A generally applicable pedotransfer function that estimates field capacity and permanent wilting point from soil texture and bulk density

2008 ◽  
Vol 88 (5) ◽  
pp. 761-774 ◽  
Author(s):  
J. A. P. Pollacco
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Bulk Density ◽  
Soil Texture ◽  
The United States ◽  
Pedotransfer Functions ◽  
Pedotransfer Function ◽  
Data Set ◽  
Wide Range ◽  
Fitting Parameters

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

scholarly journals Soil, snow, weather, and sub-surface storage data from a mountain catchment in the rain–snow transition zone

2013 ◽  
Vol 6 (2) ◽  
pp. 811-835 ◽  
Author(s):  
P. R. Kormos ◽  
D. Marks ◽  
C. J. Williams ◽  
H. P. Marshall ◽  
P. Aishlin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Transition Zone ◽  
Soil Texture ◽  
Soil Depth ◽  
Meteorological Data ◽  
Dew Point ◽  
Soil Profiles ◽  
Data Set ◽  
Hydrologic Condition ◽  
Wide Range

Abstract. A comprehensive hydroclimatic data set is presented for the 2011 water year to improve understanding of hydrologic processes in the rain-snow transition zone. This type of dataset is extremely rare in scientific literature because of the quality and quantity of soil depth, soil texture, soil moisture, and soil temperature data. Standard meteorological and snow cover data for the entire 2011 water year are included, which include several rain-on-snow events. Surface soil textures and soil depths from 57 points are presented as well as soil texture profiles from 14 points. Meteorological data include continuous hourly shielded, unshielded, and wind corrected precipitation, wind speed, air temperature, relative humidity, dew point temperature, and incoming solar and thermal radiation data. Sub-surface data included are hourly soil moisture data from multiple depths from 7 soil profiles within the catchment, and soil temperatures from multiple depths from 2 soil profiles. Hydrologic response data include hourly stream discharge from the catchment outlet weir, continuous snow depths from one location, intermittent snow depths from 5 locations, and snow depth and density data from ten weekly snow surveys. Though it represents only a single water year, the presentation of both above and below ground hydrologic condition makes it one of the most detailed and complete hydro-climatic datasets from the climatically sensitive rain-snow transition zone for a wide range of modeling and descriptive studies. Data are available at doi:10.1594/PANGAEA.819837.

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 Characterizing Soil Physical Properties for Soil Moisture Monitoring with the North Carolina Environment and Climate Observing Network

2012 ◽  
Vol 29 (7) ◽  
pp. 933-943 ◽  
Author(s):  
Weinan Pan ◽  
R. P. Boyles ◽  
J. G. White ◽  
J. L. Heitman
Keyword(s):  
North Carolina ◽  
Soil Moisture ◽  
Physical Properties ◽  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Soil Physical Properties ◽  
The North ◽  
Wide Range ◽  
Soil Moisture Monitoring

Abstract Soil moisture has important implications for meteorology, climatology, hydrology, and agriculture. This has led to growing interest in development of in situ soil moisture monitoring networks. Measurement interpretation is severely limited without soil property data. In North Carolina, soil moisture has been monitored since 1999 as a routine parameter in the statewide Environment and Climate Observing Network (ECONet), but with little soils information available for ECONet sites. The objective of this paper is to provide soils data for ECONet development. The authors studied soil physical properties at 27 ECONet sites and generated a database with 13 soil physical parameters, including sand, silt, and clay contents; bulk density; total porosity; saturated hydraulic conductivity; air-dried water content; and water retention at six pressures. Soil properties were highly variable among individual ECONet sites [coefficients of variation (CVs) ranging from 12% to 80%]. This wide range of properties suggests very different behavior among sites with respect to soil moisture. A principal component analysis indicated parameter groupings associated primarily with soil texture, bulk density, and air-dried water content accounted for 80% of the total variance in the dataset. These results suggested that a few specific soil properties could be measured to provide an understanding of differences in sites with respect to major soil properties. The authors also illustrate how the measured soil properties have been used to develop new soil moisture products and data screening for the North Carolina ECONet. The methods, analysis, and results presented here have applications to North Carolina and for other regions with heterogeneous soils where soil moisture monitoring is valuable.

scholarly journals Tree‐Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level

2020 ◽  
Vol 9 (22) ◽  
Author(s):  
Liangyuan Hu ◽  
Bian Liu ◽  
Jiayi Ji ◽  
Yan Li
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Health ◽  
Black People ◽  
The United States ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Wide Range ◽  
Neighborhood Level

Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community‐based interventions have been shown to be both effective and cost‐effective in preventing cardiovascular disease. There is a dearth of robust studies identifying the key determinants of cardiovascular disease and the underlying effect mechanisms at the neighborhood level. We aim to contribute to the evidence base for neighborhood cardiovascular health research. Methods and Results We created a new neighborhood health data set at the census tract level by integrating 4 types of potential predictors, including unhealthy behaviors, prevention measures, sociodemographic factors, and environmental measures from multiple data sources. We used 4 tree‐based machine learning techniques to identify the most critical neighborhood‐level factors in predicting the neighborhood‐level prevalence of stroke, and compared their predictive performance for variable selection. We further quantified the effects of the identified determinants on stroke prevalence using a Bayesian linear regression model. Of the 5 most important predictors identified by our method, higher prevalence of low physical activity, larger share of older adults, higher percentage of non‐Hispanic Black people, and higher ozone levels were associated with higher prevalence of stroke at the neighborhood level. Higher median household income was linked to lower prevalence. The most important interaction term showed an exacerbated adverse effect of aging and low physical activity on the neighborhood‐level prevalence of stroke. Conclusions Tree‐based machine learning provides insights into underlying drivers of neighborhood cardiovascular health by discovering the most important determinants from a wide range of factors in an agnostic, data‐driven, and reproducible way. The identified major determinants and the interactive mechanism can be used to prioritize and allocate resources to optimize community‐level interventions for stroke prevention.

scholarly journals Full Season Control of Japanese Beetle Grub Control with Merit, NH, 1994

1996 ◽  
Vol 21 (1) ◽  
pp. 352-352
Author(s):  
Stanley R. Swier
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Soil Organic Matter ◽  
Air Temperature ◽  
Soil Ph ◽  
Soil Texture ◽  
Clay Soil ◽  
Golf Course ◽  
Silt Loam ◽  
Japanese Beetle

Abstract The trial was conducted 10 May on a golf course rough, Amherst, NH. Plots were 10 X 10 ft, replicated 4 times, in a RCB design. Merit WP was applied in 4 gal water/1000 ft2 with a watering, can. Merit G granules were applied with a homemade salt shaker. Treatments were irrigated with 0.5 inch water after application. Plots were rated 30 Sep by counting the number of live grubs per 1 ft2. Conditions at the time of treatment were: air temperature 70°F; wind, 3 MPH; sky, clear; soil temperature, 1 inch, 60°F; thatch depth, 0.5 inch soil pH, 5.4; slope 0%; soil texture, silt loam, 47% sand, 50% silt, 3% clay; soil organic matter, 6.9%; soil moisture, 21.8%.

scholarly journals Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

2015 ◽  
Vol 19 (1) ◽  
pp. 209-223 ◽  
Author(s):  
A. J. Newman ◽  
M. P. Clark ◽  
K. Sampson ◽  
A. Wood ◽  
L. E. Hay ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Cycle ◽  
Model Performance ◽  
The United States ◽  
Hydrologic Model ◽  
Low Flow ◽  
Reference Level ◽  
Hydrologic Response ◽  
Data Set ◽  
Wide Range

Abstract. We present a community data set of daily forcing and hydrologic response data for 671 small- to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980–2010 was generated for three basin spatial configurations – basin mean, hydrologic response units (HRUs) and elevation bands – by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash–Sutcliffe efficiency scores ≥0.55 for the calibration period and 34% ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.

scholarly journals Estimation of the net energy value of barley for finishing beef steers1

2019 ◽  
Vol 3 (4) ◽  
pp. 1550-1560
Author(s):  
Jan G P Bowman ◽  
Darrin L Boss ◽  
Lisa M M Surber ◽  
Tom K Blake
Keyword(s):  
Particle Size ◽  
Bulk Density ◽  
Barley Grain ◽  
Regression Equations ◽  
Animal Performance ◽  
Data Set ◽  
Wide Range ◽  
Development Data ◽  
Laboratory Procedures ◽  
The Mean

Abstract The objective of this study was to identify barley grain characteristics measured by laboratory procedures that could be used to predict barley energy content for finishing beef steers. Twenty-eight different barley genotypes were evaluated including 18 cultivars and 10 experimental lines. Laboratory analysis of barley samples included bulk density, particle size, N, ADF, starch, and ISDMD (in situ DM disappearance after 3 h of ruminal incubation). Animal performance data (BW, DMI, ADG, steer NEm, and NEg requirements) were collected from 26 feedlot experiments conducted in Montana and Idaho during a 10-yr period and were used to estimate barley NEm and NEg content. A total of 80 experimental units were available with each experimental unit being a diet mean from an individual feedlot experiment. Fifty-eight of the 80 experimental units were randomly selected and used in the development data set and the remaining 22 experimental units were used in the validation data set. Forward, backward, and stepwise selection methods were used to identify variables to be included in regression equations for NEm using PROC REG of SAS. Barley samples in the model development data set represented a wide range in concentrations (DM basis): N (1.6% to 2.8%), ISDMD (25.7% to 58.7%), ADF (3.6% to 8.0%), starch (44.1% to 62.4%), particle size (1,100 to 2,814 µm), and bulk density (50.8 to 69.4 kg/hL). The barley grain characteristics of particle size, ISDMD, starch, and ADF were the most important variables in six successful models (R2 = 0.48 to 0.60; P = 0.001). The six prediction equations gave mean predicted values for NEm ranging from 1.99 to 2.05 Mcal/kg (average 2.04 Mcal/kg; 0.45% CV). The mean actual NEm values from animal performance trials ranged from 1.75 to 2.48 Mcal/kg (average 2.03 Mcal/kg; 6.5% CV). The mean bias or difference in predicted vs. actual values ranged from −0.001 to 0.005 Mcal/kg. Barley NEg values calculated from animal performance ranged from 1.13 to 1.78 Mcal/kg (average 1.39 Mcal/kg; 8.4% CV). Average predicted barley NEm and NEg were 0.02 and 0.01 Mcal/kg less, respectively, than the 2.06 Mcal/kg NEm and 1.40 Mcal/kg NEg reported by NRC. Barley NE can be predicted from simple laboratory procedures which will aid plant breeders developing new feed varieties and nutritionists formulating finishing rations for beef cattle.

scholarly journals Comparison of different models for predicting soil bulk density. Case study – Slovakian agricultural soils

2017 ◽  
Vol 31 (4) ◽  
pp. 491-498 ◽  
Author(s):  
Jarmila Makovníková ◽  
Miloš Širáň ◽  
Beata Houšková ◽  
Boris Pálka ◽  
Arwyn Jones
Keyword(s):  
Organic Carbon ◽  
Bulk Density ◽  
Monitoring System ◽  
Soil Bulk Density ◽  
Pedotransfer Functions ◽  
Research Centre ◽  
Organic Carbon Content ◽  
Joint Research ◽  
Data Set ◽  
Sustainable Resources

Abstract Soil bulk density is one of the main direct indicators of soil health, and is an important aspect of models for determining agroecosystem services potential. By way of applying multi-regression methods, we have created a distributed prediction of soil bulk density used subsequently for topsoil carbon stock estimation. The soil data used for this study were from the Slovakian partial monitoring system-soil database. In our work, two models of soil bulk density in an equilibrium state, with different combinations of input parameters (soil particle size distribution and soil organic carbon content in %), have been created, and subsequently validated using a data set from 15 principal sampling sites of Slovakian partial monitoring system-soil, that were different from those used to generate the bulk density equations. We have made a comparison of measured bulk density data and data calculated by the pedotransfer equations against soil bulk density calculated according to equations recommended by Joint Research Centre Sustainable Resources for Europe. The differences between measured soil bulk density and the model values vary from −0.144 to 0.135 g cm−3 in the verification data set. Furthermore, all models based on pedotransfer functions give moderately lower values. The soil bulk density model was then applied to generate a first approximation of soil bulk density map for Slovakia using texture information from 17 523 sampling sites, and was subsequently utilised for topsoil organic carbon estimation.

Plastic limits of agricultural soils as functions of soil texture and organic matter content

Soil Research ◽  
2012 ◽  
Vol 50 (1) ◽  
pp. 7 ◽  
Author(s):  
Thomas Keller ◽  
Anthony R. Dexter
Keyword(s):  
Organic Matter ◽  
Soil Texture ◽  
Agricultural Soils ◽  
Organic Matter Content ◽  
Clay Content ◽  
Liquid Limit ◽  
Matter Content ◽  
Pedotransfer Functions ◽  
Strongly Correlated ◽  
Yield Information

The plastic limits (lower plastic limit, PL; and liquid limit, LL) are important soil properties that can yield information on soil mechanical behaviour. The objective of this paper is to study the plastic limits of agricultural soils as functions of soil texture and organic matter (OM) content. The plastic limits were highly related to the clay content. The LL was more strongly correlated with clay than was PL, but the reasons are unclear. Interestingly, PL was virtually unaffected by clay content for soils with clay contents below ~35%. The OM had a strong effect on the plastic limits. This effect was clearly demonstrated when analysing soils of similar texture with a range of OM. We present equations (pedotransfer functions) for estimation of PL, LL, and plasticity index (PI) from soil texture and OM. Finally, we predict that the clay content must be ≥10% for soils without OM to be plastic; however, soils with <10% clay can be plastic if OM is present. More research is needed to investigate OM effects on soil consistency.

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