Wang D.,Zhejiang University |
Wang D.,Chinese National Engineering Research Center for Information Technology in Agriculture |
Li C.,Chinese National Engineering Research Center for Information Technology in Agriculture |
Song X.,Chinese National Engineering Research Center for Information Technology in Agriculture |
And 6 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2010
Temperature, rainfall, illumination time and soil nutrients are major ecological factors to influence protein content forming of winter wheat. This study focused on the evaluation of the relative weighting of those factors on winter wheat grain quality (protein) based on the wheat planting, soil and weather data in Beijing, China. artificial neural network (ANN) analysis is employed in this study. The result indicated that the 10 factors have significant impact on the formation of wheat protein. The most important factor is illumination time from 6th June to 10th June, followered by the number of days which the temperature above 32°C, available nitrogen content of soil, average temperature from 1st May to 10th June, average temperature from 26 May to 30 May, accumulated temperature from 20th May to 10th June, average temperature from 1st June to 5th June, range of temperature from 20th May to 10th June, rainfall from 20th May to 10th June, and organic matter in soil respectively. Then, the response curves for key factors are generated by the ANN models in order to reflect the wheat protein variant trend according to the different ecological factors. The results of this study can probably be used for provided the reference basis for the winter wheat quality regionalization of Beijing area.
Wang D.-C.,Zhejiang University |
Wang D.-C.,Beijing Aeronautical Science and Technology Research Institute |
Li C.-J.,Beijing Aeronautical Science and Technology Research Institute |
Song X.-Y.,Beijing Aeronautical Science and Technology Research Institute |
And 6 more authors.
Agricultural Sciences in China | Year: 2011
It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network (ANN) analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness. © 2011 Chinese Academy of Agricultural Sciences.