The Experts below are selected from a list of 200457 Experts worldwide ranked by ideXlab platform
Weiguo Liu - One of the best experts on this subject based on the ideXlab platform.
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calibrating bacterial tetraether distributions towards in situ Soil Temperature and application to a loess paleosol sequence
Quaternary Science Reviews, 2020Co-Authors: Huanye Wang, Zenghao Zhao, Weiguo LiuAbstract:Abstract Branched glycerol dialkyl glycerol tetraethers (brGDGTs) produced by Soil-dwelling bacteria offer a promising tool for reconstructing terrestrial Temperatures. However, in most previous studies, due to lack of Soil Temperature data, brGDGTs are calibrated to the air rather than the Soil Temperature. This may impede our understanding of the accurate response of brGDGTs to Temperature, and thus affect the quantitative paleoTemperature reconstruction using these lipids. Here, we investigated modern Soil brGDGTs and the corresponding Soil Temperature across a large climatic gradient in China (mean annual Soil Temperature (MAST) range: −2.7 to 26.2 °C). The results show that the MAST is higher than the mean annual air Temperature (MAAT) by 0–6 °C, and the difference is related to vegetation coverage. This supports the idea that vegetation can modulate MAST and points to the necessity of exploring the direct response of brGDGTs to Soil Temperature. Employing stepwise regression (sr), we developed MASTsr and MAATsr calibrations, which improve accuracy and reduce the error compared with previous global MAAT calibrations. In Lantian loess-paleosols, the MASTsr calibration resulted in ∼4 °C lower glacial Temperatures and a ∼10 °C deglacial warming comparable with other terrestrial records and climate models. However, the global MATmr and MBT’/CBT calibrations produced abnormally higher glacial Temperatures, while the empirical MAATsr calibration overestimated MAAT during the deglacial period with low vegetation coverage. This demonstrates that the calibration with Soil Temperature is preferred for quantitative paleoTemperature reconstruction. Nevertheless, Soil brGDGTs might be useful for inferring MAAT if underlying surface conditions are sufficiently constrained.
Shadrack O Nyawade - One of the best experts on this subject based on the ideXlab platform.
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effect of potato hilling on Soil Temperature Soil moisture distribution and sediment yield on a sloping terrain
Soil & Tillage Research, 2018Co-Authors: Shadrack O Nyawade, N K Karanja, Charles K K Gachene, Elmar Schultegeldermann, M ParkerAbstract:Abstract Soil erosion rates are exacerbated in sloping arable lands of Central Kenya due mainly to the high Soil disturbance caused by potato hilling. A field study was conducted in runoff plots to quantify the effect of potato hilling on Soil loss, Soil moisture distribution and Soil Temperature. Three hilling practices; hilling performed at before crop emergence (pre-hilling), one-pass hilling (at 15 days after potato emergence), the conventional two-pass hilling (at 15 and 30 days after potato emergence), and the control (non-hilling) constituted the treatments. Root length density, vegetal cover, Soil surface roughness and Soil water infiltration capacity were quantified at different stages of potato growth and related with the sediment yield. Soil Temperature and Soil moisture contents were monitored using Onset HOBO sensor probes throughout the potato growth cycle. Compared to the conventional two-pass hilling, pre-hilling increased the Soil moisture content by 6% and lowered the Soil Temperature by up to 3.4 °C at crop emergence, thus optimized tuber germination and growth. This ensured earlier canopy closure and reduced the cumulative sediment yield by 12 t/ha. The increased surface roughness resulting from pre-hilled ridges puddled the surface water and increased the Soil water infiltration rate by 7 to 9 mm/hr compared to the non-hilled plots. Planting potatoes in pre-hilled plots has a potential to optimize the Soil Temperature and Soil moisture conditions and can reduce the high Soil erosion rates in sloping arable lands.
M Parker - One of the best experts on this subject based on the ideXlab platform.
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effect of potato hilling on Soil Temperature Soil moisture distribution and sediment yield on a sloping terrain
Soil & Tillage Research, 2018Co-Authors: Shadrack O Nyawade, N K Karanja, Charles K K Gachene, Elmar Schultegeldermann, M ParkerAbstract:Abstract Soil erosion rates are exacerbated in sloping arable lands of Central Kenya due mainly to the high Soil disturbance caused by potato hilling. A field study was conducted in runoff plots to quantify the effect of potato hilling on Soil loss, Soil moisture distribution and Soil Temperature. Three hilling practices; hilling performed at before crop emergence (pre-hilling), one-pass hilling (at 15 days after potato emergence), the conventional two-pass hilling (at 15 and 30 days after potato emergence), and the control (non-hilling) constituted the treatments. Root length density, vegetal cover, Soil surface roughness and Soil water infiltration capacity were quantified at different stages of potato growth and related with the sediment yield. Soil Temperature and Soil moisture contents were monitored using Onset HOBO sensor probes throughout the potato growth cycle. Compared to the conventional two-pass hilling, pre-hilling increased the Soil moisture content by 6% and lowered the Soil Temperature by up to 3.4 °C at crop emergence, thus optimized tuber germination and growth. This ensured earlier canopy closure and reduced the cumulative sediment yield by 12 t/ha. The increased surface roughness resulting from pre-hilled ridges puddled the surface water and increased the Soil water infiltration rate by 7 to 9 mm/hr compared to the non-hilled plots. Planting potatoes in pre-hilled plots has a potential to optimize the Soil Temperature and Soil moisture conditions and can reduce the high Soil erosion rates in sloping arable lands.
Huanye Wang - One of the best experts on this subject based on the ideXlab platform.
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calibrating bacterial tetraether distributions towards in situ Soil Temperature and application to a loess paleosol sequence
Quaternary Science Reviews, 2020Co-Authors: Huanye Wang, Zenghao Zhao, Weiguo LiuAbstract:Abstract Branched glycerol dialkyl glycerol tetraethers (brGDGTs) produced by Soil-dwelling bacteria offer a promising tool for reconstructing terrestrial Temperatures. However, in most previous studies, due to lack of Soil Temperature data, brGDGTs are calibrated to the air rather than the Soil Temperature. This may impede our understanding of the accurate response of brGDGTs to Temperature, and thus affect the quantitative paleoTemperature reconstruction using these lipids. Here, we investigated modern Soil brGDGTs and the corresponding Soil Temperature across a large climatic gradient in China (mean annual Soil Temperature (MAST) range: −2.7 to 26.2 °C). The results show that the MAST is higher than the mean annual air Temperature (MAAT) by 0–6 °C, and the difference is related to vegetation coverage. This supports the idea that vegetation can modulate MAST and points to the necessity of exploring the direct response of brGDGTs to Soil Temperature. Employing stepwise regression (sr), we developed MASTsr and MAATsr calibrations, which improve accuracy and reduce the error compared with previous global MAAT calibrations. In Lantian loess-paleosols, the MASTsr calibration resulted in ∼4 °C lower glacial Temperatures and a ∼10 °C deglacial warming comparable with other terrestrial records and climate models. However, the global MATmr and MBT’/CBT calibrations produced abnormally higher glacial Temperatures, while the empirical MAATsr calibration overestimated MAAT during the deglacial period with low vegetation coverage. This demonstrates that the calibration with Soil Temperature is preferred for quantitative paleoTemperature reconstruction. Nevertheless, Soil brGDGTs might be useful for inferring MAAT if underlying surface conditions are sufficiently constrained.
Vijay P Singh - One of the best experts on this subject based on the ideXlab platform.
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Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model
IEEE Access, 2020Co-Authors: Liu Penghui, Ahmed A. Ewees, Beste Hamiye Beyaztas, Chongchong Qi, Sinan Q. Salih, Nadhir Al-ansari, Suraj Kumar Bhagat, Zaher Mundher Yaseen, Vijay P SinghAbstract:An enhanced hybrid artificial intelligence model was developed for Soil Temperature (ST) prediction. Among several Soil characteristics, Soil Temperature is one of the essential elements impacting the biological, physical and chemical processes of the terrestrial ecosystem. Reliable ST prediction is significant for multiple geo-science and agricultural applications. The proposed model is a hybridization of adaptive neuro-fuzzy inference system with optimization methods using mutation Salp Swarm Algorithm and Grasshopper Optimization Algorithm (ANFIS-mSG). Daily weather and Soil Temperature data for nine years (1 of January 2010 - 31 of December 2018) from five meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) in North Dakota, USA, were used for modeling. For validation, the proposed ANFIS-mSG model was compared with seven models, including classical ANFIS, hybridized ANFIS model with grasshopper optimization algorithm (ANFIS-GOA), salp swarm algorithm (ANFIS-SSA), grey wolf optimizer (ANFIS-GWO), particle swarm optimization (ANFIS-PSO), genetic algorithm (ANFIS-GA), and Dragonfly Algorithm (ANFIS-DA). The ST prediction was conducted based on maximum, mean and minimum air Temperature (AT). The modeling results evidenced the capability of optimization algorithms for building ANFIS models for simulating Soil Temperature. Based on the statistical evaluation; for instance, the root mean square error (RMSE) was reduced by 73%, 74.4%, 71.2%, 76.7% and 80.7% for Baker, Beach, Cando, Crary and Fingal meteorological stations, respectively, throughout the testing phase when ANFIS-mSG was used over the standalone ANFIS models. In conclusion, the ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting Soil Temperature based on univariate air Temperature scenario.
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modeling daily Soil Temperature using data driven models and spatial distribution
Theoretical and Applied Climatology, 2014Co-Authors: Sungwon Kim, Vijay P SinghAbstract:The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro–fuzzy inference system (ANFIS), for estimating daily Soil Temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily Soil Temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily Soil Temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily Soil Temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily Soil Temperature successfully in this study.
