河南省气象科学研究所

Based on the progress made in stress biology and epigenetics,the current theoretical framework of phenology model,which only considers the response mechanism of developmental rate to the environment factors,is being revised and validated to incorporate mechanisms of response,adaptation,and memory.The adaptation mechanism is taken as an example and the day of year(DOY)of the beginning of the growth period is proposed as the factor to characterize the adaptability,which is then integrated with linear temperature response functions to establish a phenology model coupled with both response and adaptation mechanisms(RAM).RAM model is calibrated and validated using phenology observations during the vegetative growth period(VGP)and reproductive growth period(RGP)of maize at 194 agrometeorological observation sites across China.Results show that DOY is generally positively correlated with developmental rate over all the sites during the development periods.The collinearity of DOY and temperature in the two development periods at all sites are within the acceptable range.Compared with the linear temperature response model,the RAM model improves the interpretation rate of the observed data by 0.213 and 0.274 in the VGP and RGP,respectively.Compared with the typical fully calibrated bilinear(WOFOST)and nonlinear(Gao)models,RAM can reduce the RMSE by 0.4 and 0.4 d during VGP,respectively,and by 1.6 and 0.7 d during RGP,respectively in the calibration process.In the validation process,RAM can reduce the RMSE by 0.1 and 0.4 d during VGP,respectively,and by 1.1 and 0.4 d during RGP,respectively.In addition,RAM mode has the advantage of a simple parameterization process.Our results show that the modified phenology model framework may have promising prospects in theoretical research and practical application.

Wheat is one of the most crucial global staple crops for food security.However,the continuous rainy weather during its growth,particularly at maturation,can easily cause ear germination and moldiness,thus severely impacting the yield and quality.This study aims to accurately monitor and evaluate the germination and moldiness of wheat ears under continuous rainy weather stress during the maturity period.A case study was also conducted on the continuous rainy weather in the western part of the Huang-Huai region of China in late May 2023.The wheat ear germination and moldiness were tackled using meteorological and satellite remote sensing data,with emphasis on the disaster risk elements.Then,meteorological hazard factors were determined from the weather stress mechanisms.The resilience was also characterized using remote sensing parameters,according to the state and environment of the wheat.Thirdly,the modeling factors were selected for subsequent analysis.Spearman correlation and ReliefF method were also used for the feature selection in binary and severity classification tasks,while Pearson correlation was employed to predict the ear germination and moldiness index(EGMI).The optimal factors were then combined to form the SCF,PCF,and RFF factor groups,according to the meteorological and remote sensing types.Subsequently,five classification models(including Logistic regression,LGR)and five regression methods(including multiple linear regression,MLR)were applied for the binary classification and severity grading of wheat ear germination and moldiness,in order to predict and simulate the EGMI.The effectiveness of these models was then compared to identify and grade the wheat ear germination and moldiness.The results showed that the optimal factors were achieved in the identification and severity grading of germination and moldiness using different classifiers,from the perspective of the disaster-causing process of continuous rain and the three elements of disaster risk.The accuracy score(AC)ranged from 0.649 to 0.811 in the binary classification of wheat ear germination and moldiness identification,with the Kappa coefficients between 0.245 and 0.600.In the three-category classification of severity grading,the AC value ranged from 0.432 to 0.622,with the Kappa values between 0.099 and 0.414.The R2 value of EGMI prediction ranged from 0.10 to 0.25,with an average mean absolute error(MAE)of 12.93 and an average root mean square error(RMSE)of 16.74.The PCF-XGBR model performed the best,with the R2,RMSE,and MAE values of 0.25,15.69,and 12.05,respectively,as well as the standard deviation(SDEV)and centered root-mean-square deviation(CRMSD)values of 13.10 and 15.55,respectively.Comparative analysis of the three models showed that the remote sensing model was superior to the meteorological model,in terms of the identification of germination and moldiness.While the meteorological model outperformed the remote sensing model,in terms of grading the severity of germination and moldiness.The meteorological-remote sensing model was integrated to balance their shortcomings for better performance and robustness.The estimation of continuous rainy weather disasters was achieved in the western Huang-Huai region,thus filling the technological gap in monitoring wheat ear germination and moldiness.The finding can provide the technical support to reduce the wheat disaster in post-disaster assessment.

To examine the effects of gradual increase of atmospheric CO2 concentration on soil respiration of winter wheat(Triticum aestivum)field,a gradually increased CO2 concentration experiment was conducted with automatic control system of CO2 in open top chambers(OTCs)during 2017-2019 growing seasons.In this study,a gradual in-crease of atmospheric CO2 concentration(C80 and C120,an increase of 40 μmol·mol-1 year by year from 2016)was set up based on the ambient atmospheric CO2 concentration(CK).The soil respiration rate(Rs)was measured by static chamber-gas chromatograph method.The results showed that gradually increased CO2 did not alter the seasonal patterns of soil respiration,but had significant effect on Rs during winter wheat bloom-growth period.In 2018-2019 growing season,compared to CK,C120 treatment significantly increased Rs by 50.2%(P=0.008)at the heading-flowering stage,and significantly increased cumulative amount of CO2 emissions(CAC)by 25.9%(P=0.044)during the wheat growing season;while in 2017-2018 growing season,compared to CK,C80 treatment had no signifi-cant effect on Rs.A positive exponential relationship was found between soil respiration rate and soil temperature.Compared to CK,gradually increased CO2 concentration reduced the temperature sensitivity coefficient of soil respi-ration(Q10 values).In summary,a gradual increase of atmospheric CO2 concentration of 120 μmol·mol-1 increased CAC during the growing season of winter wheat.

In order to clarify the climatic conditions for the quality formation of winter wheat, we analyzed the climatic resource characteristics of winter wheat from heading to maturity and the characteristics of winter wheat quality with four gluten types（strong gluten, medium strong gluten, medium gluten and weak gluten）, and we identified the climatic conditions for the formation of wheat quality, using the data of 10 winter wheat quality indices and their corresponding climatic data from 2006 to 2015 in five major provinces（Hebei, Shandong, Henan, Anhui, Jiangsu） of China in this study. The results showed that the dominant climate factors of the same quality index of winter wheat with different gluten types were different, and the dominant climatic factors of different quality indices of the same type winter wheat were also different. On the whole, the main factors for quality formation of winter wheat were temperature and humidity. The quality of strong gluten winter wheat was mainly affected by the number of high-temperature days ≥32 ℃ and precipitation from heading to maturity, and the quality of strong gluten winter wheat was better under good temperature and heat conditions. The quality of medium and strong gluten winter wheat was mainly affected by the number of precipitation days and the daily temperature range, which had negative effects on quality（except for extensibility）. The quality of medium gluten winter wheat was mainly affected by the maximum temperature and the number of precipitation days. For weak gluten winter wheat, the quality got worse when there were the more days with the daily maximum temperature ≥32 ℃ or greater humidity.

Crop yield separation is one of the important steps in analyzing the impact of meteorological factors on yield. Statistical rice yield data for 1985-2018 from 24 counties in Jiangsu are used to analyze the rationality of different separation methods. Six separation methods are 3-year moving mean, 5-year moving mean, five-point quadratic smoothing, quadratic exponential smoothing, HP filter and year-to-year increment. Consistencies and differences are analyzed from aspects of trend yield and meteorological yield. In order to select better methods that could accurately capture the yield variation caused by meteorological factors, the meteorological yield based on different methods are compared with the typical annual increase and decrease of rice yield records. Finally, as mentioned above, the selected methods are calibrated by the rationality of the relationship between meteorological factors and yield. Results show that the trend yield curves fitted by different methods are in line with the process of social technology development. Compared with the average trend yield, almost all the consistency correlation coefficients are greater than 0.5. It suggests that different methods do not differ much in trend fitting. Characteristics of meteorological yield separated by 3-year moving mean, 5-year moving mean, five-point quadratic smoothing and quadratic exponential smoothing in each county are simultaneously increasing or decreasing. And their standard deviation values are significantly smaller than HP filter method and year-to-year increment method. The result suggests that the rationality of separating the meteorological yields by 3-year moving mean, 5-year moving mean, five-point quadratic smoothing, and quadratic exponential smoothing is higher than the other two methods. Five-point quadratic smoothing method and 3-year moving mean method can capture almost 100% of typical annual meteorological yield changes in the whole research area. Further verification results show that the positive and negative effects of meteorological factors captured by 3-year moving mean and five-point quadratic smoothing method are more consistent with the response to meteorological factors. Overall, separation methods of five-point quadratic smoothing method and 3-year moving mean method are more suitable for this research area and match well with meteorological factors.