Table of Content

20 October 2024, Volume 45 Issue 10

Previous Issue   

Cover Download

Article Download

Retrieving Soil Moisture Based on Feature Selection and Genetic Neural Network

LIU Yun-hao, LI Xue-dong, FEI Long, YANG Fu-xiao

2024, 45(10):  1095-1108.  doi:10.3969/j.issn.1000-6362.2024.10.001

Asbtract ( 74 )   PDF (9433KB) ( 62 )  

Related Articles | Metrics

Soil moisture is a significant factor influencing crop growth and a crucial aspect in environmental factors such as hydrology, ecology, and climate. It exerts profound impacts on natural environmental processes. The development and application of remote sensing technology have provided effective means for monitoring regional surface soil moisture. This study primarily utilized Sentinel data as the data source to extract characteristic parameters and construct an input parameter dataset. BP neural network optimized by genetic algorithms was then employed to reconstruct the soil moisture inversion model. The results indicated that 20 characteristic parameters extracted from Sentinel microwave and optical remote sensing images could be used to invert soil moisture content within the study area based on the BP neural network. However, redundant characteristic parameters result in low computational efficiency and longer processing time for the model. To address this, a feature selection algorithm is utilized to reduce the dimensionality of the feature subset. Feature screening was further conducted using the importance scores obtained from XGBoost. Eight optimal feature variables were ultimately determined, which retain the main information of the feature dataset while effectively reducing data redundancy. The inversion results demonstrated an R² value of 0.62 and an RMSE of 0.59%. The network runtime and memory usage were significantly improved compared to the full-feature GA-BP neural network, with an average reduction in runtime of 75 seconds and a decrease in memory usage by an average of 863.86MB. The inversion of soil moisture within the study area throughout the year revealed that July and September had the highest soil moisture content, with a maximum soil weight water content of 38.29% and an average of 14.52%. Conversely, January exhibit the lowest soil moisture content, with a maximum soil weight water content of 15.71% and an average of 12.52%. These patterns closely align with the precipitation trends observed during the year. The results of this study demonstrate that the proposed approach achieves rapid and accurate inversion of large-area soil moisture while maintaining high inversion precision. This study offers a novel approach for combining microwave and optical remote sensing data to invert soil moisture on farmland surfaces.

Simulation of Evapotranspiration in a Well-facilitated Paddy Field Based on Machine Learning Algorithms and Energy Balance Closure

TAI Jiu, WANG Wei, XU Min, HU Ning, CHEN Shang, XU Jing-zheng, HU Xiao-xu, LV Heng, ZHU Zi-han, LAI Yu-jing

2024, 45(10):  1109-1122.  doi:10.3969/j.issn.1000-6362.2024.10.002

Asbtract ( 71 )   PDF (8100KB) ( 91 )  

Related Articles | Metrics

To find the optimal machine learning model for simulating the actual paddy evapotranspiration (ET_a) in each growth stages, and to quantify the impacts of forcing energy balance closure on simulation results, authors firstly analyzed the temporal variations in ET_a and its influencing factors (air temperature T_a, relative humidity RH, wind speed U, vapor pressure deficit VPD, soil moisture at 5cm depth SWC and incident shortwave radiation K_↓) with in-situ observations in a well-facilitated paddy filed in Nantong city, Jiangsu in 2020. Then the ET_a of each growth stages were simulated by two machine learning algorithms: back propagation (BP) neural network and random forest. Finally, the impact of forcing energy balance closure on ET_a simulation by BP model was quantified. The results showed that the relative importance of influencing factors to paddy ET_a differed among growth stages. K_↓ was the most important influencing factor for ET_a, while SWC had negligible effect on ET_a. Therefore, including K_↓significantly improved the ET_a simulation by BP neural network algorithm with correlation coefficient (R) increased by 14.9% and root mean square error (RMSE) reduced by 51.1%. BP1 model containing the five meteorological factors (T_a, RH, U, VPD and K_↓) ranked the best for simulating ET_a before tillering stage, while the BP3 model including T_a, RH, and K_↓ was more suitable after tillering stage. Forcing energy balance closure had improved the simulation performance of the BP neural network algorithm especially before tillering stage. After forcing energy balance closure, the simulation of BP2 model (T_a, RH, U and K_↓) had been improved most obviously among five variable combinations. The R between BP2 simulations and field observations increased by 3.5% and the RMSE reduced by 25.7%. 

Effect of Shelterbelt Structure on Particulate Matter Concentration in Bashang Area

YAN Bing, CUI Yue, FAN Ming-yuan, LI Zhi-xue, SUN Li-bo, CHANG Xiao-min

2024, 45(10):  1123-1130.  doi:10.3969/j.issn.1000-6362.2024.10.003

Asbtract ( 48 )   PDF (2412KB) ( 46 )  

Related Articles | Metrics

 Wind erosion and dust in farmland soil release a large amount of PM₁, PM_2.5 and PM₁₀ into the air, leading to serious air pollution. In this study, a combination of field investigation and semi-fixed monitoring methods were used to measure wind speed and particulate matter concentration at different locations before and after three kinds of shelterbelts, namely tight structure (5667plant·ha⁻¹), transparent structure (3000plant·ha⁻¹) and ventilation structure (950plant·ha⁻¹). The wind-proof effect and particle reduction rate were calculated, the wind-proof effect of different structure shelterbelts was analyzed, and the particle reduction rate of shelterbelts and its influencing factors were explored. In order to optimize the structural parameters of the shelterbelt and strengthen the retarding function of the shelterbelt to PM₁, PM_2.5, PM₁₀ and other particles. The results showed that: (1) the wind protection effect at 1m height of the shelterbelt was: transparent structure shelterbelt (37.95%) > tight structure shelterbelt (32.61%) > ventilation structure shelterbelt (25.67%). The wind protection effect at 2m height of the shelterbelt was: transparent structure shelterbelt (49.66%) > ventilation structure shelterbelt (19.64%) > tight structure shelterbelt (19.38%). (2) The reduction rate of PM_2.5 in tight structure shelterbelt was −22.5%, and the reduction rate of PM₁ in transparent structure shelterbelt and ventilation structure shelterbelt was −41.36% and −18.06%, respectively. (3) Meteorological factors had the greatest influence on PM₁₀ concentration, and there was a significant positive correlation between PM_2.5 concentration and wind speed behind the forest belt (P<0.01).

Current Status and Development Trend of Food Life-cycle Carbon Emissions Research

LIU Chang, SHANG Jie

2024, 45(10):  1131-1145.  doi:10.3969/j.issn.1000-6362.2024.10.004

Asbtract ( 66 )   PDF (5706KB) ( 55 )  

Related Articles | Metrics

The impact of food production and consumption processes on the environment has become increasingly apparent in the context of ongoing economic development and the environment. Especifically, the issue of greenhouse gas emissions in the life cycle of food has emerged as a major challenge in the realm of climate change and ecological balance. A comprehensive analysis of international and domestic research progress on food life cycle carbon emissions can reveal the research trends and frontier hotspots in this area. By conducting searches in English and Chinese literature databases, including the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI), utilizing the scientific knowledge graph and the CiteSpace literature visualization tool, an in-depth analysis and visual representation were carried out. The results showed that research on carbon emissions from food lifecycle began to emerge internationally in 2003, while domestic attention to the issue began in 2009, and both had exhibited steady growth in their respective research activities. While Chinese research had largely emphasized the food production aspect, international research had taken a more holistic approach, encompassing research on both food production and consumption, the entire food system, and the supply chains. In addition, there was a greater emphasis internationally on the management of carbon emissions within food systems and supply chains, whereas Chinese research was more skewed towards the study of regional disparities. In the future, research on carbon emissions in the food lifecycle was likely to place increased emphasis on the equilibrium between food production and consumption, with emerging industries like food delivery services gaining attention. Regional disparities in food lifecycles’ carbon emissions were crucial for the sustainable development of regional food production and consumption. Hybrid life cycle assessment was poised to become a mainstream research method, with future research comprehensively evaluating multiple aspects of food life cycle carbon emissions. The effectiveness of policies is also set to be a key focus of future research aimed at driving carbon reduction in the food life cycle.

Potential Impacts of Climate Change on Rice Yield in Sichuan-Chongqing Area

YANG Man-shan, GUAN Kai-xin, ZHANG Wen-meng, JIANG Chao-yue, LIU Zhi-juan, YANG Xiao-guang

2024, 45(10):  1146-1159.  doi:10.3969/j.issn.1000-6362.2024.10.005

Asbtract ( 74 )   PDF (24714KB) ( 42 )  

Related Articles | Metrics

 The cultivation of the double-cropping systems involving early and late rice was investigated within the Sichuan-Chongqing area, and a panel model was constructed based on historical meteorological data (1981−2020) and rice yield data (1997−2020) to analyze the impacts of future climate (2021−2060, SSP1−2.6 and SSP5−8.5) on rice yield in study area. The results showed that: (1) from 2021 to 2060, the rice yield in most areas of Sichuan and Chongqing would increase by 10.0% to 30.0% compared with the historical yield, and the southern and southeastern areas of Sichuan would decrease by 0 to 10%. (2) Under future climate change, the change of mean climate (precipitation and radiation) and extreme climate (high temperature and drought) would reduce rice yield by 0 to 2.0% in most areas of Sichuan and Chongqing, and the change of extreme state of precipitation would reduce rice yield by 0 to 2.0% in Chongqing and Dazhou. The impact of temperature on yield of early and late rice was very different. The impact of precipitation and radiation on yield of early and late rice was basically the same. During 2021−2060, the temperature, precipitation and radiation in most areas showed an upward trend, while the precipitation in a few areas (Chongqing and Panzhihua) showed a downward trend. While the change of precipitation and radiation, drought and high temperature led to the decrease of rice production in study area. The mean climate of radiation and the extreme climate of drought contributed most to rice yield under future SSP1−2.6 and SSP5−8.5, respectively. Under future climate change, the high and stable yield of rice should be ensured under extreme drought and high temperature events in Sichuan-Chongqing area.

Climate Suitability Evaluation of Jiangsu Spring Tea Growth under the Background of Climate Change

REN Yi-fang, WANG Chun-yi, ZHANG Xu-hui, LI Shi-rui, QIAN Ban-dun

2024, 45(10):  1160-1173.  doi:10.3969/j.issn.1000-6362.2024.10.006

Asbtract ( 61 )   PDF (17517KB) ( 60 )  

Related Articles | Metrics

 Based on the suitable meteorological conditions for tea growth in spring, daily meteorological data from 72 meteorological stations in Jiangsu during 1981 to 2020 were used to construct the daily scale tea climate suitability index by applying the me thod of fuzzy mathematics, and regional division and evaluation of the climatic suitability of spring tea in Jiangsu province were realized. Furthermore, the temporal and spatial characteristics of the climatic suitability of spring tea in Jiangsu in different decade under the background of climate change were analyzed. The results showed that the spatiotemporal variation characteristics of climate suitability for spring tea in Jiangsu province had similarities and differences. The daily scale climate suitability of spring tea in three dominant tea growing regions showed a single peak distribution, and the peak period located from early-April to late-May. With the change of decades, the times when the climatic suitability of tea growth entered the peak period showed an early trend of about 10 days. While, the annual scale climate suitability index of the three dominant tea growing regions reached its peak during the period from 2000 to 2010, and basically showed an increased trend. During different interdecadal, the low mountain tea area around Taihu lake always belonged to the high suitability area, Lianyungang tea area always belonged to the low suitability area, and Ningzhenyang hilly tea area mainly belonged to the medium suitability area. For Jiangsu region, the climate suitability index of spring tea showed the distribution characteristics of "low in the north and high in the south". The interdecadal variation characteristics were obvious, showing an upward trend of 0.005·10y⁻¹. While, climate resources of spring tea growth during the period from 2000 to 2010 were the best during the study period. 

Effect of Deficit Irrigation on Post-harvest Quality of Cut Chrysanthemum “Shenma”

TIAN Pei, JIANG Xiao-dong, ZOU Chun-li, ZHOU Jian-fei, JIA Hao-yu

2024, 45(10):  1174-1182.  doi:10.3969/j.issn.1000-6362.2024.10.007

Asbtract ( 44 )   PDF (589KB) ( 29 )  

Related Articles | Metrics

 In order to research the effect of deficit irrigation on post-harvest quality of cut chrysanthemums, the variety "Shenma" was selected for testing, different irrigation treatments. Referring to the reference evapotranspiration (ET₀) of fresh-cut chrysanthemums, four irrigation treatments, T1 (90% ET₀), T2 (75% ET₀), T3 (50% ET₀), and CK (100% ET₀) were established. A field experiment was conducted in a solar greenhouse at Nanjing Information Engineering University. The flower diameter, fresh weight, water balance value, and lifespan of the fresh-cut chrysanthemums were measured, and the impact of the deficit irrigation treatments on the appearance quality of the fresh-cut chrysanthemums was investigated. The findings indicate that: (1) the moderate deficit irrigation (T2) results in the longest vase life of the fresh-cut chrysanthemums, while the severe deficit irrigation (T3) results in the shortest vase life. (2) The moderate deficit treatment (T2) results in the largest increase in flower diameter, while the severe deficit irrigation (T3) results in the smallest reduction in flower diameter and the fastest wilting rate. (3) The mild deficit irrigation (T1) allows the fresh-cut chrysanthemums to maintain a significant fresh weight. (4) The moderate deficit irrigation (T2) maintains a higher water balance value for the fresh-cut chrysanthemums after vase planting and at the end of their lifespan. Treatment (T2) significantly prolongs the vase life of cut chrysanthemums, increases flower diameter, and enhances water balance value. It has good water retention and appearance quality, and is highly ornamental. Treatment T2 is the optimal irrigation amount for fresh cut chrysanthemums in Nanjing region．

Construction Climate Quality Grade Evaluation Model of Yellow Passion Fruit

LI Li-rong, YANG Kai, LIN Jing, LAN Ya-ping, CHEN Hui-ling, ZHANG Lin

2024, 45(10):  1183-1192.  doi:10.3969/j.issn.1000-6362.2024.10.008

Asbtract ( 53 )   PDF (1038KB) ( 46 )  

Related Articles | Metrics

Based on the data of 131 fruit quality indicators of yellow passion fruit and meteorological data of ten periods before fruit harvesting from 2019 to 2022, the regression relationship model was established by correlation and multivariate stepwise regression analysis, to screen out the key meteorological factors with biological significance of yellow passion fruit. Combination with the comprehensive contribution of individual quality indicators of yellow passion fruit to total fruit quality, the weighted index summation method was used to construct the climate quality index model of yellow passion fruit, which provided a reference for the comprehensive evaluation of the quality grade of yellow passion fruit. The results showed that: (1) the key meteorological factors affecting the quality of yellow passion fruit were the average daily sunshine hours and daily precipitation of 10 days, average maximum temperature of 20 and 30 days, and average daily precipitation of 60 days before havvesting, a total of five indicators. (2) According to the passion fruit quality standards, combined with the measured quality changes and relevant expert evaluation experience, the grade and threshold of the three quality indicators of edible rate, soluble solids and total acid were determined. The edible rate of "extra high" grade was≥50%, the soluble solids were≥17.5%, and the total acid was≤3.0%. The edible rate of "high" grade was between 45%−50%, the soluble solids were between 17.0%−17.5%, and the total acid was between 3.0%−3.5%. The edible rate of "medium" grade was between 40%−45%, the soluble solids were between 16.5%−17.0%, and the total acid was between 3.5%−4.0%.The edible rate of " low" grade was ＜40%, the soluble solids were ＜16.5%, and the total acid was ＞4.0%. (3) The climate quality index (I_Qc) of yellow passion fruit was divided into four grades. The I_Qc气 corresponding to the "extra excellent", "excellent", "good" and "general" grades were≥2.5, 1.5−2.5, 0.5−1.5 and ＜0.5, respectively. A total of 54 independent samples were used to test the climate quality grade evaluation model of yellow passion fruit, and the agreement rate reached 87%. The results had a good effect on the evaluation of the climate quality grade of yellow passion fruit.

Risk Assessment of Agricultural Meteorological Disasters in North China under Warming Environment II：Risk Assessment and Validation of Frozen Damage during Pear Flowering Period in Hebei Province

WANG Jin-chen, ZHU Jun, WANG Li-rong, ZHANG Qi

2024, 45(10):  1193-1203.  doi:10.3969/j.issn.1000-6362.2024.10.009

Asbtract ( 55 )   PDF (4786KB) ( 44 )  

Related Articles | Metrics

Frozen damage during flowering is one of the main meteorological disasters that affect the yield of pear trees in Hebei province. This study analyzed the spatial distribution characteristics of frost damage risk during flowering of pear trees in Hebei province, in order to provide support for the rational layout of local pear trees and the prevention and control of frozen damage risks. This paper selected four indicators from the risk of disaster causing factors, the vulnerability of disaster pregnant environment, the exposure of bearing bodies, and the ability to prevent and reduced disasters to build a risk assessment model of pear tree frozen damage in flowering period in Hebei province. The robustness of the model was tested, and the rationality of the risk assessment results was confirmed by the average yield per unit area of pear trees in 11 cities in Hebei province from 1997 to 2017. As a result, there were three conclusions as follows, (1)the risk of pear trees frozen damage during flowering in the northern part of Hebei province was high, which was related to the high risk and vulnerability of the region, the risk of frozen damage during flowering in the central and southern parts was low, mainly due to the lower risk, vulnerability, and high disaster prevention and mitigation capabilities. (2)When adjusting the indicators and methods of the risk assessment model, the change in the risk ranking of pear trees frozen damage during flowering period at each station was relatively small, with an average ranking change of 6.2, reflecting the good robustness of the model. (3)The rationality of the frost damage risk assessment results was verified using the average annual yield of pear trees. The average difference in risk and yield ranking among 11 cities was 2.0, which verified the rationality of the risk assessment results obtained based on the aggregation method proposed in this study. Establishing a risk assessment model for pear trees frozen damage during flowering period in Hebei province, with stations as the unit, can grasp the strength of frozen damage risk in each station in the study area, formulate frozen damage response strategies according to local conditions, and provide theoretical support and policy recommendations for stable and high yield pear trees in the study area.

Research on Insurance Design and Pricing of Comprehensive Remote Sensing Index of Shaanxi Winter Wheat at Kilometer Grid Scale

CHEN Yan, XUE Zi-yi, WANG Tong, WANG Dong, JI Bian-bian

2024, 45(10):  1204-1215.  doi:10.3969/j.issn.1000-6362.2024.10.10

Asbtract ( 48 )   PDF (7242KB) ( 14 )  

Related Articles | Metrics

Based on the MOD13A2 time series remote sensing data and daily meteorological data from 99 meteorological stations in Shaanxi province, the winter wheat planting area in Shaanxi was extracted using the EVI differencing method. The remote sensing index most correlated with winter wheat yield was selected and combined with meteorological indicators for agricultural meteorological disasters such as late spring frosts, drought, continuous rainy days, and hot dry winds during the winter wheat growth period to construct a comprehensive remote sensing index model to cover the agricultural meteorological disaster risk throughout the entire winter wheat growth period. A comprehensive remote sensing index insurance for winter wheat was designed based on the optimal yield prediction model. Using distribution fitting and Monte Carlo simulation methods, the claim threshold and actuarial pure premium rate for 10770 grid cells of winter wheat comprehensive remote sensing index insurance were calculated, and claim threshold maps and actuarial pure premium rate maps were generated. The results showed that: (1) using the EVI differencing method to extract the winter wheat planting area, different time periods and thresholds were used in different regions to achieve higher accuracy, with a correlation coefficient of 0.997 between the extracted area at the county level and the actual planting area in 2020, with an average absolute error of 524.9ha. (2) The EVI on the 65th and 81st days of 2000−2020 and their maximum values were highly correlated with the single yield of Shaanxi winter wheat, with a highest county-level average correlation coefficient of 0.692. (3) After integrating the meteorological indicators of drought, continuous rainy days, and late spring frosts, the optimal comprehensive remote sensing index model predicted a correlation coefficient of 0.837 between simulated and actual yield, with an R² of 0.602 for the optimal comprehensive remote sensing index model. (4) The risk of winter wheat planting in some areas of Guanzhong and southern Shaanxi was relatively low, with an average yield loss rate of less than 2%, while the risk of winter wheat planting at the junction of the Wei river and the Yellow river was higher, with an average yield loss rate of over 4%, and in other areas, the risk of winter wheat planting ranged between these two extremes. There were significant spatial differences in winter wheat yield and planting risk below the county level. Improving the spatial accuracy of calculating claim thresholds and actuarial pure premium rates can ensure that high-yield and low-yield areas have equal compensation opportunities, avoid moral hazards caused by paying more than actual losses, match rates with actual planting risks, increase rate fairness, enhance the willingness of low-risk areas to be insured, and reduce adverse selection.

Research on the Construction of Knowledge Graphs for Agricultural Meteorological Disasters: A Review

QIU Ming-hui, XIE Neng-fu, JIANG Li-hua, WU Huan-ping, CHEN Ying, LI Yong-lei

2024, 45(10):  1216-1235.  doi:10.3969/j.issn.1000-6362.2024.10.11

Asbtract ( 94 )   PDF (1642KB) ( 132 )  

Related Articles | Metrics

Efficient utilization of massive heterogeneous data is the key factor to enhance the intelligence of agricultural disaster management. Therefore, it is important to explore techniques for constructing multi-source heterogeneous agricultural meteorological disaster knowledge graphs for dynamic monitoring of agricultural meteorological disasters and intelligent management decision making. This paper analyzed the data sources, types, and characteristics required for knowledge graph construction in the agricultural meteorological disaster domain through literature studies and proposed a framework for knowledge graph construction that combined top-down and bottom-up approaches. The paper also examined key techniques and the current application status of knowledge graph construction from the perspective of schema layer construction, entity extraction, relation extraction, and knowledge fusion. In addition, it explored the applications of agricultural meteorological disaster knowledge graphs in the fields of monitoring and early warning, risk assessment, intelligent service, and decision support. It summarized the challenges of constructing agricultural meteorological disaster knowledge graphs and discussed the future development directions. Integrating information from the different modalities could make knowledge graph more comprehensive and accurate in describing and expressing the knowledge and information in the field of agricultural meteorological disasters, which could help to mitigate the losses caused by agricultural meteorological disasters and improve the accuracy and efficiency of decision-making. In the future, agricultural meteorological disaster knowledge graph will be constructed by incorporating large language models, advanced knowledge extraction methods to achieve complex entity and relationship extraction, and multi modal data. Further research is needed to advance the technical study of agricultural meteorological disaster knowledge graph.

Heavy Rains and Floods Impact on Agricultural Product Supply Chain and Countermeasures

CHEN Ning-yuan, ZHANG Xi-cai, NI fang-fang

2024, 45(10):  1236-1246.  doi:10.3969/j.issn.1000-6362.2024.10.12

Asbtract ( 84 )   PDF (371KB) ( 66 )  

Related Articles | Metrics

In the context of global climate change, Chinese mainland often faces the threat of heavy rainfall and flooding, and the impact of different regions is different, posing serious challenges to agricultural production and supply chain management. This paper analyzed the spatiotemporal heterogeneity of China's agricultural product supply chain, including complex origin distribution, seasonal production, diversified types of agricultural products, and the close connection of each link of the supply chain; analyzed the impact of heavy rain and flood disasters on the supply chain from planting to final sales, from farmland waterlogging and soil erosion in the production link, to road damage in the logistics and transportation link, the decline in the quality of agricultural products, and then to the price fluctuation and market uncertainty in the sales link. Post-disaster short-term relief operations and post-disaster long-term recovery and reconstruction measures. It aims to improve the resilience of agricultural supply chains, reduce agricultural losses, ensure the sustainability of agricultural product supply, and provide experience for future disaster risk management.

Report on Agrometeorological Conditions Analysis during Growing Season of Summer Harvest Crops in 2024

ZHENG Chang-ling, GUO An-hong, ZHAO Yun-cheng, ZHAO Xiao-feng, HE Yan-bo

2024, 45(10):  1247-1252.  doi:10.3969/j.issn.1000-6362.2024.10.13

Asbtract ( 56 )   PDF (429KB) ( 53 )  

Related Articles | Metrics

Based on the measured data of about 1800 meteorological observation stations and 295 agrometeorological stations in the main production region of summer grain and oil crops in China from 2023 to 2024, the climate suitable index and disaster index were used to analyze the agrometeorological conditions in the growing season of major summer harvest crops, including winter wheat and rape. The results showed that there were sufficient thermal conditions and sunlight in the main production region during the winter wheat and rape growing season. At the same peirod, precipitation was excessive, while soil moisture conditions were suitable for crop growth and development. In addition, agricultural meteorological disasters such as late frosts, waterlogging damage and drought had relatively light effects. Crop diseases and pests were closely and effectively guarded against, so that they only occurred on a small scale. The weather during maturity stage was clear and favorable for mechanized wheat harvesting operations. The wheat harvest progresses faster than usual and ahead of the previous year （1991-2020）, resulting in a high harvest quality. However, the increase in rapeseed yields had been affected by multiple rounds of rain, snow and freezing damage in winter, periodic cloudy rain in mid spring, and strong convective weather in the middle and lower reaches of the Yangtze river, especially in Hunan and Hubei provinces. Overall, the winter wheat growing season was generally better than the previous with the higher climate suitability, while the meteorological conditions during the rape growing season were close to the 2023.