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Modern monothetic hierarchical soil classification systems such as the Chinese Soil Taxonomy (CST) are semi‐quantitative and their future development is likely to trend towards a fully quantitative system using the concepts of numerical taxonomy. Previous researchers have calculated the taxonomic distances between individual soils based on soil physiochemical properties, not, however, based on spectra of full soil profiles with different horizons. We hypothesized that numerical taxonomy implemented by cluster analysis of the taxonomic distance matrix based on vis–NIR spectra would accord with some CST Orders assigned by pedologists, and not with others, depending on how closely spectral features represent the diagnostic features used in the classification. Taxonomic distances in spectral space were computed for all pairs of 191 profiles, resulting in a distance matrix on which hierarchical cluster analysis was performed. Different indices were calculated to determine the optimum number of clusters, resulting in four spectral soil classes. These were then compared with CST Orders assigned to the profiles by expert allocation. The numerical classes and CST Orders matched poorly because of the completely different classification philosophies behind numerical taxonomy and the CST, which is based largely on presumed genesis and uses sharp thresholds leading to very similar soils being allocated to different classes. Thus, we consider the numerical classification as information that is complementary to the monothetic hierarchical system. Numerical classification can reveal the taxonomic objective aspects of the relation between the defined classes and can suggest new groupings. HIGHLIGHTS: Taxonomic distances can serve as an objective measure of soil similarity. Soil spectra are a good candidate for numerical classification based on taxonomic distances. The conceptual basis of Chinese Soil Taxonomy (CST) does not match that of taxonomic distance. Numerical classification based on spectra can suggest revisions to the CST.

This study evaluates to what degree soil samples associated in characteristic space are also close in geographical space, i.e., the possible location from which an unknown sample was obtained in a forensic investigation. The study compares similarity computed from Munsell colors, RGB colors, and full visible-near infrared (vis-NIR) spectra by the spectral angle mapper with similarity based on six easily-measured physio-chemical properties. The reference area is Anhui Province, China with three scales of datasets: provincial, county, and field. Ten diverse “unknown” samples were selected by the Kennard-Stone algorithm from the field-scale dataset and their matches in characteristic space from the several datasets were found by the different methods. The geographic distances of the matches to the “unknowns” were used to evaluate the source identification ability. When a detailed library with local samples is present, a limited set of physio-chemical properties achieved higher geographic accuracy than the color and spectral methods. However, with a regional library the spectral and color methods are superior. Distances in RGB space reveal finer differences than exact matching in Munsell space, but whole-spectra matching outperforms both, because of the rich information influenced by more soil properties than influencing color. We recommend the use of soil vis-NIR spectra as a priority indicator for forensic soil analysis because of its success in this study and its ability to work non-destructively on small quantities of soil.

Climate change has significant impacts on crop yields and could greatly affect global food security. As an advanced process-based land surface model, the Community Land Model (CLM) includes a comprehensive crop model (CLM-Crop), which aims to simulate crop growth globally. Although this model has been evaluated in the United States, it is not clear whether it can be applied successfully in other parts of the world. In this study we evaluate the applicability of CLM-Crop for maize growth simulation in Northeast China, one of the major agricultural production areas in China. Simulated LAI, plant carbon, phenology and yields of maize were compared with observations at agricultural experiment and meteorological stations and with statistical reports. The CLM-Crop model overestimated LAI and underestimated leaf and stem carbon during the growing period. Planting and harvesting dates were overestimated in the eastern part of the study area, but underestimated in the southern part. Correlation (r = 0.26) between simulated and reported yield was poor. Yields were generally overestimated especially in the east and south parts, which may be due to imperfect farming practices on working farms. Some parameters, including temperature thresholds of planting and crop management parameters, have spatial heterogeneity rather than the default fixed parameters in CLM-Crop. Development of gridded crop parameters is expected to improve simulation of crop phenology and yield estimation at the regional and global scales.

In the tropical rain forest zone of Southern Cameroon, shifting cultivation and perennial plantations of cocoa are the main farming systems practiced by small-scale farmers to ensure subsistence food crop production and a small income. This research used scientific modelling tools to produce quantitative information on the evolution of soils under this shifting agricultural system. An analysis of farming system led to the development of a conceptual model of the spatio-temporal dynamics of shifting agriculture, including transition matrices of rotational cycles that guided the sampling strategy for the study of soil evolution under the system. The study of soil variability showed that 30-35% of the total variance of some topsoil (0-20 cm) properties was due to the influence of land use practices. Five soil properties (pH, calcium, available phosphorus, bulk density and organic carbon) that are the most sensitive to these agricultural practices were empirically modelled and linear/quadratic fractional rational functions were successfully fitted to time series soil variables to derive quantitative measures on temporal changes in soil with land use. Data and methods produced are useful for soil quality assessment and spatio-temporal dynamic simulation in order to guide decision-making for sustainable land-use planning and soil resources management.

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.”

Although pedometric approaches were taken as early as 1925, the post-WWII development of information technology radically transformed the possibilities for applying mathematical methods to soil science. The most significant development is the digital computer and associated developments in computer science. These allowed statistical inference on large pedometric datasets, e.g., numerical taxonomy of soils from the early 1960s and geostatistics from the mid-1970s, as well as simulation models of soil processes. By the time of the first Pedometrics conference in 1992 sufficient computing power was available for stochastic simulation and complex geostatistical procedures such as disjunctive kriging. In the intervening 25 years computing power has grown to almost magical proportions, allowing individual scientists to carry out complex procedures. A second development is the growth of networking. This facilitates collaboration among pedometricians, rapid communication with journals, collaborative programming and publication, and easy access to resources. A third development is the availability of on-line storage of large datasets, especially of open data, including GIS coverages and remotely-sensed images. This allows pedometricians working on geographic problems to integrate sources from multiple disciplines, most notably in digital soil mapping using a wide variety of covariates related to soil genesis. It also promotes the an open-source movement of collaborative development of computer programs useful for pedometrics. A fourth development is the wide range of digital sensors which provide data for pedometrics; sensors include spectroscopy, electromagnetic induction, and γ-ray detectors., connected to each other and to central data stores. A fifth development is wireless technology, including mobile computing and telephony, again greatly facilitating rapid and extensive data collection – in pedometrics, the more dense the data, the greater the analytical possibilities and the lower the uncertainty. A final development is a global navigation satellite system (e.g., GPS), making accurate georeference of field data a routine part of data collection, and thereby assuring the highest possible accuracy in maps made by predictive pedometric methods. As computer power increases, models can be correspondingly detailed. As sensor networks and remote sensing provide ever more abundant and spatiotemporally fine-resolution measurements, the challenge is to manage this information and maintain the link with pedologic and soil-landscape knowledge, within the context of societal needs for the results of pedometric analysis.

The soil genoform vs. soil phenoform distinction was suggested twenty years ago by Droogers and Bouma to recognize management-induced differences among pedons with the same long-term pedogenesis and included in the same soil map unit, these changes being sufficient to cause important and persistent differences in soil functions. To support the recent increased interest in soil change and soil health, we propose conceptual and operational definitions of soil genoforms and soil phenoforms, and suggest techniques to identify and map them. We define soil genoforms as soil classes as identified by the soil classification system used as the basis for detailed soil mapping in a given area. This avoids the difficulty of defining when human intervention has been sufficient to create new genoforms – by definition this is when new lowest-level classes are recognized in the classification system, based on diagnostic horizons and properties. We then define soil phenoforms as persistent variants of a genoform with sufficient physical or chemical differences to substantially affect soil functions. Soil phenoforms must be persistent enough that substantial management interventions are necessary to change them, thus seasonal and rotational variants are excluded from the concept. Soil phenoforms can be identified by measurements of indicator soil properties at locations within a soil genoform with different management and investigating if these are different enough to affect soil functions, notably soil hydrology and crop yield. Digital mapping of soil phenoforms will likely use maps of current and historical management as predictors. In areas with intensive or changed management, mapping should be repeated every few years to identify areas of changed soil phenoforms and new genoforms.

The essential element of citizen science is the participation of non-specialists in scientific research. The citizen acts as an observer or experimenter within structures established by a project run by professional scientists. The recent explosion in projects is due to the development of enabling technology, exemplified by the spatially-enabled “smart” phone with mapping applications and its supporting networks including the GPS system. Citizen science projects have two purposes: (1) to amplify scientific research; and (2) to build citizen support for, and understanding of, science. Current initiatives in citizen soil science include the OPAL Soil and Earthworm Survey, GLOBE, and mySoil, but these are not aimed at soil mapping. We propose digital soil mapping (DSM) citizen science initiatives for countries with and without well-organized extension and advisory services and existing soil surveys, and identify types of citizens who might be motivated to contribute to such initiatives. Contributions could be in the form of tacit knowledge, opportunistic or protocol-guided new information, information from precision agriculture, and physical samples submitted for analysis. The primary beneficiary of such projects would be the professional mapper using digital information to produce or enhance maps of soil properties or types. The secondary beneficiary would be the citizen scientist, who would benefit from an enhanced map, and may be better able to participate in policy debates related to the soil resource. In addition, participation would enhance the connectivity between the soil resource and the citizen.

This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called ''Universal Kriging'' (UK) and ''Kriging with External Drift'' (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, a physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different software environments, both statistical and GIS.

Land evaluation is the process of predicting the use potential of land on the basis of its attributes. A variety of analytical models can be used in these predictions, ranging from qualitative to quantitative, functional to mechanistic, and specific to general. This paper classifies land evaluation models by how they take time and space into account, and whether they use land qualities as an intermediate between land characteristics and land suitability. Temporally, models can be of a static resource base and static land suitability, a dynamic resource base but static land suitability, or both a dynamic resource base and dynamic land suitability. spatially, land evaluation models can be of a single area with no interaction between areas, with static inter-area effects, or dynamic inter-area effects. In the most complex case, land suitabilities for several land uses are interdependent.

This article defines eight key climate, soil and terrain criteria that have been developed for the future delimitation of the Intermediate Less Favoured Areas (LFAs) support, a measure of the Common Agricultural Policy. The LFA scheme has existed since 1975 and is a broad mechanism for improving the viability of agriculture in areas with natural handicaps. The common criteria have been developed for the European Commission's Directorate-General for Agriculture and Rural Development to satisfy the objectives in the Rural Development Policy 2007–2013 (Axis II), which aim to improve the environment and the countryside by more sustainable land management. The criteria were developed by experts, coordinated by the European Commissions Joint Research Centre, to meet the requirement for a robust and harmonised approach of identifying areas that experience natural constraints to agriculture throughout the EU 27 Member States. The criteria proposed are: temperature, heat stress, drainage, soil texture and stoniness, soil rooting depth, soil chemical properties, soil moisture balance and slope. Each criterion is described and an indicative threshold for assessment of its impact on agriculture is provided. The criteria are currently being tested by the EU Member States for a future possible legislation.

This chapter introduces the concepts of data rescue of legacy soil surveys, here defined as a simple conversion to archival format by scanning or direct entry into a database, and data renewal, here defined as the process of bringing these surveys up to modern standards by taking advantage of technological and conceptual advances in geoinformation technology. This is especially important in areas with sparse soil data infrastructure, as it is both more likely that the data will be lost and less likely that a new survey can be commissioned. Digital Soil Mapping (DSM) techniques, although designed for new surveys, can play an important role in data rescue and renewal, in particular as geodetic control for a GIS coverage, as a medium-resolution elevation model (DEM) and derived terrain parameters to adjust terrain-related boundaries, and synoptic satellite imagery to adjust vegetation or landuse-related boundaries. The semantic issues raised by soil-landscape modelling within DSM are especially important for data renewal and integration with supplementary surveys. As with DSM in general, a data renewal exercise may require cultural and institutional change in traditional soil survey organization.

Incomplete information is one of the main constraints for decision-making, which are then by definition risky. In this study, formal risk concepts were introduced in decision-makers’ meetings according to local demands and following a participatory approach, as a first step towards integrating risk assessment into rural decision-making in Santa Catarina, Brazil. Semi-structured interviews and meetings were conducted with extensionists and farmers. The following information was presented and discussed: (1) the time series and frequency distribution of maize yield predictions, simulated by the GAPS computer program for 16 feasible planting dates, representing climatic risks, both within and between years; and (2) a simple economic analysis (gross margin) and income probabilities for seven land-use options over a recent five-year period, followed by an interactive exercise where probabilities of achieving user-supplied target gross margins were calculated according to participants’ actual information, using the computer program @RISK. This paper also investigates decision-makers’ attitudes towards risk, and how these were influenced by objective information. Although results from a study such as this are not definitive, considering that the effects of the information on actual decision-making require some time to become evident, it was already possible to conclude that the risk-orientated information presented according to local demands and following a participatory approach had a positive impact on decision-makers’ understanding and perceptions. This approach should be further explored to effectively integrate risk assessment into rural decision-making.

). The classification accuracy was assessed One approach to semi-detailed survey is to study repusing the error matrix, calculated by comparing both the whole API resentative sample areas, typically covering about 10% maps and point samples, with the results of classification. The first of the survey area, more intensively to arrive at a better results, using a maximum-likelihood classifier, were 58.2% (hill land), understanding of the soil-landscape relations and map 39.1% (plain), and 45.3% (entire area) reproducibility of the training set. Six classes in the plain were responsible for a large proportion unit composition. Field sampling is thus concentrated of the misclassifications, due to an insufficiently detailed DEM and in comparison with the densities mentioned above, to the complex nature of landforms (point bar complexes, levees, active an observation density of one per 2.5 to 10 ha in the channel banks), which cannot be explained with the terrain parameters sample area. This is at the cost of samples over the rest only. Reproducibility for a simplified legend of 15 classes over the of the area, which is then mapped purely by photostudy area was improved to 65.8% (plain), 58.2% (hill land), and interpretation, extrapolating from the detailed under63.4% (entire area) using the whole-API training set. After the simpli- standing of the soil landscape built up in the sample fication of legend (15) and with the iterative (3) selection of point- areas. Because of the low inspection density, the only sample training set, classification was able to reproduce 97.6% (hill way that such maps can be reasonably accurate is if land), 86.7% (plain), and 90.2% (entire area) of the training set. The the surveyor is able to correctly understand the soilsupervised classification showed fine details not achieved by photointerpretation. The number of manual photo-interpretations that had landscape relations in the survey area, and then map to be prepared was reduced from 84 to 6. The methodology can be these by surface features visible on the aerial photo applied by soil survey teams to edit and update current maps and to (e.g., the landform as seen stereoscopically). enhance or replace API for new surveys. Several systematic approaches to soil-landscape photointerpretation have been developed. In this study, we use the “geo-pedological” method of Zinck (Zinck, 1988

Publisher Summary Scientists in the developed world, in an effort to develop tools that will support informed land use decision making, have created a number of very different land use analysis and agricultural production computer models. Such models should be increasingly useful in many parts of Latin America, where issues of sustainable land use are approaching critical dimensions. These include the need to maximize agricultural production while minimizing erosion, to use limited land areas in the most efficient manner, to reduce pollution pressure on the landscape, and to reduce deforestation—as well as optimize these goals. The models reviewed in this chapter are derived quite independently from one another and for very different purposes: ALES (Automated Land Evaluation System) is a computerized framework for allowing expert judgment to estimate crop and consequent economic production, and DSSAT (Decision Support System for Agrotechnology Transfer) is an explicit suite of models of crop plant production. The development of an agroecological modeling approach for land use planning in Costa Rica faced two major problems: a dispersed and not very accurate database and an official land use classification scheme that had little previous impact on decision making. There is an inherent social difficulty in applying even perfectly accurate models, especially where models show that a certain agricultural use might not be a good use for a particular land.

ALES, the Automated Land Evaluation System, is a microcomputer program that allows land evaluators to build their own knowledge-based systems with which they can compute the physical and economic suitability of land map units, in accordance with the FAO's Framework for Land Evaluation. The economic suitability of a land mapping unit for a land utilization type is determined from the predicted annual gross margin per unit area. Increasing limitations result in increased costs of production, decreased yields, or both. Evaluators build decision trees to express inferences from land characteristics to land qualities, from land qualities to predicted yields, and from land qualities to overall physical suitability. A representative model is described.

The potential economic and agronomic impacts of gradual climate warming are examined at the farm level. Three models of the relevant climatic, agronomic, and economic processes are developed and linked to address climate change impacts and agricultural adaptability. Several climate warming scenarios are analyzed, which vary in severity. The results indicate that grain farmers in southern Minnesota can effectively adapt to a gradually changing climate (warmer and either wetter or drier) by adopting later maturing cultivars, changing crop mix, and altering the timing of field operations to take advantage of a longer growing season resulting from climate warming.

A method to visualize multiple membership maps, called ‘Colour mixture’ (CM) is described and compared with alternative techniques: defuzzification and Pixel mixture. Six landform parameters were used to derive the landform classes using supervised fuzzy k-means classification. The continuous categorical map is derived by GIS calculations with colours, where colour values are considered to represent the taxonomic space spanned by the attribute variables. Coordinates of the nine class centres (landform facets) were first transformed from multivariate to two-dimensional attribute space using factor analysis, and then projected on the Hue Saturation Intensity (HSI) colourwheel. The taxonomic value was coded with the Hue and confusion with Saturation. To improve visual impression, saturation was replaced with whiteness. Classes that were closer in attribute space were merged into similar generic colours. The CM technique limits the derived mixed-colour map to seven generic hues independently of the total numb...