Sulfur processing in forest soil and litter along an elevational and vegetative gradient

1986 ◽  
Vol 16 (4) ◽  
pp. 689-695 ◽  
Author(s):  
Mary E. Watwood ◽  
John W. Fitzgerald ◽  
James R. Gosz
Keyword(s):  
New Mexico ◽  
Moisture Content ◽  
Soil Ph ◽  
Soil Samples ◽  
Total Carbon ◽  
Santa Fe ◽  
Sulfate Adsorption ◽  
Total Carbon Content ◽  
Organic S ◽  
Pool Sizes

O2 litter and A1 horizon soil samples from various locations within the Santa Fe and Cibola National Forests of New Mexico were assayed for sulfate adsorption, organic S formation, and organic S solubilization and mineralization (mobilization). During a 48-h incubation, samples of O2 litter were found to adsorb between 1.6 and 4.1 nmol g−1 of added sulfate S and to form 2.0 to 9.8 nmol g−1 of organic S from this anion. Between 17 and 48% of this organic S was mobilized within 24 h. A1 horizon soils adsorbed 1.2 to 4.9 nmol g−1 of added sulfate S and formed between 1.6 and 4.8 nmol g−1 of organic S during 48 h. Between 20 and 50% of this organic S was mobilized within 24 h. Estimations of S-accumulation potentials for both horizons were made from these determinations. Intrinsic S pools were quantified to determine the S status of the samples prior to incubation. Carbon-bonded forms of S were found to predominate in samples from both horizons, while ester sulfate accounted for most of the remaining S. Sample pH, moisture content, and total carbon content were also determined. Attempts were made to correlate these characteristics and S pool sizes with laboratory determined potentials for sulfate adsorption, organic S formation, and mobilization. For some sites, relationships were established between sulfate adsorption, soil pH, and total C, whereas the total S and organic S content of most samples agreed well with organic S formation potentials.

scholarly journals The effects of humic substances on DNA isolation from soils

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9378
Author(s):  
Ewa Wnuk ◽  
Adam Waśko ◽  
Anna Walkiewicz ◽  
Piotr Bartmiński ◽  
Romualda Bejger ◽  
...  
Keyword(s):  
Humic Substances ◽  
Dna Isolation ◽  
Arable Land ◽  
Soil Layer ◽  
Soil Samples ◽  
Total Carbon ◽  
High Concentration ◽  
Soil Dna ◽  
Total Carbon Content ◽  
Direct Cell

Background Humic substances (HS) are compounds with a complicated structure, present in the humus soil layer, water, lake sediments, peat, brown coal and shales. Due to their similar physicochemical properties to DNA, they may have an adverse effect on the subsequent use of the isolated material. The main aim of this research was to examine the effect of HS on DNA isolation depending on the soil type and land use, taking into account the spectroscopic full characteristics of HS fractions. Methods The research was conducted on eight types of soil sample. Soils represented the most important Soil Reference Groups for temperate climates: Fluvisols, Regosols, Cambisols, Arenosols, Histosols and Luvisols. Soil samples were also collected from areas diversified in terms of use: arable land, grassland and forest. The extraction of HS fractions was performed using the procedure recommended by the International HS Society. The fractional composition of HS was characterized by UV–Vis and fluorescence methods. Soil DNA is extracted by direct cell lysis in the using a CTAB-based method with a commonly-used commercial soil DNA isolation kit. The basis for assessing the quantity and quality of extracted DNA was the Polymerase chain reaction (PCR) reaction since the analysis of soil DNA often relies on the use of PCR to study soil microorganisms. Results Based on the results, it can be concluded that in the presence of a high concentration of HS, the isolated DNA was low quality and the additional purification procedure was necessary. Despite the differentiation of the internal structure of HS fractions, the decisive factor in the efficiency of DNA isolation from soil samples was the total carbon content in HS. Reduced DNA yields can significantly constrain PCR detection limits to levels inadequate for metagenomic analysis, especially from humus-rich soils.

scholarly journals The Influence of Afforestation on Upland Soils: The Use of ‘Bomb 14C’ Enrichment as a Quantitative Tracer for Changes in Organic Status

Radiocarbon ◽  
1989 ◽  
Vol 31 (03) ◽  
pp. 637-643 ◽  
Author(s):  
D D Harkness ◽  
A F Harrison
Keyword(s):  
Organic Matter ◽  
Carbon Content ◽  
Growth Period ◽  
Soil Samples ◽  
Total Carbon ◽  
Soil Horizons ◽  
Total Carbon Content ◽  
Soil Carbon Content ◽  
Fresh Organic Matter ◽  
Carbon Inventory

A series of soil samples were collected in November 1984 from five stands of Sitka spruce planted at recorded times between 1951 and 1968. Within a comprehensive program of ecologic and biogeochemical analyses, natural 14C measurements on selected organic components of the 0 to 5cm soil horizons serve to quantify progressive changes induced in the organic carbon inventory and relative to that of the original grassland. Points of particular interest are: 1) an enhanced input of fresh organic matter in the years immediately following planting; this, in parallel with a net decrease in the total carbon content of the topsoil; 2) this freshly introduced carbon predominates in the soil profile even after 30 years of afforestation; 3) during the 15- to 30-year growth period, the soil carbon content remains constant but progressive changes occur in its biogeochemical composition and rate of turnover.

scholarly journals African Soil Properties and Nutrients Mapped at 30--m Spatial Resolution using Two-scale Ensemble Machine Learning

2020 ◽  
Author(s):  
Tomislav Hengl ◽  
Matthew Miller ◽  
Josip Krizan ◽  
Keith Shepherd ◽  
Andrew Sila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Ph ◽  
Soil Samples ◽  
Total Carbon ◽  
Climatic Data ◽  
Continental Scale ◽  
Ensemble Machine Learning ◽  
Bioclimatic Variables ◽  
Extractable Phosphorus ◽  
Sentinel 2

Abstract Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mappedat all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGOfunded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. Inthis paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensivecompilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and totalnitrogen (N), total carbon, Cation Exchange Capacity (eCEC), extractable — phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg),sulfur (S), sodium (Na), iron (Fe), zinc (Zn) — silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariatelayers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives)images. Our 5–fold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC=0.900) tomore poorly predictable extractable phosphorus (CCC=0.654) and sulphur (CCC=0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11,B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 mresolution covariates. Climatic data images — SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature — however, remainedas the overall most important variables for predicting soil chemical variables at continental scale. The publicly available 30–m soil maps aresuitable for numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmentalprograms, or targeting of nutrition interventions.

scholarly journals Nitrogen forms, pH and total carbon in a soil incubated with treated sewage effluent

2007 ◽  
Vol 50 (5) ◽  
pp. 743-752 ◽  
Author(s):  
Adriel Ferreira da Fonseca ◽  
Uwe Herpin ◽  
Carlos Tadeu dos Santos Dias ◽  
Adolpho José Melfi
Keyword(s):  
Soil Ph ◽  
Total Carbon ◽  
Sewage Effluent ◽  
Total Carbon Content ◽  
Carbon And Nitrogen ◽  
Application Rates ◽  
Randomized Design ◽  
Treated Sewage ◽  
Nitrogen Concentrations ◽  
Treated Sewage Effluent

In this study, an experiment under controlled conditions was carried out to determine the effects of secondary-treated sewage effluent (STSE) application on soil nitrogen concentrations (mineral and total), total carbon and soil pH. The soil and STSE used were collected at Lins, São Paulo State, Brazil. A completely randomized design was used, in completed factorial 4x11 (weekly application rates of 0, 100, 150 and 200 mL STSE per kg soil; and, eleven soil incubation periods from 0 to 10 weeks) with four replicates. The STSE was applied simulating common surface irrigation. Seven days after each incubation period, ammonium and nitrate contents were determined. Additionally, pH and total carbon and nitrogen contents were measured in the soil after 10 weeks. STSE application increased the nitrogen content (total and mineral - mainly as nitrate) and soil pH. For the total carbon content no differences were observed.

scholarly journals African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomislav Hengl ◽  
Matthew A. E. Miller ◽  
Josip Križan ◽  
Keith D. Shepherd ◽  
Andrew Sila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Soil Ph ◽  
Soil Samples ◽  
Total Carbon ◽  
Continental Scale ◽  
Ensemble Machine Learning ◽  
Soil Information ◽  
Extractable Phosphorus ◽  
Sentinel 2

AbstractSoil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ($$N \approx 150,000$$ N ≈ 150 , 000 ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the (Machine Learning in ) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.

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