Soil Moisture and Aromatic-Containing Compounds Control Soil Organic Carbon Associated with Iron Oxides in Permafrost Wetland Soils Along the Yarlung Tsangbo River, Tibet

2022 ◽  
Author(s):  
Xuehui Zhang ◽  
Yuan Xin ◽  
Zhongsheng Zhang ◽  
Zhenshan Xue ◽  
Haitao Wu
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Iron Oxides ◽  
Wetland Soils ◽  
Control Soil

scholarly journals Soil greenhouse gas fluxes from different tree species on Taihang Mountain, North China

2014 ◽  
Vol 11 (6) ◽  
pp. 1649-1666 ◽  
Author(s):  
X. P. Liu ◽  
W. J. Zhang ◽  
C. S. Hu ◽  
X. G. Tang
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Temperature ◽  
Soil Ph ◽  
Tree Species ◽  
Soil Bulk Density ◽  
Total N ◽  
Gas Fluxes ◽  
Greenhouse Gas Fluxes

Abstract. The objectives of this study were to investigate seasonal variation of greenhouse gas fluxes from soils on sites dominated by plantation (Robinia pseudoacacia, Punica granatum, and Ziziphus jujube) and natural regenerated forests (Vitex negundo var. heterophylla, Leptodermis oblonga, and Bothriochloa ischcemum), and to identify how tree species, litter exclusion, and soil properties (soil temperature, soil moisture, soil organic carbon, total N, soil bulk density, and soil pH) explained the temporal and spatial variation in soil greenhouse gas fluxes. Fluxes of greenhouse gases were measured using static chamber and gas chromatography techniques. Six static chambers were randomly installed in each tree species. Three chambers were randomly designated to measure the impacts of surface litter exclusion, and the remaining three were used as a control. Field measurements were conducted biweekly from May 2010 to April 2012. Soil CO2 emissions from all tree species were significantly affected by soil temperature, soil moisture, and their interaction. Driven by the seasonality of temperature and precipitation, soil CO2 emissions demonstrated a clear seasonal pattern, with fluxes significantly higher during the rainy season than during the dry season. Soil CH4 and N2O fluxes were not significantly correlated with soil temperature, soil moisture, or their interaction, and no significant seasonal differences were detected. Soil organic carbon and total N were significantly positively correlated with CO2 and N2O fluxes. Soil bulk density was significantly negatively correlated with CO2 and N2O fluxes. Soil pH was not correlated with CO2 and N2O emissions. Soil CH4 fluxes did not display pronounced dependency on soil organic carbon, total N, soil bulk density, and soil pH. Removal of surface litter significantly decreased in CO2 emissions and CH4 uptakes. Soils in six tree species acted as sinks for atmospheric CH4. With the exception of Ziziphus jujube, soils in all tree species acted as sinks for atmospheric N2O. Tree species had a significant effect on CO2 and N2O releases but not on CH4 uptake. The lower net global warming potential in natural regenerated vegetation suggested that natural regenerated vegetation were more desirable plant species in reducing global warming.

scholarly journals Soil Water Characteristics of Gleysols in the Bamenda (Cameroon) Wetlands and Implications for Agricultural Management Strategies

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Alice Mufur Magha ◽  
Primus Azinwi Tamfuh ◽  
Lionelle Estelle Mamdem ◽  
Marie Christy Shey Yefon ◽  
Bertrand Kenzong ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Water ◽  
Total Variance ◽  
Organic Carbon Content ◽  
Available Water ◽  
High Soil ◽  
Local Soil ◽  
Very High

Water budgeting in agriculture requires local soil moisture information as crops depend mainly on moisture available at root level. The present paper aims to evaluate the soil moisture characteristics of Gleysols in the Bamenda (Cameroon) wetlands and to evaluate the link between soil moisture content and selected soil characteristics affecting crop production. The work was conducted in the field and laboratory, and data were analyzed by simple descriptive statistics. The main results showed that the soils had a silty clayey to clayey texture, high bulk density, high soil organic carbon content, and high soil organic carbon stocks. The big difference between moisture contents at wilting point and at field capacity testified to very high plant-available water content. Also, the soils displayed very high contents of readily available water and water storage contents. The soil moisture characteristics give sigmoid curves and enabled noting that the Gleysols attain their full water saturation at a range of 57.68 to 91.70% of dry soil. Clay and SOC contents show a significant positive correlation with most of the soil moisture characteristics, indicating that these soil properties are important for soil water retention. Particle density, coarse fragments, and sand contents correlated negatively with the soil moisture characteristics, suggesting that they decrease soil water-holding capacity. The principal component analysis (PCA) enabled reducing 17 variables described to only three principal components (PCs) explaining 73.73% of the total variance; the first PC alone expressed 45.12% of the total variance, associating clay, SOC, and six soil moisture characteristics, thus portraying a deep correlation between these eight variables. Construction of contoured ditches, deep tillage, and raised ridges management techniques during the rainy season while channeling water from nearby water bodies into the farmland, opportunity cropping, and usage of water cans and other irrigation strategies are used during the dry season to combat water constraints.

Influence of Plastic Mulching on Soil Moisture, Nutrient and Microbial Biomass in Pineapple Cultivation on Undulating Hilly Areas of Nagaland

Agropedology ◽  
2019 ◽  
Vol 29 (1) ◽  
Author(s):  
Christy Sangma ◽  
◽  
A. Thirugnanavel ◽  
Ph. Romen Sharma ◽  
G. Rajesha ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Microbial Biomass ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Moisture Content ◽  
Microbial Biomass Carbon ◽  
Fertility Level ◽  
Biomass Carbon ◽  
Available N ◽  
Plastic Mulching

The pineapple var. Kew was planted on black polythene film mulching with double hedgerow planting to find out the influence of mulches on soil and plant. The soil samples were collected twice (kharif and rabi) at two different depths (0-15 and 15-30 cm), and the pH, soil organic carbon (SOC), nitrogen, phosphorus, potassium, basal respiration and soil microbial biomass carbon were analysed. The data revealed that soil organic carbon and available N, P, and K content were slightly higher in the bottom hill than the top hill. The mulched field had higher nutrients than the non-mulched field. The fertility level varied slightly between the seasons. The biological parameters (microbial biomass carbon) were observed to be significantly higher (P≤0.05) in the bottom hill in both the seasons than the non-mulched field. The soil moisture content ranged from 5.9 % in March to 24.24 % August in the bottom hill (15-30 cm depth). The moisture content in the non-mulched field was lower than the mulched field.

scholarly journals Mini review: Intensification of mulching to improve soil moisture in vanilla plantation

2018 ◽  
Vol 3 (2) ◽  
pp. 42
Author(s):  
I Gede Ketut Adiputra
Keyword(s):  
Global Warming ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Practices ◽  
Basic Mechanism ◽  
Global Warming Scenario ◽  
The Impact ◽  
Evaporation Increase ◽  
Growth And Reproduction

Continuous water uptake from soil via the root system and it transport into the leaves system is a basic mechanism in plants to maintain growth and reproduction.  Consequently, sustaining soil moisture to keep water supply into the plants should continuously occurred to maintain growth.  Under condition of global warming scenario and robust agricultural practices, soil organic carbon which plays as a key for soil moisture and fertility are continuously diminished.  This condition could subsequently endanger the growth of shallow rooted plants, such as vanilla.  To mitigate the impact of global warming and robust agricultural practices, enhancing carbon sequestration to inhibit water loss is regarded crucial.  However, although mulch materials are locally available in most land crop plantations, those materials are rarely viewed as functional for maintaining soil moisture.  Both water stress and mulching might have not been seriously anticipated in conventional agricultural practices.  For example, continuous decreased in yield of vanilla plants are usually handled by applying pesticide or fertilizer, without addition of mulch.  The objective of this review was to gain a better understanding of soil moisture to increase vanilla growth and reproduction.  This review found that mulching could reduce evaporation, increase soil organic carbon and soil fertility.  It is concluded that intensification of mulching could enhance sustainability of vanilla plantations.  

scholarly journals Soil Organic Carbon Mineralization and its Temperature Sensitivity Along a Vegetation Restoration Gradient in Subtropical China

2021 ◽  
Author(s):  
Xiong Fang ◽  
Haozhao Sun ◽  
Yunpeng Huang ◽  
Jundi Liu ◽  
Yulin Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Temperature Sensitivity ◽  
Interactive Effect ◽  
Carbon Mineralization ◽  
Terrestrial Ecosystems ◽  
Vegetation Restoration ◽  
Soil Organic Carbon Mineralization ◽  
Soc Mineralization

Abstract Background and aims Soil organic carbon (SOC) mineralization produces important CO2 flux from terrestrial ecosystems which can provide feedbacks to climates. Vegetation restoration can affect SOC mineralization and its temperature sensitivity (Q10), but how this effect is related to soil moisture remains uncertain. Methods We performed a laboratory incubation using soils of different vegetation restoration stages (i.e., degraded vegetation [DS], plantation [PS], and secondary natural forest [SFS]) maintained under different moisture and temperature conditions to explore the combined effects of vegetation restoration and soil moisture on SOC mineralization and Q10. Results We found that cumulative SOC mineralization in PS and SFS were about 11.7 times higher than that in the DS, associated with higher SOC content and microbial biomass. Increased soil moisture and temperature led to higher SOC mineralization in the SFS and PS. However, in the DS, soil moisture did not affect SOC mineralization, but temperature enhancement solely increased (158.7%) SOC mineralization at the 60%MWHC treatment. Furthermore, significant interactive effect of vegetation restoration and soil moisture on Q10 was detected. At the 60%MWHC treatment, Q10 declined with vegetation restoration age. Nevertheless, at the 30%MWHC treatment, Q10 was lower in the DS than that in the PS. Higher soil moisture did not affect Q10 in the PS and SFS, but enhanced Q10 in the DS. Conclusions Our results highlight that the responses of SOC mineralization and Q10 to vegetation restoration were highly dependent on soil moisture and substrate availability, and vegetation restoration reduced the influence of soil moisture on Q10.

scholarly journals Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect

2020 ◽  
Vol 12 (8) ◽  
pp. 1308 ◽  
Author(s):  
Said Nawar ◽  
Muhammad Abdul Munnaf ◽  
Abdul Mounem Mouazen
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
External Parameter ◽  
Machine Learning Method ◽  
Learning Method ◽  
Orthogonal Signal Correction ◽  
Negative Effect ◽  
On Line

It is well-documented in the visible and near-infrared reflectance spectroscopy (VNIRS) studies that soil moisture content (SMC) negatively affects the prediction accuracy of soil attributes. This work was undertaken to remove the negative effect of SMC on the on-line prediction of soil organic carbon (SOC). A mobile VNIR spectrophotometer with a spectral range of 305–1700 nm and spectral resolution of 1 nm (CompactSpec, Tec5 Technology, Germany) was used for the spectral measurements at four farms in Flanders, Belgium. A total of 381 fresh soil samples were collected and divided into a calibration set (264) and a validation set (117). The validation samples were processed (air-dried and grind) and scanned with the same spectrophotometer in the laboratory. Three SMC correction methods, namely, external parameter orthogonalization (EPO), piecewise direct standardization (PDS), and orthogonal signal correction (OSC) were used to correct the on-line fresh spectra based-on its corresponding laboratory spectra. Then, the Cubist machine learning method was used to develop calibration models of SOC using the on-line spectra (after correction) of the calibration set. Results indicated that the EPO-Cubist outperformed the PDS-Cubist and the OSC-Cubist, with considerable improvements in the prediction results of SOC (coefficient of determination (R2) = 0.76, ratio of performance to deviation (RPD) = 2.08, and root mean square error of prediction (RMSEP) = 0.12%), compared with the corresponding uncorrected on-line spectra (R2 = 0.55, RPD = 1.24, and RMSEP = 0.20%). It can be concluded that SOC can be accurately predicted on-line using the Cubist machine learning method, after removing the negative effect of SMC with the EPO method.

Sentinel-2 exposed soil composite for soil organic carbon prediction: the ‘greening-up’ method for detecting suitable images

2021 ◽  
Author(s):  
Klara Dvorakova ◽  
Bas van Wesemael
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Satisfactory Result ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sentinel 2

<p>Pilot studies have demonstrated the potential for remote sensing techniques for soil organic carbon  (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface such as photosynthetic active and nonphotosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, many studies have focused on stabilizing the soil reflectance by building image composites that are generated using a set of criteria. These composites tend to minimize and cancel out the disturbing effects. Here we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels that are not affected by the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Loam Belt of Belgium from January 2019 to December 2020 (in total 38 images). We then built four exposed soil composites based on four sets of criteria: (1) NDVI < 0.25, (2) NDVI < 0.25 & Normalized Burn Ratio (NBR2) < 0.07, (3) the ‘greening-up’ period of a crop and (4) the ‘greening-up’ period of a crop & NBR2 < 0.07. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is bare (NDVI < 0.25) before the crop develops (NDVI > 0.6).,We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the four above described composites. We obtained a non-satisfactory result for composites (1) to (3): R² = 0.22, RMSE = 3.46 g C kg<sup>-1</sup> and RPD 1.12 for (1), R² = 0.19, RMSE = 3.43 g C kg<sup>-1</sup> and RPD 1.10 for (2) and R² = 0.15, RMSE = 2.74 g C kg<sup>-1</sup> and RPD 1.06 for (3). We, however, obtained a satisfactory result for composite (4): R² = 0.54, RMSE = 2.09 g C kg<sup>-1</sup> and RPD 1.68. Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be the surface coverage, which for a two-year period reached 47% of croplands, compared to 89% exposure if only the NDVI threshold is applied.</p>

Modelling soil organic carbon turnover with assimilation of satellite soil moisture data

2021 ◽  
pp. 97-99
Author(s):  
Olha Stepanchenko ◽  
Liubov Shostak ◽  
Olena Kozhushko ◽  
Viktor Moshynskyi ◽  
Petro Martyniuk
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Abiotic Factors ◽  
Carbon Turnover ◽  
Century Model ◽  
Soil Moisture Data ◽  
Factors Influencing ◽  
Moisture Regimes ◽  
Satellite Retrievals

The content of organic carbon is one of the essential factors that define soil quality. It is also notoriously challenging to model due to a multitude of biological and abiotic factors influencing the process. In this study, we investigate how decomposition of soil organic matter is affected by soil moisture and temperature. Soil organic carbon turnover is simulated by the CENTURY model. The accuracy of soil moisture data used is ensured by data assimilation approach, combing mathematical model and satellite retrievals. Numerical experiments demonstrate the influence of soil moisture regimes and climate on the quantity of soil humus stocks.

scholarly journals Soil moisture influence on the interannual variation in temperature sensitivity of soil organic carbon mineralization in the Loess Plateau

2015 ◽  
Vol 12 (11) ◽  
pp. 3655-3664 ◽  
Author(s):  
Y. J Zhang ◽  
S. L Guo ◽  
M. Zhao ◽  
L. L. Du ◽  
R. J. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Moisture Content ◽  
Carbon Cycle ◽  
Temperature Sensitivity ◽  
Interannual Variation ◽  
Soil Moisture Content ◽  
The Loess Plateau ◽  
Soc Mineralization

Abstract. Temperature sensitivity of soil organic carbon (SOC) mineralization (i.e., Q10) determines how strong the feedback from global warming may be on the atmospheric CO2 concentration; thus, understanding the factors influencing the interannual variation in Q10 is important for accurately estimating local soil carbon cycle. In situ SOC mineralization rate was measured using an automated CO2 flux system (Li-8100) in long-term bare fallow soil in the Loess Plateau (35°12' N, 107°40' E) in Changwu, Shaanxi, China from 2008 to 2013. The results showed that the annual cumulative SOC mineralization ranged from 226 to 298 g C m−2 yr−1, with a mean of 253 g C m−2 yr−1 and a coefficient of variation (CV) of 13%, annual Q10 ranged from 1.48 to 1.94, with a mean of 1.70 and a CV of 10%, and annual soil moisture content ranged from 38.6 to 50.7% soil water-filled pore space (WFPS), with a mean of 43.8% WFPS and a CV of 11%, which were mainly affected by the frequency and distribution of precipitation. Annual Q10 showed a quadratic correlation with annual mean soil moisture content. In conclusion, understanding of the relationships between interannual variation in Q10, soil moisture, and precipitation are important to accurately estimate the local carbon cycle, especially under the changing climate.

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