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Highlights: [•] Land surface diurnal temperature differences (DTD) were negatively correlated with soil organic matter (SOM) in low-relief plains. [•] The soil-adjusted vegetation index (SAVI) was positively correlated with SOM in these areas. [•] Regression kriging with a mixed model using DTD, SAVI and soil type predicted SOM. [•] The generic mixed model RK method must be calibrated for each area separately. [Copyright &y& Elsevier]

• A gridded soil colour map of China at 1 km resolution and 9 depths was produced. • Predictive soil mapping from 4600 profile descriptions and 40 covariates. • Regional patterns and details of soil colour are clearly shown. • Random forest models did not predict less common colours. • Maps can be used for education or linked to soil processes and classification. 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. [ABSTRACT FROM AUTHOR]

Abstract 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 (R2 adj = 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. Highlights • Soils in Jharkhand, India generally show physical, biological and chemical constraints related to crop production • P and K are low due to negative nutrient balances, although minor nutrients are generally sufficient • Cultivation practices, organic matter additions, and natural processes affect organic matter dynamics and soil health [ABSTRACT FROM AUTHOR]

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. [Copyright &y& Elsevier]

Abstract: The complexity of temporal and spatial changes of soil characteristics under shifting cultivation in the tropics and the expense of comprehensive data collection motivates the development of a minimum data set (MDS) for characterizing soil productivity status and potential. We define a multi-criteria quantitative procedure for MDS selection: (i) the development of selectors based on objective selection criteria; (ii) the transformation of these selectors into combinable scores; and (iii) the combination of transformed selectors scores into a single rating for each soil variable. Selectors are (a) the norm of the vector representing a soil property in the space spanned by the standardized principal components that explain most of the variance; (b) the coefficient of determination of a one-way ANOVA of land use change on a property; (c) time of earliest response; (d) recovery time; (e) expense of sampling and measurement. These are justified heuristically. The method was applied to a set of MDS candidates: 13 soil variables collected within a chronosequence of shifting cultivation system in southern Cameroon. In this case the method selected five soil properties (pH in water, exchangeable calcium, available phosphorous, bulk density and organic carbon). These can be used individually or in combination to assess the effect of this practice on soil condition. The selected variables were easily interpretable in terms of their relation to land management practices and land use changes. The procedure was robust to soil orders and depths at which properties were measured. This method of choosing a MDS is expected to work well for studies of soil dynamics in other agro-ecosystems. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The biogeochemical cycles of nitrogen (N) and base cations (BCs), (i.e., K + , Na + , Ca 2+ , and Mg 2+ ), play critical roles in plant nutrition and ecosystem function. Empirical correlations between large experimental N fertilizer additions to forest ecosystems and increased BCs loss in stream water are well demonstrated, but the mechanisms driving this coupling remain poorly understood. We hypothesized that protons generated through N transformation (PPR N )—quantified as the balance of NH 4 + (H + source) and NO 3 − (H + sink) in precipitation versus the stream output will impact BCs loss in acid-sensitive ecosystems. To test this hypothesis, we monitored precipitation input and stream export of inorganic N and BCs for three years in an acid-sensitive forested watershed in a granite area of subtropical China. We found the precipitation input of inorganic N (17.71 kg N ha −1  year −1 with 54% as NH 4 + –N) was considerably higher than stream exported inorganic N (5.99 kg N ha −1 year −1 with 83% as NO 3 − –N), making the watershed a net N sink. The stream export of BCs (151, 1518, 851, and 252 mol ha −1 year −1 for K + , Na + , Ca 2+ , and Mg 2+ , respectively) was positively correlated ( r = 0.80, 0.90, 0.84, and 0.84 for K + , Na + , Ca 2+ , and Mg 2+ on a monthly scale, respectively, P < 0.001, n = 36) with PPR N (389 mol ha −1 year −1 ) over the three years, suggesting that PPR N drives loss of BCs in the acid-sensitive ecosystem. A global meta-analysis of 15 watershed studies from non-calcareous ecosystems further supports this hypothesis by showing a similarly strong correlation between ∑BCs output and PPR N ( r = 0.89, P < 0.001, n = 15), in spite of the pronounced differences in environmental settings. Collectively, our results suggest that N transformations rather than anions (NO 3 − and/or SO 4 2− ) leaching specifically, are an important mediator of BCs loss in acid-senstive ecosystems. Our study provides the first definitive evidence that the chronic N deposition and subsequent transformation within the watershed drive stream export of BCs through proton production in acid-sensitive ecosystems, irrespective of their current relatively high N retention. Our findings suggest the N-transformation-based proton production can be used as an indicator of watershed outflow quality in the acid-sensitive ecosystems. [ABSTRACT FROM AUTHOR]