Association between irrigation thresholds and promotion of soil organic carbon decomposition in sandy soil

AbstractSoil organic carbon (SOC) has a significant effect on the carbon cycle, playing a vital role in environmental services and crop production. Increasing SOC stock is identified as an effective way to improve carbon dioxide sequestration, soil health, and plant productivity. Knowing soil water is one of the primary SOC decomposition driver, periods in the crops growth stages with increased water movement might influence the SOC dynamics. Here, we evaluate the temporal effect of four precision irrigation thresholds ($$-15$$ - 15 , $$-30$$ - 30 , $$-45$$ - 45 , and $$-60$$ - 60 kPa) in potato crop on SOC dynamics using the Partial Least Square algorithm and the Tea Bag Index in a sandy soil under potato production. The difference of SOC decomposition rate between the precision irrigation thresholds is developed in the second quarter of the growing season, between 38 and 53 days after planting. This critical period occurred in a stage of strong vegetative growth and rapid irrigation cycles. The precision irrigation threshold affected the decomposition rate of SOC. A faster decomposition of labile organic carbon was promoted by water excess ($$-15$$ - 15 kPa). The dryer ($$-30$$ - 30 , $$-45$$ - 45 , and $$-60$$ - 60 kPa) precision irrigation thresholds did not show any differences. The advancement of this knowledge may promote soil health conservation and carbon sequestration in agricultural soil.

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Isotope Analysis Reveals Differential Impacts of Artificial and Natural Afforestation on Soil Organic Carbon Dynamics in Abandoned Farmland

10.21203/rs.3.rs-238894/v1 ◽

2021 ◽

Author(s):

Dongrui Di ◽

GuangWei Huang

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Respiration ◽

Decomposition Rate ◽

Dominant Role ◽

Soil Aggregates ◽

Isotope Analysis ◽

Aggregate Size ◽

Vegetation Restoration ◽

Soc Decomposition

Abstract Backgrounds A multitude of studies have applied different methods to study the dynamics of soil organic carbon (SOC), but the differential impact of artificial and natural vegetation restoration on SOC dynamic are still poorly understood. Methods and aims We investigated the SOC dynamics following artificial and natural afforestation in Loess Plateau of China, characterizing soil structure and stoichiometry using stable isotope carbon and radiocarbon models. We aim to compare SOC dynamics under both natural and artificial afforestation and examine how soil aggregate size classes control SOC dynamics based on stoichiometry and soil respiration.Results Total top soil SOC stocks, C:N and C:P of differently sized soil aggregates significantly increased following vegetation restoration. 13C results and Radiocarbon models indicated that the SOC decomposition rate and new SOC input rate were lower under natural afforestation than artificial afforestation and revealed the highest SOC decomposition rate under natural afforestation compared to other two ecosystems. Conclusions Vegetation restorations can accumulate SOC in top soils. Soil aggregates alternately play a dominant role in SOC accumulation following vegetation restoration; SOC loss from soil respiration was derived from microaggregates during afforestation. Recovery time is a key factor for the accumulation of SOC following afforestation.

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Sustaining Soil Organic Carbon, Soil Quality, and Soil Health in Organic Field Crop Management Systems

Managing Energy, Nutrients, and Pests in Organic Field Crops ◽

10.1201/b16044-13 ◽

2014 ◽

pp. 129-154

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Quality ◽

Soil Health ◽

Crop Management ◽

Management Systems ◽

Field Crop ◽

Crop Management Systems

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Predicting Soil Organic Carbon and Total Nitrogen at the Farm Scale Using Quantitative Color Sensor Measurements

Agronomy ◽

10.3390/agronomy8100212 ◽

2018 ◽

Vol 8 (10) ◽

pp. 212 ◽

Cited By ~ 4

Author(s):

Roxanne Stiglitz ◽

Elena Mikhailova ◽

Julia Sharp ◽

Christopher Post ◽

Mark Schlautman ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Total Nitrogen ◽

Low Cost ◽

Rapid Assessment ◽

Soil Health ◽

Sensor Technology ◽

P Value ◽

Color Sensor ◽

Farm Scale

Sensor technology can be a reliable and inexpensive means of gathering soils data for soil health assessment at the farm scale. This study demonstrates the use of color system readings from the Nix ProTM color sensor (Nix Sensor Ltd., Hamilton, ON, Canada) to predict soil organic carbon (SOC) as well as total nitrogen (TN) in variable, glacial till soils at the 147 ha Cornell University Willsboro Research Farm, located in Upstate New York, USA. Regression analysis was conducted using the natural log of SOC (lnSOC) and the natural log of TN (lnTN) as dependent variables, and sample depth and color data were used as predictors for 155 air dried soil samples. Analysis was conducted for combined samples, Alfisols, and Entisols as separate sample sets and separate models were developed using depth and color variables, and color variables only. Depth and L* were significant predictors of lnSOC and lnTN for all sample sets. The color variable b* was not a significant predictor of lnSOC for any soil sample set, but it was for lnTN for all sample sets. The lnSOC prediction model for Alfisols, which included depth, had the highest R2 value (0.81, p-value < 0.001). The lnSOC model for Entisols, which contained only color variables, had the lowest R2 (0.62, p-value < 0.001). The results suggest that the Nix ProTM color sensor is an effective tool for the rapid assessment of SOC and TN content for these soils. With the accuracy and low cost of this sensor technology, it will be possible to greatly increase the spatial and temporal density of SOC and TN estimates, which is critical for soil management.

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

Water ◽

10.3390/w12010261 ◽

2020 ◽

Vol 12 (1) ◽

pp. 261 ◽

Cited By ~ 2

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.

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Prediction of Soil Organic Carbon for Ethiopian Highlands Using Soil Spectroscopy

ISRN Soil Science ◽

10.1155/2013/720589 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 9

Author(s):

Tadele Amare ◽

Christian Hergarten ◽

Hans Hurni ◽

Bettina Wolfgramm ◽

Birru Yitaferu ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Diffuse Reflectance ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Calibration Models ◽

The Stability

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used. The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9), Maybar (84. 0.57, 2.5), Megech (85, 0.15, 2.6), and Wondo Genet (86, 0.52, 2.7) indicating that the models were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

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Effect of low temperature and soil type on the decomposition rate of soil organic carbon and clover leaves, and related priming effect

Soil Biology and Biochemistry ◽

10.1016/j.soilbio.2014.10.004 ◽

2015 ◽

Vol 80 ◽

pp. 156-166 ◽

Cited By ~ 26

Author(s):

R.B. Frøseth ◽

M.A. Bleken

Keyword(s):

Low Temperature ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Decomposition Rate ◽

Soil Type ◽

Priming Effect

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Predicting Soil Organic Carbon Content Using Hyperspectral Remote Sensing in a Degraded Mountain Landscape in Lesotho

Applied and Environmental Soil Science ◽

10.1155/2020/2158573 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Freddy Bangelesa ◽

Elhadi Adam ◽

Jasper Knight ◽

Inos Dhau ◽

Marubini Ramudzuli ◽

...

Keyword(s):

Random Forest ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Organic Carbon Content ◽

Random Forest Regression ◽

Least Square Regression ◽

Field Spectroscopy

Soil organic carbon constitutes an important indicator of soil fertility. The purpose of this study was to predict soil organic carbon content in the mountainous terrain of eastern Lesotho, southern Africa, which is an area of high endemic biodiversity as well as an area extensively used for small-scale agriculture. An integrated field and laboratory approach was undertaken, through measurements of reflectance spectra of soil using an Analytical Spectral Device (ASD) FieldSpec® 4 optical sensor. Soil spectra were collected on the land surface under field conditions and then on soil in the laboratory, in order to assess the accuracy of field spectroscopy-based models. The predictive performance of two different statistical models (random forest and partial least square regression) was compared. Results show that random forest regression can most accurately predict the soil organic carbon contents on an independent dataset using the field spectroscopy data. In contrast, the partial least square regression model overfits the calibration dataset. Important wavelengths to predict soil organic contents were localised around the visible range (400–700 nm). This study shows that soil organic carbon can be most accurately estimated using derivative field spectroscopy measurements and random forest regression.

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Should we manage soil organic carbon in Vertosols in the northern grains region of Australia?

Australian Journal of Experimental Agriculture ◽

10.1071/ea00163 ◽

2003 ◽

Vol 43 (3) ◽

pp. 261 ◽

Cited By ~ 13

Author(s):

R. J. Farquharson ◽

G. D. Schwenke ◽

J. D. Mullen

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

New South ◽

New South Wales ◽

Soil Health ◽

Organic Carbon Content ◽

Continuous Cropping ◽

South Wales ◽

On Farm ◽

Carbon Concentrations

Two issues prompted this paper. The first was the measured soil organic carbon decline in fertile northern Australian soils under continual cropping using traditional management practices. We wanted to see whether it was theoretically possible to maintain or improve soil organic carbon concentrations with modern management recommendations. The second was the debate about use of sustainability indicators for on-farm management, so we looked at soil organic carbon as a potential indicator of soil health and investigated whether it was useful in making on-farm crop decisions. The analytical results indicated first that theoretically the observed decline in soil organic carbon concentrations in some northern cracking clay soils can be halted and reversed under continuous cropping sequences by using best practice management. Second, the results and associated discussion give some support to the use of soil organic carbon as a sustainability indicator for soil health. There was a consistent correlation between crop input decisions (fertilisation, stubble management, tillage), outputs (yield and profits) and outcomes (change in soil organic carbon content) in the short and longer term. And this relationship depended to some extent on whether the existing soil organic carbon status was low, medium or high. A stock dynamics relationship is one where the change in a stock (such as soil organic carbon) through time is related not only to the management decisions made and other random influences (such as climatic effects), but also to the concentration or level of the stock itself in a previous time period. Against such a requirement, soil organic carbon was found to be a reasonable measure. However, the inaccuracy in measuring soil organic carbon in the paddock mitigates the potential benefit shown in this analysis of using soil organic carbon as a sustainability indicator.These results are based on a simulation model (APSIM) calibrated for a cracking clay (Vertosol) soil typical of much of the intensively-cropped slopes and plains region of northern New South Wales and southern Queensland, and need to be interpreted in this light. There are large areas of such soils in north-western New South Wales; however, many of these experience lower rainfalls and plant-available soil water capacities than in this case, and the importance of these characteristics must also be considered.

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Using a data-driven network to understand the drivers of soil organic carbon dynamics across the tropic

10.5194/egusphere-egu2020-15028 ◽

2020 ◽

Author(s):

Leigh Winowiecki ◽

Tor-Gunnar Vågen

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Land Cover ◽

Soil Properties ◽

Carbon Content ◽

Land Management ◽

Land Degradation ◽

Soil Health ◽

Carbon Dynamics ◽

Soil Organic Carbon Dynamics

Maintaining soil organic carbon (SOC) content is recognized as an important strategy for a well-functioning soil ecosystem. The UN Convention to Combat Desertification (UNCCD) recognizes that reduced SOC content can lead to land degradation, and ultimately low land and agricultural productivity. SOC is almost universally proposed as the most important indicator of soil health, not only because SOC positively influences multiple soil properties that affect productivity, including cation exchange capacity and water holding capacity, but also because SOC content reflects aboveground activities, including especially agricultural land management. To be useful as an indicator, it is crucial to assess the importance of both inherent soil properties as well as external factors (climate, vegetation cover, land management, etc.) on SOC dynamics across space and time. This requires large, reliable and up-to-date soil health data sets across diverse land cover classes. The Land Degradation Surveillance Framework (LDSF), a well-established method for assessing multiple biophysical indicators at georeferenced locations, was employed in nine countries across the tropics (Burkina Faso, Cameron, Honduras, India, Indonesia, Kenya, Nicaragua, Peru, and South Africa) to assess the influence of land use, tree cover and inherent soil properties on soil organic carbon dynamics. The LDSF was designed to provide a biophysical baseline at landscape level, and monitoring and evaluation framework for assessing processes of land degradation and the effectiveness of rehabilitation measures over time. Each LDSF site has 160 &#8211; 1000 m2 plots that were randomly stratified among 16 - 1 km2 sampling clusters. A total of 6918 soil samples were collected (3478 topsoil (0-20 cm) and 3435 subsoil (20-50 cm)) within this study. All samples were analyzed using mid-infrared spectroscopy and 10% of the samples were analyzed using traditional wet chemistry to develop calibration prediction models. &#160;Validation results for soil properties (soil organic carbon (SOC), sand, and total nitrogen) showed good accuracy with R2 values ranging between 0.88 and 0.96. Mean organic carbon content was 21.9 g kg-1 in topsoil and 15.2 g kg-1 in subsoil (median was 18.3 g kg-1 &#160;for topsoil and 10.8 g kg-1 in subsoil). Forest and grassland had the highest and similar carbon content while bushland/shrubland had the lowest. Sand content played an important role in determining the SOC content across the land cover types. Further analysis will be conducted and shared on the role of trees, land cover and texture on the dynamics of soil organic carbon and the implications for LDN reporting, land restoration initiatives as well as sustainable land management recommendations.

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