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Understanding yield potential and yield-limiting factors is essential for improving profitability and grain yields while avoiding adverse environmental effects. In the USA, grain yield monitors are widely used but the information they provide is rarely used to understand within-field yield variations and associated yield constraints. The objectives of this research were to understand the influence of in-season precipitation on within-field spatio-temporal variation of maize (Zea mays L.) yield and to determine manageable yield variation in two contrasting Major Land Resource Areas of the Mid-Atlantic USA. It does this by assessing the association of grain yield monitor data and in-season precipitation information to be used for variable rate management. Maize yields, as evaluated by baseline functions, were more closely associated with in-season precipitation in the Coastal Plain than in the Piedmont. The study then used standardized principal component analysis (stdPCA) to reveal within-field yield patterns. These varied only under moisture-limited conditions in the Coastal Plain. In the Piedmont, the within-field yield pattern was more consistent under a range of in-season precipitation conditions. In Coastal Plain rainfed fields, the yield predictability increased at the end of June, indicating the possibility of predicting within-field spatial patterns in mid-season. These approaches were successful in deciding whether within-field site- and time-specific management is beneficial for a particular region or field. The presented method, combining stdPCA and geostatistical assessment, is useful in strategizing precision crop management, but do not reveal causes. Detailed soil information and topography could additionally be valuable for understanding constraints to crop yield.

We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by digital soil mapping (DSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales and compare them to spatial patterns known from detailed field surveys (gNATSGO and gSSURGO). The methods are illustrated with an example, i.e. topsoil pH for an area in central New York state. A companion report examines other areas, soil properties, and depth intervals. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case, we discover and discuss substantial discrepancies between DSM products and large differences between the DSM products and legacy field surveys. These differences are in whole-map statistics, visually identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, and co-occurrence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not substantially different from the global model and, in some cases, introduced artefacts from a lithology covariate. Uncertainty (5 %–95 % confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gNATSGO/gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the DSM products as a (partial) replacement for field-based soil surveys. There is no substitute for actually examining and interpreting the soil–landscape relation, but despite the issues revealed in this study, DSM can be an important aid to the soil surveyor.

The admixture of loess in soils formed in sandy parent materials has considerable impact on pedogenesis and soil ecological functions. This study aimed to evaluate these effects on soil hydrology by quantifying the relationships between loess content and soil hydrological properties in a sandstone landscape covered by Pleistocene periglacial slope deposits in central Europe. Studied properties were saturated soil water capacity, field capacity, permanent wilting point, available water capacity, macro-porosity, matrix porosity and saturated hydraulic conductivity. The effect of loess addition on soil hydrological properties differed between topsoil (pedogenic A horizons) and subsoil (pedogenic B and C horizons). In the subsoil, the studied soil hydrological properties were mainly controlled by loess content. By contrast, in the topsoil the effects of loess content on soil hydrological properties were largely masked by the effects of soil organic matter (reflected by soil organic carbon, SOC). The enhancing effect of SOC on soil hydrological properties was most prominent for saturated soil water capacity and was also significant for all other parameters except permanent wilting point. After the effects of SOC were accounted for, the residual effects of loess on soil hydrological properties were the same in both topsoils and subsoils, with one exception: Saturated hydraulic conductivity decreased with increasing loess content for subsoils, but not for topsoils. This study highlighted the ecohydrological significance of loess admixture in coarse-textured soils, especially for subsoils with low SOC contents. However, for coarse-textured topsoils, SOC content plays the dominant role in affecting soil hydrological properties.

Highlights • Sample Size Determination (SSD) is a crucial step in sampling design. • Bayesian, mixed Bayesian-likelihood (MBL) and frequentist SSD approaches compared. • Bayesian and MBL SSD approach account for uncertainty about design parameters. • Various SSD criteria derived from probability distribution of credible intervals . • Legacy data on Zn concentration in soil used for postulating prior distributions., A crucial decision in designing a spatial sample for soil survey is the number of sampling locations required to answer, with sufficient accuracy and precision, the questions posed by decision makers at different levels of geographic aggregation. In the Indian Soil Health Card (SHC) scheme, many thousands of locations are sampled per district. In this paper the SHC data are used to estimate the mean of a soil property within a defined study area, e.g., a district, or the areal fraction of the study area where some condition is satisfied, e.g., exceedence of a critical level. The central question is whether this large sample size is needed for this aim. The sample size required for a given maximum length of a confidence interval can be computed with formulas from classical sampling theory, using a prior estimate of the variance of the property of interest within the study area. Similarly, for the areal fraction a prior estimate of this fraction is required. In practice we are uncertain about these prior estimates, and our uncertainty is not accounted for in classical sample size determination (SSD). This deficiency can be overcome with a Bayesian approach, in which the prior estimate of the variance or areal fraction is replaced by a prior distribution. Once new data from the sample are available, this prior distribution is updated to a posterior distribution using Bayes’ rule. The apparent problem with a Bayesian approach prior to a sampling campaign is that the data are not yet available. This dilemma can be solved by computing, for a given sample size, the predictive distribution of the data, given a prior distribution on the population and design parameter. Thus we do not have a single vector with data values, but a finite or infinite set of possible data vectors. As a consequence, we have as many posterior distribution functions as we have data vectors. This leads to a probability distribution of lengths or coverages of Bayesian credible intervals, from which various criteria for SSD can be derived. Besides the fully Bayesian approach, a mixed Bayesian-likelihood approach for SSD is available. This is of interest when, after the data have been collected, we prefer to estimate the mean from these data only, using the frequentist approach, ignoring the prior distribution. The fully Bayesian and mixed Bayesian-likelihood approach are illustrated for estimating the mean of log-transformed Zn and the areal fraction with Zn-deficiency, defined as Zn concentration

In recent years, mobile phone location (MPL) data have been widely used to determine the spatial trajectories of users. Although this massive amount of MPL data can provide insight into human movement, definite conclusions cannot be drawn because of positioning bias: the locations of MPL data are usually not the phone users' actual locations. In recent years, the spatial accuracy of MPL data has been increasingly evaluated. Such efforts have led to many insights regarding the quality and applicability of MPL data. Despite these achievements, to the best of our knowledge, no studies have quantitatively assessed the spatial accuracy of MPL data by considering geographical influencing factors. In this study, we built a linear evaluation model based on geographical weighted regression (GWR) and a nonlinear evaluation model based on a random forest (RF) to quantify the relationship between geographical factors and the positioning bias of MPL data. Nanjing city in China is used as the test case. The results show that both the GWR model and RF model have good stability. However, the RF model's overall prediction performance is much better than that of the GWR model. The RF model can estimate the spatial accuracy of the MPL data within narrow margins of error. The importance ranking of geographical variables shows that the population density, elevation and building density are the three most important factors, while the normalised difference water index (NDWI) and distance to the nearest cell tower (DNCT) are the least important variables. The RF model constructed in this study can be used to evaluate the spatial accuracy of MPL data and simulate the spatial distribution of the positioning bias of the MPL data covering the study area.

We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gSSURGO). The methods are illustrated with an example: topsoil pH for an area in central New York State. A companion report examines other areas, soil properties, and depth slices. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case we discover and discuss substan- tial discrepancies between PSM products, as well as large differences between the PSM products and legacy field surveys. These differences are in whole-map statistics, visually-identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, co-occurence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine-learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not better than the global model, and in some cases introduced artefacts from a lithology covariate. Uncertainty (5–95% confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the PSM products as a (partial) replacement for field-based soil surveys.

Soils provide a variety of goods and services and are a key natural resource, non-renewable on the time scale of a human life-span, to realise several UN-Sustainable-Development–Goals, including zero hunger (SDG2), climate action (SDG13) and life on land (SDG15). Consistent soil information is required to underpin a large range of global assessments, such as soil and land degradation, sustainable land management, and environmental conservation. In this study, we present an application addressing the modelling of soil functions at global scale, using erosivity risk and soil carbon sequestration potential as examples. We used SoilGridsv2.0, as set of soil property maps at 250m resolution, derived from a large set of standardized soil profile observations (WoSIS), an updated set of environmental covariates, and improved machine learning models. It provides global assessments of prediction uncertainty, quantified with a 90% prediction interval, and considers an evaluation procedure that provides more realistic metrics of map accuracy. We used simplified models to derive soil functions (e.g. erosivity or carbon sequestration potential) from basic soil properties, meaningful for different pedo-climatic regions. We provide an indication of areas of low/high risk of soil degradation to support sustainable soil management planning. The uncertainty limits of the input soil layers were used to provide a preliminary assessment of the uncertainty of the derived layers. The present modelling framework, using soil properties maps to derive soil functions, offers great flexibility and may be applied to a diverse set of models to generate soil information products tailored to specific applications. We highlight some of the challenges of assessing soil functions at global scale.

Soil health (SH) of managed lands in India is affected by anthropogenic activities such as nutrient mining, excessive tillage, and monocropping, which reduce the productive capacity of soils. A comprehensive SH characterization was conducted in 27 catchments in six districts of Jharkhand, India. Each was stratified into four landscape positions: (i) uncultivated upland in tree vegetation, (ii) cultivated upland in garden or orchard use, and (iii) midland and (iv) lowland areas in rice-fallow fields, yielding 113 soil samples from 0 to 15 cm and 20 from 30 to 40 cm depths. Soil textural separates as well as 15 dynamic physical, biological, and chemical properties were assessed using the Comprehensive Assessment of Soil Health framework. Nutrient analyses indicate low to very low P and K values, but high micronutrient levels. A district level ANOVA shows effects of inherent soil factors on the indicators. The influence of tillage, nutrient extraction as well as landscape hydrology on soil health indicators was apparent, notably showing uncultivated soils with higher overall SH. Puddle tillage affected the surface and subsurface soil, the latter showing reduced water holding capacity and less favorable biological indicators. Multivariate analyses showed directional separation of biological and chemical indicators in the first two principal components. A Best Subsets Regression analysis revealed organic matter, soil respiration and active carbon as the most predictive in determining overall SH scores (R2adj = 0.87). In conclusion, a comprehensive soil health assessment using a spatial framework in Jharkhand, India identified multiple SH constraints related to farmer management, associated organic matter dynamics, and natural factors.

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.

Maps of soil properties and classes are key inputs to land management, especially with the increasing awareness of ecosystem services provided by soils. Due to limited resources for new soil surveys, legacy soil inventories are often the major public source of soil data, and this is the case in Iran. One line of previous work has presented methods for data archaeology, rescue and renewal, and another line has shown the value of legacy surveys as covariates for digital soil mapping (DSM). The present study therefore aimed to integrate these two streams, adding another step of adequacy evaluation of the rescued surveys according to the Cornell guidelines. The study area is a 10,480 km2 region located at the border of Isfahan and Chaharmahal-va-Bakhtiari provinces, Iran, covered by three legacy studies at the scale of 1: 50,000. The legacy maps were georeferenced and geocorrected, after which the Cornell adequacy guidelines were used to assess the quality of soil unit separation as evaluated at four levels of Soil Taxonomy. Evaluation was by weighted accuracy and the Tau coefficient, based on forty-one legacy soil profiles from a correlation study. The weighted accuracy of the map and the Tau index at all classification levels were respectively greater than 70% and 50%, which can be considered as satisfactory separation of soil units. Multinomial logistic regression (MLR) predictive models of soil classes with and without the legacy map as covariate were fit. The predictive accuracy of the models was improved at all taxonomic levels when the renewed legacy soil map was included as a covariate. We conclude that the selected legacy soil maps are of reasonable quality and can be used as reliable and useful inputs to DSM, and we propose that these procedures be widely applied.

Soil colour can indicate soil physical, chemical and biological properties and processes, and is an important indicator for soil classification, soil quality evaluation and soil management. It varies in both horizontal and vertical dimensions, and thus regional maps of soil colour can reveal spatial patterns of these properties, processes, and indicators. However, although soil regions are sometimes named for their dominant soil colour, it is directly measured only at “point” support, i.e., during soil profile description, whereas it is desirable to know soil colour over the entire soilscape. To achieve this for China we used predictive soil mapping methods to produce soil colour maps (dry and moist colours) at 1 km2 grid cell size and over multiple depths from a consistent dataset of approximately 4 600 full profile descriptions taken as part of a national survey to define soil series in Chinese Soil Taxonomy, and a set of environmental covariates covering the national territory. The covariates characterized soil forming factors including climate, parent materials, terrain, vegetation, land surface water and thermal conditions. Soil colour descriptions in the Munsell system were extracted from the genetic horizon descriptions at the selected depths and converted to the sRGB and L*a*b* colour spaces. Dry and moist colour separates were not well-correlated in either space ( r 0.76 ). Random forest models were constructed in both spaces, for dry and moist colours separately. Models in sRGB space were moderately successful ( R 2 ≈ 0.43 , RMSE ≈ 26 / 255 ) at 5 cm, with success decreasing with depth. Models smoothed the colour space and thus did not predict the more extreme values or chromas, nor the rarer hues. Models in L*a*b* space were less successful. The fitted sRGB models were used to produce predictive maps over all of China. Regional patterns as well as local detail are clearly shown. Solar radiation, wind exposure, regolith thickness, and Landsat TM bands 7 and 5 contributed most to the predictions, followed by elevation, mean annual precipitation, terrain wetness index, air temperature seasonality, precipitation standard deviation and standard deviation of NDVI. These suggest pedological processes acting on the development of soil colours, including weathering of parent materials, oxidation-reduction chemistry and biochemistry of the decomposing of organic matter. This study shows that predictive methods from points using suitable covariates are an alternative to spatial predictions over map units from their representative profiles.

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.

Soil class maps are useful representations of the landscape distribution of holistic soil functions. However these are often only available as generalized classes at small cartographic scales. One reason is that allocating a soil profile to a class in most current soil classification system requires laboratory determination of many diagnostic soil properties. The advantage of reflectance spectroscopy along with the development of spectral libraries can provide a relatively low-cost solution to this problem. Reflectance spectroscopy has demonstrated its ability to rapidly predict soil physio-chemical properties; however prediction accuracy varies among soil properties. When properties predicted with different accuracies are used to substitute for traditional laboratory determinations in allocating a soil profile to a class, the resulting reliability of the allocation is questionable. The objective of this research is to explore whether the soil properties predicted by reflectance spectroscopy can be used to correctly allocate soil profiles into soil taxa at different hierarchical levels. Two hundred and six soil profiles were allocated to eight Orders, 12 Suborders, 23 Groups and 49 Subgroups according to Chinese Soil Taxonomy, with the help of ten soil properties predicted by spectra using ten-fold cross-validated PLSR modelling. The overall allocation accuracy at Order, Suborder, Group and Subgroup level was 98.5%, 98.5%, 87.7% and 76.0% respectively. These results show that soil reflectance spectroscopy can assist in allocation of profiles. When predicted soil properties with varying accuracy are used for soil allocation, propagation of prediction errors and model uncertainties must be considered. We propose the use of multiple indicators (RPD, confidence intervals, comparison of RMSE and threshold requirements) to evaluate the allocation results.

Calcareous soils are those soils containing layers of carbonate accumulation formed by either secondary accumulation or inheritance of CaCO 3 from calcareous parent materials. These soils are widely distributed in arid, semi-arid and even some humid climate environments. However, in the Chinese Soil Taxonomy (CST, 3rd version), the soils that contain large amounts of CaCO 3 but do not meet requirements of Aridosols are classified as Hapli-Ustic Cambosols. This group also includes many non-calcareous soils and those soils strongly affected by secondary carbonate accumulation together with quite dissimilar soils in terms of their morphology and properties. This study was conducted to determine calcification patterns and calcic processes occurring in representative pedons of calcareous soils in northwestern Henan Province, China and to classify these soils in CST. A Calcic subgroup was proposed to add within the Hapli-Ustic Cambosols. A diagnostic key was also provided to separate this subgroup from the Typic subgroup. In this way the different calcification degrees of soils were better reflected in the classification, which should lead to more uniform interpretive groups for better soil management.

Summary Holocene loess has been recognized as the primary source of the silty topsoil in the northeast Qinghai-Tibetan Plateau. The processes through which these uniform loess sediments develop into diverse types of soil remain unclear. In this research, we examined 23 loess-derived soil samples from the Qilian Mountains with varying amounts of pedogenic modification. Soil particle-size distribution and non-calcareous mineralogy were changed only slightly because of the weak intensity of chemical weathering. Accumulation of soil organic carbon (SOC) and leaching of carbonate were both identified as predominant pedogenic responses to soil forming processes. Principal component analysis and structural analysis revealed the strong correlations between soil carbon (SOC and carbonate) and several soil properties related to soil functions. Accretion of SOC effectively decreased soil bulk density (R2 = 0.81) and increased cation exchange capacity (R2 = 0.96), soil water retention at saturation (R2 = 0.77), field capacity (R2 = 0.49) and wilting point (R2 = 0.56). These results indicate that soil ecological functions are strengthened during pedogenic modification of such loess sediments. Soil C/N ratio was constant at small SOC contents, but after reaching a threshold of approximately 35 g kg−1 SOC, soil C/N increased linearly with SOC. This indicates a change from a carbon-limited loess ecosystem in arid regions to a nitrogen-limited one in alpine settings. This research suggests that loess sequences within environmental gradients offer great potential as natural experiments to explore intrinsic soil behaviour and ecosystem evolution because the effect of parent material is well constrained. Highlights We examined pedogenic modifications of loess with uniform origin from contrasting environments. Accumulation of SOC and depletion of carbonate coincide during pedogenesis of loess-derived soil. Pedogenesis underpins functional evolution of loess-derived soil across the Qilian Mountains. Loess sequences provide ideal natural experiments to study soil and ecosystem evolution.

Evaluating the accuracy of allocation to classes in monothetic hierarchical soil classification systems, including the World Reference Base for Soil Classification, US Soil Taxonomy, and Chinese Soil Taxonomy, is poorly-served by binomial methods (correct/incorrect allocation per evaluation observation), since some errors are more serious than others in terms of soil properties, map use, pedogenesis, and ease of mapping. Instead, evaluations should account for the taxonomic distance between classes, expressed as class similarities, giving partial credit to some incorrect allocations. These can then be used in weighted accuracy measures, either direct measures of agreement or measures that account for chance agreement, such as the tau index. Similarities can be determined in one of four ways: (1) by the expert opinion of a soil classification specialist; (2) by the distance between classes in a numerical taxonomy assessment; (3) by distance within a taxonomic hierarchy; or (4) by an error loss function. Expert opinion can be from the point of view of the map user, to assess map utility, or map producer, to assess mapping skill. Examples are given of determining similarity between a subset of Chinese Soil Taxonomy classes by expert opinion and by numerical taxonomy from soil spectra, and then using these for weighted accuracy assessment. A method for assessing the accuracy of probabilistic predictions of several classes at a location is also proposed.

The dynamics of Pinus mugo krummholz during concomitant change in pastoral land use and climate in central Italy since the mid-20th century was investigated. Krummholz dynamics were detected using sequential aerial photography and fitted to a logistic regression model with elevation, grazing, proximity to beech forest, and proximity to krummholz as explanatory variables. Dendrochronological series were correlated with temperature and precipitation and fitted to a linear model. During this period krummholz doubled in extent and migrated 35–65 m upslope. Expansion was positively associated with krummholz proximity, residual pastoral grazing, and proximity to beech forest beyond 10 m and negatively associated with elevation and beech forest closer than 10 m. The logistic regression model forecasts krummholz migration by an additional 30 m upslope by 2060. During the 20th century, winter and spring minimum temperatures increased but did not result in increased radial stem growth of P. mugo. The combined evidence suggests that krummholz dynamics can be explained by the legacy of summer pastoralism and the dispersal limitations of P. mugo, rather than by climate change.

Previous studies have demonstrated that the pattern of land surface dynamic feedbacks (LSDF) based on remote sensing images after a rainfall event can be used to derive environmental covariates to assist in predicting soil texture variation over low-relief areas. However, the impact of the rainfall magnitude on the performance of these covariates has not been thoroughly investigated. The objective of this study was to investigate this impact during ten observation periods following rainfall events of different magnitudes (0–40 mm). An individual predictive soil mapping method (iPSM) was used to predict soil texture over space based on the environmental covariates derived from land surface dynamic feedbacks. The prediction error showed strong negative correlation with rainfall magnitude (Pearson’s r between root-mean squared error of prediction and rainfall magnitude = −0.943 for percentage of sand and −0.883 for percentage of clay). When the rainfall reaches a certain magnitude, the prediction error becomes stable. The recommended rain magnitude (threshold) using LSDF method in this study area is larger than 20 mm for both sand and clay percentage. The predictive maps based on different observed periods with similar rainfall magnitudes show only slight differences. Rainfall magnitude can thus be said to have a significant impact on the prediction accuracy of soil texture mapping. Greater rainfall magnitude will improve the prediction accuracy when using the LSDF. And high wind speed, high evaporation and low relative humidity during the observed periods also improved the prediction accuracy, all by stimulating differential soil drying.

Plant growth contributes to mineral weathering, but this contribution remains poorly understood. Weathering rates in an aggrading forested watershed in subtropical China were studied by means of geochemical mass balance. Rainfall, dry deposition, and streamwater were monitored from March 2007 to February 2012. Samples of vegetative components, rainfall, dry deposition, streamwater, representative soils, and parent rock were collected and determined for mass balance calculation and clarifying plant-driven weathering mechanisms stoichiometrically. Ignoring biomass, weathering rates of Ca 2+ , Mg 2+ , Na + , and Si were 25.6, 10.7, 2.8, and 51.0 kg ha −1 year −1 , respectively. Taking biomass into consideration, weathering rates of Ca 2+ , Mg 2+ , and Si and the sum of weathering rates of Ca 2+ , Mg 2+ , Na + , K + , and Si were 2.6, 1.8, 1.2, and 1.5-fold higher than those ignoring biomass, respectively. This is attributed to plant-driven weathering due to the nutrient ( e.g ., Ca 2+ , Mg 2+ , and K + ) absorption by vegetation and substantial proton production during assimilation of these nutrients, with the former acting as a pump for removing weathering products and the latter being a source of weathering agents solubilizing mineral components. The same pattern of weathering, i.e ., higher rates of weathering with than without including biomass in mass balance calculation, was reported in previous studies; however, the extent to which plants drive weathering rates varied with vegetation types and climatic zones. The documented biological weathering driven by plants is expected to play a critical role in regulating nutrient cycling and material flows within the Earth's Critical Zone.

Visible near-infrared (VNIR) spectra can provide rich information on soil physical and chemical properties, which implies the possibility of using soil spectra to aid in the discrimination of soil types. Pedological soil classification systems use a selected set of soil properties to identify diagnostic horizons and features, and to build a classification key. This research explored the application of VNIR spectra to classify typical soil profiles collected in Anhui province, China. The 279 soil profiles used are classified into five orders (Cambosols, Vertosols, Argosols, Primosols and Anthrosols), six suborders and 21 groups according to Chinese Soil Taxonomy. Soil spectra were collected within 350–2500 nm and principal component analysis (PCA) was applied to reduce data dimension. These principal components were used as independent variables in multinomial logistic regression for soil classification. Topsoil spectra, subsoil spectra and their combination were compared for prediction accuracy. Accuracy achieved at the level of suborder using spectra of topsoil, subsoil and combined horizons were 76.3%, 71.3% and 70.3% respectively, while the results for the level of soil group using the topsoil horizon was 40.5%. Since topsoil spectra alone achieved a prediction accuracy of more than 75%, reflectance spectroscopy can be judged a promising tool for soil classification. Taxonomic distances between classes calculated on the basis of physio-chemical properties and spectra were quite different, showing that the concept of distance between classes in feature space depends on the features chosen for evaluation. Taxonomic distances can serve as a supplement for better selection and evaluation of prediction models.

The present regression models in digital soil mapping usually assume that relationships between soil properties and environmental variables are always fixed (as in MLR) or varying (as in GWR) in geographical space. In reality, some of the environmental variables may be fixed in affecting soil property variation and some are local varying. In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales. Three groups of sample sets were created based on the total samples in the study areas to evaluate the robustness and stability of the model. Multiple linear regression (MLR), geographically weighted regression (GWR), GWR-kriging (GWRK), local regression-kriging (LRK), kriging with an external drift (KED), and ordinary kriging (OK) were used for comparison with MGWR. The validation results showed that the use of MGWR reduced the RMSE of GWR by 10.5% and 7.6% on average, reduced the RMSE of MLR by 12.8% and 9.9% on average for Heshan and Xuancheng study areas respectively. MGWR also showed a good competitiveness when compared with GWRK, LRK, KED and OK. In Heshan study area, the influence of flow length, relative position index, foot slope and distance to the nearest drainage were constant, whereas the elevation, topographic wetness index and valley index showed different influence in different regions. In Xuancheng study area, the fixed environmental variables were profile curvature, topographic wetness index and slope, whereas the varying environmental variables were precipitation, temperature, elevation, and limestone. The results indicate that the accuracy of predictions can be improved by adaptive coefficient according to the variation of environmental variables as implemented in MGWR compared with others considering only the local or global relationships. It was concluded that mixed geographically weighted regression model could be a potential method for digital soil mapping.

Process-based land surface models are important tools to study the historical and future effects of climate change and land use change. The planting date has a considerable effect on crop growth and consequently on dynamic parameters used in land surface models, for example albedo and actual evapotranspiration. If planting dates can be related to climate, scenarios can use this relation to estimate planting dates. Such a relation is expected to differ according to agro-ecological zone. In this study, spring and summer maize planting date observations at 188 agricultural meteorological experiment stations of China, as well as monthly weather records, over the years 1992&ndash, 2010 were used as the data source. In order to quantify the relation between planting dates and climate parameters, growing season monthly average minimum temperature (Tmin), mean temperature (T), and precipitation (P) were used. The time trend analysis of planting dates and weather data, principal component analysis (PCA) of weather data, and multivariate regression of planting dates as affected by weather data were used. Both Tmin and T increased during this period in most zones, whereas precipitation showed no trend. In southwest and northwest China, maize planting dates advanced significantly for both spring and summer maize. However, in the north China plain (summer maize) and northeast China (spring maize), the planting date was significantly delayed. Ordinary least squares multivariate regression models were able to explain 33% and 59% of the variance of planting dates in the southwest China (i.e., the humid subtropics zone) for spring and summer maize, respectively. However, only 3% could be explained in the Loess Plateau. Thus, adjusting planting dates in scenario analysis using land surface models is indicated for some zones, but not others, where socioeconomic factors are dominant.

Very frequently biological databases are used for analysing distribution of different taxa. These databases are usually the result of variable sampling effort and location uncertainty. The aim of this study was to test the influence of geographically biased sampling effort and spatial uncertainty of locations on models of species richness. For this purpose, we assessed the pattern of invasive alien plants in Croatia using the Flora Croatica Database. The procedure applied in testing of the sensitivity of models consisted of sample area sectioning into coherent ecological classes (hereinafter Gower classes). The quadrants were then ranked based on sampling effort per class. This resulted in creation of models using varying numbers of quadrants whose performance was tested with independent validation points. From this the best fitting model was determined, as well as a threshold of sampling effort. The data from quadrants with sampling effort below the threshold were considered too unreliable for modelling. Further, spatial uncertainty was simulated by adding a random term to each location and re-running the models using the simulated locations. Biased sampling effort and spatial uncertainty of locations had similar effects on model performance in terms of the magnitude of the affected area, as in both cases 7% of the quadrants showed statistically significant deviations in alien plant species richness. The model using only on the quandrants with the highest 35% quantile sampling effort best balanced the sampling effort per quadrant and overall geographical coverage. It predicted a mean number of 3.2 invasive alien plant species per quadrant for the Alpine region, 5.2 for the Continental, 6.1 for the Mediterranean and 5.3 for the Pannonian region of Croatia. Thus, the observational databases can be considered as a reliable source for species richness models and, most likely, for other types of species distribution models, given that their limitations are accounted for in the data selection process. In order to obtain precise estimates of species richness it is required to sample the whole range of ecological conditions of the study area.

The stability of soil colloids influences soil physicochemical properties, soil development, and transfer of nutrients and contaminants to surface and ground waters. A better understanding of soil colloids stability dynamics during soil evolution is important for the evaluation of soil's capacity to retain nutrients and/or accommodate toxic contaminants. This study was aimed to determine changes in the stability of water-dispersible soil colloids that accompany mineral transformation and surface charge evolution during pedogenesis using a well characterized chronosequence derived from basalt in the humid tropical region of Hainan Island, South China. The results demonstrated that the pH-dependent colloid stability decreased significantly with tropical soil development, which we attribute to the substantial changes in clay mineral compositions and colloid surface charge properties. Clay minerals in the studied chronosequence were characterized by an increase of kaolinite, gibbsite and Fe oxides and a decrease of quartz and halloysite towards more advanced stages of weathering, which resulted in the decline of permanent negative charges in the older soils. The point of zero charge (pHPZC) increased while ∆ pH decreased across the tropical soil chronosequence, being in good agreement with the observed lower colloid stability in aged soils dominated by kaolinitic minerals. Our study of colloid stability at long-term pedogenic time scale suggests young tropical soils (

The accuracy and precision of predictions of soil texture based on visible–near-infrared (Vis-NIR) spectra are affected by the soil moisture content at the time of measurement. This study aimed to quantify these effects. We also developed a method to improve the accuracy of soil texture prediction when the difference in moisture content is large and unknown, as is usually the case in field measurements. Reflection spectra (380–2400 nm) of 89 soil samples were obtained in the laboratory under nine moisture conditions. Prediction models for each moisture condition were built separately using partial least-squares (PLS) regression. Each model was applied to independent validation sets under the same moisture condition as the calibration sets, and then the model based on air-dried soils (dry model) was applied to the other eight moisture conditions to quantify the effect of soil moisture on the prediction accuracy. Finally, the perpendicular drought index (PDI) was used as an indicator of soil moisture, and the nine moisture conditions were regrouped to four groups using the PDI. Prediction models for each group with the same PDI were built and evaluated. The results show that Vis-NIR spectra can be directly applied to predict the soil texture of soils in different known moisture states. When the moisture state is unknown, the models based on PDI grouping markedly improve prediction accuracy. The RMSEₚ of prediction from PDI-grouped models of prediction of the content of clay and sand separates were between 1 and 2% and between 8 and11%, respectively, for the different moisture classes. This method has potential for direct application in the field with on-the-go sensors.

In temperate climates, a significant fraction of annual emissions of nitrous oxide (N2O) from agricultural land can occur during soil thaw in late winter and early spring. The objective of this study is to determine the impact of land use change from long-term fallow grassland to managed perennial grass crops on these thaw-related N2O emissions, and to identify field-scale drivers that influence emissions. Using static chambers, we monitored mid-afternoon N2O fluxes during the 2013 spring thaw from March 27 to April 7, observing fallow grassland and second year reed canarygrass (Phalaris arundinaceae L., v. Bellevue) over a short topographical gradient. Soil temperature, soil moisture and residual above-ground biomass were also observed, as were hourly air temperature and precipitation. Fluxes of N2O were generally low (-9.8 to 21.3 μg N2O-N m-2 hr-1) except for one observation of 77.6 μg N2O-N m-2 hr-1. Hot-moment analysis, non-parametric statistical tests, and ANOVA results showed that downslope positions converted to managed and fertilized grass had significantly higher N2O emissions compared to the fallow and upslope positions. We found that these downslope managed grass sites had mean soil moisture of 75.0 % water filled pore space (WFPS) and less insulating residual above-ground biomass than the fallow grassland. Our results suggest that converting fallow grassland to managed perennial grass cropping systems for bioenergy or other uses could increase springthaw N2O emissions in wetness-prone areas.

This paper presents an approach to predicting three-dimensional (3D) variation of soil organic matter (SOM) concentration by integrating a similarity-based method with depth functions. It was tested in a small hilly landscape. A depth function model was constructed to fit SOM profile distribution using a linear relation in the topsoil and a power function in the subsoil. Then, under the assumption that similar environmental conditions at two sites would lead to the development of similar profile morphologies and thus similar depth function parameters, the similarity-based method was used to spatially interpolate the depth function parameters based on their relationships with environmental variables. With the values of the parameters for every location, a 3D map of SOM distribution was generated. The predicted SOM pattern well reproduced the statistical distribution of the pedon dataset used in this study. The overall mean error (ME) was 0.06 g kg− 1 and ratio of performance to deviation (RPD) was 2.34. We conclude that the proposed approach is effective and accurate for 3D SOM prediction. It overcomes two drawbacks of the frequently used pseudo 3D soil mapping approach: (1) the neglect of vertical soil pattern when performing horizontal soil predictions, and (2) the repeated applications of depth function fittings in the mapping process, both of which may lead to prediction errors. Moreover, the similarity-based method is a transparent and traceable prediction process, allowing for easy interpretation of its results. This is useful for understanding soil–environmental relationships and processes. The method thus is an attractive alternative to the commonly used non-linear “black-box” techniques such as artificial neural networks.

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.

In geography, a terrain sketch map is necessary to understand the features and internal structures of a landscape due to its ability to depict key information in a geographical scene using as few lines as possible. Previous computer-drawn sketch maps have focused on the artistic effect rather than on depicting terrain features and landform structures, and thus, they differ considerably from hand-drawn sketch maps by geographers or geologists. This study develops a DEM-based method for terrain sketch mapping that considers the typical feature descriptions of a loess landform and the visual hierarchy expression in line with the law of visual perspective. The method was tested with experiments on two landforms of Chinese Loess Plateau, based on digital elevation models (DEM) with a horizontal resolution of 5 m. In the developed method, first, typical terrain features, including the visual outline, shoulder line, gully and flow lines, are extracted from the DEM. Second, the map is divided into three visual levels in accordance with the data extension and viewing point. Then, terrain feature lines are assigned to different visual levels. Finally, the visual outlines in the distant view are generalized following the law of visual perspective. Results are assessed through a questionnaire with specialists (experts) and students (non-experts). The sketch map was able to characterise loess landforms, and is somewhat similar to traditional hand-drawn maps. The generalisation method realises the near and distant view characteristics of a sketch map, which are detailed and simplified, respectively. The results of the questionnaire also showed that our method presents terrain morphology and geographical scene more accurately and reliably than a hand-drawn sketch map.

Numerous pedotransfer functions (PTFs) have been developed to predict the soil properties of interest from other soil properties and, less commonly, from environmental variables. However, only a few PTFs have been developed to predict soil nutrients using environmental variables and to extrapolate them to characterize spatial soil variations at a regional scale. In this study, we attempted to develop PTFs for the total nitrogen (TN), total phosphorus (TP) and total potassium (TK) concentrations in three typical pedo-climatic areas of China (Fujian Province, Jiangsu Province and Qilian Mountains) with diverse climate, terrain and soil types. A series of linear PTFs were developed to quantify the effect of terrain and climate on the predictive relations between the soil nutrients and other measured soil properties and environmental variables. In addition, digital soil mapping (DSM) based on the random forest (RF) technique was performed to test the hypothesis that the best-fit PTFs could be extrapolated, based on soil maps and environmental variables, to describe regional soil variations in the soil nutrients. The root mean square errors (RMSEs) of the best-fit PTFs for TN, TP and TK ranged from 0.21 to 0.79 g kg−1, 0.20 to 0.58 g kg−1, and 3.68 to 5.00 g kg−1, respectively. Different RMSEs were produced by DSM, namely 0.37-1.89 g kg−1, 0.19−0.56 g kg−1 and 3.79-4.83 g kg−1 for TN, TP and TK, respectively. PTFs provided a sound basis for database compilation if the soil properties were highly correlated. However, the extrapolation of best-fit PTFs to regional scales yielded greater errors than those produced by DSM. The comparison results reveal the limitations of PTFs and suggest that their performance could be improved by using environmental covariates or by fitting data in areas with relatively homogeneous soil landscapes. The DSM techniques may provide satisfactory alternatives to predict soil data at both regional and plot scales.

Observations of soil at georeferenced sample points are the indispensable input to digital soil mapping (DSM) models that relate soil properties and types to the soil-forming environment. Many existing DSM methods require soil samples to be collected following a pre-defined sampling pattern. This requirement is often not met due to various operational reasons. The individual sample-based predictive soil mapping (iPSM) method has been developed to avoid this requirement. However, the prediction accuracy of iPSM depends heavily on the reliability of the known sample points used in prediction. Unreliable sample points are those with unreliable soil-environment relationship: the target soil property value and environmental covariate data are not correctly paired at the sample point location. Such unreliable sample points will lead to poorly constructed models and low prediction accuracy. This paper presents a new method to estimate the reliability of soil-environment relationship at each sample location and to evaluate the trustworthiness of prediction at each unvisited location. Under the assumption that sample points with similar environmental conditions have similar soil property values, the method first identifies the sample points that are environmentally similar to the sample point to be evaluated, then uses their agreement on the targeted soil property to evaluate the reliability of soil-environment relationship at the sample point. The effectiveness of the method was assessed in a case study located in Anhui Province in China to map soil organic matter content (SOM, %) in the topsoil. When the reliability threshold increased from 0.5 to 0.8, more unreliable sample points are excluded from prediction: 41% of total sample points were excluded when the threshold was set to 0.5, and 88% were excluded when the threshold was set to 0.8. As a result, less unknown area could be predicted – only nine validation points were predictable with the 0.8 reliability threshold. However, the prediction accuracy was improved: the root mean squared error, RMSE, decreased from 1.37% to 0.63%, and R2 increased from 0.21 to 0.98. Prediction trustworthiness at each unvisited location was also produced along with prediction accuracy, which was negatively related to the absolute prediction residuals. This study shows that the reliability of individual sample points is an important determinant of the prediction accuracy of the iPSM method. When applying iPSM method, an optimal trade-off between prediction accuracy and completeness of the predicted map needs to be found by adjusting the reliability threshold on the sample points used in prediction.

Process-based crop models are popular scientific tools to study the impacts of environmental conditions and management decisions on crop growth. Some cultivar parameters in crop models cannot be measured directly and need to be estimated. In this research, two most popular Bayesian methods, namely generalized likelihood uncertainty estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM), were used for the first time to estimate parameters of the maize module of the Agricultural Productions Systems sIMulator model (APSIM-maize). Both theoretical and real-world evaluations were conducted to compare the performances of these two methods. The maize yields from 2003 to 2006 were used for model calibration, and the yields from 2007 to 2013 were used for model validation. Both GLUE and DREAM performed well in the theoretical and real-world evaluation. During the validation period (2007–2013), when the heteroscedastic model error assumption was adopted in DREAM, on average approximate 90% of observed yield values were captured in the 95% confidence band of DREAM (P-factor = 90.47%), which was larger than that using GLUE (P-factor = 80.93%). Meanwhile the uncertainty bands of DREAM (R-factor = 4.42) were wider than those of GLUE (R-factor = 2.32). If only one parameter set was allowed to be used in the simulation, the weighted mean parameter values according to the likelihood of each parameter set performed better than the parameter set with maximum likelihoods for GLUE while the opposite is true for DREAM. But considering future analysis in the real-world evaluation, the moderate performance of these two methods suggests that a single parameter set obtained by neither GLUE nor DREAM is satisfactory and ensemble simulation is needed. Overall, GLUE and DREAM had similar performance, but GLUE is more convenient and simpler to use than DREAM. So we think GLUE is a better choice than DREAM for estimating cultivar parameters of APSIM-maize.

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.

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 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.