Affiliation:
1. Department of Soil Science, FD Hole Soils Lab University of Wisconsin‐Madison Madison Wisconsin USA
2. Department of Soil Science, Institute of Environmental Sciences Hungarian University of Agriculture and Life Sciences Gödöllő Hungary
3. Soil Research Institute, Soil Genesis, Survey and Classification Division Council for Scientific and Industrial Research Kwadaso‐Kumasi Ghana
4. Department of Crop and Soil Science Oregon State University Corvallis Oregon USA
Abstract
AbstractSoil exchangeable acidity (EA) is an indicator of aluminium toxicity potential in acidic soils. Predicting the distribution and dynamics of EA is needed for the identification and management of acidic soils. In this study, we used datasets of 355 pedons from across Ghana and the Cubist rule‐based algorithm to generate pedotransfer functions (PTFs) of EA. Eight soil properties (pH, organic carbon, calcium, sodium, magnesium, total exchangeable bases, cation exchange capacity [CEC] to clay ratio and soil depth) were used to predict EA. We first used the whole dataset to construct generic PTFs and then stratified the dataset based on World Reference Base‐Reference Soil Groups (WRB‐RSGs) to generate soil‐specific PTFs. Goodness‐of‐fit statistics comprising the root mean squared error (RMSE), Lin's concordance correlation coefficient () and coefficient of determination (R2) were used to evaluate the prediction accuracy and reliability of the developed PTF models on both calibration and validation datasets. The Fluvisols EA‐PTF exhibited lower performance metrics in the validation (RMSE = 0.17 cmolc kg−1, = 0.19, R2 = 0.24), whereas the EA‐PTFs for all other WRB‐RSGs and whole dataset had above‐average performance metrics in the validation (0.05 ≤ RMSE ≤ 0.97 cmolc kg−1; 0.34 ≤ ≤ 0.94; 0.52 ≤ R2 ≤ 0.96). Soil pH sufficed for predicting EA in soils with pH above 5.0, but in soils with a pH < 5.0, the levels of exchangeable bases (e.g., Na+, K+, Mg2+, Ca2+), CEC to clay ratio (CCR) and soil depth improved the prediction of EA. The developed EA‐PTFs are useful for estimating the missing values of EA in soil databases.
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