Unravelling spatial drivers of topsoil total carbon variability in tropical paddy soils of Sri Lanka

Abstract

This study aimed to map and identify the spatial drivers of total carbon (TC) concentration in topsoil (0–15 cm) across paddy-growing regions in tropical climates using Sri Lanka as a case study. For model calibration, a total of 888 sampling locations were sampled using the conditioned Latin Hypercube sampling approach with a sample density of one sample per 11 km2. Additionally, 99 sampling sites were selected using a design-based (probabilistic) stratified random strategy for independent evaluation of the developed models. Two distinct spatial random forest (RF) models were developed using a variety of environmental covariates: Model 1: using all environmental covariates without variable selection; Model 2: only incorporated covariates selected based on the forward selection process. Evaluation of model quality using fully independent validation sites revealed that both Model 1 and Model 2 performed similarly. Based on the spatial estimates of Model 1 across the paddy-growing regions of Sri Lanka, the predicted TC concentration varied from 0.89% to 13.15%. The highest predicted TC concentration range was in the Wet zone (2.06% to 13.15%), followed by the Intermediate zone (1.18% to 7.23%), and the lowest was reported in the Dry zone (0.86% to 4.30%). In the spatial estimates of Model 2, the predicted values varied between 0.86% and 13.29% and were similar to Model 1. The highest predicted TC concentration range was in the Wet zone (2.09% to 13.29%), followed by the Intermediate zone (1.08% to 6.99%), and the lowest was reported in the Dry zone (0.86% to 4.30%) following the similar pattern to Model 1. In fact, this clearly showed the importance of mean annual rainfall on the dynamics of TC in tropical rice production systems. Furthermore, the variable importance plot of the RF models revealed that out of all considered environmental covariates, the mean annual rainfall was identified as being the most important variable in the developed spatial prediction function. Moreover, we deployed an area of applicability (AOA) calculation to quantify and identify regions where prediction is less reliable and quantified the prediction uncertainty using a bootstrapping approach. Additionally, we assessed the influence of increasing the number of calibration sites on model prediction quality and reliability using user defined sequence of calibration sites. Independent evaluations of each model indicated that model performance quality indices were improved up to n = 400 and thereafter stagnated. For AOA results, an improvement in model reliability is observed for Wet and Intermediate zones when models are developed using 400 calibration sites. Derived estimates of TC can be used for regional-scale planning to enhance the soil carbon and provide a baseline for designing a future land-based carbon accounting system for Sri Lanka.