SIMPLE REGRESSION MODELS FOR PREDICTING SOIL HYDROLYTIC ACIDITY

Abstract

Soil acidity is global factor limiting soil fertility of about 40% of the cultivable land which are acid. The common liming recommendations are based on different soil properties and therefore the calculation could differ according to available and used soil data. The aim of this paper was to determine the suitability of simple regression models for prediction soil hydrolytic acidity for precise liming recommendation using just actual (pHH2O) and exchangeable soil pH (pHKCl) and humus content as basic soil data. These agrochemical analyses were done on basic set of 2600 soil samples and on validation set of 375 soil samples. The simple regression model could be accurate enough using just actual soil pH only for soils with lower humus content. Model accuracy increases including more soil data in prediction model, starting from adding soil exchangeable pH and then including humus data. Because of possible high soil sample variance, the best simple models are model including both actual and exchangeable soil pH, and humus, but with different regression equation for each range of soil pH or/and for each range of humus content. These kinds of models are sensitive to soil cation exchange capacity, humus content, texture and soil acidity, indicating that model adjustment to soil types could result in increasing model accuracy. The model error correlate to humus content and soil acidity, and the lowest model error were about 14% in average for soil pHKCl 4-5, and 16% for soil pHKCl <4.