Spatial modeling of soil salinity using kriging interpolation techniques: A study case in the Great Hungarian Plain

The world’s current task is to ensure food security for an ever-growing population of 7.674 billion in 2019. Soil degradation threatens sustainable agriculture arid and semi-arid climates, where evaporation rates outweigh precipitation. Soluble salts concentrated the subsoil under certain climatic conditions influence soil physicochemical properties, leading fertility biodiversity losses. Hence, understanding salinity behavior its spatial variation are crucial natural resources management achieve maintain sustainability. This study aims model distribution using four kriging interpolation methods, i.e., ordinary (OK), empirical Bayesian (EBK), co-kriging (CK), indicator (IK). Two hundred twenty-two samples were collected this purpose during a field campaign conducted Hungarian Monitoring System framework 2016. performance methods was assessed compared two cross-validations, leave-one-out cross-validation (LOOCV) holdout method. Pearson correlation analysis has been used expose significant moderate between salt content cation exchange capacity (CEC) with coefficient 0.4 p-value 0.003. Thus, relationship (SSC) CEC integrated into enhance predictions areas no measurements accessible. demonstrated efficiency by reducing mean squared error (MSE) (OK) from 0.8 g/kg 0.85 LOOCV 0.3 g/kg.