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Long homogeneous meteorological data is important to study climate change. Evaluating the impact of data inhomogeneity on analyzing average temperature trends in Huai river basin is value for accurately understanding the response of agriculture, ecology and water resources to climate change. In this study, based on the homogeneous data and observation data of daily average air temperature from the National Meteorological Information Center, the trends of annual and seasonal air temperatures during 1961 to 2018 were calculated using simple linear regression at 172 meteorological stations over Huai river basin. Then the impacts of data inhomogeneity on analyzing mean air temperature trends during 1961 to 2018 were evaluated by using two terms Crop rhizosphere microorganisms is crucial for their growth, development and adaptability to stress. A field pot experiment was conducted using Yannong19(YN) with strong resistance to late spring coldness and which were inhomogeneity impacts and contribution rates. The results showed that the average air temperature series of 96 meteorological stations in Huai river basin were inhomogeneous, accounting for 55.8% of the 172 total stations. Before and after the homogenization, the regional annual average air temperature both increased significantly. But the increasing rate was underestimated due to the data heterogeneity, and the influence was -0.015°C•10y-1 with a contribution of -6.6%. For each station, 35 stations (20.3%) was positively affected and the warming rate was overestimated with an average contribution of 21.3%, while 61 stations (35.5%) were negatively affected and the warming rate was underestimated with an average contribution of -43.6%. The influences of inhomogeneity for four seasons showed little difference, which were -0.016°C•10y-1, -0.014°C•10y-1, -0.016°C•10y-1 and -0.015°C•10y-1, respectively. However, because of the slowest increasing rate, the absolute value of inhomogeneity contribution was largest in summer with a contribution of -40.0%. The contributions of spring, autumn and winter were -4.7%, -8.0% and -4.3%. Inhomogeneity mostly affected the temperature rising rate at each station in spring, autumn and winter, while led to a turning of temperature trend before and after homogenization at 20 stations (11.6%) in summer. [ABSTRACT FROM AUTHOR]

Based on the traditional black tea processing technology, the hot air withering process was used to replace the traditional indoor natural withering process on the basis of the traditional black tea processing technology. The effects of four hot air withering processes of 35 °, 45 °, 55 ° and 65 ° on the quality of summer and autumn black tea were studied and analyzed from three aspects of sensory quality, taste quality and aroma quality. The results show that compared with 25 ° traditional indoor natural withering black tea, the comprehensive sensory quality of black tea processed by 45 ° hot air withering process was better. The color of dry tea was dark brown and moist, the soup color was red and bright, the taste was mellow and refreshing, and the quality characteristics was sweet aroma. At the same time, the contents of tea polyphenols, gallic acid, EGCG, theaflavins, thearubigins and the ratio of the sum contents of theaflavins and thearubigins to the content of theabrownins (TFRB) significantly increased (P<0.05), while the content of soluble sugar decreased significantly (P<0.05). The types of aroma quality components increased, among which alcohols accounted for the highest proportion (70.47%), and the relative contents of alcohols, ketones and esters increased significantly (P<0.05), while pyrroles and aldehydes decreased significantly (P < 0.05). And the aroma components with flower and fruit aroma such as trans-3-hexenol, 2,3-dihydro-2,2,6-trimethylbenzaldehyde and α-ionone were detected. This shows that hot air withering with appropriate temperature is conducive to improving the quality of black tea, which can be applied to the production practice of improving the quality of black tea in summer and autumn. [ABSTRACT FROM AUTHOR]

The seasonal temperature forecasting models in greenhouse based on multiple regression (MR), BP artificial neural networks (BPANN), random forest (RF) and support vector machine (SVM) were constructed using meteorological observations inside and outside the Venlo-type glass greenhouses from 27th February 2021 to 4th March 2023 at Nanjing University of Information Science & Technology, and then to validate all the models. The results showed that the fitting accuracy of the seasonal forecasting models for daily average and minimum air temperatures in the greenhouse was significantly higher than that of the seasonal forecasting models for daily maximum air temperature; the fitting accuracy of each model for air temperature inside the greenhouse in spring, summer and autumn was higher than that for winter. For the seasonal forecasting models of daily average air temperature and minimum air temperature, the fitting accuracy of the spring, summer and autumn forecasting models constructed by the four algorithms was higher, but the simulation effect of the RF model was more stable, and the coefficient of determination (R²) of the simulated values and the actual observed values were above 0.94, and the root mean square error (RMSE) and absolute error (MAE) were within 1.5 °C. For the seasonal forecast models of daily maximum temperature, the fitting accuracy of RF model for spring, summer and autumn was higher than other models in general, and its R² value was above 0.75. The MR model had higher accuracy for winter indoor air temperature and was more suitable for predicting winter greenhouse air temperature. The study concluded that it is feasible to choose the RF model to forecast the air temperature inside the glass greenhouse in spring, summer and autumn and the MR model to forecast the air temperature inside the glass greenhouse in winter, and this study can provide an important reference for the seasonal forecast of air temperature in Venlo-type glass greenhouse. [ABSTRACT FROM AUTHOR]

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