Effect of soil moisture content on the splash phenomenon reproducibility.
Ontology highlight
ABSTRACT:
SUBMITTER: Ryzak M
PROVIDER: S-EPMC4364956 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Widespread afforestation programs sequester carbon from the atmosphere and mitigate the rising of atmospheric carbon dioxide (CO2). Meanwhile, afforestation carbon sequestration may cost soil water. However, changes in soil moisture content (SMC) after large-scale afforestation or reforestation have rarely been quantified. In this study, we measured changes in SMC following afforestation using a paired plots method with data from 757 plots in Northeastern China. We found a marginally significant decline in soil moisture content of the top 1-m soil (SMC0-1m) after afforestation (P = 0.08) at the regional scale. The SMC responses to afforestation also vary across species. For example, significant SMC decrease are found for Populus spp. plantations (P < 0.05) and plantations of Pinus sylvestris var. mongolica (P < 0.05). Splitting the first meter of the soil profile into different depth intervals revealed that SMC declined significantly in shallow layers (0-30 cm) for Populus spp. and Pinus sylvestris var. mongolica. We also found that when SMC in the control exceeded a specific threshold, SMC for all five tree species considered tended to decrease, suggesting that the effects of afforestation on soil hydrology vary across different regions.
| S-EPMC4981471 | biostudies-literature
Project description:The dataset contains thermal properties of soil such as thermal conductivity, thermal diffusivity, temperature and specific heat capacity in an agricultural farm within the University of Ibadan, Ibadan, Nigeria. The data were acquired in forty (40) sampling points using thermal analyzer called KD-2 Pro. Soil samples taken at these sampling points were analyzed in the laboratory for their moisture content following the standard reference of American Association of State Highway and Transport Officials (AASHTO) T265. The data were acquired within the first and second weeks in the month of April, 2012. Statistical analyses were performed on the data set to understand the data. The data is made available publicly because thermal properties of soils have significant role in understanding the water retention capacity of soil and could be helpful for proper irrigation water management.
| S-EPMC5988408 | biostudies-literature
Project description:The rheological properties parameters of paddy soil affect the interaction between the tillage tools and soil, thus influencing the operation quality and power consumption. In order to study the effects of tillage methods and moisture content on the rheological properties parameters of paddy soil in the middle and lower reaches of the Yangtze River, uniaxial compression creep tests of paddy soils with four moisture contents under no tillage (moisture contents: 26.71%, 24.52%, 23.26%, 21.28%) and plough tillage (moisture contents: 26.77%, 25.55%, 23.40%, 20.56%) were carried out using a TMS-PRO texture analyzer. The creep properties curves obtained from the tests, and the rheological constitutive equation of paddy soil under compression was established by Burgers viscoelastic model. Respectively, the quantitative change rules of creep properties of paddy soil with different moisture contents under different tillage methods and the correlation between these parameters were explored. The results showed that the moisture content under the three-year plough tillage and no tillage methods had significant influence on the rheological properties parameters of paddy soil (P < 0.05). The instantaneous elastic modulus, delay elastic modulus, and viscosity coefficient of the two paddy soils (no tillage and plough tillage soils) decreased with the increase of moisture content. However, the variation rules of relaxation time and delay viscosity coefficient with moisture content differed between these two paddy soils. Specifically, the strain rate of the two paddy soils decreased as moisture content decreased, where the total strain combines elastic strain, viscous strain, and viscoelastic strain. The initial strain rate and steady strain rate of the plough tillage paddy soils were lower than that of the no tillage paddy soils. The established creep model equation could be used to obtain viscoelastic rheological parameters of paddy soil in a wide range. The fitting equations between rheological parameters and moisture content were introduced into Burgers model, and the coupling equations between creep deformation and moisture content and time were derived, which could be used to predict the creep properties and deformation behavior of paddy soil in a certain range of no tillage and ploughed field. Overall, this study has a certain theoretical significance for the development and improvement of paddy soil rheology theory, and can also provide theoretical basis and technical support for the research of agricultural machinery design optimization, field water, soil conservation, soil tillage and compaction related simulation analysis in the middle and lower reaches of the Yangtze River.
| S-EPMC8224953 | biostudies-literature
Project description:Microbial communities are known as the primary decomposers of all the carbon accumulated in the soil. However, how important soil structure and its conventional or organic management, moisture content, and how different plant species impact this process are less understood. To answer these questions, we generated a soil microcosm with decomposing corn and soy leaves, as well as soil adjacent to the leaves, and compared it to control samples. We then used high-throughput amplicon sequencing of the ITS and 16S rDNA regions to characterize these microbiomes. Leaf microbiomes were the least diverse and the most even in terms of OTU richness and abundance compared to near soil and far soil, especially in their bacterial component. Microbial composition was significantly and primarily affected by niche (leaves vs. soil) but also by soil management type and plant species in the fungal microbiome, while moisture content and pore sizes were more important drivers for the bacterial communities. The pore size effect was significantly dependent on moisture content, but only in the organic management type. Overall, our results refine our understanding of the decomposition of carbon residues in the soil and the factors that influence it, which are key for environmental sustainability and for evaluating changes in ecosystem functions.
| S-EPMC10461183 | biostudies-literature
Project description:Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model's SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0-10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R 2 val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R 2 val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.
| S-EPMC6501779 | biostudies-literature
Project description:Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
| S-EPMC9609775 | biostudies-literature
Project description:The stabilization of slopes by vegetation has been a topical issue for many years. Root mechanical characteristics significantly influence soil reinforcement; therefore it is necessary to research into the indicators of root tensile properties. In this study, we explored the influence of root moisture content on tensile resistance and strength with different root diameters and for different tree species. Betula platyphylla, Quercus mongolica, Pinus tabulaeformis, and Larix gmelinii, the most popular tree species used for slope stabilization in the rocky mountainous areas of northern China, were used in this study. A tensile test was conducted after root samples were grouped by diameter and moisture content. The results showedthat:1) root moisture content had a significant influence on tensile properties; 2) slightly loss of root moisture content could enhance tensile strength, but too much loss of water resulted in weaker capacity for root elongation, and consequently reduced tensile strength; 3) root diameter had a strong positive correlation with tensile resistance; and4) the roots of Betula platyphylla had the best tensile properties when both diameter and moisture content being controlled. These findings improve our understanding of root tensile properties with root size and moisture, and could be useful for slope stabilization using vegetation.
| S-EPMC4803321 | biostudies-literature
Project description:Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the ?-? types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all ?-? types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.
| S-EPMC7070557 | biostudies-literature
Project description:BACKGROUND:Although the effect of cold stratification on seed dormancy release has been extensively studied for many species, knowledge of the role of stratifying temperature, soil moisture content and duration of stratification on seed dormancy release at the population level is limited. Here, we aimed to determine the response of seed dormancy release to these factors in six populations of Halenia elliptica. RESULTS:Seed dormancy release was more responsive to low than high temperatures, and no dormancy break occurred at 8?°C. Seed germination percentage increased first and then remained unchanged as stratifying soil moisture content increased from 0 to 24%. Seed dormancy release of populations from low altitude was more sensitive to increased stratifying temperature and decreased soil moisture content than those from high altitudes. CONCLUSIONS:Temperature and soil moisture changes resulting from global warming could affect seed dormancy release and consequently seedling establishment. Thus, incorporating data on seed dormancy release involving temperature, soil moisture content and stratification duration is beneficial for predicting plant species regeneration, migration and coexistence in a scenario of climate change.
| S-EPMC7388213 | biostudies-literature
Project description:Mineral nitrogen (N) availability to heterotrophic micro-organisms is known to impact organic matter (OM) decomposition. Different pathways determining the N accessibility depend to a large extent on soil structure. Contact between soil mineral and OM substrate particles can facilitate N transport toward decomposition hot spots. However, the impact of soil structure on N availability to microbes and thus heterotrophic microbial activity and community structure is not yet fully understood. We hypothesized that carbon mineralization (Cmin) from low-N substrate would be stimulated by increased N availability caused by closer contact with soil particles or by a higher moisture level, enhancing potential for N-diffusion. Under opposite conditions retarded heterotrophic activity and a dominance of fungi were expected. A 128-days incubation experiment with CO2 emission monitoring from artificially reconstructed miniature soil cores with contrasting soil structures, viz. high or low degree of contact between soil particles, was conducted to study impacts on heterotrophic activity. The soil cores were subjected to different water filled pore space percentages (25 or 50% WFPS) and amended with either easily degradable OM high in N (grass) or more resistant OM low in N (sawdust). X-ray μCT image processing allowed to quantify the pore space in 350 μm around OM substrates, i.e., the microbial habitat of involved decomposers. A lower local porosity surrounding sawdust particles in soils with stonger contact was confirmed, at least at 25% WFPS. Mineral N addition to sawdust amended soils with small particle contact at 25% WFPS resulted in a stimulated respiration. Cmin in the latter soils was lower than in case of high particle contact. This was not observed for grass substrate particles or at 50% WFPS. The interactive effect of substrate type and soil structure suggests that the latter controls Cmin through mediation of N diffusion and in turn N availability. Phospholipid fatty acid did not reveal promotion of fungal over bacterial biomarkers in treatments with N-limited substrate decomposition. Combining X-ray μCT with tailoring soil structure allows for more reliable investigation of effects on the soil microbial community, because as also found here, the established soil pore network structure can strongly deviate from the intended one.
| S-EPMC6043809 | biostudies-literature