scholarly journals Unconfined strength of an unsaturated residual soil struck lightning

2016 ◽  
Vol 9 ◽  
pp. 14006 ◽  
Author(s):  
Thiago de S. Carnavale ◽  
Mariana F.B. Motta ◽  
Tácio. M.P. de Campos ◽  
Haimon D.L. Alves ◽  
Antônio R.M.B. de Oliveira ◽  
...  
Geotecnia ◽  
2014 ◽  
Vol 130 ◽  
pp. 79-99
Author(s):  
David Jorge Pereira Fernandes ◽  
◽  
<br>António Viana da Fonseca ◽  

2002 ◽  
Vol 12 (2) ◽  
pp. 250-256 ◽  
Author(s):  
Hudson Minshew ◽  
John Selker ◽  
Delbert Hemphill ◽  
Richard P. Dick

Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Chen ◽  
Xue-wen Lei ◽  
Han-lin Zhang ◽  
Zhi Lin ◽  
Hui Wang ◽  
...  

AbstractThe problems caused by the interaction between slopes and hydrologic environment in traffic civil engineering are very serious in the granite residual soil area of China, especially in Guangdong Province. Against the background of two heavy rainfall events occurring during a short period due to a typhoon making landfall twice or even two typhoons consecutively making landfall, laboratory model tests were carried out on the hydrological effects of the granite residual soil slope considering three vegetation types under artificial rainfall. The variation in slope surface runoff, soil moisture content and rain seepage over time was recorded during the tests. The results indicate that surface vegetation first effectively reduces the splash erosion impact of rainwater on slopes and then influences the slope hydrological effect through rainwater forms adjustment. (1) The exposed slope has weak resistance to two consecutive heavy rains, the degree of slope scouring and soil erosion damage will increase greatly during the second rainfall. (2) The multiple hindrances of the stem leaf of Zoysia japonica plays a leading role in regulating the hydrological effect of slope, the root system has little effect on the permeability and water storage capacity of slope soil, but improves the erosion resistance of it. (3) Both the stem leaf and root system of Nephrolepis cordifolia have important roles on the hydrological effect. The stem leaf can stabilize the infiltration of rainwater, and successfully inhibit the surface runoff under continuous secondary heavy rainfall. The root system significantly enhances the water storage capacity of the slope, and greatly increases the permeability of the slope soil in the second rainfall, which is totally different from that of the exposed and Zoysia japonica slopes. (4) Zoysia is a suitable vegetation species in terms of slope protection because of its comprehensive slope protection effect. Nephrolepis cordifolia should be cautiously planted as slope protection vegetation. Only on slopes with no stability issues should Nephrolepis cordifolia be considered to preserve soil and water.


Author(s):  
Muttaqa Uba Zango ◽  
Khairul Anuar Kassim ◽  
Radzuan Sa'ari ◽  
Mohd Fadhli Abd Rashid ◽  
Abubakar Sadiq Muhammed ◽  
...  

Author(s):  
Nilo Cesar Consoli ◽  
Naiara da Costa Reginato ◽  
Luizmar da Silva Lopes Júnior ◽  
Marcelo Maia Rocha ◽  
Vítor Pereira Faro ◽  
...  

1996 ◽  
Vol 76 (2) ◽  
pp. 153-164 ◽  
Author(s):  
B. J. Zebarth ◽  
J. W. Paul ◽  
O. Schmidt ◽  
R. McDougall

Manure-N availability must be known in order to design application practices that maximize the nutrient value of the manure while minimizing adverse environmental impacts. This study determined the effect of time and rate of liquid manure application on silage corn yield and N utilization, and residual soil nitrate at harvest, in south coastal British Columbia. Liquid dairy or liquid hog manure was applied at target rates of 0, 175, 350 or 525 kg N ha−1, with or without addition of 100 kg N ha−1 as inorganic fertilizer, at two sites in each of 2 yr. Time of liquid-dairy-manure application was also tested at two sites in each of 2 yr with N-application treatments of: 600 kg N ha−1 as manure applied in spring; 600 kg N ha−1 as manure applied in fall; 300 kg N ha−1 as manure applied in each of spring and fall; 200 kg N ha−1 applied as inorganic fertilizer in spring; 300 kg N ha−1 as manure plus 100 kg N ha−1 as inorganic fertilizer applied in spring; and a control that received no applied N. Fall-applied manure did not increase corn yield or N uptake in the following growing season. At all sites, maximum yield was attained using manure only. Selection of proper spring application rates for manure and inorganic fertilizer were found to be equally important in minimizing residual soil nitrate at harvest. Apparent recovery of applied N in the crop ranged from 0 to 33% for manure and from 18 to 93% for inorganic fertilizer. Key words: N recovery, manure management


2021 ◽  
Vol 61 (2) ◽  
pp. 520-532
Author(s):  
Xinyu Liu ◽  
Xianwei Zhang ◽  
Lingwei Kong ◽  
Xinming Li ◽  
Gang Wang

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