A simple pipeline for the assessment of legacy soil datasets: An example and test with soil organic carbon from a highly variable area

Schillaci, Calogero; Acutis, Marco; Vesely, Fosco M.; Saia, Sergio

doi:10.1016/j.catena.2018.12.015

Cited by 17 publications

(17 citation statements)

References 54 publications

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“…A power analysis (Lakens, 2013) was used to nd the minimum number of samples needed to determine the SOC change with time (from 1994 to 2017). The expected change with time was derived from a modelled SOC variation (1994 to 2008) at the regional level, as observed in Schillaci et al (2019). In particular, samples from the same soil layer, land use and sub-area were used (Fig.…”

Section: Sampling Campaignmentioning

confidence: 99%

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

Schillaci

Saia

Lipani

et al. 2021

Preprint

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Background Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use can pose 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-aligned 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 a=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-aligned data), when compared to 1994 observed data (Z = -9.119; 2-tailed asymptotic significance < 0.001). Such a result implies 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.

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Section: Sampling Campaignmentioning

confidence: 99%

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

Schillaci

Saia

Lipani

et al. 2021

Preprint

Self Cite

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“…Data obtained from a sampling campaign carried out in 1993-1994 were used in the present work. In particular, data about the rainfed arable land (CORINE code 211) were identified using the optimization procedure shown in Schillaci et al (2019). Briefly, the whole legacy database consists of 6674 checked samples.…”

Section: Expected Soc Difference Assumptions and Sampling Size Determinationmentioning

confidence: 99%

“…Soil and SOC conservation is critically important in semi-arid areas, where desertification risk is increasing [ 18 ]. For these areas, a recent increase in the output of literature regarding SOC accounting and spatial modelling [ 19 ], legacy databases [ 20 ], and digital soil mapping [ 21 ] has been noted.…”

Section: Introductionmentioning

confidence: 99%

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

Schillaci

Saia

Lipani

et al. 2021

Carbon Balance Manage

Self Cite

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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.

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“…Moreover, many soil properties in samples collected from large areas show inherent non-linearities 14 . Legacy soil data also carry information on both spatial variability and non-linearity 23 . In addition, the size and volume of data available in many legacy soil databases are large 24 .…”

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confidence: 99%

Spatial structure, parameter nonlinearity, and intelligent algorithms in constructing pedotransfer functions from large-scale soil legacy data

Chakraborty

Das

Vasava

et al. 2020

Sci Rep

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Pedotransfer function (PTF) approach is a convenient way for estimating difficult-to-measure soil properties from basic soil data. Typically, PTFs are developed using a large number of samples collected from small (regional) areas for training and testing a predictive model. National soil legacy databases offer an opportunity to provide soil data for developing PTFs although legacy data are sparsely distributed covering large areas. Here, we examined the Indian soil legacy (ISL) database to select a comprehensive training dataset for estimating cation exchange capacity (CEC) as a test case in the PTF approach. Geostatistical and correlation analyses showed that legacy data entail diverse spatial and correlation structure needed in building robust PTFs. Through non-linear correlation measures and intelligent predictive algorithms, we developed a methodology to extract an efficient training dataset from the ISL data for estimating CEC with high prediction accuracy. The selected training data had comparable spatial variation and nonlinearity in parameters for training and test datasets. Thus, we identified specific indicators for constructing robust PTFs from legacy data. Our results open a new avenue to use large volume of existing soil legacy data for developing region-specific PTFs without the need for collecting new soil data.

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A simple pipeline for the assessment of legacy soil datasets: An example and test with soil organic carbon from a highly variable area

Cited by 17 publications

References 54 publications

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

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

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

Spatial structure, parameter nonlinearity, and intelligent algorithms in constructing pedotransfer functions from large-scale soil legacy data

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