Pedometrics

Why do we need pedometrics? What is its agenda? Pedometrics addresses certain key soil-related questions from a quantitative point of view. The need for the quantitative approach arises from a general demand for quantitative soil information for improved economic production and environmental management. Pedometrics addresses four main areas which are akin to the problems of conventional pedology:

A soil scientist in most instances can only measure and describe soil at a few points in a landscape; at each location, he has ways to describe and measure soil features. These may be based on field observations, e.g. presence of mottling in the subsoil, or a sample may be collected for subsequent laboratory analysis, e.g. clay content. Many different measurements of soil properties can be made, and each of these has what we call a measurement scale which in simplistic terms tells us whether it is measured as numbers, e.g. clay content, or categories, e.g. texture class.

A basic idea concerning collections of soil observations is to obtain statistical parameters from the data distribution. In soil, we recognise two kinds of statistical distributions relating to discrete or continuous random variables.

When we wish to characterize soil, it soon becomes very clear that one or two properties of soil materials, horizons, profiles or pedons will not suffice to give an adequate description. Soil classification, land capability, soil quality, condition and health assessments often involve the observation of tens or scores of soil properties on a single soil entity; e.g. the new soil microbial DNA descriptions involve hundreds or thousands of attributes. For analysis of such high-dimensional data, multivariate statistical techniques are most appropriate, particularly ordination techniques which help to reduce the dimensionality down to a few (typically 2 or 3) which can be graphed simply and the relationships between soil entities, and between observed soil attributes on those entities, displayed.

There are some universally described soil attributes that are worthy of more detailed pedometric description. Here, we largely concentrate on field properties which have particular issues associated with them. We are not attempting to be exhaustive. Over the years most countries have performed field descriptions, and laboratory analysis of soil based on some kind of standard technique. The methods that can be utilized for in-situ field description were already developed in the 1950s and refined and standardised by most countries in the 1970s and 1980s. These efforts have resulted in what is referred to as soil legacy data. The aim of this chapter is to illustrate how these soil legacy data can be modified for pedometric, quantitative and in general terms more objective soil analysis. Here, we will also discuss how we can use these quantitative descriptions to perform a more quantitative analysis of soil attributes, utilizing mathematical descriptors or how we can achieve a more quantitative measurement and assessment of soil attributes utilizing new technology and computational advances.

As previously discussed in Chap. 5, soil structure is defined by the spatial arrangement of soil primary particles and aggregates. There is increasing evidence that quantitative characterization of the soil structure and of its heterogeneity and complexity holds the key to a deeper understanding on physical, chemical, and biological processes that take place within them (Vogel 2000; Rockhold et al. 2004; Young et al. 2008; Blair et al. 2007; Pajor et al. 2010; Kravchenko et al. 2010; Dullien 2012).Therefore, it is very important to obtain an accurate description of it which best approximates to reality. Although many parameters may be used to attempt to describe irregular morphology, the spatial arrangement of the most prominent features is a challenging problem across a wide range of disciplines (Ripley 1988; Griffith 1988; Baveye and Boast 1988).

The term pedotransfer function (PTF) was coined by Bouma (1989) as ‘translating data we have into what we need’. Pedotransfer functions are regression functions used to predict soil properties that would be otherwise infeasible to obtain. Typical reasons for this infeasibility include, but are not limited to, the cost, time, difficulty or hazard involved in procuring direct measurements. Each PTF is developed around some insight into a soil’s physical, chemical or biological properties that relates a set of input parameters (predictor properties) to an output parameter (a predicted property).

Soil classification is really about answering the question what makes a soil? Or, perhaps, what makes one soil different from another? To answer questions like these, soil classifiers create taxonomic rules to separate one kind of soil from another and categorise and make sense of the diverse pattern of the soil continuum. Traditionally a great deal of consideration has been given to characterising and classifying the whole soil profile in a top-down fashion. Pedometric methods allow us to answer the same questions in a bottom-up trajectory. Thus, the starting point is not the whole soil profile or even its major constituents, the soil horizons. Rather we start by classifying the actual, tangible, skeleton of soil itself: the soil material.

The previous chapter discussed the possibility of using pedometric techniques to make numerical classifications of soil material and soil layers. Of course it is not a step too far to use pedometric techniques to make classifications of entire profiles also. That is the subject of this chapter.

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In Chap. 10, we described how classical geostatistical methods can be used to interpolate measurements of soil properties at locations where they have not been observed and to calculate the uncertainty associated with these predictions. The idea that soil properties can be treated as realizations of regionalized random functions in this manner has perhaps been the most significant ever in pedometrics (Webster 1994). The approach has been applied in thousands of studies for every imaginable soil property at scales varying from the microscopic to the global and has greatly enhanced our understanding of the spatial variability of soil properties.

Soil is often described as mantling the land more or less continuously with the exception being where there is bare rock and ice (Webster and Oliver 2006). Our understanding of soil variation in any region is usually based on only a small number of observations made in the field. Across the spatial domain of the region of interest, predictions of the spatial distribution of soil properties are made at unobserved locations based on the properties of the small number of soil observations. There are two principal approaches for making predictions of soil at unobserved locations. The first approach subdivides the soil coverage into discrete spatial units within which the soils conform to the characteristics of a class in some soil classification (Heuvelink and Webster 2001). The second approach treats soils as a suite of continuous variables and attempts to describe the way these variables vary across the landscape (Heuvelink and Webster 2001). The second approach is necessarily quantitative, as it requires numerical methods for interpolation between the locations of actual soil observations.

Since the early ages of soil surveys, air photographs have been intensively used by soil surveyors for depicting the soil variations across landscapes. The variations of soil surfaces, specifically color and ratio of vegetation cover, that were revealed by this early remote sensing product were a great help for interpolating the scarce soil observations and for delineating the soil class boundaries. This was further transposed in digital soil mapping (McBratney et al. 2003), thanks to the large availability of remote sensing images provided by the emerging spatial data infrastructures. Up to now, digital soil mappers have mainly used remote sensing images as spatial data inputs for representing the landscape variables that are related with soil, such as vegetation and parent material (the soil covariates). Boettinger et al. (2008) reviewed the main indicators that could be retrieved for estimating these soil covariates, using multispectral data acquired in the visible near-infrared and short-wave infrared (VIS, 400–700 nm; NIR, 700–1100 nm; SWIR, 1100–2500 nm) spectral domain. After a spatial overlay with the sparse sets of observed and measured sites collected in a given area, the indicators derived from remote sensing have been used as independent variables in regression-like models or as external drift in geostatistic models (McBratney et al. 2003, Chap. 12 of this book).

In previous chapters, the use of geostatistical modelling for soil mapping was addressed. We learnt that one of the advantages of kriging is that it not only produces a map of predictions but that it also quantifies the uncertainty about the predictions, through the kriging standard deviation. In this chapter we will look into this in more detail. We will also examine another way to assess the accuracy of soil prediction maps, namely, through independent validation. This approach has the advantage that it is model-free and hence makes no assumptions about the structure of the spatial variation and relationships between the target soil property and covariates. Finally, we will examine how uncertainties in soil maps propagate through environmental models and spatial analyses. Throughout this chapter we will use the Allier data set and case study, Limagne rift valley, central France, to illustrate concepts and methods. We will only consider soil properties that are measured on a continuous-numerical scale. Many of the concepts presented can also be extended to categorical soil variables, but this is more complicated and beyond the scope of this chapter.

Like Swift’s fleas, the soil is organized at multiple spatial scales from the clay particle, interacting with its neighbours and the soil solution according to the laws of electrochemistry, to the continent, at which the properties of the soil are organized according to general climate trends and the contingencies of geological history. Pedometrics can help the soil scientist to understand these processes in so far as it is possible to analyse soil properties into scale-dependent components which can be modelled and visualized. This is done, for example, by the spatially nested sampling introduced by Youden and Mehlich (1937). Geostatistical methods achieve it to some extent, with the use of nested models of regionalization and coregionalization, and methods of analysis to visualize components of different spatial scales (factorial kriging, reference) and scale-dependent correlation between variables (e.g. Goovaerts and Webster 1994). However, geostatistical analysis is primarily undertaken to support spatial prediction; the variogram, while reflecting the influence of processes at multiple spatial scales, is not particularly suited to the interpretation of such processes. For this we must look elsewhere.

Pedodiversity deals with the analysis of the number and complexity of pre-classified soil entities and/or their properties in the landscape (Fridland 1974; Jacuchno 1976; Linkeš et al. 1983; Ibañez et al. 1987, 1995; McBratney 1992; McBratney and Minasny 2007; Ibáñez and Bockheim 2013). If we consider that soil directly and/or indirectly affects every single biotic structure, it is easy to see that the study of its distribution needs to be a priority when trying to preserve the biodiversity of our planet. Whereas the pedologist might regard pedodiversity a great boon, an agronomist might regard the exact same situation a nuisance.

Soil forms the thin skin of the Earth and is the site of many ecological processes, transformations, and fluxes. It forms the substrate for most of the activities that take place at the Earth’s surface, including almost all food production and human occupation, and underpins both natural and managed ecosystems. Soils differ in their structure, composition, and ability to function under a use. Soil is a multifunctional resource that affects human well-being both directly (e.g., food provision) and indirectly (e.g., surface and groundwater supplies) and that affects all near-land surface ecological processes. Clearly, soil is “valuable” as that term is understood in common language. The pedometric program as outlined in this book, i.e., the development of “quantitative methods for the study of soil distribution … as a sustainable resource,” should therefore include an attempt to quantify this value. Chapter 1 of the present book lists as the third of four items on the pedometric agenda “evaluating the utility and quality of soil,” and it is in this sense that we attempt in this chapter to define and quantify the value of the soil resource. This process is referred to as “valuation.”

The extent of soil formation is dependent on local site characteristics. To model the evolution of soil in the landscape, we therefore need to know which factors and processes are important for describing pedogenesis quantitatively. There are several ways to go about it, and in the following the main approaches of how to formalize soil formation are therefore outlined.

Various methods have been used to measure or estimate pedogenic processes that are responsible for the differentiation of a soil profile. In this chapter, the modelling and quantification of these processes will be reviewed and discussed.

This chapter takes a look at the impetus and main approaches for a crop management system that utilises high-resolution spatial and temporal data derived mainly from proximal and remote sensors. With the development of pedometrics and the associated tools (e.g. kriging, multivariate techniques) providing the ability to describe and understand soil variability in space and time (well explored in Chaps. 10, 11, 12, 13, 14 and 15), the concept of Precision Agriculture was born in the early 1990s (Robert 1993) as a practical crop management response to the impact of the described soil variation on crop production.

The previous chapter describes how precision agriculture can be used to improve farm management to achieve economic and environmental benefits. Short-range differences in soil attributes mean that spatially differentiated management can create economic or environmental benefits. Effective precision agriculture requires accurate soil mapping at subfield scales so that management practices can be modified. Improvements in farming technology, for instance, GPS-controlled farm equipment, decrease the difficulty and cost associated with spatially differentiated management. This improves the ease of implementation and makes high-resolution soil maps more valuable.

Soil resources provide many important ecosystem goods and services. However, they are at risk from a variety of threats operating over a broad range of scales. Political awareness that soil is threatened by increasing pressures has been rising for several years (European Commission 2006). Indeed, the demand for soil information is increasing continuously (Richer de Forges and Arrouays 2010). Although rates of soil degradation are often slow and only detectable over long timescales, they are often irreversible. Therefore, monitoring soil quality and condition is essential in order to detect adverse changes in their status at an early stage.

The soil system is recognized as a significant terrestrial sink of carbon. Estimates for the top meter of soil in the world range between 1,200 and 2,500 petagrams for organic C (Batjes 1996; Lal 2004). The reliable assessment and monitoring of soil carbon stocks is of key importance for soil conservation and in mitigation strategies for increased atmospheric carbon (Stockmann et al. 2013). Carbon credits are the heart of a cap-and-trade scheme, by offering a way to quantify carbon sequestered from the atmosphere; carbon credits gain a monetary value to offset a given amount of carbon dioxide releases (Paustian et al. 2009). The agricultural industry worldwide has the capacity to capture and store carbon emissions in soil (Paustian et al. 2000). However, there is still a debate on how soil can benefit for the offsets in the carbon economy because there is no good and efficient way of measuring soil carbon storage with appropriate statistical confidence (Post et al. 2001; Smith 2004b). A scheme that can measure and monitor soil carbon storage on a farm, which is crucial to the participation of the agricultural sector in the carbon economy, is essential.