scholarly journals Estimating soil bulk density with information metrics of soil texture

Geoderma ◽  
2017 ◽  
Vol 287 ◽  
pp. 66-70 ◽  
Author(s):  
Miguel Ángel Martín ◽  
Miguel Reyes ◽  
F. Javier Taguas
Keyword(s):  
Bulk Density ◽  
Soil Texture ◽  
Soil Bulk Density ◽  
Information Metrics

A pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs

Soil Research ◽  
2002 ◽  
Vol 40 (5) ◽  
pp. 847 ◽  
Author(s):  
Ravinder Kaur ◽  
Sanjeev Kumar ◽  
H. P. Gurung
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Carbon Content ◽  
Bulk Density ◽  
Soil Texture ◽  
Soil Bulk Density ◽  
Organic Carbon Content ◽  
Data Set ◽  
The Absolute ◽  
Relative Errors

Collection of non-destructive soil core samples for determination of bulk densities is costly, difficult, time- consuming, and often impractical. To overcome this difficulty, several attempts have been made in the past to estimate soil bulk densities through pedo-transfer functions (PTFs), requiring soil texture and organic carbon (OC) content data. Although many studies have shown that both organic carbon and texture predominantly determine soil bulk density, a majority of the PTFs developed so far are a function only of organic matter (OM)/OC. In addition, no attempts have been made to test and compare the applicability of these PTFs on an independent soil data set. Thus, through this study efforts have been made not only to develop a robust soil bulk density estimating PTF, based on both soil texture and organic carbon content data, but also to compare its predictive potential with the existing PTFs on an independent soil data set from 4 ecologically diverse micro-watersheds in Almora district of Uttaranchal State in India. Effects of varying levels of soil particle size distributions and/or OC/OM contents on the absolute relative errors associated with these PTFs were also analysed for assessing their applicability to the independent soil data set. Amongst the existing PTFs, Curtis and Post, Adams, Federer, and Huntington-A methods were found to be associated with positive bias or mean errors (ME) and root mean square prediction differences (RMSPD) ranging between 0.10 and 0.38, and between 0.23 and 0.45, respectively, whereas Alexander-A, Alexander-B, Manrique and Jones-A, Manrique and Jones-B, and Rawls methods were found to be associated with negative ME and RMSPD values ranging between -0.08 and -0.15, and 0.18 and 0.23, respectively. In contrast, Bernoux, Huntington-B, and Tomasella and Hodnett-PTFs, with RMSPD values ranging between 0.18 and 0.20, were the only methods associated with little or no bias. However, on comparing the predictive potential of the existing PTFs, in terms of their 1 : 1 relationships between the observed and predicted soil bulk densities and ME and RMSPD values, only Manrique and Jones-B (ME: -0.08; RMSPD: 0.18), Alexander-A (ME: -0.08; RMSPD: 0.19), and Rawls (ME: -0.11; RMSPD: 0.22) methods were observed to give somewhat more realistic soil bulk density estimations. The study revealed very limited predictive potential of the existing PTFs, due to their development on specific soils and/or ecosystems, use of an indirectly computed organic matter (instead of directly measured organic carbon) content as a predictor variable, poor predictive potential of developed regression model(s), and/or subjective errors. In contrast to this, the new soil bulk density estimating PTF was found to be associated with far better 1 : 1 relationship between the observed and predicted soil bulk densities and zero ME (or bias) and lowest (0.15 g/cm3) RMSPD values. The absolute relative errors associated with both the new and the existing soil OC/OM and texture-dependent PTFs were observed to be almost insensitive to the varying levels of silt and clay. However, compared with the existing PTFs, these errors associated with the new PTF were observed to be much more insensitive to the varying levels of OC/OM, thereby indicating the applicability of the new PTF to a wide range of soil types.

scholarly journals Influence of soil physical factors on productivity of herbaceous forage species in Kivaa and Ntugi rangelands in Eastern Kenya

2020 ◽  
Vol 1 (1) ◽  
pp. 17-24
Author(s):  
E. Karugia ◽  
F. Kariuki ◽  
J. Mwaniki
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Bulk Density ◽  
Soil Texture ◽  
Soil Bulk Density ◽  
Cyperus Rotundus ◽  
Grass Species ◽  
Physical Factors ◽  
Forage Species ◽  
Experimental Plots

Rangelands are extensive tracts of land with natural vegetation which is the main forage resource for domestic and wild ungulates. This study investigated the influence of soil texture, bulk density, and moisture content on the production of herbaceous forage species biomass in Kivaa and Ntugi rangelands in Eastern Kenya. Stratified random sampling was used where one out of four blocks of the target rangelands was picked and three belt transects established. Along the belt transects, ten experimental plots of 5 metres by 5 metres were established at intervals of 5 metres. Key forage species were identified using a structured questionnaire administered to key informants. Soil samples were collected from the experimental plots and analyzed for soil texture, bulk density and moisture content. Forage samples were collected and dry matter weight determined. The data collected were analyzed using both descriptive and inferential statistics especially tabulation and regression respectively. The study identified the most valuable forage species namely, Dichanthium annulatum, Themeda triandra, Cenchrus ciliaris, Rhynchelytrum repens, Digitaria abyssinica, Chloris roxburghiana and Cyperus rotundus. Soils in Ntugi ranged from clay loam to sandy clay while those from Kivaa ranged from silty loam to sandy loam. There was higher moisture content in soils in Ntugi than soils in Kivaa (t = 7.71, P ≤ 0.05). Soil moisture content had significant influence on herbage production in both Kivaa (R= 0.968, P ≤ 0.05) and Ntugi (R = 0.962, P ≤ 0.05). Soil bulk density significantly influenced forage grass species herbage production in Ntugi in 2014 (R= 0.513, P ≤ 0.05) and in 2016 ((R = 0.632, P ≤ 0.05). This study concluded that soil texture, bulk density and moisture content significantly influenced herbaceous forage production in the two rangelands. The study recommends use of rotational grazing systems combined with proper stocking rates to maintain moderate soil bulk density and good levels of soil moisture for the herbaceous forage species to continue producing adequate biomass while maintaining residual foliage for continued primary production.

MEASUREMENT AND PALEOCLIMATIC SIGNIFICANCE OF SOIL BULK DENSITY FOR LOESS IN EXTREME ARID REGION

2010 ◽  
Vol 30 (2) ◽  
pp. 127-132
Author(s):  
Jinbo ZAN ◽  
Shengli YANG ◽  
Xiaomin FANG ◽  
Xiangyu LI ◽  
Yibo YANG ◽  
...  
Keyword(s):  
Bulk Density ◽  
Arid Region ◽  
Soil Bulk Density ◽  
Paleoclimatic Significance

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

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Salt Marsh ◽  
Bulk Density ◽  
Remote Sensing Data ◽  
Soil Bulk Density ◽  
Soil Samples ◽  
Marsh Soil ◽  
Landsat 7 ◽  
Salt Marsh Soil

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.

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
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