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.

Drought stress at the germination and seedling stages is a key factor in reducing crop yields in arid and semi−arid 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 F_v/F_m 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.

Based on the observation data of single-season rice phenology, daily average temperature, maximum temperature, minimum temperature, sunshine duration and other meteorological data in 51 mid−altitude areas of Guizhou province from May to September 1991 to 2020, the key climatic factors of the initial period of six stages were determined by using factor analysis and correlation analysis method, including green turning stage, tillering stage, jointing−booting stage, head−flowering stage, filling−ripening stage and harvest stage. In order to explore a simple and easy to operate method for simulating the initial period of single-season rice phenology, multiple linear regression method was applied to construct a simulated model for single-season rice phenology with the above data. The model was verified in observation data of the initial period of single-season rice phenology in 25 experimental stations in Guizhou from 2016 to 2021, as well as temperature and light factors during the same period. The results showed as follows: the meteorological factors that were significantly correlated with the initial period of single-season rice phenology were daily average temperature and daily sunshine duration. There was significant negative correlation between single-season rice phenology and daily average temperature and daily sunshine duration in the first 10 days and the first 20 days, and the correlation coefficients were −0.70 to −0.51 and −0.63 to −0.42, respectively. The correlation coefficient between head-flowering stage and daily average temperature was −0.70 as the largest, and the correlation coefficient between jointing-booting stage and daily sunshine duration was −0.42 as the smallest, all of which passed the 0.01 significance level test, which indicating that the higher temperature and the more sunshine duration before each single-season rice phenology, the shorter the length of each rice phenology and the easier earlier date of each rice phenology. The average coefficient of determination R² of the simulated models of the initial period of single-season rice phenology in the middle altitude area of Guizhou was between 0.88 and 0.94, and except tillering stage and jointing−booting stage passed 0.05 significance level test, other growth stages passed 0.01 significance level test. The observation data of 25 experimental spots in Guizhou was given to verify the model. And when the absolute error was from 3 to 4 days, it was defined as reaching the basic-accurateness level. The simulation accuracy of green turning stage and harvest stage reached above basic-accurateness level was 100%, and that of tillering stage, jointing stage and heading and flowering stage was 83.3%, and that of filling and ripening stage was 100%, which showed that the simulated model could effectively simulate the initial period of rice phenology of the mid-altitude area in Guizhou. It provided scientific and technological support for the meteorological service of rice production in the mid-altitude area of low latitude plateau.

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 time−frequency 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 sub−seasonality. 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 1−4 ten−days, 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. 

Revealing the effects of water and nitrogen coupling at different growth stages on yield and water productivity of potato in agro-pastoral ecotone has great significance for the efficient utilization of water and fertilizer of potato. In this study, soil data, meteorological data and management data from Zhangbei and Wuchuan in the northern of agro-pastoral ecotone were used to drive the calibrated APSIM−Potato model. Different gradients were set for irrigation and nitrogen fertilizer, respectively, irrigation gradients were 0, 60, 120, 180, 240mm, and nitrogen application amounts were 0, 50, 100, 150, 200, 250, 300kg·ha⁻¹, respectively. Two growth stages of seedling stage, tuber formation stage and tuber expansion stage were selected as the combination of irrigation and nitrogen fertilizer application, with the same amount of irrigation and N fertilizer applied for the both two stages. The effects of water and nitrogen coupling on potato yield, water consumption and water productivity at different growth stages were analyzed the optimal water-nitrogen coupling scheme was recommended based on the highest yield and the highest water productivity respectively. The results showed that the potato yield of Wuchuan and Zhangbei stations were 12700−25600kg·ha⁻¹ and 14200−24900kg·ha⁻¹ under different water-nitrogen coupling scenarios at different growth stages. Under the scenario of maximum yield, the combination of potato tuber formation stage and expansion stage was selected; the irrigation amount and N fertilizer amount were 90mm and fertilized 75kg·ha⁻¹ for the formation stage and expansion stage at Wuchuan, and the amounts were 60mm and 75kg·ha⁻¹ for the formation stage and expansion stage at Zhangbei, and the highest yields were 25600kg·ha⁻¹ and 24900kg·ha⁻¹ for the two stations. The potato water productivity of Wuchun and Zhangbei was 5.3−9.0kg·m⁻³ and 5.9−8.5kg·m⁻³. Under the scenario of maximum water productivity, two growth stages of potato tuber formation stage and expansion stage were selected; Wuchuan irrigated 90mm and fertilized 50kg·ha⁻¹, Zhangbei irrigated 60mm and fertilized 50kg·ha⁻¹ for the formation stage and expansion stage, and the highest water productivities were 9.0kg·m⁻³ and 8.5kg·m⁻³. 

 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 mid−July. (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 tobacco−targeted endemic diseases.. Specifically, higher soil temperatures, greater precipitation, and increased relative humidity 15−30 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 tobacco−targeted scrofula. 

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.

Assessing the responses of crop growth to elevated CO₂, irrigation and nitrogen restriction in the latest version of the Community Land Model (CLM5) is crucial for the further development of its crop module. In this study, spring wheat observations from the pioneer Free-Air CO₂ Enrichment Experiment Site (FACE Experiment) located in Maricopa, Arizona, USA were used to validate the response of the CLM5 model spring wheat growth to elevated CO₂ concentration, irrigation and nitrogen fertilization, as well as the interactions among them. The results showed that: (1) after parameter calibration, the CLM5 model was able to reasonably simulate the seasonal growth of spring wheat, but there was a certain degree of bias, with an overestimation in simulating above-ground biomass by 92.0g×m⁻², and an underestimation in modeling grain yield by 39.0g×m⁻². (2) Elevated CO₂ concentration promoted spring wheat growth and yield enhancement. Under increased CO₂ conditions, the observed yield increased by an average of 14.3%, while the simulated yield showed an average increase of 22.7%, indicating a certain degree of overestimation in the model compared to actual observations. (3) Irrigation and nitrogen limitations were detrimental to the growth of spring wheat and led to a decrease in spring wheat yield, with the observed yield decreased by an average of 28.5% and 22.4%, respectively. The elevated CO₂ mitigated the yield reductions caused by moisture restriction but exacerbated those caused by nitrogen fertilizer restriction. The CLM5 model was able to capture these environmental changes effects on spring wheat yields, but with bias. Simulated spring wheat yields were decreased by 5.5% under water restriction and 44.5% under nitrogen fertilizer restriction. The CLM5 model can reasonably simulate the growth of spring wheat, and can to some extent simulate the effects of increased CO₂ concentration and water and fertilizer restriction on the growth and yield of spring wheat.

Temporal and spatial variation characteristics of spring maize wind damage at different growth stages were analyzed based on the daily meteorological data of 43 meteorological stations, growth stages of spring maize in 18 agricultural observation stations and the data of wind damage of spring maize in 43 counties in Jilin province from 1971 to 2023. The meteorological grade index at different growth stages of spring maize wind damage was constructed and verified based on precipitation and maximum wind speed, in order to improve the monitoring and evaluation technology of spring maize damage under different wind and rain conditions. The results showed that: (1) the numbers of wind damage in heading-milk maturity of spring maize in Jilin province from 1971 to 2022 was the highest, reaching 61 times, accounting for 41.5% of the whole growth period from 1971 to 2022; the number of spring maize wind damage showed a significant upward trend, with an increase rate of 1.3 times∙10y⁻¹; the number of occurrences in 2010s was the highest, reaching 76 times, accounting for 52.8% of the total number from 1970s to 2010s; the spring maize wind damage mainly occurred in the central and western part and Dunhua of eastern part of Jilin  from 1971 to 2022, which Dunhua had the largest number of occurrences, 10 times. (2) There was a significant positive correlation between the process precipitation of 2 days before and on the day of the occurrence of the wind damage and the percentage of damage area, and the fitting effect was the best, R² was 0.616; the wind damage regression equations of spring maize in different growth stages were established by using the maximum wind speed and process precipitation, each regression equation showed a significant linear positive correlation (P＜0.01), which conformed to the sample independence and had a good fitting effect, R² was greater than 0.5. (3) The jointing−heading stage was most susceptible to less rainfall type wind damage, and the moderate wind damage was most prone to occur; the milk maturity−maturity stage was most susceptible to heavy rainfall type wind damage, and it was most prone to moderate and above wind damage. (4) The disaster grade of typical cases of wind damage in jointing-maturity stage of spring maize based on the meteorological grade index of less rainfall type wind damage and heavy rainfall type wind damage was determined and verified, the proportion of wind damage grade determined by the index completely consistent with the actual situation was 72.7%, and the proportion of one grade different from the actual situation was 18.2%; the accuracy of heavy rainfall type wind damage was higher, and the proportion of complete consistent was 85.7%, the proportion of coincidence in less rainfall type wind damage was 50%. In summary, the meteorological grade index of spring maize wind damage can reasonably determine the degree of wind damage in each growth stage of spring maize under different wind and rain conditions, which can provide reference for disaster prevention and mitigation of local agricultural production.

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.

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.

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%.  

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 units，the 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.

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 late−season snowmelt and freeze−thaw cycle disrupted grain storage and transportation in northeast China. In east−central 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 open−field vegetables.