A preliminary assessment of the space‐for‐time substitution method in soil carbon change prediction

Abstract

Prediction of the soil C response to environmental change is often limited by the absence of temporal soil monitoring data from which applicable models can be developed. This constrains the understanding of the processes governing soil C cycling in data‐poor areas. Here, we demonstrate a method to predict soil organic C (SOC) change over time based on the space‐for‐time (SFT) substitution method by constructing spatial and temporal datasets of SOC change from soil datasets pertaining to four periods over the past 40 yr in China. We empirically test the assumption that environmental drivers of the spatial gradients of SOC variation also drive SOC change over time. We find that the accuracy of SOC change predictions obtained with the SFT method is comparable to that of predictions directly obtained with temporal models, with the prediction performance ranging from 86 to 118%. Our results generally support the application of the SFT method to model SOC dynamics over time and highlight the importance of this method to better understand soil C cycling in ecosystems associated with insufficient soil monitoring data. Our findings provide a priori guidance for the suitability of the SFT method as an approach to infer the soil C response to environmental change.

Reply to: Field experiments show no consistent reductions in soil microbial carbon in response to warming

“…Due to the very short duration of these time series, the mean annual temperature of most sites only varied by a few degrees Celsius within a site, strongly limiting the ability of this dataset to discern any relationship. This weak statistical power is accentuated by the lack of additional relevant explanatory variables, such as vegetation and soil water content 8 . We hope that additional datasets will be available in the future to clarify these relationships.…”

“…On the contrary, SFT is often used in ecological studies and can provide valuable mechanistic insights 9 . Especially Yang et al 8 found high validity in the SFT approach for modeling soil carbon processes. Most importantly, we disagree with the rationale of the approach taken by Yue et al to test for SFT biases, and question how bootstrapping can be used for that purpose.…”

BIS-4D: a high-resolution soil modelling and mapping platform for the Netherlands in 3D space and time

“…However, the overwhelming majority of DSM studies ignore time. This is because spacetime mapping of soil properties remains a major challenge, with relatively few studies having mapped temporal changes and mostly only at the field scale (Meersmans et al, 2011(Meersmans et al, , 2016Sun et al, 2012;Stockmann et al, 2015;Gasch et al, 2015;Minasny et al, 2016;Gray & Bishop, 2016Yigini & Panagos, 2016;Schillaci et al, 2017;Hengl et al, 2017a;Sanderman et al, 2017;Song et al, 2018;Stumpf et al, 2018;Reyes Rojas et al, 2018;Adhikari et al, 2019;Huang et al, 2019;Zhou et al, 2019;Heuvelink et al, 2020;Sun et al, 2021;OpenGeoHub et al, 2022;Yang et al, 2022). These studies have used various space-time modelling approaches.…”

“…Another approach is space-for-time-substitution (e.g. Adhikari et al, 2019;Yang et al, 2022), although differences in a dynamic soil property due to temporal changes are not necessarily comparable to differences due to spatial variation. Commonly, soil property changes are mapped using a static model, by simply replacing covariates such as land use or climate during prediction with a historic map or future scenario (Yigini & Panagos, 2016;Meersmans et al, 2016;Gray & Bishop, 2016Adhikari et al, 2019;Huang et al, 2019;Reyes Rojas et al, 2018).…”