Comparing the Ability of Multiple Soil Sensors to Predict Soil Properties in a Scottish Potato Production System

Taylor, James A.; Short, Michael G.; McBratney, Alex B.; Wilson, J. H.

doi:10.1007/978-90-481-8859-8_33

Cited by 9 publications

(10 citation statements)

References 5 publications

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“…() and Piikki et al . () used EC a measurements in combination with proximal measurements of gamma ray spectrometry, together with elevation (Taylor et al ., ), other terrain attributes and a panchromatic aerial photograph (Piikki et al ., ).…”

Section: Introductionmentioning

confidence: 97%

“…Studies have been published on predicting soil carbon or SOM (Kweon et al ., ; Knadel et al ., ; Kweon, ; Mahmood et al ., ), pH and macronutrients (Taylor et al ., ; Mahmood et al ., ; Schirrmann et al ., ), and soil texture and clay content (Taylor et al ., ; Mahmood et al ., ; Piikki et al ., ). Both Piikki et al .…”

Section: Introductionmentioning

confidence: 99%

“…Studies have been published on predicting soil carbon or SOM (Kweon et al, 2009;Knadel et al, 2011;Kweon, 2012;Mahmood et al, 2012), pH and macronutrients (Taylor et al, 2010;Mahmood et al, 2012;Schirrmann et al, 2012), and soil texture and clay content (Taylor et al, 2010;Mahmood et al, 2012;Piikki et al, 2013). Both Piikki et al (2013) and Mahmood et al (2012) combined data from sensor readings that were measured independently in the sense that the data were not recorded simultaneously and not always at exactly the same location.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Wetterlind

Piikki

Stenberg

2015

European J Soil Science

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SummaryIn soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (EC a ), reflectance in the visible and near-infrared (vis-NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5-cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm-specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis-NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross-validation (RMSECV) of 0.3-0.5% for SOM, about 6% for clay and silt and 10-11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including EC a reduced the RMSECV on average for all particle size fractions by 5-10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis-NIR sensor measurements with measurements of EC a and or IF.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Wetterlind

Piikki

Stenberg

2015

European J Soil Science

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“…This investigation focused on one combination of soil sensors. Other combinations (De Benedetto et al, 2011) are possible and should be investigated to meet site-specific requirements but, as stressed by Taylor et al (2008), the benefits of the extra information need to be assessed against the extra cost.…”

Section: Resultsmentioning

confidence: 99%

“…Wong et al (2008Wong et al ( , 2010 used these two sensors to map soil properties in a West Australian cropping field containing sandy, clayey, gravelly and salt-affected soils which are poorly identified using an EMI or a γ-ray sensor alone. Taylor et al (2010) carried out a study in eastern Scotland to investigate the potential of γ-radiometric data for soil mapping. Their results showed that the gamma radiometer produced better predictions in the top soil than the EMI sensor, but EMI predicted clay content better in the subsoil.…”

Section: Introductionmentioning

confidence: 99%

Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation

Castrignanò¹,

Wong²,

Stelluti³

et al. 2012

Geoderma

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Field‐scale digital soil mapping of clay: Combining different proximal sensed data and comparing various statistical models

Arshad

Bella

et al. 2020

Soil Science Soc of Amer J

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Knowledge about clay fraction is important for soil management. Laboratory analysis is time consuming, however, because information is required at the topsoil (0‒0.3 m), subsurface (0.3‒0.6 m), and subsoil (0.9‒1.2 m). Herein, we developed digital soil mapping (DSM) of clay by coupling limited laboratory data with proximal sensed data (i.e., elevation, γ‐ray, and electromagnetic). Our first aim was to determine whether individual or combined data were better for modeling using various models (i.e., regression kriging [RK], linear mixed model [LMM], and a Bayesian model named integrated nested Laplace approximation [INLA] with stochastic partial differential equation [SPDE]). We also evaluated DSM of clay and associated errors at different depths using various indices (e.g., Lin's concordance). Digital soil mapping performance was compared by mean square prediction error (MSPE). Considering proximal data, the Akaike information criteria and Log‐likelihood showed that RK and LMM performed best by combining proximal data, whereas for INLA‐SPDE individual and combined data performed equally well. Considering prediction uncertainty, RK error was larger where predicted clay was larger, whereas LMM and INLA‐SPDE had larger errors associated with texture or field boundaries. With increasing depth, model performance decreased, as exemplified by LMM and for topsoil (Lin's = 0.79), subsurface (0.72), and subsoil (0.71). The MSPE showed that the DSM map for topsoil clay was best for LMM (28), followed by INLA‐SPDE (29) and RK (39). The LMM DSM was also best in the subsurface (37) and subsoil (84); however, RK was superior to INLA‐SPDE.

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Comparing the Ability of Multiple Soil Sensors to Predict Soil Properties in a Scottish Potato Production System

Cited by 9 publications

References 5 publications

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation

Field‐scale digital soil mapping of clay: Combining different proximal sensed data and comparing various statistical models

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