TY  - JOUR
TI  - Using Different Regression Tree Algorithms to Predict Soil Organic Matter with Digital Color Parameters in Soil Profile Wall
AB  - Soil organic matter has a critical role for the physical, chemical and biological propertiesof the soil and for sustainable soil and agriculture. Quick and cost-effective prediction of soil organicmatter can provide basic data support for precision agriculture. The study area is located in theMuttalip pasture of Tepebaşı, Eskişehir. The soil profile wall (1x1 m) was dug and divided into 10x10cm raster cell. A total of 100 soil samples were taken from center of each raster cell of the soil profilewall. The field-based and lab-based digital color parameters (CIE Lab) were measured depending onthe grid sampling model. The ordinary Kriging interpolation method was used in geostatisticaldistribution maps of the amount of organic matter (OM) and field-based and lab-based CIE Labvalues. CHAID, Ex-CHAID, and CART regression tree algorithms were used to predict the OM withfield-based and lab-based CIE Lab values. The OM in the soil profile wall varies between 4.65-10.54%in the topsoils, while it varies between 0.01-0.41% in the subsoils. According to the results, lab-basedCIE Lab values obtained high predicting performance and more effective than field-based CIE Labvalues. It concluded that the CART algorithm can be used rapidly and economically in prediction OMwith high prediction performance (R2=0.89) with lab-based digital color parameters.
AU  - ALTAY, Yasin
AU  - GÖZÜKARA, Gafur
DO  - 10.24180/ijaws.907028
PY  - 2021
JO  - Uluslararası Tarım ve Yaban Hayatı Bilimleri Dergisi
VL  - 7
IS  - 2
SN  - 2149-8245
SP  - 326
EP  - 336
DB  - TRDizin
UR  - http://search/yayin/detay/441819
ER  -