Can organic carbon and water supplementation sustain soil moisture–carbon balance under long-term plastic mulched semiarid farmland?

2022 ◽  
Vol 260 ◽  
pp. 107303
Author(s):  
Xucheng Zhang ◽  
Huizhi Hou ◽  
Yanjie Fang ◽  
Hongli Wang ◽  
Xianfeng Yu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Carbon Balance ◽  
Water Supplementation

scholarly journals Higher Long-Term Soil Moisture Increases Organic Carbon Accrual Through Microbial Conversion of Organic Inputs

2021 ◽  
Author(s):  
Itamar Shabtai ◽  
Srabani Das ◽  
Thiago Inagaki ◽  
Behrooz Azimzadeh ◽  
Carmen Martínez ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Chemical Composition ◽  
Organic Carbon ◽  
Fold Increase ◽  
Sodium Dithionite ◽  
Sodium Pyrophosphate ◽  
Microbial Conversion ◽  
High Moisture

High long-term soil moisture may either stimulate or inhibit soil organic carbon (SOC) losses through changes to mineral and chemical composition, and resultant organo-mineral interactions. Yet, the trade-off between mineralization and accrual of SOC under long-term variation in unsaturated soil moisture remains an uncertainty. In this study, we tested the underexplored relationships between long-term soil moisture and organo-mineral chemical composition, and its implications for SOC persistence. The results provide new insights into SOC accrual mechanisms under different long-term moisture levels commonly observed in well-drained soils. Differences in long-term mean volumetric water content ranging from 0.4 - 0.63 (v/v) on fallow plots in an experimental field in New York, USA, were positively correlated with SOC contents (R2 = 0.228; P = 0.019, n = 20), mineral-associated organic matter (MAOM) (R2 = 0.442; P = 0.001; n = 20) and occluded particulate organic matter (oPOM) contents (R2 = 0.178; P = 0.033; n = 20). Higher long-term soil moisture decreased the relative content of sodium pyrophosphate extractable Fe (R2 = 0.33; P < 0.005; n = 20), increased that of sodium dithionite extractable Fe (R2 = 0.443; P < 0.001; n = 20), and increased the overall importance of non-crystalline Al pools (extracted with sodium pyrophosphate and hydroxylamine extractable) for SOC retention. Higher long-term soil moisture supported up to a four-fold increase in microbial biomass (per unit SOC), and lower C:N ratios in MAOM fractions of high-moisture soils (from C:N 9.5 to 9, R2 = 0.267, P = 0.011, n =20). This was reflected by a 15% and 10% greater proportion of oxidized carboxylic-C to aromatic-C and O-alkyl C, respectively, as measured with 13C-NMR, and a more pronounced FTIR signature of N-containing proteinaceous compounds in high-moisture MAOM fractions, reflective of microbial metabolites. SOC accrual increased with increasing soil moisture (P = 0.019), exchangeable Ca2+ (P = 0.013), and pyrophosphate-extractable Al content (P = 0.0001) and Al/Fe ratio (P = 0.017). Taken together, our results show that high long-term soil moisture resulted in SOC accrual by enhancing microbial conversion of plant inputs to metabolites that interact with reactive minerals.

scholarly journals Higher Long-Term Soil Moisture Increases Organic Carbon Accrual Through Microbial Conversion of Organic Inputs

2021 ◽  
Author(s):  
Itamar Shabtai ◽  
Srabani Das ◽  
Thiago Inagaki ◽  
Behrooz Azimzadeh ◽  
Carmen Martínez ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Chemical Composition ◽  
Organic Carbon ◽  
Fold Increase ◽  
Sodium Dithionite ◽  
Sodium Pyrophosphate ◽  
Microbial Conversion ◽  
High Moisture

High long-term soil moisture may either stimulate or inhibit soil organic carbon (SOC) losses through changes to mineral and chemical composition, and resultant organo-mineral interactions. Yet, the trade-off between mineralization and accrual of SOC under long-term variation in unsaturated soil moisture remains an uncertainty. In this study, we tested the underexplored relationships between long-term soil moisture and organo-mineral chemical composition, and its implications for SOC persistence. The results provide new insights into SOC accrual mechanisms under different long-term moisture levels commonly observed in well-drained soils. Differences in long-term mean volumetric water content ranging from 0.4 - 0.63 (v/v) on fallow plots in an experimental field in New York, USA, were positively correlated with SOC contents (R2 = 0.228; P = 0.019, n = 20), mineral-associated organic matter (MAOM) (R2 = 0.442; P = 0.001; n = 20) and occluded particulate organic matter (oPOM) contents (R2 = 0.178; P = 0.033; n = 20). Higher long-term soil moisture decreased the relative content of sodium pyrophosphate extractable Fe (R2 = 0.33; P < 0.005; n = 20), increased that of sodium dithionite extractable Fe (R2 = 0.443; P < 0.001; n = 20), and increased the overall importance of non-crystalline Al pools (extracted with sodium pyrophosphate and hydroxylamine extractable) for SOC retention. Higher long-term soil moisture supported up to a four-fold increase in microbial biomass (per unit SOC), and lower C:N ratios in MAOM fractions of high-moisture soils (from C:N 9.5 to 9, R2 = 0.267, P = 0.011, n =20). This was reflected by a 15% and 10% greater proportion of oxidized carboxylic-C to aromatic-C and O-alkyl C, respectively, as measured with 13C-NMR, and a more pronounced FTIR signature of N-containing proteinaceous compounds in high-moisture MAOM fractions, reflective of microbial metabolites. SOC accrual increased with increasing soil moisture (P = 0.019), exchangeable Ca2+ (P = 0.013), and pyrophosphate-extractable Al content (P = 0.0001) and Al/Fe ratio (P = 0.017). Taken together, our results show that high long-term soil moisture resulted in SOC accrual by enhancing microbial conversion of plant inputs to metabolites that interact with reactive minerals.

Effects of biotic and abiotic indices on soil water content in a decade-long grassland biodiversity experiment

2020 ◽  
Author(s):  
Christine Fischer ◽  
Sophia Leimer ◽  
Christiane Roscher ◽  
Janneke Ravenek ◽  
Hans de Kroon ◽  
...  
Keyword(s):  
Species Richness ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Functional Groups ◽  
Plant Species ◽  
Plant Species Richness

&lt;p&gt;Soil moisture is the dynamic link between climate, soil and vegetation and the dynamics and variation are affected by several often interrelated factors such as soil texture, soil structural parameters (soil organic carbon) and vegetation parameters (e.g. belowground- and aboveground biomass). For the characterization of soil moisture, including its variability and the resulting water and matter fluxes, the knowledge of the relative importance of these factors is of major challenge. Because of the spatial heterogeneity of its drivers soil moisture varies strongly over time and space. Our objective was to assess the spatio-temporal variability of soil moisture and factors which could explain that variability, like soil properties and vegetation cover, in in a long term biodiversity experiment (Jena Experiment).&lt;/p&gt;&lt;p&gt;The Jena Experiment consist 86 plots on which plant species richness (0, 1, 2, 4, 8, 16, and 60) and functional groups (legumes, grasses, tall herbs, and small herbs) were manipulated in a factorial design Soil moisture measurements were performed weekly April to September 2003-2005 and 2008-2013 in 0.1, 0.2, 0.3, 0.4, and 0.6 m soil depth using Delta T theta probe.&lt;/p&gt;&lt;p&gt;The analysis showed that both plant species richness and the presence of particular functional groups affected soil water content, while functional group richness per se played no role. Plots containing grasses was consistently drier than average at the soil surface in all observed years while plots containing legumes comparatively moister, but only up to the year 2008.&lt;/p&gt;&lt;p&gt;Interestingly, plant species richness led to moister than average subsoil at the beginning of the experiment (2003 and 2004), which changed to lower than average up to the year 2010 in all depths.Shortly after establishment, increased topsoil water content was related to higher leaf area index in species&amp;#8208;rich plots, which enhanced shading. In later years, higher species richness increased topsoil organic carbon, likely improving soil aggregation. Improved aggregation, in turn, dried topsoils in species&amp;#8208;rich plots due to faster drainage of rainwater.&lt;/p&gt;&lt;p&gt;Our decade&amp;#8208;long experiment shows that besides abiotic factors like texture, soil water patterns are consistently affected by biotic factors such as species diversity and plant functional types, but also properties that originate from biotic-abiotic interactions such as soil structure. Especially the effect of plant species richness propagated to deeper soil layers 8 years after the establishment of the experiment, and while originally caused by shading it was later related to altered soil physical characteristics in addition to modification of water uptake depth. Functional groups affected soil water distribution, likely due to plant traits affecting root water uptake depths, shading, or water&amp;#8208;use efficiency. Our results highlight the role of vegetation composition for soil processes and emphasize the need for long-term experiments to discover diversity effects in slow reacting systems like soil.&lt;/p&gt;

scholarly journals Effect of Long-Term Soil Management Practices on Tree Growth, Yield and Soil Biodiversity in a High-Density Olive Agro-Ecosystem

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1036
Author(s):  
Sauro Simoni ◽  
Giovanni Caruso ◽  
Nadia Vignozzi ◽  
Riccardo Gucci ◽  
Giuseppe Valboa ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Structure ◽  
Soil Management ◽  
Carbon Pools ◽  
Grass Cover ◽  
Soil Biodiversity ◽  
Canopy Effect ◽  
Soil Organic Carbon Pools

Edaphic arthropod communities provide valuable information about the prevailing status of soil quality to improve the functionality and long-term sustainability of soil management. The study aimed at evaluating the effect of plant and grass cover on the functional biodiversity and soil characteristics in a mature olive orchard (Olea europaea L.) managed for ten years by two conservation soil managements: natural grass cover (NC) and conservation tillage (CT). The trees under CT grew and yielded more than those under NC during the period of increasing yields (years 4–7) but not when they reached full production. Soil management did not affect the tree root density. Collecting samples underneath the canopy (UC) and in the inter-row space (IR), the edaphic environment was characterized by soil structure, hydrological properties, the concentration and storage of soil organic carbon pools and the distribution of microarthropod communities. The soil organic carbon pools (total and humified) were negatively affected by minimum tillage in IR, but not UC, without a loss in fruit and oil yield. The assemblages of microarthropods benefited, firstly, from the grass cover, secondly, from the canopy effect, and thirdly, from a soil structure ensuring a high air capacity and water storage. Feeding functional groups—hemiedaphic macrosaprophages, polyphages and predators—resulted in selecting the ecotonal microenvironment between the surface and edaphic habitat.

scholarly journals Long-Term Nitrogen Addition Decreases Soil Carbon Mineralization in an N-Rich Primary Tropical Forest

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 734
Author(s):  
Xiankai Lu ◽  
Qinggong Mao ◽  
Zhuohang Wang ◽  
Taiki Mori ◽  
Jiangming Mo ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
Organic Carbon ◽  
Tropical Forest ◽  
Soil Carbon ◽  
Tropical Forests ◽  
Carbon Mineralization ◽  
N Deposition ◽  
Soil Carbon Mineralization ◽  
N Additions

Anthropogenic elevated nitrogen (N) deposition has an accelerated terrestrial N cycle, shaping soil carbon dynamics and storage through altering soil organic carbon mineralization processes. However, it remains unclear how long-term high N deposition affects soil carbon mineralization in tropical forests. To address this question, we established a long-term N deposition experiment in an N-rich lowland tropical forest of Southern China with N additions such as NH4NO3 of 0 (Control), 50 (Low-N), 100 (Medium-N) and 150 (High-N) kg N ha−1 yr−1, and laboratory incubation experiment, used to explore the response of soil carbon mineralization to the N additions therein. The results showed that 15 years of N additions significantly decreased soil carbon mineralization rates. During the incubation period from the 14th day to 56th day, the average decreases in soil CO2 emission rates were 18%, 33% and 47% in the low-N, medium-N and high-N treatments, respectively, compared with the Control. These negative effects were primarily aroused by the reduced soil microbial biomass and modified microbial functions (e.g., a decrease in bacteria relative abundance), which could be attributed to N-addition-induced soil acidification and potential phosphorus limitation in this forest. We further found that N additions greatly increased soil-dissolved organic carbon (DOC), and there were significantly negative relationships between microbial biomass and soil DOC, indicating that microbial consumption on soil-soluble carbon pool may decrease. These results suggests that long-term N deposition can increase soil carbon stability and benefit carbon sequestration through decreased carbon mineralization in N-rich tropical forests. This study can help us understand how microbes control soil carbon cycling and carbon sink in the tropics under both elevated N deposition and carbon dioxide in the future.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Tracking Changes on Soil Structure and Organic Carbon Sequestration after 30 Years of Different Tillage and Management Practices

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 291
Author(s):  
Ramón Bienes ◽  
Maria Jose Marques ◽  
Blanca Sastre ◽  
Andrés García-Díaz ◽  
Iris Esparza ◽  
...  
Keyword(s):  
Triticum Aestivum ◽  
Organic Carbon ◽  
Management Practices ◽  
Management Strategies ◽  
Triticum Aestivum L ◽  
Field Trials ◽  
Conventional Tillage ◽  
Soil Parameters ◽  
Wilting Point

Long-term field trials are essential for monitoring the effects of sustainable land management strategies for adaptation and mitigation to climate change. The influence of more than thirty years of different management is analyzed on extensive crops under three tillage systems, conventional tillage (CT), minimum tillage (MT), and no-tillage (NT), and with two crop rotations, monoculture winter-wheat (Triticum aestivum L.) and wheat-vetch (Triticum aestivum L.-Vicia sativa L.), widely present in the center of Spain. The soil under NT experienced the largest change in organic carbon (SOC) sequestration, macroaggregate stability, and bulk density. In the MT and NT treatments, SOC content was still increasing after 32 years, being 26.5 and 32.2 Mg ha−1, respectively, compared to 20.8 Mg ha−1 in CT. The SOC stratification (ratio of SOC at the topsoil/SOC at the layer underneath), an indicator of soil conservation, increased with decreasing tillage intensity (2.32, 1.36, and 1.01 for NT, MT, and CT respectively). Tillage intensity affected the majority of soil parameters, except the water stable aggregates, infiltration, and porosity. The NT treatment increased available water, but only in monocropping. More water was retained at the permanent wilting point in NT treatments, which can be a disadvantage in dry periods of these edaphoclimatic conditions.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

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