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.

Comparison of Field and Laboratory VNIR Spectroscopy for Profile Soil Property Estimation

2017 ◽  
Vol 60 (5) ◽  
pp. 1503-1510 ◽  
Author(s):  
Yongjin Cho ◽  
Alexander H. Sheridan ◽  
Kenneth A. Sudduth ◽  
Kristen S. Veum
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Precision Agriculture ◽  
Soil Property ◽  
Soil Cores ◽  
Estimation Errors ◽  
Vnir Spectroscopy ◽  
Dry Soil

Abstract. In-field, in-situ data collection with soil sensors has potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate important soil properties, such as soil carbon, nitrogen, water content, and texture. Most previous work has focused on laboratory-based visible and near-infrared (VNIR) spectroscopy using dried soil. The objective of this research was to compare estimates of laboratory-measured soil properties from a laboratory DRS spectrometer and an in-situ profile DRS spectrometer. Soil cores were obtained to approximately 1 m depth from treatment blocks representing variability in topsoil depth located at the South Farm Research Center of the University of Missouri. Soil cores were split by horizon, and samples were scanned with the laboratory DRS spectrometer in both field-moist and oven-dried conditions. In-situ profile DRS spectrometer scans were obtained at the same sampling locations. Soil properties measured in the laboratory from the cores were bulk density, total organic carbon (TOC), total nitrogen (TN), particulate organic matter carbon and nitrogen (POM-C and POM-N), water content, and texture fractions. The best estimates of TOC, TN, and bulk density were from the laboratory DRS spectra on dry soil (R2 = 0.94, 0.91, and 0.71, respectively). Estimation errors with the field DRS system were at most 25% higher for these soil properties. For POM-C and POM-N, the field system provided estimates of similar accuracy to the best (dry soil) laboratory measurements. Clay and silt texture fraction estimates were most accurate from laboratory DRS spectra on field-moist soil (R2 = 0.91 and 0.93, respectively). Estimation errors for clay and silt were almost doubled with the field DRS system. Considering the efficiency advantages, in-field, in-situ DRS appears to be a viable alternative to laboratory DRS for TOC, TN, POM-C, POM-N, and bulk density estimates, but perhaps not for soil texture estimates. Keywords: In-situ sensing, Precision agriculture, Reflectance spectra, Soil properties, Soil spectroscopy.

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 Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 52
Author(s):  
Gustavo M. Vasques ◽  
Hugo M. Rodrigues ◽  
Maurício R. Coelho ◽  
Jesus F. M. Baca ◽  
Ricardo O. Dart ◽  
...  
Keyword(s):  
Soil Properties ◽  
Ordinary Kriging ◽  
Soil Property ◽  
Clay Content ◽  
Exchange Capacity ◽  
Organic C ◽  
Covariate Data ◽  
Independent Validation ◽  
Soil Sensors ◽  
Dense Grid

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

The Effect of Soil Strength on the Growth of Young Wheat Plants

1987 ◽  
Vol 14 (6) ◽  
pp. 643 ◽  
Author(s):  
J Masle ◽  
JB Passioura
Keyword(s):  
Water Content ◽  
Bulk Density ◽  
Soil Phosphorus ◽  
Soil Strength ◽  
Leaf Expansion ◽  
Wheat Seedlings ◽  
Penetrometer Resistance ◽  
Carbon Supply ◽  
High Soil ◽  
Carbon Demand

Wheat seedlings were grown in soil of various strengths, obtained by changing the bulk density or the water content of the soil. Leaf expansion and transpiration rate were monitored from emergence until the main stem had 5-7 leaves. Leaf area, and shoot and root dry weights, were negatively correlated with soil strength as measured by penetrometer resistance. The growth of roots was less affected than that of shoots. Leaf expansion was reduced before the first leaf was fully expanded. Relative rates of leaf expansion thereafter were consistently lower at high soil strength, although not always significantly. High soil strength also produced substantially smaller stomatal conductances. All effects were the same whether variations of soil strength were brought about by changes in water content or in bulk density. Three possible causes of reduced shoot growth were examined: (1) a limiting supply of nutrients; or (2) of water, because of a restricted root system; or (3) a reduced carbon supply because of a higher carbon demand from the roots, or because of the low stomatal conductance. We conclude that these are all unlikely explanations for the onset of the effects of soil strength, which were independent of soil phosphorus content, of leaf water potential, and of the amount of carbon reserves in the seed. We suggest that growth of the shoot is primarily reduced in response to some hormonal message induced in the roots when they experience high soil strength.

scholarly journals Experimental Comparison of Runoff Generation and Initial Soil Erosion Between Vineyards and Croplands of Eastern Croatia: A Case Study

2020 ◽  
Vol 13 ◽  
pp. 117862212092832 ◽  
Author(s):  
Igor Bogunovic ◽  
Leon Josip Telak ◽  
Paulo Pereira
Keyword(s):  
Soil Erosion ◽  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Loss Factor ◽  
Available Phosphorus ◽  
Hydrological Response ◽  
N Loss ◽  
Water Stable Aggregates ◽  
P Loss

Soil and water loss in agricultural fields is a global problem. Although studies about soil erosion in croplands and vineyards exist, the direct comparison between these land uses is missing, especially under continental climates in Europe. Therefore, it is needed to find control measures to the impacts of these land-use management strategies on soil properties and hydrological response. The objective of this work is to estimate and compare the impacts of croplands and vineyards under conventional management croplands and vineyards on soil properties (water holding capacity—WHC; bulk density—BD; soil water content—SWC; water stable aggregates—WSA; mean weight diameter—MWD; soil organic matter—SOM; available phosphorus—AP; total nitrogen—TN) and hydrological response (runoff—Run; sediment content—SC; sediment loss—SL; carbon loss—C loss; phosphorus loss—P loss; nitrogen loss—N loss) in Eastern Croatia. To achieve these goals, a study was set up using rainfall simulation tests at 58 mm h−1 over 30 minutes on 2 locations (Zmajevac: 45°48′N; 18°46′E; Erdut: 45°30′N; 19°01′E). In total, 32 rainfall simulations were carried out, 8 repetitions in vineyards and 8 in cropland plots of 0.876 m2, per location. Bulk density was significantly higher in cropland plots compared with the vineyard. Soil water content was significantly higher in Zmajevac cropland compared with Erdut plots. Also, SWC was significantly lower in Zmajevac vineyard than in the cropland located in the same area. Water stable aggregates and MWD were significantly higher in vineyard plots than in the cropland. Also, SOM and TN were significantly lower in Zmajevac cropland compared with the vineyard located in the same area. Available phosphorus was significantly high in Zmajevac plots than in Erdut. The rainfall simulations showed that Run was significantly higher in Erdut vineyard (8.2 L m−2) compared with Zmajevac (3.8 L m−2). Also, the Run in Erdut Cropland was significantly lower than in the vineyard. Sediment content did not show significant differences among locations. In Erdut, vineyard plots had a significantly lower SL (28.0 g m−2) than the cropland ones (39.1 g m−2). C loss was significantly higher in Zmajevac cropland than in Erdut. Also, C loss was significantly lower in Zmajevac vineyard compared with the cropland. We did not observe significant differences in P loss, and N loss also did not show significant differences. The principal component analysis showed that SOM was associated with WSA, AP, and TN. These variables were negatively related to slope, SWC, and C loss (factor 1). Also, MWD was inversely related to SL, P, and N loss (factor 2). Bulk density and SC were negatively related to Run. Overall, we conclude that noninvertive tillage practices in vineyards preserve soil structure, enhance soil quality, and reduce the extent of soil degradation.

Variation in soil strength, bulk density and gravel concentration along a toposequence in Abeokuta, south-western Nigeria

Soil Research ◽  
2007 ◽  
Vol 45 (8) ◽  
pp. 643 ◽  
Author(s):  
F. K. Salako ◽  
P. O. Dada ◽  
J. K. Adesodun ◽  
F. A. Olowokere ◽  
I. O. Adekunle
Keyword(s):  
Land Use ◽  
Water Content ◽  
Bulk Density ◽  
Extreme Values ◽  
Soil Depth ◽  
Density Measurement ◽  
Soil Strength ◽  
Slope Position ◽  
Fine Earth ◽  
Wet Season

This study was carried out at Abeokuta, south-western Nigeria, to understand the variation in soil strength, gravel distribution, and bulk density along a toposequence. In 2003, a 120-m transect on a fallowed land was sampled at every 1 m for topsoil bulk density measurement by excavation (3278 cm3 pits), while soil strength was measured at every soil depth increment of 25 mm to 0.50 m depth. Total dry (ρt) and fine earth (<2 mm) (ρf) bulk densities were determined. Soil water content was also determined. Gravel was divided into classes of 2–4, 4–8, 8–16, and >16 mm. In 2006, four 100-m transects were considered; two each on adjacent fallowed and cultivated lands. Soil strength and water content were measured. The fine earth fraction of topsoil ranged from 62 to 90.6%. Gravel in the 2–4 mm class was dominant with a range of 0.8–35.7%. Thus, cores ≥50 mm could be used in the topsoil to obtain reliable estimates of bulk density. Total bulk density (ρt) was reduced by 4–19% when corrected for gravel to obtain ρf. Soil strength of the lower slope was highest in 2003 (1981–4482 kPa) and lowest in 2006 (1546 kPa). In spite of the apparent significant influence of water content on soil strength, the relationship was weakly expressed by regression analysis, as only 35% of variation in soil strength was explained by water content at 0.10–0.15 m soil depth in 2003. No relationship was found in 2006; the cultivated segment had higher soil strength (2045 kPa) than the fallowed segment (1970 kPa) even though the water contents were similar. Also, only the 2–4 mm gravel significantly influenced ρt. Land use, soil depth, and slope position significantly affected soil strength. Root-limiting soil strength (>2000 kPa) would certainly be encountered below 0.20 m soil depth in the wet season irrespective of land use. Management of this gravelly landscape must be based on the heterogeneous nature of soil physical properties along the toposequence, and this could be made effective by grouping the soils according to slope position and taking interest in the few portions of the landscape with extreme values of gravel distribution and high soil strength.

scholarly journals Effects of reduced and traditional tillage on soil properties and diversity of diatoms under winter wheat

2018 ◽  
Vol 32 (3) ◽  
pp. 403-409 ◽  
Author(s):  
Jadwiga Stanek-Tarkowska ◽  
Ewa A. Czyż ◽  
Anthony R. Dexter ◽  
Cezary Sławiński
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Winter Wheat ◽  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Reduced Tillage ◽  
Research Station ◽  
Organic Carbon Content ◽  
Dispersible Clay

Abstract The aim of this study was to quantify soil properties, microbial biodiversity and crop yield under two tillage systems used for winter wheat production in monoculture. The study was conducted in the period 2013-2016, on a long-term field experiment on a silt loam at the Krasne Research Station near Rzeszów, Poland. Traditional tillage involved soil inversion whereas reduced tillage was a non-inversion system. The following soil properties: chemical (soil organic carbon, pH, available P, K, Mg), physical (soil bulk density, water content, stability in water), and biological (the diversity of diatoms) were measured on samples collected throughout the growing season and at harvest. Soil organic carbon content, water content and bulk density in the 0-5 and 5-10 cm layers were greater in reduced tillage than in traditional tillage. Under reduced tillage the amount of readily dispersible clay was reduced giving increased soil stability in water. Soil under reduced tillage had greater diversity of diatoms (139 taxa) than that under traditional tillage (102 taxa). Wheat yields were positively correlated with precipitation, soil water content and soil organic carbon, and negatively correlated with readily dispersible clay.

Interactions between landscape defined management zones and grazing management systems

2017 ◽  
Vol 8 (2) ◽  
pp. 787-791 ◽  
Author(s):  
E. M. Pena-Yewtukhiw ◽  
D. Mata-Padrino ◽  
J. H. Grove
Keyword(s):  
Soil Properties ◽  
Bulk Density ◽  
Weather Conditions ◽  
Soil Strength ◽  
Forage Yield ◽  
Grazing Management ◽  
Weather Extremes ◽  
Near Surface ◽  
Management Zones ◽  
Management Zone

Yield and landscape are commonly used to guide management zone delineation. However, production system choice and management can interact with landscape attributes and weather. The objective of this study was to evaluate forage yield and soil properties in three landscape defined (elevation based) management zones, and under two different grazing systems. Changes in soil properties (soil strength, bulk density, moisture, bioavailable nutrients) and forage productivity (biomass), as related to grazing management and management zone, were measured. Bulk density, moisture, and forage biomass were greater at higher elevation. Soil strength decreased as elevation increased, and was greater near-surface after winter grazing ended. The response of landscape delineated management zones varied with extreme weather conditions and treatment. Lower zones were more sensitive to weather extremes than higher elevations, directly affecting biomass accumulation. In conclusion, we observed interactions between the grazing treatments and the management zones.

Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

2014 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Set Foong Ng ◽  
Pei Eng Ch’ng ◽  
Yee Ming Chew ◽  
Kok Shien Ng
Keyword(s):  
Soil Properties ◽  
Lagrange Multipliers ◽  
Unbiased Estimator ◽  
Soil Property ◽  
Minimum Variance ◽  
Distance Method ◽  
True Value ◽  
A Site ◽  
Method Of Lagrange Multipliers ◽  
Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

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