scholarly journals Modelling soil moisture – soil strength relationship of fine-grained upland forest soils

Silva Fennica ◽  
2019 ◽  
Vol 53 (1) ◽  
Author(s):  
Jori Uusitalo ◽  
Jari Ala-Ilomäki ◽  
Harri Lindeman ◽  
Jenny Toivio ◽  
Matti Siren
Keyword(s):  
Water Content ◽  
Forest Soils ◽  
Model Performance ◽  
Rolling Resistance ◽  
Absolute Error ◽  
Field Studies ◽  
Soil Bulk Density ◽  
Soil Strength ◽  
Fine Grained ◽  
Strength Models

The strength of soil is known to be dependent on water content but the relationship is strongly affected by the type of soil. Accurate moisture content – soil strength models will provide forest managers with the improved ability to reduce soil disturbances and increase annual forest machine utilization rates. The aim of this study was to examine soil strength and how it is connected to the physical properties of fine-grained forest soils; and develop models that could be applied in practical forestry to make predictions on rutting induced by forest machines. Field studies were conducted on two separate forests in Southern Finland. The data consisted of parallel measurements of dry soil bulk density (BD), volumetric water content (VWC) and penetration resistance (PR). The model performance was logical, and the results were in harmony with earlier findings. The accuracy of the models created was tested with independent data. The models may be regarded rather trustworthy, since no significant bias was found. Mean absolute error of roughly 20% was found which may be regarded as acceptable taken into account the character of the penetrometer tool. The models can be linked with mobility models predicting either risks of rutting, compaction or rolling resistance.

Least limiting water range: a potential indicator of physical quality of forest soils

Soil Research ◽  
2000 ◽  
Vol 38 (5) ◽  
pp. 947 ◽  
Author(s):  
C. Zou ◽  
R. Sands ◽  
G. Buchan ◽  
I. Hudson
Keyword(s):  
Forest Management ◽  
Plant Growth ◽  
Water Content ◽  
Forest Soils ◽  
Soil Compaction ◽  
Soil Strength ◽  
Matric Potential ◽  
Potential Indicator ◽  
Least Limiting Water Range ◽  
Water Contents

The interactions of the 4 basic soil physical properties—volumetric water content, matric potential, soil strength, and air-filled porosity—were investigated over a range of contrasting textures and for 3 compaction levels of 4 forest soils in New Zealand, using linear and non-linear regression methods. Relationships among these properties depended on texture and bulk density. Soil compaction increased volumetric water contents at field capacity, at wilting point, and at the water contents associated with restraining soil strength values, but decreased the water content when air-filled porosity was limiting. The integrated effect of matric potential, air-filled porosity, and soil strength on plant growth was described by the single parameter, least limiting water range (LLWR). LLWR defines a range in soil water content within which plant growth is least likely to be limited by the availability of water and air in soil and the soil strength. Soil compaction narrowed or decreased LLWR in most cases. In coarse sandy soil, initial compaction increased LLWR, but further compaction decreased LLWR. LLWR is sensitive to variations in forest management practices and is a potential indicator of soil physical condition for sustainable forest management.

Measurement of Soil Bulk Density and Water Content with Time Domain Reflectometry: Algorithm Implementation and Method Analysis

2021 ◽  
pp. 126389
Author(s):  
Marco Bittelli ◽  
Fausto Tomei ◽  
Anbazhagan P. ◽  
Raghuveer Rao Pallapati ◽  
Puskar Mahajan ◽  
...  
Keyword(s):  
Water Content ◽  
Bulk Density ◽  
Time Domain ◽  
Soil Bulk Density ◽  
Time Domain Reflectometry ◽  
Algorithm Implementation ◽  
Method Analysis

Experimental study on dual capillary barrier using recycled asphalt pavement materials

2014 ◽  
Vol 51 (10) ◽  
pp. 1165-1177 ◽  
Author(s):  
F.R. Harnas ◽  
H. Rahardjo ◽  
E.C. Leong ◽  
J.Y. Wang
Keyword(s):  
Water Content ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Water Storage ◽  
Pressure Head ◽  
Volumetric Water Content ◽  
Capillary Barrier ◽  
Recycled Asphalt ◽  
Fine Grained ◽  
Drainage Rate

The performance of a capillary barrier cover as a cover system is affected by the ability of the capillary barrier to store water. To increase the water storage of a capillary barrier cover, the dual capillary barrier (DCB) concept is proposed. The objective of this paper is to investigate the water storage of the proposed DCB as compared to the storage of a traditional single capillary barrier (SCB). The investigation is conducted using two one-dimensional infiltration column tests under different rainfall conditions. The results show that a DCB stores more water as compared to SCB. The results show that the fine-grained layers of a DCB have higher volumetric water contents during drainage as compared to that of the fine-grained layer of an SCB. The higher volumetric water content is caused by the fact that the thickness of the layers in a DCB corresponds to a pore-water pressure head range where the material has the highest volumetric water content. In addition, a slower drainage rate is resulted from additional layering in a DCB.

Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe

2017 ◽  
Vol 60 (3) ◽  
pp. 683-692 ◽  
Author(s):  
Yongjin Cho ◽  
Kenneth A. Sudduth ◽  
Scott T. Drummond
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Soil Property ◽  
Soil Strength ◽  
Reflectance Spectra ◽  
Land Resource ◽  
Strength Data ◽  
Soil Sensors ◽  
Combining Data

Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.

Forecasting Of Covid-19 Cases Using Machine Learning Approach

2021 ◽  
Vol 17 ◽  
Author(s):  
Sachin Kumar ◽  
Karan Veer
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Model Performance ◽  
Real Data ◽  
Absolute Error ◽  
Viral Disease ◽  
Support Vector ◽  
Family Welfare ◽  
Accuracy Score ◽  
Learning Approaches

Aims: The objective of this research is to predict the covid-19 cases in India based on the machine learning approaches. Background: Covid-19, a respiratory disease caused by one of the coronavirus family members, has led to a pandemic situation worldwide in 2020. This virus was detected firstly in Wuhan city of China in December 2019. This viral disease has taken less than three months to spread across the globe. Objective: In this paper, we proposed a regression model based on the Support vector machine (SVM) to forecast the number of deaths, the number of recovered cases, and total confirmed cases for the next 30 days. Method: For prediction, the data is collected from Github and the ministry of India's health and family welfare from March 14, 2020, to December 3, 2020. The model has been designed in Python 3.6 in Anaconda to forecast the forecasting value of corona trends until September 21, 2020. The proposed methodology is based on the prediction of values using SVM based regression model with polynomial, linear, rbf kernel. The dataset has been divided into train and test datasets with 40% and 60% test size and verified with real data. The model performance parameters are evaluated as a mean square error, mean absolute error, and percentage accuracy. Results and Conclusion: The results show that the polynomial model has obtained 95 % above accuracy score, linear scored above 90%, and rbf scored above 85% in predicting cumulative death, conformed cases, and recovered cases.

Modeling unconfined compressive strength of fine-grained soils: Application of pocket penetrometer for predicting soil strength

CATENA ◽  
2021 ◽  
Vol 196 ◽  
pp. 104890
Author(s):  
Fatemeh Mousavi ◽  
Ehsan Abdi ◽  
Shaaban Ghalandarayeshi ◽  
Deborah S. Page-Dumroese
Keyword(s):  
Compressive Strength ◽  
Unconfined Compressive Strength ◽  
Soil Strength ◽  
Fine Grained

scholarly journals Tillage and crop rotation effects on soil carbon and selected soil physical properties in a Haplic Cambisol in Eastern Cape, South Africa

2019 ◽  
Vol 15 (No. 1) ◽  
pp. 47-54 ◽  
Author(s):  
Mxolisi Mtyobile ◽  
Lindah Muzangwa ◽  
Pearson Nyari Stephano Mnkeni
Keyword(s):  
South Africa ◽  
Water Content ◽  
Bulk Density ◽  
Soil Water ◽  
Soil Water Content ◽  
Crop Rotation ◽  
Soil Bulk Density ◽  
Crop Rotations ◽  
Soil Porosity ◽  
No Till

The effects of tillage and crop rotation on the soil carbon, the soil bulk density, the porosity and the soil water content were evaluated during the 6<sup>th</sup> season of an on-going field trial at the University of Fort Hare Farm (UFH), South Africa. Two tillage systems; conventional tillage (CT) and no-till and crop rotations; maize (Zea mays L.)-fallow-maize (MFM), maize-fallow-soybean (Glycine max L.) (MFS); maize-wheat (Triticum aestivum L.)-maize (MWM) and  maize-wheat-soybean (MWS) were evaluated. The field experiment was a 2 × 4 factorial, laid out in a randomised complete design. The crop residues were retained for the no-till plots and incorporated for the CT plots, after each cropping season. No significant effects (P &gt; 0.05) of the tillage and crop rotation on the bulk density were observed. However, the values ranged from 1.32 to1.37 g/cm<sup>3</sup>. Significant interaction effects of the tillage and crop rotation were observed on the soil porosity (P &lt; 0.01) and the soil water content (P &lt; 0.05). The porosity for the MFM and the MWS, was higher under the CT whereas for the MWM and the MWS, it was higher under the no-till. However, the greatest porosity was under the MWS. Whilst the no-till significantly increased (P &lt; 0.05) the soil water content compared to the CT; the greatest soil water content was observed when the no-till was combined with the MWM rotations. The soil organic carbon (SOC) was increased more (P &lt; 0.05) by the no-till than the CT, and the MFM consistently had the least SOC compared with the rest of the crop rotations, at all the sampling depths (0–5, 5–10 and 10–20 cm). The soil bulk density negatively correlated with the soil porosity and the soil water content, whereas the porosity positively correlated with the soil water content. The study concluded that the crop rotations, the MWM and the MWS under the no-till coupled with the residue retention improved the soil porosity and the soil water content levels the most.

scholarly journals Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1786
Author(s):  
Linh T. T. Ho ◽  
Laurent Dubus ◽  
Matteo De Felice ◽  
Alberto Troccoli
Keyword(s):  
Power Generation ◽  
Random Forest ◽  
Model Performance ◽  
Absolute Error ◽  
Random Forest Model ◽  
Low Carbon ◽  
Climate Data ◽  
Hydro Power ◽  
Forest Model ◽  
Continental Scale

Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power. Specifically, multiple lagged values of temperature and precipitation are used. Overall, the model shows promising results, with the correlation values ranging between 0.85 and 0.98 for run-of-river and between 0.73 and 0.90 for reservoir-based generation. Compared to the more standard optimal lag approach the normalised mean absolute error reduces by an average of 10.23% and 5.99%, respectively. The model was also implemented over six Italian bidding zones to also test its skill at the sub-country scale. The model performance is only slightly degraded at the bidding zone level, but this also depends on the actual installed capacity, with higher capacities displaying higher performance. The framework and results presented could provide a useful reference for applications such as pan-European (continental) hydro power planning and for system adequacy and extreme events assessments.

scholarly journals A Comparison of Soil Compaction Associated with Four Ground-Based Harvesting Systems

2011 ◽  
Vol 28 (4) ◽  
pp. 194-198 ◽  
Author(s):  
Oscar Bustos ◽  
Andrew Egan
Keyword(s):  
Bulk Density ◽  
Forest Soils ◽  
Soil Compaction ◽  
Group Selection ◽  
Soil Bulk Density ◽  
Soil Productivity ◽  
Equipment Selection ◽  
Farm Tractor ◽  
Harvesting Systems

Abstract A study of soil compaction associated with four harvesting systems—a forwarder working with a mechanized harvester and a rubber-tired cable skidder, a farm tractor, and a bulldozer, each of them coupled with a chainsaw felling—was conducted in a group selection harvest of a mixed hardwood stand in Maine. The bulldozer system was associated with the highest percentage differences in soil bulk density measured in machine tracks (16.9%), trail centerlines (15.7%), and harvested group selection units (13.1%) versus adjacent untrafficked areas, whereas the forwarder system was associated with the lowest percentage differences in soil bulk density measured in machine tracks (3.5%), trail centerlines (1.2%), and harvested group selection units (6.3%) versus adjacent untrafficked areas. Results will help to inform loggers and foresters on equipment selection, harvest planning, and the conservation of forest soils and soil productivity.

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