Variation in soil bulk density and hydraulic conductivity within a quasi-circular vegetation patch and bare soil area

Liu, Qingsheng; Liu, Gaohuan; Huang, Chong; He, Li

doi:10.1007/s11368-019-02549-3

Cited by 14 publications

(11 citation statements)

References 64 publications

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“…Soil moisture contents were determined by drying the soil at 80 • C in the oven to a constant weight. Soil bulk density was determined by the cutting ring method (Liu et al, 2020).…”

Section: Determination Of Soil Physicochemical Propertiesmentioning

confidence: 99%

Biochar and Intercropping With Potato–Onion Enhanced the Growth and Yield Advantages of Tomato by Regulating the Soil Properties, Nutrient Uptake, and Soil Microbial Community

Xie

Gao

et al. 2021

Front. Microbiol.

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The application of biochar stimulates the activities of microorganisms that affect soil quality and plant growth. However, studies on the impacts of biochar mainly focus on a monoculture, its effects on interspecific interactions are rarely reported. Here, we investigated the impacts of biochar on tomato/potato–onion intercropped (TO) in a pot experiment. Tomato monoculture (T) and TO were treated with no, 0.3, 0.6, and 1.2% biochar concentrations in a pot experiment. Microbial communities from tomato rhizosphere soil were analyzed by quantitative PCR and Illumina MiSeq. The results showed that compared with the tomato monoculture, 0.6%TO and 1.2%TO significantly increased tomato yield in 2018. TO and 1.2%TO significantly increased plant height and dry weight in 2018 and 2019. Biochar treatments increased soil pH, decreased NO3--N and bulk density, and increased the absorption of N, P, and K by tomato. Bacterial and fungal abundances increased with an increase in biochar concentration, while Bacillus spp. and Pseudomonas spp. abundances showed an “increase-decrease-increase” trend. Biochar had a little effect on bacterial diversities but significantly lowered fungal diversities. TO, 0.6%TO, and 1.2%TO increased the potentially beneficial organisms (e.g., Pseudeurotium and Solirubrobacter) and lowered the potentially pathogenic organisms (e.g., Kribbella and Ilyonectria). Different concentrations of biochar affected the bacterial and fungal community structures. Redundancy analysis indicated that the bacterial community was strongly correlated with soil pH, NO3--N, and EC, while the fungal community was closely related to soil NO3--N and moisture. The network analysis showed that biochar and intercropping affected the symbiosis pattern of the microorganisms and increased the proportion of positive interactions and nitrifying microorganisms (Nitrospirae) in the microbial community. Overall, our results indicated that monoculture and intercropping with biochar improved soil physicochemical states and plant nutrient absorption, and regulated soil microbial communities, these were the main factors to promote tomato growth and increase tomato productivity.

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“…Soil moisture contents were determined by drying the soil at 80 • C in the oven to a constant weight. Soil bulk density was determined by the cutting ring method (Liu et al, 2020).…”

Section: Determination Of Soil Physicochemical Propertiesmentioning

confidence: 99%

Biochar and Intercropping With Potato–Onion Enhanced the Growth and Yield Advantages of Tomato by Regulating the Soil Properties, Nutrient Uptake, and Soil Microbial Community

Xie

Gao

et al. 2021

Front. Microbiol.

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“…The actual number of QVPs was 139 at the study site, which were obtained through the visual interpretation of the 1 m resolution IKONOS images, GF-2 satellite images, and high-resolution images from Google TM Earth. According to the previous studies [5]- [8], generally the area of the QVPs was from 50 to 1206 m 2 . Due to the adhesion of multiple QVPs, the area of some QVPs can reach 3000 m 2 .…”

Section: A Study Sitementioning

confidence: 91%

“…A total of 43 datasets (including plant species, diameter of the QVPs, soil physiochemical properties, and so on) of the QVPs from five field surveys in April 2019, May 2018 [5], June 2017, August 2017 [8], and October 2013 were included into the reference dataset for landscape classification and the detection of the QVPs from the GF-1 imagery, and accuracy assessments. Because the areas of QVPs have changed little since 2005 [5], so field survey samples, although not obtained synchronously with the acquisition of satellite images, can be used as the reference data.…”

Section: A Study Sitementioning

confidence: 99%

“…Analogous to arid and semi-arid areas around the world, vegetation pattern in the Yellow River Delta (YRD), China is marked by the bare soils interspersed with the quasi-circular vegetation patches (QVPs) [6], which was first discovered from high spatial resolution imagery in 2011. The QVPs have a quick succession rate and can develop to their maximum size within one to three years, rendering them optimum for examining the mechanisms of spontaneous plant colonization, evolution, resilience, and degradation [5]- [8]. It has been suggested that patch-size distributions can act as good indicators of discontinuous transitions and imminent desertification in arid and semi-arid regions, which are issues that increasingly draw attention from the scientific community [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery

et al. 2020

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“…In wetlands, bulk density is a fundamental parameter influencing hydraulic conductivity [ 37 , 39 ], and this parameter is used for calculating the total storage of a given nutrient per unit area [ 2 , 40 ]. Nutrient stock (carbon or nitrogen) in a wetland environment could be estimated by soil bulk density, and soils with low bulk density have a greater capacity for holding nitrogen and carbon than soils with high bulk density [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Hikouei

Kim

Mishra

2021

Sensors

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Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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Variation in soil bulk density and hydraulic conductivity within a quasi-circular vegetation patch and bare soil area

Cited by 14 publications

References 64 publications

Biochar and Intercropping With Potato–Onion Enhanced the Growth and Yield Advantages of Tomato by Regulating the Soil Properties, Nutrient Uptake, and Soil Microbial Community

Biochar and Intercropping With Potato–Onion Enhanced the Growth and Yield Advantages of Tomato by Regulating the Soil Properties, Nutrient Uptake, and Soil Microbial Community

Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

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