Comparison of Three Models for Predicting The Spatial Distribution of Soil Organic Carbon in Boalemo Regency, Sulawesi: Comparison of Three Models for Predicting The Spatial Distribution of Soil Organic Carbon in Boalemo Regency, Sulawesi | Comparison of Three Models for Predicting The Spatial Distribution of Soil Organic Carbon in Boalemo Regency, Sulawesi: Comparison of Three Models for Predicting The Spatial Distribution of Soil Organic Carbon in Boalemo Regency, Sulawesi

anglais. Information on the spatial distribution of soil organic carbon content is required for sustainable land management. But, creating this map is time-consuming and costly. Digital soil mapping methodology makes use of legacy soil data to create provisional soil organic carbon map. This map helps soil surveyors in allocating the next soil observation. This study aimed: (i) to develop predictive statistical soil organic carbon models for Sulawesi, and (ii) to evaluate the best model between the three obtained models. Boalemo Regency in Gorontalo Province (Sulawesi) was selected as a studying area due to abundant legacy soil data. The study covered dataset preparation, model development, and model comparison. Dataset of soil organic carbon at 6 different depths as the target was established from 176 soil profiles and 7 terrain parameters were selected as predictors. Soil-landscape models for each soil depth were created using a regression tree, conditional inference tree, and multiple linear regression techniques.  The result showed that model performance differed among 3 modeling techniques and soil depths. The tree models were better than the multiple linear regression model as they have the lowest RMSE index. The best model in the mountainous area seems to be the regression tree model, whereas in the plains it may be the conditional inference tree. In creating the provisional map, several models should be developed and the median of the predicted value is used as a provisional map. Keywords: Digital soil mapping, multiple linear regression, regression tree, soil-landscape model, soil organic carbon map

indonésien. Information on the spatial distribution of soil organic carbon content is required for sustainable land management. But, creating this map is time-consuming and costly. Digital soil mapping methodology makes use of legacy soil data to create provisional soil organic carbon map. This map helps soil surveyors in allocating the next soil observation. This study aimed: (i) to develop predictive statistical soil organic carbon models for Sulawesi, and (ii) to evaluate the best model between the three obtained models. Boalemo Regency in Gorontalo Province (Sulawesi) was selected as a studying area due to abundant legacy soil data. The study covered dataset preparation, model development, and model comparison. Dataset of soil organic carbon at 6 different depths as the target was established from 176 soil profiles and 7 terrain parameters were selected as predictors. Soil-landscape models for each soil depth were created using a regression tree, conditional inference tree, and multiple linear regression techniques.  The result showed that model performance differed among 3 modeling techniques and soil depths. The tree models were better than the multiple linear regression model as they have the lowest RMSE index. The best model in the mountainous area seems to be the regression tree model, whereas in the plains it may be the conditional inference tree. In creating the provisional map, several models should be developed and the median of the predicted value is used as a provisional map. Keywords: Digital soil mapping, multiple linear regression, regression tree, soil-landscape model, soil organic carbon map