Change of active accumulated temperature nearly 50 years in Hunchun and corresponding adjustment of agricultural climate division based on GIS

2015 ◽  
pp. 1055-1060
Author(s):  
Hai-fu Cui ◽  
Zhen-ming He ◽  
Ning Wang
Keyword(s):  
Climate Division ◽  
Accumulated Temperature

Suitable Maturity Period and Accumulated Temperature of Summer Maize in Wheat-maize Double Cropping System

2018 ◽  
Vol 44 (1) ◽  
pp. 137
Author(s):  
Bai-Zhao REN ◽  
Fei GAO ◽  
Yu-Jun WEI ◽  
Shu-Ting DONG ◽  
Bin ZHAO ◽  
...  
Keyword(s):  
Cropping System ◽  
Summer Maize ◽  
Double Cropping ◽  
Maturity Period ◽  
Accumulated Temperature

A Novel Forecast Method for Air-Conditioning Load of Public Building Considering Accumulated Temperature Effect

2015 ◽  
Vol 9 (1) ◽  
pp. 363-367
Author(s):  
Qingshan Xu ◽  
Xufang Wang ◽  
Chenxing Yang ◽  
Hong Zhu ◽  
Qingguo Yan
Keyword(s):  
Neural Network ◽  
Temperature Effect ◽  
Air Conditioning ◽  
Forecast Accuracy ◽  
Power Network ◽  
Forecast Method ◽  
Elman Neural Network ◽  
Public Building ◽  
Effective Accumulated Temperature ◽  
Accumulated Temperature

It has great significance to estimate the schedulable capacity of air-conditioning load of public building for participating the power network regulation by forecasting the air-conditioning load accurately. A novel forecast method considering the accumulated temperature effect is proposed in this paper based on Elman neural network. Firstly, the starting and ending date for forecast considering the accumulated temperature effect are determined by providing the five day sliding average thermometer algorithm which is usually adopted in aerology research. Then, the effective accumulated temperature of each day is calculated. Finally, take the effective accumulated temperature, temperature and humidity into consideration, the air-conditioning load of public building in the forecast day is acquired by Elman neural network. Simulated results show that the higher forecast accuracy can be achieved by considering the accumulated temperature effect.

Modelling leaf phenology of some trees with accumulated temperature in a temperate forest in northeast China

2021 ◽  
Vol 489 ◽  
pp. 119085
Author(s):  
Zhenzhao Xu ◽  
Qijing Liu ◽  
Wenxian Du ◽  
Guang Zhou ◽  
Lihou Qin ◽  
...  
Keyword(s):  
Northeast China ◽  
Temperate Forest ◽  
Leaf Phenology ◽  
Accumulated Temperature

scholarly journals Prediction of accumulated temperature in vegetation period using artificial neural network

2010 ◽  
Vol 51 (11-12) ◽  
pp. 1453-1460 ◽  
Author(s):  
Chunqiao Mi ◽  
Jianyu Yang ◽  
Shaoming Li ◽  
Xiaodong Zhang ◽  
Dehai Zhu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vegetation Period ◽  
Accumulated Temperature ◽  
Artificial Neural

Changes of annual accumulated temperature over Southern China during 1960–2011

2015 ◽  
Vol 25 (10) ◽  
pp. 1155-1172 ◽  
Author(s):  
Shengpei Dai ◽  
Hailiang Li ◽  
Hongxia Luo ◽  
Yifei Zhao ◽  
Kexin Zhang
Keyword(s):  
Southern China ◽  
Accumulated Temperature

Effects of Rice Yield and Quality Across Accumulated Temperature Zone Planting in Cold Area

2015 ◽  
Vol 22 (2) ◽  
pp. 1-7 ◽  
Author(s):  
Wang Qiu-ju ◽  
Liu Feng ◽  
Gao Pan ◽  
Gao Zhong-chao ◽  
Chang Ben-chao ◽  
...  
Keyword(s):  
Rice Yield ◽  
Temperature Zone ◽  
Yield And Quality ◽  
Cold Area ◽  
Accumulated Temperature

scholarly journals Effects of accumulated temperature on growth curve of Gynostemma pentaphyllum stem and leaf under the conditions of Simulated light under the forest

2021 ◽  
Author(s):  
Chunyao Dun ◽  
Wan Songsheng ◽  
Li Shuanglong ◽  
Wu Daikun
Keyword(s):  
Growth Curve ◽  
Leaf Growth ◽  
Three Dimensional ◽  
Logistic Function ◽  
Leaf Number ◽  
Stem Length ◽  
Suitable Habitat ◽  
Three Dimensional Model ◽  
Gynostemma Pentaphyllum ◽  
Accumulated Temperature

The effects of accumulated temperature on the growth curve and leaf number growth curve of Gynostemma pentaphyllum were studied. The growth curve of twigs and strands were studied by curve regression analysis. The results showed that the growth of Gynostemma pentaphyllum Leaf growth curve was optimized, and the growth curve of stem and leaf of Gynostemma pentaphyllum was established, and the curve was fitted and analyzed. Three - dimensional model of the effect of accumulated temperature on the stem length and leaf number of Gynostemma pentaphyllum . The results show that the stem length L and the accumulated temperature t of the strands are in the logistic function, the mathematical model was L=-112.69/(1+(t/892.1) 3.31 )+130.54, the number of gibbere leaf number n is logistic function with the accumulated temperature t, The model was n=-27.86/(1+(t/1159.77) 0.26 )+30.37, and the growth model could reflect the dynamic growth of Gynostemma pentaphyllum growth. The experimental results provide a theoretical basis for choosing suitable habitat for Gynostemma pentaphyllum under the forest.

scholarly journals Reconstruction of Rice Drying Model and Analysis of Tempering Characteristics Based on Drying Accumulated Temperature

2021 ◽  
Vol 11 (23) ◽  
pp. 11113
Author(s):  
Yi Jin ◽  
Jun Yin ◽  
Huihuang Xie ◽  
Zhongjie Zhang
Keyword(s):  
Moisture Ratio ◽  
Drying Process ◽  
Hot Air Drying ◽  
Exponential Relationship ◽  
Transfer Processes ◽  
Data Comparison ◽  
Hot Air ◽  
Accumulated Temperature ◽  
Mass And Heat Transfer ◽  
Rice Drying

Previous research has shown that the accumulated temperature can describe drying processes as well as crop growth. To describe the mass and heat transfer processes in the rice drying process more accurately, a mathematical model of rice drying was proposed based on the drying accumulated temperature, and the optimal tempering ratio for conventional hot air drying was obtained through data comparison and analysis. First, it was proven that there was an exponential relationship between the moisture ratio and the drying accumulated temperature of rice. Second, by comparing and analyzing the fitting results of seven different drying mathematical models, the model with the highest fitting degree was selected and reconstructed to obtain the drying accumulated temperature–moisture ratio model. Finally, the new model was used to fit the results of two drying experiments without and with tempering, and the tempering characteristics of rice drying were proved by comparing and analyzing the coefficient difference between the two models. The results showed that the optimal tempering ratio was 3. This study thus provides a reference for rice drying process parameters.

scholarly journals A Method for Monitoring and Forecasting the Heading and Flowering Dates of Winter Wheat Combining Satellite-Derived Green-up Dates and Accumulated Temperature

2020 ◽  
Vol 12 (21) ◽  
pp. 3536
Author(s):  
Xin Huang ◽  
Wenquan Zhu ◽  
Xiaoying Wang ◽  
Pei Zhan ◽  
Qiufeng Liu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Heading Date ◽  
Series Data ◽  
Index Method ◽  
Thermal Requirements ◽  
Flowering Date ◽  
Starting Point ◽  
Monitoring And Forecasting ◽  
Accumulated Temperature

Heading and flowering are two key phenological stages in the growth process of winter wheat. It is of great significance for agricultural management and scientific research to accurately monitor and forecast the heading and flowering dates of winter wheat. However, the monitoring accuracy of existing methods based on remote sensing needs to be improved, and these methods cannot realize forecasting in advance. This study proposed an accumulated temperature method (ATM) for monitoring and forecasting the heading and flowering dates of winter wheat from the perspective of thermal requirements for crop growth. The ATM method consists of three key procedures: (1) extracting the green-up date of winter wheat as the starting point of temperature accumulation with the dynamic threshold method from remotely sensed vegetation index (VI) time-series data, (2) calculating the accumulated temperature and determining the thermal requirements from the green-up date to the heading date or the flowering date based on phenology observation samples, and (3) combining the satellite-derived green-up date, daily temperature data, and thermal requirements to monitor and forecast the heading date and flowering date of winter wheat. When applying the ATM method to winter wheat in the North China Plain during 2017–2019, the root mean square error (RMSE) for the estimated heading date was between 4.76 and 6.13 d and the RMSE for the estimated flowering date was between 5.30 and 6.41 d. By contrast, the RMSE for the heading and flowering dates estimated by the widely used maximum vegetation index method was approximately 10 d. Furthermore, the forecasting accuracy of the ATM method was also high, and the RMSE was approximately 6 d. In summary, the proposed ATM method can be used to accurately monitor and forecast the heading and flowering dates of winter wheat in large spatial scales and it performs better than the existing maximum vegetation index method.

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