Twenty-five years of observations of soil organic carbon in Swiss croplands showing stability overall but with some divergent trends

Abstract

The temporal evolution of soil organic carbon (SOC) is of major importance given its status as a key parameter in many soil functions. Furthermore, soils constitute an important reservoir of carbon in our environment. In light of climate change, consistent SOC data over extended periods in combination with information on agricultural management are much required, but still scarce. We report SOC changes in the topsoil (0–20 cm) of Swiss cropland measured at well-defined monitoring sites resampled every 5 years from 1990 to 2014 by the Swiss Soil Monitoring Network NABO using consistent sampling protocols and quality assurance. Data on agricultural management practices were retrieved from farmers. Overall, SOC remained stable for the ensemble of monitoring sites, although increasing and decreasing trends were observed for individual sites, ranging from − 11 to + 16% relative change per decade. Changes in the agricultural management of cropland triggered substantial changes in SOC contents for some sites. Moreover, sites with a low ratio of SOC/clay (< 0.1) generally showed more positive trends than sites with higher ratios. We presume that SOC was either at or near steady state, given the consistency of management practices over the last few decades. Finally, our study provides insights into the uncertainties related to (real-world) SOC monitoring and underlines the relevance of short-term SOC variations that could hamper the detection of long-term trends. The minimum detectable change (MDC) by the applied monitoring scheme is estimated at 0.35% per year, in relative terms. Electronic supplementary material The online version of this article (10.1007/s10661-019-7435-y) contains supplementary material, which is available to authorized users.

Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands

“…In this study, no evidence of this magnitude of increase was found, and no such difference was seen in neither the original data nor in the log transformed data. When using a similar amount of data from meadows, Gubler et al [76] found that the minimum detectable change in 10 to 100 years (at a power of the 80%, such as in the present study) spanned from approximately 2 to 6%, respectively. Bellamy et al [15] showed that variation in time strongly depended on the initial SOC content, e.g., sites with low SOC had more opportunity to increase their SOC than sites with high SOC.…”

“…The previous analyses based on modelled data over a 15-year span (from 1994 to 2008) predicted a mean relative increase of around the 21% of SOC content in arable lands, but such an increase was affected by a strong variability in the real plot scale land use when survey data was reviewed. By using a monitoring network spanning 30 years, Gubler et al [76] found that SOC dynamic is more determined by a change in land use than other predictors in a colder climate (Switzerland) agro-ecosystem.…”

Determination of minimum number of samples allowing to detect long term soil organic carbon changes in Mediterranean arable lands using paired-sites

“…In this study, no evidence of this magnitude of increase was found, and no such difference was seen in neither the original data nor in the log transformed data. When using a similar amount of data from meadows, Gubler et al (2019) found that the minimum detectable change in 10 to 100 years (at a power of the 80%, such as in the present study) spanned from approximately 2 to 6%, respectively. Bellamy et al (2005) showed that variation in time strongly depended on the initial SOC content, e.g., sites with low SOC had more opportunity to increase their SOC than sites with high SOC.…”

“…The previous analyses based on modelled data over a 15-year span (from 1994 to 2008) predicted a mean relative increase of around the 21% of SOC content in arable lands, but such an increase was affected by a strong variability when survey data was reviewed. By using a monitoring network spanning 30 years, Gubler et al (2019) found that SOC dynamic is more determined by a change in land use than other predictors in a colder climate (Switzerland).…”

Machine learning in space and time for modelling soil organic carbon change

“…Modelling temporal change can benefit greatly from such repeated measurements, because it filters out spatial variation. In recent years, various countries and organizations have implemented soil monitoring campaigns (e.g., Gubler, Wächter, Schwab, Müller, & Keller, 2019;Orgiazzi, Ballabio, Panagos, Jones, & Fernández-Ugalde, 2018;Smith et al, 2020); making these data available for modelling could greatly improve prediction accuracy. Such monitoring schemes are becoming much easier to practically implement with the use of soil sensing technologies, as reviewed in Smith et al (2020).…”