Greenhouse gas inventories: Deriving soil organic carbon change factors and assessing soil depth relevance in Argentinean Semiarid Chaco

CATENA ◽  
2018 ◽  
Vol 169 ◽  
pp. 164-174 ◽  
Author(s):  
Sebastián Horacio Villarino ◽  
Guillermo Alberto Studdert ◽  
Pedro Laterra
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Greenhouse Gas ◽  
Soil Depth ◽  
Change Factors ◽  
Greenhouse Gas Inventories ◽  
Carbon Change ◽  
Depth Relevance

scholarly journals Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?

2021 ◽  
Vol 13 (12) ◽  
pp. 2265
Author(s):  
Jonathan Sanderman ◽  
Kathleen Savage ◽  
Shree Dangal ◽  
Gabriel Duran ◽  
Charlotte Rivard ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Carbon ◽  
Diffuse Reflectance Spectroscopy ◽  
Low Cost ◽  
Soil Survey ◽  
Mid Infrared ◽  
Mir Spectroscopy ◽  
Induced Changes ◽  
Carbon Change

A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring.

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 ◽  
2017 ◽  
Vol 55 (3) ◽  
pp. 296 ◽  
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.

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

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;After the collection of the 30 samples, SOC concentration in the newly collected samples was determined in lab using the same method&lt;/p&gt;&lt;p&gt;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 (not always significant) SOC concentration than in 2017.&lt;/p&gt;&lt;p&gt;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 &lt; 0.001).&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;Bibliography&lt;/p&gt;&lt;p&gt;[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.&lt;/p&gt;&lt;p&gt;[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.&amp;#160;&lt;/p&gt;

scholarly journals 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 ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 532 ◽  
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.

scholarly journals Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 261 ◽  
Author(s):  
Maria Marques ◽  
Ana Álvarez ◽  
Pilar Carral ◽  
Iris Esparza ◽  
Blanca Sastre ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Prediction Ability ◽  
Greenhouse Gas Inventories ◽  
Starting Point

Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

scholarly journals Soil Organic Carbon Depletion from Forests to Grasslands Conversion in Mexico: A Review

Agriculture ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 181 ◽  
Author(s):  
Deb Aryal ◽  
Danilo Morales Ruiz ◽  
César Tondopó Marroquín ◽  
René Pinto Ruiz ◽  
Francisco Guevara Hernández ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Land Use Change ◽  
Soil Organic Carbon ◽  
Tropical Forests ◽  
Soil Depth ◽  
Carbon Stocks ◽  
Soil Horizon ◽  
Native Forests ◽  
Soc Stocks

Land use change from forests to grazing lands is one of the important sources of greenhouse gas emissions in many parts of the tropics. The objective of this study was to analyze the extent of soil organic carbon (SOC) loss from the conversion of native forests to pasturelands in Mexico. We analyzed 66 sets of published research data with simultaneous measurements of soil organic carbon stocks between native forests and pasturelands in Mexico. We used a generalized linear mixed effect model to evaluate the effect of land use change (forest versus pasture), soil depth, and original native forest types. The model showed that there was a significant reduction in SOC stocks due to the conversion of native forests to pasturelands. The median loss of SOC ranged from 31.6% to 52.0% depending upon the soil depth. The highest loss was observed in tropical mangrove forests followed by highland tropical forests and humid tropical forests. Higher loss was detected in upper soil horizon (0–30 cm) compared to deeper horizons. The emissions of CO2 from SOC loss ranged from 46.7 to 165.5 Mg CO2 eq. ha−1 depending upon the type of original native forests. In this paper, we also discuss the effect that agroforestry practices such as silvopastoral arrangements and other management practices like rotational grazing, soil erosion control, and soil nutrient management can have in enhancing SOC stocks in tropical grasslands. The results on the degree of carbon loss can have strong implications in adopting appropriate management decisions that recover or retain carbon stocks in biomass and soils of tropical livestock production systems.

scholarly journals Mathematical Functions to Model the Depth Distribution of Soil Organic Carbon in a Range of Soils from New South Wales, Australia under Different Land Uses

Soil Systems ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 46 ◽  
Author(s):  
Brian W. Murphy ◽  
Brian R. Wilson ◽  
Terry Koen
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Exponential Function ◽  
Depth Distribution ◽  
Soil Depth ◽  
New South ◽  
Exponential Functions ◽  
Two Phase ◽  
Mathematical Functions ◽  
South Wales

The nature of depth distribution of soil organic carbon (SOC) was examined in 85 soils across New South Wales with the working hypothesis that the depth distribution of SOC is controlled by processes that vary with depth in the profile. Mathematical functions were fitted to 85 profiles of SOC with SOC values at depth intervals typically of 0–5, 5–10, 10–20, 20–30, 30–40, 40–50, 50–60, 60–70, 70–80, 80–90 and 90–100 cm. The functions fitted included exponential functions of the form SOC = A exp (Bz); SOC = A + B exp (Cz) as well as two phase exponential functions of the form SOC = A + B exp (Cz) + D exp (Ez). Other functions fitted included functions where the depth was a power exponent or an inverse term in a function. The universally best-fitting function was the exponential function SOC = A + B exp (Cz). When fitted, the most successful function was the two-phase exponential, but in several cases this function could not be fitted because of the large number of terms in the function. Semi-log plots of log values of the SOC against soil depth were also fitted to detect changes in the mathematical relationships between SOC and soil depth. These were hypothesized to represent changes in dominant soil processes at various depths. The success of the exponential function with an added constant, the two-phase exponential functions, and the demonstration of different phases within the semi-log plots confirmed our hypothesis that different processes were operating at different depths to control the depth distributions of SOC, there being a surface component, and deeper soil component. Several SOC profiles demonstrated specific features that are potentially important for the management of SOC profiles in soils. Woodland and to lesser extent pasture soils had a definite near surface zone within the SOC profile, indicating the addition of surface materials and high rates of fine root turnover. This zone was much less evident under cropping.

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