Evaluation Model of Winter Wheat Yield Based on Soil Properties

Abstract

AbstractIn order to realize precision management of winter wheat, two prediction models of winter wheat yield based on soil parameters were proposed and compared. The field tests were carried out in two years. The variety of the experimental winter wheat was Jingdong 12, and the test area was divided into 60 zones with 5m×5m grids. The sampling point was put in the center of the zone, and the depth of the sampling point was 5cm. Soil EC was measured by a DDB-307 EC meter, and the winter wheat yield data were provided by a CASE2366 grain harvester with GPS receiver. Gray theory were used to analyze the gray relation between soil EC value and each of other soil parameters, such as total nitrogen content, K + 、NO\(_{\rm 3}^{\rm -}\) and pH of soil. Results showed that there were high gray relation between soil EC and three other indexes, total nitrogen content, K + , pH of soil. Since soil organic horizons had high correlation with soil negative charge capacity, when soil had more organic horizons, there were more soil negative ions, and the soil EC was higher. Hence, the gray relation between K + and EC was high. Using nitrogen fertilizer could removal caution from soil, and increase the content of K + ,Na + , Ca2 + and Mg2 + , so that there was also high correlation between total nitrogen content and EC. The reason of high correlation between EC and soil pH was attributed to that the change of pH had influence on negative charge. After analyzing the correlation between winter wheat yield and soil EC, total nitrogen content, K + , NO\(_{\rm 3}^{\rm -}\), pH of soil in different growth period respectively, two prediction algorithms of yield were proposed, Least Square-Support Vector Machine (LSSVM) and Fuzzy Least Square-Support Vector Machine (FLSSVM). LSSVM prediction model took soil EC, total nitrogen content and K + as the input factors and winter wheat yield as output. While FLSSVM prediction model took soil EC, nitrogen content, K + and gray relation as input factors and also winter wheat yield as output. Results showed that the prediction and validation R2 of LSSVM model were 0.772 and 0.685 respectively. Prediction R2 of FLSSVM was 0.8625, and validation R2 of FLSSVM model was 0.8003. FLSSVM used Fuzz Similar Extent to fuzz input samples so that it could avoid over-training. Also because it was based on membership function, it had several advantages such as simple structure, efficient convergence, precise forecasting, and etc. FLSSVM had high accuracy prediction result and could be used in estimating yield and providing theory and technical support for precision management of crops.Keywordsnear infrared spectroscopywaste cooking oilsupport vector machineparameters optimization