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The sustainable use of agricultural lands is significantly affected by the implemented management and land processing methods. In sugar beet and wheat cropping, because the agronomic characteristics of plants are different, the tillage methods applied also exhibit significant variability. Soil quality concept is used, as a holistic approach to determining the effects of these applications on the sustainable use of soil. Agricultural soil quality evaluation is essential for economic success and environmental stability in rapidly developing regions. At present, a variety of methods are used to evaluate soil quality using different indicators. This study was conducted in one of the most important irrigated agriculture areas of Çumra plain in Central Anatolia, Turkey. In the soil under sugar beet and wheat cultivation, 12 soil quality indicators (aggregate stability (AS), available water capacity (AWC), surface penetration resistance (PR), subsurface penetration resistance (PR), organic matter (OM), active carbon (AC), potentially mineralizable nitrogen (PMN), root health value (RHV), pH, available phosphorus (AP), potassium (K), and macro-micro elements (ME) (Mg, Fe, Mn, and Zn)) were measured and scored according to the Cornell Soil Health Assessment (CSHA) and the Soil Management Assessment Framework (SMAF). The differences among 8 (AS, AWC, PR, PR, AC, PMN, AP, and ME) of these 12 soil quality characteristics measured in two different plant cultivation were found statistically significant. The result of the soil quality evaluation with scoring function in the examined area revealed a soil quality score of 61.46 in the wheat area and of 51.20 in the sugar beet area, which can be classified as medium and low, respectively. Low soil quality scores especially depend on physical and biological soil properties. Therefore, improvement of soil physical and biological properties with sustainable management is necessary to enhance the soil quality in the study area soils.

Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P is influenced by both natural and anthropogenic factors. This study aimed at (1) predicting SOC and Ava-P in a piedmont plain of Northeast Iran using the Random Forests (RF) and Cubist mathematical models and hybrid models (Regression Kriging), (2) comparing the models’ results, and (3) identifying the key variables that influence the spatial dynamics of soil SOC and Ava-P under different agricultural practices. The machine learning models were trained with 201 composite surface soil samples and 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) and key soil features (S) according to the SCORPAN digital soil mapping framework, which can predictively represent soil formation factors spatially. Clay, one of the most critical soil properties with a well-known relationship to SOC, was the most important predictor of SOC, followed by open-access multispectral satellite images-based vegetation and soil indices. Ava-P had a similar set of effective variables. Hybrid approaches did not improve model accuracy significantly, but they did reduce map uncertainty. In the validation set, Ava-P was calculated using the RF algorithm with a normalized root mean square (NRMSE) of 96.8, while SOC was calculated using the Cubist algorithm with an NRMSE of 94.2. These values did not change when using the hybrid technique for Ava-P; however, they changed just by 1% for SOC. The management of SOC content and the supply of Ava-P in agricultural activities can be guided by SOC and Ava-P digital distribution maps. Produced digital maps in which the soil scientist plays an active role can be used to identify areas where concentrations are high and need to be protected, where uncertainty is high and sampling is required for further monitoring.

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