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 Geochemical Distribution Characteristics of Rare Earth Elements in Different Soil Profiles in Mun River Basin, Northeast Thailand

2020 ◽  
Vol 12 (2) ◽  
pp. 457 ◽  
Author(s):  
Wenxiang Zhou ◽  
Guilin Han ◽  
Man Liu ◽  
Chao Song ◽  
Xiaoqiang Li
Keyword(s):  
Rare Earth ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Rare Earth Elements ◽  
River Basin ◽  
Soil Ph ◽  
Enrichment Factors ◽  
Soil Profiles ◽  
Distribution Characteristics ◽  
Northeast Thailand

Exploring the distributions of rare earth elements (REEs) in soil profiles is essential to understanding how natural and anthropogenic factors influence the geochemical behaviors of REEs. This study aimed to learn about the distribution characteristics of REEs in soils, including their fractionation and enrichment, and to explore the influence of soil pH and soil organic carbon (SOC) on REEs. One hundred and three samples were collected from six soil profiles under different land uses (paddy field: T1, T3; forest land: T2, T6; wasteland: T4; building site: T5) in the Mun River Basin, Northeast Thailand. The average total REE contents (∑REE) are much lower (<80 mg kg−1) than that of Earth’s crust (153.80 mg kg−1) in soil profiles T2, T3, T4, and T6. The contents of REEs tend to increase slightly with depth in all soil profiles. The ratios of (La/Yb)N range from 0.35 to 0.96 in most samples, indicating that the enrichment of heavy REEs (HREEs) relative to light REEs (LREEs) is the main fractionation pattern. Samples from profile T2 show relatively obvious negative Ce anomalies (0.55–0.78) and positive Eu anomalies (1.41–1.56), but there are almost no anomalies of Ce and Eu in other soil profiles. Enrichment factors of LREEs (EFLREEs) range from 0.23 to 1.54 and EFHREEs range from 0.34 to 2.27, which demonstrates that all soil samples show no LREE enrichment and only parts of samples show minor HREE enrichment. Soil organic carbon (SOC) contents positively correlate with the enrichment factors of REEs (EFREE) in soil profiles T1 (R = 0.56, p < 0.01) and T6 (R = 0.71), while soil pH values correlate well with EFREE in soil profiles T2 (R = 0.75) and T4 (R = −0.66, p < 0.01), indicating the important influence of soil pH and SOC on the mobility of REEs in some soil profiles.

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

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.

scholarly journals Effects of soil pH and texture on soil carbon and nitrogen in soil profiles under different land uses in Mun River Basin, Northeast Thailand

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7880 ◽  
Author(s):  
Wenxiang Zhou ◽  
Guilin Han ◽  
Man Liu ◽  
Xiaoqiang Li
Keyword(s):  
Soil Carbon ◽  
River Basin ◽  
Soil Ph ◽  
Soil Samples ◽  
Soil Profiles ◽  
Land Uses ◽  
Agricultural Lands ◽  
Carbon And Nitrogen ◽  
Soil Carbon And Nitrogen ◽  
Forest Lands

Soil carbon and nitrogen are essential factors for agricultural production and climate changes. A total of 106 soil samples from three agricultural lands (including two rice fields and one sugarcane field) and four non-agricultural lands (including two forest lands, one wasteland and one built-up land) in the Mun River Basin were collected to determine soil carbon, nitrogen, soil pH, soil particle sizes and explore the influence of pH and soil texture on soil C and N. The results show that total organic carbon (TOC) and nitrogen (TON) contents in topsoil (TOC: 2.78 ~ 18.83 g kg−1; TON: 0.48 ~ 2.05 g kg−1) are much higher than those in deep soil (TOC: 0.35 ~ 6.08 g kg−1; TON: <0.99 g kg−1). In topsoil, their contents of forest lands and croplands (TOC: average 15.37 g kg−1; TON: average 1.29 g kg−1) are higher than those of other land uses (TOC: average 5.28 g kg−1; TON: average 0.38 g kg−1). The pH values range from 4.2 to 6.1 in topsoil, and with increase in soil depth, they tend to increase and then decrease. Soil carbon, nitrogen and the C/N (TC/TN ratio) are negatively correlated with soil pH, demonstrating that relatively low pH benefits the accumulation of organic matter. Most soil samples are considered as sandy loam and silt loam from the percentages of clay, silt and sand. For soil profiles below 50 cm, the TOC and TON average contents of soil samples which contain more clay and silt are higher than those of other soil samples.

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;

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

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.

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

2021 ◽  
Vol 16 (1) ◽  
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 ◽  
Paired Data ◽  
Soil Database ◽  
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 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.

scholarly journals Vertical Distribution and Controlling Factors Exploration of Sc, V, Co, Ni, Mo and Ba in Six Soil Profiles of The Mun River Basin, Northeast Thailand

2020 ◽  
Vol 17 (5) ◽  
pp. 1745
Author(s):  
Wenxiang Zhou ◽  
Guilin Han ◽  
Man Liu ◽  
Chao Song ◽  
Xiaoqiang Li ◽  
...  
Keyword(s):  
Heavy Metals ◽  
River Basin ◽  
Soil Ph ◽  
Soil Texture ◽  
Heavy Metal Contamination ◽  
Geoaccumulation Index ◽  
Controlling Factors ◽  
Soil Profiles ◽  
Northeast Thailand ◽  
Mo Content

Exploring the enrichment and controlling factors of heavy metals in soils is essential because heavy metals can cause severe soil contamination and threaten human health when they are excessively enriched in soils. Soil samples (total 103) from six soil profiles (T1 to T6) in the Mun River Basin, Northeast Thailand, were collected for the analyses of the content of heavy metals, including Sc, V, Co, Ni, Mo, Ba. The average contents of soil heavy metals decrease in the following order: Ba, V, Ni, Sc, Co, and Mo (T1, T3, T4 and T5); Ni, V, Ba, Co, Sc, Mo, and Ba (T2); Ba, V, Sc, Ni, Mo, and Co (T6). An enrichment factor (EF) and geoaccumulation index were calculated to assess the degree of heavy metal contamination in the soils. The EFs of these heavy metals in most samples range from 0 to 1.5, which reveals that most heavy metals are slightly enriched. Geoaccumulation indexes show that only the topsoil of T1 and T2 is slightly contaminated by Ba, Sc, Ni, and V. Soil organic carbon (SOC), soil pH and soil texture are significantly positively correlated with most heavy metals, except for a negative correlation between soil pH and Mo content. In conclusion, the influence of heavy metals on soils in the study area is slight and SOC, soil pH, soil texture dominate the behavior of heavy metals.

Use of color parameters in the grouping of soil samples produces more accurate predictions of soil texture and soil organic carbon

2020 ◽  
Vol 177 ◽  
pp. 105710
Author(s):  
José Janderson Ferreira Costa ◽  
Élvio Giasson ◽  
Elisângela Benedet da Silva ◽  
João Augusto Coblinski ◽  
Tales Tiecher
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Texture ◽  
Soil Samples ◽  
Color Parameters

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

Forests ◽  
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.

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

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.

Sign in / Sign up

Export Citation Format

Share Document