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Auswahl der wissenschaftlichen Literatur zum Thema „Grain China Forecasting“
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Zeitschriftenartikel zum Thema "Grain China Forecasting"
TOYODA, Takashi, Jin CHEN und Hidefumi IMURA. „An empirical analysis and forecasting of grain production in China.“ ENVIRONMENTAL SYSTEMS RESEARCH 25 (1997): 111–19. http://dx.doi.org/10.2208/proer1988.25.111.
Der volle Inhalt der QuelleLIU, L., Y. WANG, J. WU, J. WANG und C. XI. „New optimized grey derivative models for grain production forecasting in China“. Journal of Agricultural Science 153, Nr. 2 (05.03.2014): 257–69. http://dx.doi.org/10.1017/s002185961400001x.
Der volle Inhalt der QuelleZeng, Bo, Hui Li und Xin Ma. „A novel multi-variable grey forecasting model and its application in forecasting the grain production in China“. Computers & Industrial Engineering 150 (Dezember 2020): 106915. http://dx.doi.org/10.1016/j.cie.2020.106915.
Der volle Inhalt der QuelleWilson, William W., Won W. Koo, Richard Taylor und Bruce Dahl. „Long-Term Forecasting of World Grain Trade and U.S. Gulf Exports“. Transportation Research Record: Journal of the Transportation Research Board 1909, Nr. 1 (Januar 2005): 22–30. http://dx.doi.org/10.1177/0361198105190900104.
Der volle Inhalt der QuelleZhao, Yue Ling, Hai Yan Han, Li Ying Cao, Li Ma und Gui Fen Chen. „Study of Application of Time Series Model in Grain Yield Predition“. Advanced Materials Research 1049-1050 (Oktober 2014): 1392–95. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1392.
Der volle Inhalt der QuelleLuo, Zhong Hui, Qi Jun Xiao und Jun Lan Wu. „Research on the Multi-Parameter Modeling of Submarine Sediment Prediction“. Applied Mechanics and Materials 462-463 (November 2013): 13–16. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.13.
Der volle Inhalt der QuelleZhuo, Wen, Jianxi Huang, Xinran Gao, Hongyuan Ma, Hai Huang, Wei Su, Jihua Meng, Ying Li, Huailiang Chen und Dongqin Yin. „Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model“. Remote Sensing 12, Nr. 18 (07.09.2020): 2896. http://dx.doi.org/10.3390/rs12182896.
Der volle Inhalt der QuelleWu, Xianghua, Jieqin Zhou, Huaying Yu, Duanyang Liu, Kang Xie, Yiqi Chen, Jingbiao Hu, Haiyan Sun und Fengjuan Xing. „The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis“. Atmosphere 12, Nr. 1 (06.01.2021): 74. http://dx.doi.org/10.3390/atmos12010074.
Der volle Inhalt der QuelleGe, Jun, Andrew J. Pitman, Weidong Guo, Beilei Zan und Congbin Fu. „Impact of revegetation of the Loess Plateau of China on the regional growing season water balance“. Hydrology and Earth System Sciences 24, Nr. 2 (04.02.2020): 515–33. http://dx.doi.org/10.5194/hess-24-515-2020.
Der volle Inhalt der QuelleLi, Wei, Lu Li, Jie Chen, Qian Lin und Hua Chen. „Impacts of land use and land cover change and reforestation on summer rainfall in the Yangtze River basin“. Hydrology and Earth System Sciences 25, Nr. 8 (24.08.2021): 4531–48. http://dx.doi.org/10.5194/hess-25-4531-2021.
Der volle Inhalt der QuelleDissertationen zum Thema "Grain China Forecasting"
Shea, Esther Yi Ping. „The political economy of China's grain policy reform“. Title page, contents and abstract only, 2003. http://web4.library.adelaide.edu.au/theses/09PH/09phs5393.pdf.
Der volle Inhalt der QuelleCheing, Mei, und 鄭玫. „Can China Feed Itself:A Study on China’s Grain Demand Forecasting“. Thesis, 2007. http://ndltd.ncl.edu.tw/handle/51755928425105050544.
Der volle Inhalt der Quelle國立臺灣大學
國家發展研究所
95
As the coming of the 21st centuries, China is becoming a dominating country in the world; hence, there are many predictions about China''s future--"the collapsing of China", "the threatening of China" and "the rising of China". China-related issues have been closely studied worldwide. All kinds of reports provided by different Officials, institutes and private researches can be found. Some of them are predicting that the increase of Chinese population may destroy its ecological equilibrium and China will suffer from famine. as a result, the whole world will be affected. According to China''s history, the ample staple food supply has a significant political implication-it is a necessity to governing the country well. If the providing of staple food is well secured, a basis for a prosperous China is promised. This study focuses on observing the trend of China’s staple food demand, and further offer to a reasonable prediction. The models used are:GARCH model, Co-integration model, and forecasting process. The Co-integration model may have a non-linear effect; hence, this study uses both Linear and Non-linear approaches in the Co-integration models. The data used in the study are from the Food and Agriculture Organization (U.N.), 1961-2004. The variables are:Average consumer quantity of major grain, average price of grain, and average income. The empirical results are summarized as follow: Firstly, in the first two periods, the declining of consuming quantity on grain will cause the increase of grain price. The farmer could even hold down his sales. This is due to the commodity price a rise but the grain price doesn''t go up as well. Since the price of grain keeps unchanged, the costs on equipment and labor are increasing. Secondly, in this study, China''s staple food demand is offered to have both long-term and short-term stability, and it fits the "best linear unbiased estimator (BLUE)", goodness of fit , hence it is predictable and has long-term developing co-relation--the rise of food consumption causes the rise of food price and the increase of food consumption stimulate the increase of income. Third, many studies on this topic have been made from the supply-side. Most of the investigated periods are within 5-20. However, the Chinese data before China''s "reform" are usually fragmentary, inconsistent and incompleteness. This study overcomes many limitations and obstacles, probe into this topic from the demand-side, in an effort to make it a rare one. Fourth, China''s data of the earlier years'' can be obtained from database of FAOSTAT. Obviously, it has better credibility and is more comprehensive. It''s a pity that it only represents the country''s total supply, but the differences between rural vs. urban, poor vs. well-off are not included. What we can conclude from the data is:Over the years, the Chinese'' dietary habit has changed significantly. Fifth, according to this investigation, all the indexes are aiming to the same direction:China is having a good opportunity, and is not "developing to perishing". However, this topic deserves follow up and further investigation. Finally, this study investigates the sensitiveness of China''s staple food demand and observes the price of World staple food as well, then, examines Taiwan agriculture''s developing opportunities and provides some recommendations.
Konferenzberichte zum Thema "Grain China Forecasting"
Hu, Hai-Qing, Dan Zhang und Qiu-Ping Wang. „Application of trigonometric grey prediction approach to forecasting China grain yield“. In 2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/gsis.2009.5408255.
Der volle Inhalt der QuelleGao, Mingjie, Qiyou Luo, Yang Liu und Jian Mi. „Grain consumption forecasting in China for 2030 and 2050: Volume and varieties“. In 2014 Third International Conference on Agro-Geoinformatics. IEEE, 2014. http://dx.doi.org/10.1109/agro-geoinformatics.2014.6910669.
Der volle Inhalt der QuelleDuan, Shanshan, Weidong Yang, Xuyu Wang, Shiwen Mao und Yuan Zhang. „Grain Pile Temperature Forecasting from Weather Factors: A Support Vector Regression Approach“. In 2019 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2019. http://dx.doi.org/10.1109/iccchina.2019.8855910.
Der volle Inhalt der QuelleWen, Jian, und Lijuan Lei. „A Combined Forecasting Method of Grain Yield in China Based on GM(1,1) and BP Network“. In 2010 Third International Conference on Information and Computing Science (ICIC). IEEE, 2010. http://dx.doi.org/10.1109/icic.2010.289.
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