The implicit loss function for errors in soil information

Lark, R. M.; Knights, K.V.

doi:10.1016/j.geoderma.2015.03.014

Cited by 18 publications

(28 citation statements)

References 17 publications

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“…We do not claim that the advantage of the larger throughput with a portable spectrometer is necessarily more cost-effective regardless of its accuracy. First, a tolerable error is arbitrary (Lark & Knights, 2015) and depends on the data and the application. For instance, an RMSE of 0.5% SOC might be acceptable when the mean SOC is large or when many measurements are made for mapping by kriging, for example, kriging would diminish the prediction error variances further.…”

Section: Factors Affecting the Cost Effectivenessmentioning

confidence: 99%

The cost‐effectiveness of reflectance spectroscopy for estimating soil organic carbon

Rossel

Webster

2021

European J Soil Science

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Reflectance spectra of soil can be used to estimate the concentrations of organic carbon in soil (SOC). The estimates are more or less imprecise, but spectroscopy is quicker, less laborious and cheaper than conventional dry combustion analysis. Are the greater economy and efficiency sufficient to justify the loss of information arising from errors in estimation? We measured soil spectra with three instruments: a bench-top mid-infrared (mid-IR) (mid-IR b ) spectrometer, a portable mid-IR (mid-IR p ) spectrometer and a portable visiblenear infrared (vis-NIR p ) spectrometer. We calculated a quantity E to express the cost-effectiveness of spectroscopic estimates relative to the conventional analysis, by accounting for their inaccuracy, their cost and their capacity, namely the maximum number of samples that can be prepared and measured daily. In all, 562 samples of soil were collected from 150 locations at four depths on a farm. The samples were dried and ground to particle sizes of ≤2 and ≤0.5 mm before measurements were made by dry-combustion analysis.The machine learning algorithm Cubist was used to derive spectroscopic models of SOC concentrations and their uncertainties. We found that the mid-IR b on the ≤0.5 mm samples was the most accurate and expensive but nevertheless sufficiently cost-effective (large value of E) for determining the organic C. The mid-IR p was somewhat more accurate, but its E was smaller than vis-NIR p on corresponding samples because it required more time to record the spectra. We also found that, with the portable spectrometers, the SOC predictions made on the ≤0.5 mm samples were somewhat more accurate than those made on the ≤2 mm samples, but their E was smaller because of the additional cost of sample preparation. The vis-NIR p on the ≤2 mm samples was the most cost-effective for estimating SOC because it is cheap, accurate and has a large capacity for measurements. Highlights• Concentrations of soil organic carbon (SOC) were determined by standard dry combustion and estimated from reflectance spectra recorded by three instruments.• The labour required for each of the techniques and the cost, including that of the equipment, were recorded.

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Section: Factors Affecting the Cost Effectivenessmentioning

confidence: 99%

The cost‐effectiveness of reflectance spectroscopy for estimating soil organic carbon

Rossel

Webster

2021

European J Soil Science

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“…However, this has also been applied in the management of marine and coastal resources; for example, in identifying optimal funding allocation for marine mammal monitoring (Bisack and Magnusson, 2014) and in valuing additional fisheries benefit due to improved spatial information (Costello et al, 2010). The benefit of additional information can also be measured by the loss that is avoided (Lark and Knights, 2015) because of the availability of this information. This focuses on the value of the loss that will be incurred due to erroneous or insufficient information used in decision making, which could be avoided if additional information is obtained.…”

Section: Value Of Informationmentioning

confidence: 99%

A Review of the Tools Used for Marine Monitoring in the UK: Combining Historic and Contemporary Methods with Modeling and Socioeconomics to Fulfill Legislative Needs and Scientific Ambitions

et al. 2017

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Marine environmental monitoring is undertaken to provide evidence that environmental management targets are being met. Moreover, monitoring also provides context to marine science and over the last century has allowed development of a critical scientific understanding of the marine environment and the impacts that humans are having on it. The seas around the UK are currently monitored by targeted, impact-driven, programmes (e.g., fishery or pollution based monitoring) often using traditional techniques, many of which have not changed significantly since the early 1900s. The advent of a new wave of automated technology, in combination with changing political and economic circumstances, means that there is currently a strong drive to move toward a more refined, efficient, and effective way of monitoring. We describe the policy and scientific rationale for monitoring our seas, alongside a comprehensive description of the types of equipment and methodology currently used and the technologies that are likely to be used in the future. We contextualize the way new technologies and methodologies may impact monitoring and discuss how whole ecosystems models can give an integrated, comprehensive approach to impact assessment. Furthermore, we discuss how an understanding of the value of each data point is crucial to assess the true costs and benefits to society of a marine monitoring programme.

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“…The target kriging variance, v t , might be selected on various grounds (de Gruijter et al, 2006;Black et al, 2008;Lark and Knights, 2015). For illustrative purposes we chose to select a target kriging variance such that the kriging standard error was 10 % of the overall mean concentration.…”

Section: Kriging Variance As a Random Variablementioning

confidence: 99%

“…This could be directly translated to a cost given analytical costs and logistical costs (e.g. Lark and Knights, 2015). In practice we may use our m MCMC samples of the variogram parameters, θ i , to compute a set of tolerable grid spacings, where this is defined as…”

Section: Tolerable Grid Spacing As a Random Variablementioning

confidence: 99%

Planning spatial sampling of the soil from an uncertain reconnaissance variogram

Lark

Hamilton

Kaninga

et al. 2017

SOIL

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Abstract. An estimated variogram of a soil property can be used to support a rational choice of sampling intensity for geostatistical mapping. However, it is known that estimated variograms are subject to uncertainty. In this paper we address two practical questions. First, how can we make a robust decision on sampling intensity, given the uncertainty in the variogram? Second, what are the costs incurred in terms of oversampling because of uncertainty in the variogram model used to plan sampling? To achieve this we show how samples of the posterior distribution of variogram parameters, from a computational Bayesian analysis, can be used to characterize the effects of variogram parameter uncertainty on sampling decisions. We show how one can select a sample intensity so that a target value of the kriging variance is not exceeded with some specified probability. This will lead to oversampling, relative to the sampling intensity that would be specified if there were no uncertainty in the variogram parameters. One can estimate the magnitude of this oversampling by treating the tolerable grid spacing for the final sample as a random variable, given the target kriging variance and the posterior sample values. We illustrate these concepts with some data on total uranium content in a relatively sparse sample of soil from agricultural land near mine tailings in the Copperbelt Province of Zambia.

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The implicit loss function for errors in soil information

Cited by 18 publications

References 17 publications

The cost‐effectiveness of reflectance spectroscopy for estimating soil organic carbon

The cost‐effectiveness of reflectance spectroscopy for estimating soil organic carbon

A Review of the Tools Used for Marine Monitoring in the UK: Combining Historic and Contemporary Methods with Modeling and Socioeconomics to Fulfill Legislative Needs and Scientific Ambitions

Planning spatial sampling of the soil from an uncertain reconnaissance variogram

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