Using Different Regression Tree Algorithms to Predict Soil Organic Matter with Digital Color Parameters in Soil Profile Wall
نویسندگان
چکیده
Soil organic matter has a critical role for the physical, chemical and biological properties of soil sustainable agriculture. Quick cost-effective prediction can provide basic data support precision The study area is located in Muttalip pasture Tepebaşı, Eskişehir. profile wall (1x1 m) was dug divided into 10x10 cm raster cell. A total 100 samples were taken from center each cell wall. field-based lab-based digital color parameters (CIE Lab) measured depending on grid sampling model. ordinary Kriging interpolation method used geostatistical distribution maps amount (OM) CIE Lab values. CHAID, Ex-CHAID, CART regression tree algorithms to predict OM with varies between 4.65-10.54% topsoils, while it 0.01-0.41% subsoils. According results, values obtained high predicting performance more effective than It concluded that algorithm be rapidly economically (R2=0.89) parameters.
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ژورنال
عنوان ژورنال: Uluslararas? Tar?m ve Yaban Hayat? Bilimleri Dergisi
سال: 2021
ISSN: ['2149-8245']
DOI: https://doi.org/10.24180/ijaws.907028