources was distributed in Taihang mountain, central and southern planting area. (2) The quality of millet in 2019 was better than that of 2020 and 2021, with moisture content reaching high−quality standards across different planting areas. Amylose level was higher in the central and southern planting areas, while crude fat and protein content were higher in the northern planting area. (3) The excessive rainfall before jointing and excessive sunshine hours after jointing were the main reasons for insufficient protein content in millet in Shanxi. Excessive precipitation during the sowing−jointing period, heading−maturity period, and entire growth period were the primary factors leading to an inappropriate amylose content. 

Variation Characteristics of Climate Suitability for Honeysuckle and Its Relationship with Yield in the Yimeng Mountain Area

LV Xue-mei, LIU Ke-xin, AI Xin, ZHANG Lei, WU Dong-li

2025, 46(8):  1165-1177.  doi:10.3969/j.issn.1000-6362.2025.08.009

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As a geo−authentic medicinal herb in the Yimeng mountain area, honeysuckle (Lonicera japonica Thunb.) exhibits significant economic and ecological value, with its quality and yield being highly sensitive to climatic conditions. Based on meteorological data (1991−2020) from the Pingyi national basic meteorological station, this study developed temperature, water, sunshine hours and comprehensive climate suitability models for the key growth periods of honeysuckle (including sprouting−leaf expansion period, shoot growth period, bud formation period and flower bud enlargement period). Temporal variation characteristics of climate suitability and their correlations with yield were systematically analyzed to identify key determinants governing climatic adaptability of honeysuckle. The findings provided scientific support for addressing geo−authenticity degradation risks under climate change and achieving yield stability and quality improvement. The results were as follows: (1) during 1991–2020, the temperature suitability of the whole growth period for honeysuckle exhibited a significant increasing trend (0.016·10y⁻¹, P<0.05), with the most rapid rise observed during the bud formation period (0.032·10y⁻¹, P<0.01). The flower bud enlargement period demonstrated optimal thermal conditions (0.953±0.045), while frost prevention remained critical during sprouting–leaf expansion period. (2) The water suitability for honeysuckle in the Yimeng mountain area exhibited substantial interannual variability of the whole growth period, with a coefficient of variation (18.9%) significantly higher than that of temperature suitability (5.0%) and sunshine suitability (9.5%). The uneven distribution of precipitation led to suboptimal water suitability in certain years, determining water conditions as the primary limiting factor for honeysuckle production. Crucially, optimized water management during the flower bud enlargement period had been identified as a key determinant for yield stabilization. (3) The sunshine suitability of the whole growth period for honeysuckle in the Yimeng mountain area showed a significant declining trend (−0.030·10y⁻¹, P<0.05), with the most rapid decline occurring during the sprouting−leaf expansion period (−0.077·10y⁻¹, P<0.05), which adversely affected early growth. The flower bud enlargement period exhibited the highest sunshine suitability, promoting photosynthesis and flower bud formation in honeysuckle. (4) The comprehensive climate suitability remained generally stable and exhibited a progressive increase throughout the developmental process. The light, temperature and water conditions during the flower bud enlargement period had been synergistically optimized to support the production of honeysuckle. (5) The meteorological yield of honeysuckle showed a highly significant positive correlation with the comprehensive climate suitability (P<0.001), with the strongest correlation observed during the flower bud enlargement period, followed by the bud formation period. In summary, honeysuckle yield results from the synergistic effects of light, temperature and water conditions. Of these, water availability emerges as the primary limiting factor for yield in Yimeng mountain area, with water suitability during flower bud enlargement period being the key determinant. Secondly, sunshine also has a significant effect on yield. Optimizing water management during the flower bud enlargement period and enhancing comprehensive climate suitability constitute critical measures for improving yield stability in honeysuckle production.

Characteristics and Prediction Model Construction of High Temperature Heat Damage in Changsha Tobacco District of Hunan Province

ZHANG Chao, HUANG Wan-hua, CHEN Zhi-feng, LIU Si-hua, LU Kui-dong

2025, 46(8):  1178-1191.  doi:10.3969/j.issn.1000-6362.2025.08.010

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Using the daily maximum temperature during June and July from 1961 to 2020 at four national meteorological stations in Changsha tobacco district of Hunan province, the characteristics of high temperature heat damage during the maturation period of tobacco in Changsha tobacco district were analyzed by using M−K test, ROC curve and kernel density estimation methods. High temperature heat damage forecast models in Changsha tobacco district were constructed to provide early warning for decision−making service. The results showed that: the average high temperature days were 15.2 days per year, with an overall increasing trend from 1961 to 2020, averaging an increase of 1.0 days per decade. High temperature days start as early as June 1, with an average start date of June 13 at the 80% guarantee rate. On average, heat−damaged processes occured 0.9 times per year. The earliest occurrence date of high temperature heat damage was June 14, and the occurrence dates of high temperature heat damage increased significantly after July 1 (P<0.05). The average starting date for the 80% guarantee rate was July 5. The heat damage of high temperature was mainly mild, accounting for 86.3% of the entire mature period. The high temperature days during the ripening period of flue−cured tobacco in the eastern tobacco region of Changsha were 3.9 days longer than in the western tobacco region, and the average start date of the 80% guarantee rate was 7 days earlier than in the western tobacco region. The high temperature damage process occurred 0.3 times more often than in the western tobacco region, with the average start date of the 80% guarantee rate being 5 days earlier. The high temperature heat damage threshold model for Changsha tobacco district based on accumulated heat temperature had a good classification effect, with an AUC value of the ROC curve reaching 0.94. The classification threshold of model heat accumulation temperature was 4.6℃·d. The accuracy of predicting high temperature heat damage was close to 90%, and the start and end time of the prediction process, as well as the impact range, were consistent with the actual situation. The models were well−suited and could be applied to the monitoring and early warning services for flue−cured tobacco high temperature disasters, providing technical support for disaster prevention and mitigation. The probabilistic model of high temperature damage was established as an effective complement to the threshold model.

Temporal and Spatial Variations of Drought in the Yellow River Basin from 1980 to 2020

GU Yang-yang, ZHAO Wen-ji, WU Shu-qi

2025, 46(8):  1192-1205.  doi:10.3969/j.issn.1000-6362.2025.08.011

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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.

Impact Mechanism and Moderating Effects of Agricultural Insurance on Food Security Under Climate Change

AN Min, MA Quan, WEI Ya-qian

2025, 46(8):  1206-1220.  doi:10.3969/j.issn.1000-6362.2025.08.012

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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.

Report on Weather Impacts to Agricultural Production in Spring 2025

LI Xuan, ZHANG Yan-hong, WU Men-xin, TAN Fang-ying, ZHAO Yun-cheng

2025, 46(8):  1221-1224.  doi:10.3969/j.issn.1000-6362.2025.08.013

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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.