Abstract
<p><em>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 make use legacy soil data to create provisional soil organic carbon map. This map helps soil surveyors in allocating 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 Regeny in Gorontalo Province (Sulawesi) was selected as 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 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 regression tree, conditional inference tree, and multiple linear regression technique. Result showed that model performance differed among 3 modelling 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 mountanious area seems to be the regression tree model, whereas in the plains it may be the conditional inference tree. In creating provisional map, several model should be developed and the median of predicted value is used as provisional map.</em></p><p><em> </em></p><p><em>Keywords: Digital soil mapping, multiple linear regression, regression tree, soil-landscape model, soil organic carbon map</em></p>
Highlights
Sulawesi is a fertile land and is a cacao and rice national production centre
It is very important to assess the spatial distribution of soil organic carbon (SOC) content
Multiple linear regression analysis establishes a functional relationship between SOC data and all derived parameters
Summary
It is very important to assess the spatial distribution of soil organic carbon (SOC) content. Data in Sulawesi are limited and difficult to be obtained, especially in the mountainous area. It is useful to develop other technique to assess useful soil organic carbon data. Using digital soil mapping is a solution to improve soil mapping in poorly accessible areas (Moore et al, 1991; Florinsky, 1998). Lagarcherie and McBratney (2007) defines digital soil mapping as "the creation and population of spatial soil information systems by numerical models inferring the spatial and temporal variations of soil types and soil properties from soil observation and knowledge and from related environmental variables". The statistical analysis is used to create predictive models of soil properties, requiring less human intervention than traditional soil mapping techniques.
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