R. A. Viscarra Rossel

Using a legacy soil sample to develop a mid-IR spectral library

This paper describes the development of a diffuse reflectance spectral library from a legacy soil sample. When developing a soil spectral library, it is important to consider the number of samples that are needed to adequately describe the soil variability in the region in which the library is to be used; the manner in which the soil is sampled, handled, prepared, stored, and scanned; and the reference analytical procedures used. As with any type of modelling, the dictum is ‘garbage in = garbage out’ and hopefully the converse ‘quality in = quality out’. The aims of this paper are to: (i) develop a soil mid infrared (mid-IR) diffuse reflectance spectral library for cotton-growing regions of eastern Australia from a legacy soil sample, (ii) derive soil spectral calibrations for the prediction of soil properties with uncertainty, and (iii) assess the accuracy of the predictions and populate the legacy soil database with good quality information. A scheme for the construction and use of this spectral library is presented. A total of 1878 soil samples from different layers were scanned. They originated from the Upper Namoi, Namoi, and Gwydir Valley catchments of north-western New South Wales (NSW) and the McIntyre region of southern Queensland (Qld). A conditioned Latin hypercube sampling (cLHS) scheme was used to sample the spectral data space and select 213 representative samples for laboratory soil analyses. Using these data, partial least-squares regression (PLSR) was used to construct the calibration models, which were validated internally using cross validation and externally using an independent test dataset. Models for organic C (OC), cation exchange capacity (CEC), clay content, exchangeable Ca, total N (TN), total C (TC), gravimetric moisture content θg, total sand and exchangeable Mg were robust and produced accurate results (R2adj. > 0.75 for both cross and test set validations). The root mean squared error (RMSE) of mid-IR-PLSR predictions was compared to those from (blind) duplicate laboratory measurements. Mid-IR-PLSR produced lower RMSE values for soil OC, clay content, and θg. Finally, bootstrap aggregation-PLSR (bagging-PLSR) was used to predict soil properties with uncertainty for the entire library, thus repopulating the legacy soil database with good quality soil information.

Proximal Soil Sensing: An Effective Approach for Soil Measurements in Space and Time

Abstract This chapter reviews proximal soil sensing (PSS). Our intent is for it to be a source of up-to-date information on PSS, the technologies that are currently available and their use for measuring soil properties. We first define PSS and discuss the sampling dilemma. Using the range of frequencies in the electromagnetic spectrum as a framework, we describe a large range of technologies that can be used for PSS, including electrochemical and mechanical sensors, telemetry, geographic positioning and elevation, multisensor platforms, and core measuring and down-borehole sensors. Because soil properties can be measured with different proximal soil sensors, we provide examples of the alternative techniques that are available for measuring soil properties. We also indicate the developmental stage of technologies for PSS and the current approximate cost of commercial sensors. Our discussion focuses on the development of PSS over the past 30 years and on its current state. Finally, we provide a short list of general considerations for future work and suggest that we need research and development to: (i) improve soil sampling designs for PSS, (ii) define the most suitable technique or combination of techniques for measuring key soil properties, (iii) better understand the interactions between soil and sensor signals, (iv) derive theoretical sensor calibrations, (v) understand the basis for local versus global sensor calibrations, (vi) improve signal processing, analysis, and reconstruction techniques, (vii) derive and improve methods for sensor data fusion, and (viii) explore the many and varied soil, agricultural, and environmental applications where proximal soil sensors could be used. PSS provides soil scientists with an effective approach to learn more about soils. Proximal soil sensors allow rapid and inexpensive collection of precise, quantitative, high-resolution data, which can be used to better understand soil spatial and temporal variability. We hope that this review raises awareness about PSS to further its research and development and to encourage the use of proximal soil sensors in different applications. PSS can help provide sustainable solutions to the global issues that we face: food, water, and energy security and climate change.

The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project

Information on the geographic variation in soil has traditionally been presented in polygon (choropleth) maps at coarse scales. Now scientists, planners, managers and politicians want quantitative information on the variation and functioning of soil at finer resolutions; they want it to plan better land use for agriculture, water supply and the mitigation of climate change land degradation and desertification. The GlobalSoilMap project aims to produce a grid of soil attributes at a fine spatial resolution (approximately 100 m), and at six depths, for the purpose. This paper describes the three-dimensional spatial modelling used to produce the Australian soil grid, which consists of Australia-wide soil attribute maps. The modelling combines historical soil data plus estimates derived from visible and infrared soil spectra. Together they provide a good coverage of data across Australia. The soil attributes so far include sand, silt and clay contents, bulk density, available water capacity, organic carbon, pH, effective cation exchange capacity, total phosphorus and total nitrogen. The data on these attributes were harmonised to six depth layers, namely 0–0.05 m, 0.05–0.15 m, 0.15–0.30 m, 0.30–0.60 m, 0.60–1.00 m and 1.00–2.00 m, and the resulting values were incorporated simultaneously in the models. The modelling itself combined the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. At each layer, values of the soil attributes were predicted at the nodes of a 3 arcsecond (approximately 90 m) grid and mapped together with their uncertainties. The assessment statistics for each attribute mapped show that the models explained between 30% and 70% of their total variation. The outcomes are illustrated with maps of sand, silt and clay contents and their uncertainties. The Australian three-dimensional soil maps fill a significant gap in the availability of quantitative soil information in Australia.

Soil and Landscape Grid of Australia

The Soil and Landscape Grid of Australia (SLGA) is the first continental version of the GlobalSoilMap concept and the first nationally consistent, fine spatial resolution set of continuous soil attributes with Australia-wide coverage. The SLGA relies on digital soil mapping methods and integrates historical soil data, new measurement with spectroscopic sensors, novel spatial modelling and a web-service delivery architecture. The SLGA provides soil, regolith and landscape estimates at the centre point of 3 arcsecond grid cells (~90 × 90 m) across Australia. At each point, there are estimates of 11 soil attributes and confidence intervals for each estimate to a depth of 2 m or less, depth of regolith and a set of terrain descriptors. The information system also includes a library of mid-infrared spectra, an inference engine that allows estimation of additional soil parameters and an information model that enables users to access the system via web services. The explicit mapping of depth, bulk density and coarse fragments allows estimation of material stores and fluxes on a volumetric basis. The SLGA therefore has immediate applications in carbon, nitrogen and water process modelling. The map of regolith depth will find immediate application to studies of vadose zone processes, including solute transport, groundwater and nutrient fluxes beyond the root zone. Landscape attributes at 1 and 3 arcseconds are useful for a wide spectrum of ecological, hydrological and broader environmental applications. The SLGA can be accessed at no cost from www.csiro.au/soil-and-landscape-grid. It is managed and delivered as part of the Australian Soil Resource Information System (ASRIS).

Diffuse Reflectance Spectroscopy for High-Resolution Soil Sensing

Diffuse reflectance spectroscopy in the visible–near-infrared (vis–NIR) and mid-infrared (mid-IR) is a practical analytical technique that can be used for both laboratory and in situ soil analysis. The techniques are sensitive to both organic and mineral soil composition. They are particularly well suited to situations where the primary (conventional) analytical method is laborious and costly or where a large number of analyses and samples are required, e.g. for high-resolution digital soil mapping or precision agriculture. This chapter will describe diffuse reflectance spectroscopy of soil in the vis–NIR (400–700–2,500 nm) and mid-IR (2,500–25,000 nm) portions of the electromagnetic spectrum. The theory of the mechanisms of absorbance in soil will be explained briefly, followed by aspects of data pretreatments, chemometrics, and multivariate calibrations. Finally, both laboratory and in situ applications are discussed and the focus of future research suggested.

Rapid, quantitative and spatial field measurements of soil pH using an Ion Sensitive Field Effect Transistor

Abstract The objectives of this work are threefold. Firstly, to determine adequate times for rapid and accurate field-based measurements of soil pH (field pH). Secondly, to develop a simple protocol for quantitative spatial field measurements of soil pH using an Ion Sensitive Field Effect Transistor (ISFET) and thirdly, to demonstrate and validate its implementation. In order to determine adequate times, the kinetics of soil pH reactions in 1:5 soil/0.01 M CaCl2 (pHCa) and soil/H2O (pHw) suspensions were quantified for 17 representative agricultural soil samples from Brittany, France. The mean and variance of pH deviations at various times, from the long-term equilibrium pH as well as the accuracy of the measurements, were calculated for both pHCa and pHw. These data together with the required accuracy of the measurements may be used to determine an adequate time for field pH measurements. The mean and variance of the deviations decreased and the accuracy of measurements increased with longer reaction times. For example, the expected accuracy of 10 s pHCa and pHw measurements was 0.15 and 0.33 pH units, respectively. The expected accuracy of 20 s pHCa and pHw measurements was 0.11 and 0.15 units, respectively. Based on these data, a simple protocol for rapid field pH measurements was developed. This protocol was implemented by making 10 s pHw measurements in a 4-ha agricultural field near Rennes, France. A total of 476 pHw data were collected in 6 h. At 57 validation sites, measurements were obtained using conventional grid sampling and laboratory analysis (laboratory pHw). The observed accuracy of the 10 s field pHw measurements was 0.34 pH units. The efficiency of the field and laboratory pHw measurements was also compared. Field measurements were more efficient in terms of both the time and cost involved in obtaining the measurements. Semivariograms and kriged maps of both laboratory and field pHw measurements were compared, the latter appearing to more truthfully depict the spatial structure of soil pHw in the field. A cross-semivariogram of the laboratory and the 10 s field pHw measurements was also derived and co-kriging performed. Where time and/or economic restrictions prevent the use of sampling and laboratory analysis or the collection of more accurate field pH measurements, a co-kriging of a smaller set of these data as the primary variable along with a more exhaustive data set of more rapid but less accurate field pH measurements as the secondary variable may be practical and advantageous. Temporal measurements of field pHw at the 57 validation sites over 2 years showed good agreement.

Development of On-the-Go Proximal Soil Sensor Systems

To implement sustainable agricultural and environmental management, a better understanding of the soil at increasingly finer scales is needed. Conventional soil sampling and laboratory analyses cannot provide this information because they are slow and expensive. Proximal soil sensing (PSS) can overcome these shortcomings. PSS refers to field-based techniques that can measure soil properties from 2 m or less above the soil surface. The sensors may be invasive, or not, and may or may not be mounted on vehicles for on-the-go operation. Much research is being conducted worldwide to develop sensors and techniques that may be used for proximal soil sensing. These are based on electrical and electromagnetic, optical and radiometric, mechanical, acoustic, pneumatic, and electrochemical measurement concepts. This chapter reviews the latest of these technologies and discuss their applications.

Diffuse Reflectance Spectroscopy as a Tool for Digital Soil Mapping

This paper discusses the potential of soil diffuse reflectance spectroscopy (DRS) for rapid and cheap soil analysis and its application to digital soil mapping. We consider both visible-near infrared (vis-NIR) and mid infrared (mid-IR) spectroscopy, the use of multivariate calibrations, the development of soil spectral libraries and the cost and benefits of soil DRS. Finally, we conclude with some thoughts on the potential use of the techniques for digital soil mapping and soil science generally.

A response-surface calibration model for rapid and versatile site-specific lime-requirement predictions in south-eastern Australia

The development of response-surface calibration models that may be used in conjunction with the lime requirement buffer methods is described. The buffer methods tested were the Woodruff, New Wooruff, Mehlich, and Shoemaker, McLean and Pratt lime-requirement buffers. Model predictions were compared with those obtained from multivariate models and buffer methods calibrated using conventional linear regressions. The multivariate models described lime requirement as a function of a number of soil variables. All of the models were validated against soil : CaCO 3 incubations using a statistical jackknifing procedure for error and bias estimations. The advantages of the derived response-surface models were their improved prediction accuracy and flexibility, with a choice of any target pH CaCl 2 value between 5.5 and 7 without need for individual calibrations. The response-surface model for the Woodruff buffer method produced the most accurate predictions of lime requirement. The uncertainty of its lime requirement predictions for acid soil in an agricultural field at Kelso, New South Wales, Australia, measured by 95% confidence intervals, was 0.22 Mg/ha. A spatial analysis of lime requirement for the field showed a range of 4–11 Mg/ha. This range provides a reason for site-specific lime applications. Under- and over-applications resulting from a ‘blanket’ 7.13 Mg/ha single-rate application of lime over the field were estimated to range from –4 to 2.9 Mg/ha.

Modelling the kinetics of buffer reactions for rapid field predictions of lime requirements

Abstract This research pertains to the modelling of the kinetics of soil/lime-requirement buffer reactions. These reactions were characterised using different, though not incompatible techniques. First, using two separate log-linear functions, each with a single rate coefficient, which characterise the first-order nature of the reactions and, second, using a continuous double exponential model with two rate coefficients. The former approach to kinetic modelling has been reported in the literature for various ions, e.g. potassium. Both types of modelling described the sequential, biphasic nature of the soil/lime-requirement buffer reactions; however, the continuous model predicted the reactions more accurately. That is, it represented the ion exchange reaction of the agricultural soils used in the study in a more accurate manner than the two separate log-linear functions. A simple algorithm was devised to predict equilibrium pH buffer measurements at much shorter times than those suggested in the literature. The methodology used pH buffer measurements of the first 3 s of the ongoing reactions to predict equilibrium pH buffer values, which occurred at 12 min. Prediction accuracy was 0.1 pH buffer unit and the bias was small. Ultimately, the technique may be incorporated into the data acquisition and data handling algorithms of a soil pH and lime-requirement sensing system for field-based predictions of lime requirement and continuous liming.