Due to intensified agricultural mechanization, soils are exposed to increased levels of degradation. Soil compaction is the beginning of this degradation which further proceeds to decrease organic matter content of soils. Among the various methods applied to solve these problems, the addition of organic matter to the soil has an important place. Thus, agricultural land application of organic wastes, such as biochar and compost, could alter the soil properties to offset the degradation effects. The aim of this research was to determine the effect of compost and biochar application on the soil compaction and its quality score. Biochar and compost of the Elaeagnus tree pruning residues were applied in a field at a similar rate of 0%, 2%, and 4%. Results indicated that the soil penetration resistance (PR) statistically experienced a downward trend compared to the control when applied with compost and biochar. The application of 2–4% biochar and compost lowered soil PR by exactly 8.80%, 9.33%, 18.22%, and 28.89%, respectively, throughout the period of 7 months. Furthermore, the infusion of biochar and compost into the soil significantly increased the soil compaction quality score compared to the control, and the least value of PR was observed for the compost-treated soil at the application rate of 4%. Compost and biochar applications were effective to improve the physical quality of a sandy clay loam soil, making it more suitable for plant development. On the other hand, the effect of compost treatments was more impact than biochar treatments.

This study focuses on addressing the challenges associated with labor-intensive soil penetration resistance (SPR) measurements, which are prone to errors due to varying soil moisture levels. The innovative approach involves developing SPR estimation models using artificial neural networks (ANN) for soils with optimal moisture levels determined by van Genuchten (WG) calculations. Sampling and measurements were conducted at 280 points (0–30 cm depth), with an additional 324 samples used for model testing. Considering six scenarios, this study aimed to identify the best estimation model using key soil properties (sand, clay, silt, bulk density, organic carbon, and aggregate stability) in different combinations affecting SPR. Results from all ANN scenarios demonstrated satisfactory SPR estimation performance, with the sand and clay content scenario exhibiting the highest accuracy, characterized by a mean square error (MSE) of 0.0029 and a coefficient of determination (R²) value of 0.9707. This selected scenario were further validated with different test data, yielding an MSE of 0.7891 and an R² value of 0.67. In conclusion, this study suggests that, by standardizing moisture levels through WG calculations, ANN-based SPR estimation can effectively be applied to soils with specific sand and clay contents.

The direct estimation of soil hydraulic conductivity (Ks) requires expensive laboratory measurement to present adequately soil properties in an area of interest. Moreover, the estimation process is labor and time-intensive due to the difficulties of collecting the soil samples from the field. Hence, innovative methods, such as machine learning techniques, can be an alternative to estimate Ks. This might facilitate agricultural water and nutrient management which has an impact on food and water security. In this spirit, the study presents neural-network-based models (artificial neural network (ANN), deep learning (DL)), tree-based (decision tree (DT), and random forest (RF)) to estimate Ks using eight combinations of soil data under calcareous alluvial soils in a semi-arid region. The combinations consisted of soil data such as clay, silt, sand, porosity, effective porosity, field capacity, permanent wilting point, bulk density, and organic carbon contents. The results compared with the well-established model showed that all the models had satisfactory results for the estimation of Ks, where ANN7 with soil inputs of sand, silt, clay, permanent wilting point, field capacity, and bulk density values showed the best performance with mean absolute error (MAE) of 2.401 mm h⁻¹, root means square error (RMSE) of 3.096 mm h⁻¹, coefficient of determination (R²) of 0.940, and correlation coefficient (CC) of 0.970. Therefore, the ANN could be suggested among the neural-network-based models. Otherwise, RF could also be used for the estimation of Ks among the tree-based models.

The aim of this study is to estimate soil quality using observable soil color, thereby simplifying the assessment process that traditionally requires expert intervention and extensive analysis. A total of 324 soil samples were collected from a depth of 0–20 cm in the Konya Çumra Plain. These samples underwent color readings and principal component analysis. To estimate soil quality, three different scoring methods Soil Management Assessment Framework (SMAF), (Comprehensive Assessment of Soil Health) CASH, and Linear scoring were employed. The soil quality indicators identified by the analysis include clay, organic carbon, active carbon, calcium, available phosphorus, potassium, available water capacity, and potentially mineralizable nitrogen. The average soil quality scores calculated using SMAF, CASH, and Linear scoring were 0.73, 0.43, and 0.65, respectively. Artificial Neural Network (ANN) analysis revealed R² values of 0.18 for SMAF, 0.32 for CASH, and 0.70 for Linear scoring. The study shows that soil color can be used to predict soil quality with a high degree of accuracy, with the Linear scoring function being the most effective for soil quality assessments. The results highlight the potential of artificial intelligence (AI) algorithms in facilitating rapid and efficient prediction of soil quality. By leveraging the synergy between observable soil characteristics and advanced AI methodologies, this research simplifies soil quality assessment and enables more accessible and scalable environmental analysis.

Soil quality is a measurement used to calculate how well the soil will perform its functions and ensures the assessment of soils based on their physical, chemical and biological characteristics. The aim of this study was to facilitate the determination of soil quality, with soil properties selected by multi-criteria application, in a shorter time compared to that of conventional soils tests. A soil quality index (SQI) was determined by measuring the soil parameters under laboratory and field conditions in lands where agriculture has been taking place for many years. Based on the results of the factor analysis the 12 soil characteristics were selected for assessing the SQIs of each of the soil samples. The SQI results determined from physical, chemical, and biological soil properties with regards to total weight was calculated as 35.96%, 33.92%, and 30.12%, respectively. Based on the results of this study, it was determined that only 4 physical, 5 chemical and 3 biological properties could be used to evaluate soil quality. The selected indicators can be used in subsequent studies for the assessment of soil quality in areas where intensive agriculture has been used for many years.