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COMPARISION OF MINIMUM DATA SETS FOR SOIL QUALITY
USING EXPERT OPINION AND PRINCIPAL COMPONENT
Mesut Budak1* 2 3, Nurullah Acir4 5,Mert Acar3
1 m.budak@siirt.edu.tr 2Gazios hikmetgunal@gmail.com 3 icelik@cu.edu.tr, macar@cu.edu.tr 4 nurullah.acir@ahievran.edu.tr 5 m.sirri@siirt.edu.tr
Long standing anthropogenic impacthas substantially altered ecosystems and affected the ecosystem services by changing the functioning ability of soils around the globe. The agricultural production potential of soils has severely been impaired by some of changes in soil quality. The aims of this study are to establish aminimum data set (MDS) that can be used and topographical features, and to compare the quality scores obtained by two different methods. One hundred thirty seven soil samples were collected and analyzed to obtain data set composed of physical and chemical characteristics and slope of the sampling locations. Principal component analyses (PCA) and expert opinion approaches were used to select the least number of soil attributes that best represent the variability in original data set. The indicator scores between 0 and 1.0 obtained by linear scoring curves were multiplied by weights calculated by PCA and analytic hierarchy process methods, and 3 soil quality indexes (SQI) were obtained by using weighted additive approach. In addition, two more SQI values were calculated with the additive approach and a total of 5 SQIs were evaluated. In the PCA approach, OM, pH, CaCO3, P, SAR and slope were considered as the indicators of MS. These
indicators had the highest loading values within the PCs that have eigenvalue above 1.0. By the expert opinion, SAR was only excluded from MDS and replaced by AS. One way variance analyses revealed that SQI values were significantly different from each other. The highest SQI value (0.852) was obtained with EO weighted additiveand the lowest SQI value (0.807) was obtained with EO additive approach.
Keywords: Soil quality, multivariate statistics, PCA, expert opinion, AHP * This study has been funded by TUBITAK (grants number TOVAG 214O374)
