Predicting and comparative soil quality assessment in organic and conventional hazelnut orchards using XGBoost approach

Abstract The aim of this study was to determine soil quality index (SQI) for hazelnut gardens managed under organic and conventional agricultural systems. Additionally, the predictability of soil quality was evaluated using the XGBoost algorithm. To determine soil quality, a multi-criteria decision-making process was applied to the total data set (TDS) using standard scoring functions (linear and non-linear). Additionally, the minimum data set (MDS) was obtained using principal component analysis (PCA). Then, the model verification process was performed using SQI and yield data. According to the results, although SQI values in conventional agriculture were statistically significantly higher, the correlation between yield and soils under organic agriculture was higher than in conventional agriculture. The SQI averaged 0.4576 in conventionally farmed soils and 0.4417 in organically farmed areas. Root mean square error values obtained for SQI estimation with the XGBoost algorithm using basic soil properties ranged from 0.038 to 0.065. The mean error rate was approximately 8%. Lin’s concordance correlation coefficients for the SQI estimated by MDS and TDS were 0.60 and 0.61, respectively. The most effective basic soil properties for estimating SQI with the XGBoost algorithm were N, K, organic matter, and P. It was concluded that the XGBoost algorithm can be evaluated for soil quality prediction. In addition, the spatial distribution patterns of the values predicted by this algorithm and of the observed values were similar. The exclusive use of soil analyses in the study can be considered a limiting factor for the model. More comprehensive studies are planned using reflectance measurements from remote sensing technologies.