Heavy metals are the most environmentally hazardous pollutions in agricultural soils, threatening humans and several ecosystem services. Cadmium (Cd) is a highly toxic element but distinctively different from other heavy metals with its high mobility in soil environments. The study aimed to evaluate the Cd concentration of soils in the Konya plain with a specific attribute to soil fertilization, mainly phosphorous fertilizers. A total of 538 surface (0–20 cm) soil samples were analyzed to determine basic physical and chemical properties and total phosphorus (P) and Cd concentrations. Descriptive statistics, machine learning, and regression models were used to assess the accumulation of Cd in soils. Decision Trees, Linear Regression, Random Forest, and XGBoost machine learning methods were used in Cd prediction. The XGBoost model proved to be the best prediction model, with a coefficient of determination of 98.1%. Electrical conductivity, pH, CaCO₃, silt, and P were used in the Cd estimation of the XGBoost model and explained 56.51% of the total variance in relation to measured soil properties. The results revealed that a machine learning algorithm could be useful for estimating Cd concentration in soils using basic physical and chemical soil properties.

Soil formation processes and agricultural practices determine the amount of plant-available boron (B) concentration in soils. In this study, the relationships between soil characteristics and plant-available B concentrations of 54 soil samples collected from Gelendost and Eğirdir districts of Isparta province were investigated using the Spearman correlation and eXtreme gradient boosting regression (XGBoost) model. Plant-available B concentration was significantly correlated with the soils' phosphorus, potassium, copper, and electrical conductivity. The XGBoost model explained 63% of the variation in five components defining soil behavior, and one of these components showed the variance resulting from the plant-available B. The effects of explanatory variables on B concentration determined in the XGBoost model were the parameters that were also significant in the correlation analysis. The results indicated that the model could successfully estimate B availability from the routinely analyzed soil properties (Fig. 1).

Soil formation processes and cropping and management practices affect plant-available amounts of boron (B) in soils. Using Pearson's correlation and principal component analyses, this study investigated the relationships between soil properties and plant-available boron concentrations in 69 soil samples. In principal component analysis (PCA), 73.079% of the variance was explained with four components. Plant-available B in the soil was significantly correlated with phosphorus, potassium, pH, and electrical conductivity (EC), showing that phosphorus and potassium fertilizer producers would be adding boron to fertilizers, although it is not mentioned on their packages. Also, the tide correlation between B and either soil pH and EC was attributed to greater solubility B, pH and EC. The majority of the experimental soils were deficient in boron. Therefore, boron fertilizer must be added to the fertilization program to mitigate the losses caused by boron deficiency in low-input agricultural production fields.

The purpose of this study was to determine the effects of the dietary addition of zinc (Zn) in the form of sulphate (Zn-S), glycine (Zn-G), and hydroxychloride (Zn-H) on quail performance, eggshell quality, antioxidant status, mineral excretion, biomechanical properties and mineralization of bone. A total of 75 female quails (10-weeks-old) were randomly distributed into 3 groups with 5 replications, each with 5 female quails. Treatment groups were set up by adding Zn-S and Zn-H as the inorganic form and Zn-G as the organic form of zinc to the corn-soybean basal diet (34.14 mg/kg Zn) to obtain 50 mg/kg Zn and the feeding experiment lasted for 12 weeks. Performance parameters and egg production were not impaired by the Zn source (P > 0.05). The inclusion of Zn-S in the diet produced a reduction in eggshell thickness, while an improvement in yolk antioxidant capacity (measured as MDA concentration) was detected compared to the other Zn sources (P < 0.05). Shear strength and cortical bone area increased (P < 0.05) with Zn-G supplementation, however, the mineral concentration of the tibia was higher (P < 0.01) in those quails who had received Zn-H. Lower levels of mineral excretion were observed in both types of supplementations compared to Zn-S. Therefore, it can be stated that Zn-G or Zn-H supplementation in the diet of laying quails could be an interesting strategy to reduce mineral excretion and improve bone mineralization without affecting performance compared to Zn-S. However, further studies are needed to determine the differences between Zn-G and Zn-H.

Hasan Hadi Mahdi MAHDI, Fatih GÖKMEN, & Veli UYGUR - FERTILIZER-INDUCED GEOCHEMICAL FRACTIONS OF PHOSPHORUS IN FRUIT ORCHARDSS, Carpathian Journal of Earth and Environmental Sciences February 2023, Vol. 18, No. 1, p. 139 – 147; DOI:10.26471/cjees/2023/018/247

In acidic tea soil, boron (B) adsorption and desorption processes are dominated by the complex relationship between soil acidity, mineralogy, and organic matter. This study investigated B adsorption–desorption behavior in five acidic tea soils (pH 3.8–5.6) collected from the Eastern Black Sea region of Türkiye and evaluated the potential of machine learning (ML) algorithms to predict B desorption. Laboratory batch experiments were conducted using five initial B concentrations, and adsorption data were interpreted using the Langmuir isotherm model. Adsorption experiments indicated that B interacted with Fe/Al-oxide-containing clay minerals, which had low but favorable binding affinity, as indicated by Langmuir maximum adsorption capacities (Qmax) ranging from 46.5 to 181.8 mg kg⁻¹. Desorption experiments revealed a high degree of reversibility, particularly in soils with lower adsorption capacities, ensuring potential B leaching. To capture the governing B desorption, six machine learning (ML) algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Gaussian Process Regression (GP), Elastic Net Regression (EN), and Multivariate Adaptive Regression Splines (MARS)—were trained on 75 data points. Among the tested models, Elastic Net showed the highest predictive accuracy (R² = 0.735). This model does not replace adsorption experiments. It offers a within-assay determination of desorption given measured adsorption, which may reduce the requirement for separate desorption equilibration and analyses. Permutation importance analysis identified B_ads as the dominant predictor of B desorption, with smaller contributions from pH_ads and EC_ads. The results demonstrate that integrating laboratory experiments with machine learning provides an effective framework for predicting B mobility in acidic tea soils, offering a parameterized experimental framework for describing boron desorption behavior in acidic tea soils.