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.

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Matching legacy estimation of soil organic carbon changes from non-paired data with measured values in paired soil samples after two decades: a case study

10.5194/egusphere-egu21-10639 ◽

2021 ◽

Author(s):

Calogero Schillaci ◽

Sergio Saia ◽

Aldo Lipani ◽

Alessia Perego ◽

Claudio Zaccone ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Depth ◽

Soil Samples ◽

Wilcoxon Test ◽

Reliable Estimate ◽

Model Errors ◽

Paired Data ◽

Concentration Changes ◽

Legacy Data

Legacy data are frequently unique sources of data for the estimation of past soil properties. With the rising concerns about greenhouse gases (GHG) emission and soil degradation due to intensive agriculture and climate change effects, soil organic carbon (SOC) concentration might change heavily over time.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. The sampling schemes adopted to capture SOC variation usually involve the resampling of the original sample using a so called paired-site approach.In the present work, a regional (Sicily, south of Italy) soil database, consisting of N=302 georeferenced soil samples from arable land collected in 1993 [1], was used to select coinciding sites to test a former temporal variation (1993-2008) obtained by a comparison of models built with data sampled in non-coinciding locations [2]. A specific sampling strategy was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0-30 cm soil depth and tested.To spot SOC changes the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years has been estimated. 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.After the collection of the 30 samples, SOC concentration in the newly collected samples was determined in lab using the same methodA 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 (not always significant) SOC concentration than in 2017.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) [2], 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 changes need 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.Bibliography[1]Schillaci C, et al.,2019. A simple pipeline for the assessment of legacy soil datasets: An example and test with soil organic carbon from a highly variable area. CATENA.[2]Schillaci C, et al., 2017. Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling. Sci Total Environ.&#160;

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

10.21203/rs.3.rs-150726/v1 ◽

2021 ◽

Author(s):

Calogero Schillaci ◽

Sergio Saia ◽

Aldo Lipani ◽

Alessia Perego ◽

Claudio Zaccone ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Depth ◽

Soil Samples ◽

Reliable Estimate ◽

Model Errors ◽

Soil Database ◽

Minimum Number ◽

Concentration Changes ◽

Legacy Data

Abstract 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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Determining the Distribution and Interaction of Soil Organic Carbon, Nitrogen, pH and Texture in Soil Profiles: A Case Study in the Lancangjiang River Basin, Southwest China

Forests ◽

10.3390/f11050532 ◽

2020 ◽

Vol 11 (5) ◽

pp. 532 ◽

Cited By ~ 1

Author(s):

Wenxiang Zhou ◽

Guilin Han ◽

Man Liu ◽

Jie Zeng ◽

Bin Liang ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

River Basin ◽

Soil Ph ◽

Soil Texture ◽

Soil Depth ◽

Soil Samples ◽

Soil Profiles ◽

Soil Organic Nitrogen ◽

Silty Loam

The profile distributions of soil organic carbon (SOC), soil organic nitrogen (SON), soil pH and soil texture were rarely investigated in the Lancangjiang River Basin. This study aims to present the vertical distributions of these soil properties and provide some insights about how they interact with each other in the two typical soil profiles. A total of 56 soil samples were collected from two soil profiles (LCJ S-1, LCJ S-2) in the Lancangjiang River Basin to analyze the profile distributions of SOC and SON and to determine the effects of soil pH and soil texture. Generally, the contents of SOC and SON decreased with increasing soil depth and SOC contents were higher than SON contents (average SOC vs. SON content: 3.87 g kg−1 vs. 1.92 g kg−1 in LCJ S-1 and 5.19 g kg−1 vs. 0.96 g kg−1 in LCJ S-2). Soil pH ranged from 4.50 to 5.74 in the two soil profiles and generally increased with increasing soil depth. According to the percentages of clay, silt, and sand, most soil samples can be categorized as silty loam. Soil pH values were negatively correlated with C/N ratios (r = −0.66, p < 0.01) and SOC contents (r = −0.52, p < 0.01). Clay contents were positively correlated with C/N ratios (r = 0.43, p < 0.05) and SOC contents (r = 0.42, p < 0.01). The results indicate that soil pH and clay are essential factors influencing the SOC spatial distributions in the two soil profiles.

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Distribution Characteristics of Soil Organic Carbon in Estuarine Wetland of Qingdao Dagu River

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.737.469 ◽

2015 ◽

Vol 737 ◽

pp. 469-472

Author(s):

Fan Long Kong ◽

Min Xi ◽

Yue Li ◽

Wen Hao Zhang ◽

Yang Liu

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Coastal Wetland ◽

Soil Depth ◽

Jiaozhou Bay ◽

River Estuary ◽

Soil Samples ◽

Downward Trend ◽

Distribution Characteristics ◽

Different Depths

Distribution characteristics of content of soil organic carbon in wetland were studied by the analysis of four soil samples from areas, which were at different depths of soil, collected in the Dagu River estuary of Qingdao during summer of 2014. The result showed that the content of soil organic carbon in coastal wetland of Jiaozhou bay had an overall downward trend with the increase of soil depth. Because of the influence of hydro-salinity environment and tidal action, in regions near the sea, the content of soil organic carbon was less than its counterpart in regions away from the ocean.

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Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR

Remote Sensing ◽

10.3390/rs12203451 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3451

Author(s):

Kathrin J. Ward ◽

Sabine Chabrillat ◽

Maximilian Brell ◽

Fabio Castaldi ◽

Daniel Spengler ◽

...

Keyword(s):

Remote Sensing ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Prediction Accuracy ◽

Remote Sensing Data ◽

Soil Samples ◽

Wet Chemistry ◽

Sensing Data ◽

Soil Database ◽

Local Soil

Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.

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Changes in Soil Organic Carbon Concentration and Stock after Forest Regeneration of Agricultural Fields in Taiwan

Forests ◽

10.3390/f12091222 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1222

Author(s):

Yi-Han Lin ◽

Pei-Chen Lee ◽

Oleg V. Menyailo ◽

Chih-Hsin Cheng

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Forest Regeneration ◽

Soil Depth ◽

Accumulation Rate ◽

Soil Samples ◽

Agricultural Fields ◽

Subtropical Climate ◽

Accumulation Rates ◽

Organic Carbon Concentration

Afforestation or abandonment of agricultural fields to forest regeneration is a method of sequestering carbon to offset the increasing atmospheric concentration of CO2. We selected 11 sites with altitudes ranging from 14 to 2056 m and with paired forest regenerated and adjacent agricultural fields. Our objectives were to (1) examine the changes in soil organic carbon (SOC) concentration and stock after forest regeneration of agricultural fields and (2) identify the factors related to elevation and adjacent agricultural practices that affect the SOC accumulation rate. Our results demonstrated overall increases in both SOC concentrations and stocks after forest regeneration of the abandoned agricultural fields. The average increase rates of SOC concentrations in the forest regenerated soil samples were 1.65 and 0.95 g C kg−1 at 0–10 and 10–20 cm depths, respectively, representing 101% and 65% increases relative to those in the soil samples from agricultural fields. The average accumulation rates of SOC stocks in the regenerated forests were 13.0 and 6.7 ton C ha−1 at the 0–10 and 10–20 cm depths, respectively, representing 96% and 62% increases relative to those in the agricultural soil samples. The average annual sequestration rate was 1.03 Mg C ha−1 year−1 for the top 0–20 cm soils, which is greater than that observed by previous reviews and meta-analyses. The tropical/subtropical climate, sampling soil depth, forest regeneration period, and tree species in this study are likely to have contributed to the high average SOC accumulation levels. In addition, the SOC stock accumulation rates were higher at low-elevation sites than at middle-elevation sites, which could also be attributed to the favorable climatic conditions at the low-elevation sites. Along with the build-up of carbon sequestration in the forest floor and tree biomass, the afforestation/abandonment of agricultural fields to forest regeneration appears to be a promising carbon offset mechanism.

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Distribution of organic carbon in Ultisol soils with citronella and pine vegetation, at Gayo Highlands, Aceh

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/951/1/012009 ◽

2022 ◽

Vol 951 (1) ◽

pp. 012009

Author(s):

A Karim ◽

Hifnalisa ◽

Y Jufri ◽

Y D Fazlina ◽

Megawati

Keyword(s):

Organic Matter ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Depth ◽

Soil Surface ◽

Soil Samples ◽

Soil Profiles ◽

Pine Tree ◽

Total Soil ◽

Pine Trees

Abstract Soil organic matter is an indicator of soil fertility. The purpose of this study was to analyse various forms of soil organic carbon in citronella plantation, citronella plantation under pine tree, and soil under pine tree. Soil organic carbon in various forms was analysed from soil samples taken from each horizon and soil profile. The soil profiles observed were ultisol profiles planted with citronella, citronella under pine tree, and under pine tree, and slopes; 0-8%, 8-15%, 15 -25%, and 25-40%, in order to obtain 12 soil profiles with a total of 39 soil samples. Ultisols planted with citronella had higher soil organic carbon than ultisols planted with citronella under pine tree and ultisols under pine trees. Based on the slope, the highest soil organic carbon was obtained in the soil with a slope of 0-8%, and decreased with increasing slope. Based on soil depth, the highest soil organic carbon was obtained in the upper horizon, compared to the horizon below. The highest total soil organic carbon was obtained at the soil surface horizon with a slope of 0-8% and citronella was planted. This pattern of total soil organic carbon is similar to that of sesquioxide bound organic carbon, but is not consistent with that of free clay bound organic carbon.

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A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability

Land ◽

10.3390/land10050517 ◽

2021 ◽

Vol 10 (5) ◽

pp. 517

Author(s):

Sunwei Wei ◽

Zhengyong Zhao ◽

Qi Yang ◽

Xiaogang Ding

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Guangdong Province ◽

Soil Samples ◽

Estimation Accuracy ◽

Ann Model ◽

Soil Layers ◽

Second Stage ◽

Organic Carbon Storage

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.

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Long-term effects of fertilisers and organic sources on soil organic carbon fractions under a rice–wheat system in the Indo-Gangetic Plains of north-west India

Soil Research ◽

10.1071/sr16097 ◽

2017 ◽

Vol 55 (3) ◽

pp. 296 ◽

Cited By ~ 6

Author(s):

D. Das ◽

B. S. Dwivedi ◽

V. K. Singh ◽

S. P. Datta ◽

M. C. Meena ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Crop Yields ◽

Soil Depth ◽

Farmyard Manure ◽

Microbial Biomass C ◽

Gangetic Plain ◽

Organic C ◽

Labile C ◽

Labile C Fractions

Decline in soil organic carbon (SOC) content is considered a key constraint for sustenance of rice–wheat system (RWS) productivity in the Indo-Gangetic Plain region. We, therefore, studied the effects of fertilisers and manures on SOC pools, and their relationships with crop yields after 18 years of continuous RWS. Total organic C increased significantly with the integrated use of fertilisers and organic sources (from 13 to 16.03gkg–1) compared with unfertilised control (11.5gkg–1) or sole fertiliser (NPKZn; 12.17gkg–1) treatment at 0–7.5cm soil depth. Averaged across soil depths, labile fractions like microbial biomass C (MBC) and permanganate-oxidisable C (PmOC) were generally higher in treatments that received farmyard manure (FYM), sulfitation pressmud (SPM) or green gram residue (GR) along with NPK fertiliser, ranging from 192 to 276mgkg–1 and from 0.60 to 0.75gkg–1 respectively compared with NPKZn and NPK+cereal residue (CR) treatments, in which MBC and PmOC ranged from 118 to 170mgkg–1 and from 0.43 to 0.57gkg–1 respectively. Oxidisable organic C fractions revealed that very labile C and labile C fractions were much larger in the NPK+FYM or NPK+GR+FYM treatments, whereas the less-labile C and non-labile C fractions were larger under control and NPK+CR treatments. On average, Walkley–Black C, PmOC and MBC contributed 29–46%, 4.7–6.6% and 1.16–2.40% towards TOC respectively. Integrated plant nutrient supply options, except NPK+CR, also produced sustainable high yields of RWS.

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