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

Abstract

Background: Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0-30 cm soil depth and tested.

Results: The measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05. A Wilcoxon test applied to the variation of SOC from 1994 to 2017 suggested that there was not a statistical difference in SOC concentration after 23 years (Z = - 0.556; 2-tailed asymptotic significance = 0.578). In particular, only 40% of resampled sites showed a higher SOC concentration than in 2017.

Conclusions: This finding contrasts with a previous SOC concentration increase that was found in 2008 (75.8% increase when estimated as differences of 2 models built with non-paired data), when compared to 1994 observed data (Z = - 9.119; 2-tailed asymptotic significance < 0.001). This suggests that the use of legacy data to estimate SOC concentration dynamics requires soil resampling in the same locations to overcome the stochastic model errors. Further experiment is needed to identify the percentage of the sites to resample in order to align two legacy datasets in the same area.

Keywords: Minimum detectable change (MDC); Power analysis; Semi-arid; Soil monitoring; Soil organic carbon (SOC).

Fig.1

Study area and main soil systems from the Italian soil information system http://www.soilmaps.it/. The samples collected only in 1994 (black dots) and both in 1994 and 2017 (red triangles) were projected on the map

Fig. 2

Example of checking of the actual land use along the course from the 1994 to the 2017 in a sampling location by means of a visual interpretation of the Orthophoto taken from a Web Mapping Services (WMS)

Fig. 3

Study area, original soil data (dots) and original compared to new soil samples (histograms, background mask the Arable land cover from CORINE 2000, code 211 (yellow). Blue bars for SOC94, red bars for SOC17

Fig. 4

Distribution of the 30 samples of SOC 94 (blue bars) and SOC17 (red bars) and relative probability distribution function. Upper panels are for raw data, lower panels for log

Fig. 5

Distribution of the difference between pairs of the 30 samples of SOC17-SOC94 (expressed as raw data, green bars; or their logs, purple bars) and relative probability distribution function. Differences between the 302 original data (LEG-SOC94) and the 302 estimated data of 2008 (EST-SOC08) is also shown (grey bars). Positive values indicate an increase in SOC with time