Spatial prediction of soil properties for the Busia area, Kenya using legacy soil data

Abstract

Legacy soil data often contains considerable agronomic information that can help revitalize agriculture in countries with poor spatial data infrastructures. The objective of this study was to determine whether existing legacy soil data could be used to quantitatively predict soil properties at a higher resolution than the original legacy soil map using digital soil mapping techniques without conducting additional field work. A dataset of 76 soil profiles mined from the Reconnaissance Soil Survey of the Busia Area in Western Kenya together with a total of 23 environmental covariates were used to capture the soil forming factors and test the effectiveness of the individual predictive soil mapping (iPSM) technique to predict soil organic carbon, clay, silt, and sand over large areas in cases where data is limited. iPSM was compared to the stepwise multiple linear regression (SMLR) under two scenarios and ordinary kriging. In scenario one, SMLR prediction was achieved using the 23 original environmental soil covariates as predictors whereas in scenario two, SMLR prediction was achieved using principal components, generated from the 23 original covariates, as predictors. Ordinary kriging used only the soil property data for prediction. Ordinary kriging outperformed all the prediction models (RMSE = 0.02) for SOC, (RMSE = 0.30) for clay, (RSME = 0.10) for silt, and (RMSE = 0.32) for sand. This was attributed to the proximity between the calibration and the evaluation datasets and that these two datasets were not entirely independent. Ordinary kriging maps, however, provided very little spatial detail. Considering only those prediction models that used soil covariates, iPSM did not perform well in predicting soil properties compared to SMLR under the two scenarios. Regardless of the poor performance from iPSM relative to the other prediction models, SMLR (under the two scenarios) and ordinary kriging, iPSM best captured soil properties' spatial variability. A combination of the four models, using model averaging methods, resulted in a better prediction using the equal weights method for clay (%) (RMSE = 14.31), silt (%) (RMSE = 9.00), and sand (%) (RMSE = 16.67). On the contrary, SOC content (%) prediction from SMLR under the two scenarios performed best (RMSE = 0.43). Predicted soil property maps were at a resolution of 30 m, better than the current soil property maps for the study area with a 250 m resolution. The ability of predicted soil property maps to capture the variability of soils in our study area, except for ordinary kriging, supports the overall objective of this study. The sample size within legacy soil data, however, limits the use of complex DSM techniques. Therefore, efforts should continue to ensure that information mined from legacy soil data can be used to bolster the efforts made by various soil agencies to create harmonized soil profile databases to increase their usefulness for complex DSM methods.