Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale

Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes

Statistical sampling design impact on predictive quality of harmonization functions between soil monitoring networks

Regulations about soil quality are normally imposed at international level while many countries have set up monitoring networks at national scale. Since these networks use different sampling strategies, there is a strong need to harmonize a posteriori the collected data from the national networks in order to answer questions raised by the global regulations. For that purpose, calibration sites where different sampling strategies are carried out are necessary in order to construct harmonization functions between measurements from different sampling protocols. A case study is available for French forest soils that have been sampled twice simultaneously on the same sampling grid but with different sampling and analytical strategies: a first sampling for the French soil quality monitoring network (RMQS) and a second one for the European forest monitoring network (ICP Forests level I second survey i.e. Biosoil). However, the way to define the number and the position of these calibration sites remains a key issue. In this work, we compare both RMQS and Biosoil strategies for a set of measured variables of interest (carbon, potassium and lead contents and pH) and aim to define the minimum number of sites and their best location to establish reliable harmonization functions. Three statistical methods for construction of sampling designs are tested: random sampling, conditioned Latin Hypercube Sampling (cLHS, Minasny and McBratney, 2006) and D-Latin Hypercube Sampling (DLHS, Minasny and McBratney, 2010). With each method, we investigate the effects of the number of calibration data on the predictive quality of the harmonization functions. First, we show that both cLHS and DLHS are better than simple random sampling. Then, the difference between cLHS and DLHS performance depends mainly on the size of the samples, the nature of the soil property and the form of the pedotransfer functions. (C) 2013 Elsevier B.V. All rights reserved

Mapping soil Pb stocks and availability in mainland France combining regression trees with robust geostatistics

Maps of lead (Pb) stocks in soils and estimates of its availability are needed to assess risks of contamination. Stocks in soils of total and ethylenediamine tetraacetic acid (EDTA) extractable Pb, as well as Pb availability, assessed by EDTA/total Pb ratio, were measured and calculated to a depth of 30. cm with the French soil monitoring network at sites defined by a regular 16 × 16. km grid. Setting aside punctual anomalies by winsorizing, these properties were mapped using linear mixed models (LMM). LMMs combined conditional partitioning trees upon 5 predictors (pH, texture, parent material, land use, population density) with robust geostatistics to avoid distortion due to outlying values. Rather than selecting the fixed effects according to expert-knowledge, regression trees were used to account for explanatory variables in a single classification. This original method stressed both the necessity for a geostatistical component to complement regression tree models when spatial correlation is evident, and the usefulness of these trees to interpret maps. Pb stocks varied widely with peak concentrations and availability in densely populated areas. Lithology, texture and forestation also affected total Pb stocks. With regards to availability, forestation and pH appeared as key factors. © 2011 Elsevier B.V

Estimation of Soil Carbon Input in France: An Inverse Modelling Approach

Development of a quantitative understanding of soil organic carbon (SOC) dynamics is vital for management of soil to sequester carbon (C) and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component (i.e., C input) constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network (the RMQS database). Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values (P < 0.05) for 14 out of 17 crop types in the range from 1.84 ± 0.69 t C ha-1 year-1 (silage corn) to 5.15 ± 0.12 t C ha-1 year-1 (grassland/pasture). Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction. © 2013 Soil Science Society of China

Regolith mass balance inferred from combined mineralogical, geochemical and geophysical studies : Mule Hole gneissic watershed, South India

The aim of this study is to propose a method to assess the long-term chemical weathering mass balance for a regolith developed on a heterogeneous silicate substratum at the small experimental watershed scale by adopting a combined approach of geophysics, geochemistry and mineralogy. We initiated in 2003 a study of the steep climatic gradient and associated geomorphologic features of the edge of the rifted continental passive margin of the Karnataka Plateau, Peninsular India. In the transition sub-humid zone of this climatic gradient we have studied the pristine forested small watershed of Mule Hole (4.3 km(2)) mainly developed on gneissic substratum. Mineralogical, geochemical and geophysical investigations were carried out (i) in characteristic red soil profiles and (ii) in boreholes up to 60 m deep in order to take into account the effect of the weathering mantle roots. In addition, 12 Electrical Resistivity Tomography profiles (ERT), with an investigation depth of 30 m, were generated at the watershed scale to spatially characterize the information gathered in boreholes and soil profiles. The location of the ERT profiles is based on a previous electromagnetic survey, with an investigation depth of about 6 m. The soil cover thickness was inferred from the electromagnetic survey combined with a geological/pedological survey. Taking into account the parent rock heterogeneity, the degree of weathering of each of the regolith samples has been defined using both the mineralogical composition and the geochemical indices (Loss on Ignition, Weathering Index of Parker, Chemical Index of Alteration). Comparing these indices with electrical resistivity logs, it has been found that a value of 400 Ohm m delineates clearly the parent rocks and the weathered materials, Then the 12 inverted ERT profiles were constrained with this value after verifying the uncertainty due to the inversion procedure. Synthetic models based on the field data were used for this purpose. The estimated average regolith thickness at the watershed scale is 17.2 m, including 15.2 m of saprolite and 2 m of soil cover. Finally, using these estimations of the thicknesses, the long-term mass balance is calculated for the average gneiss-derived saprolite and red soil. In the saprolite, the open-system mass-transport function T indicates that all the major elements except Ca are depleted. The chlorite and biotite crystals, the chief sources for Mg (95%), Fe (84%), Mn (86%) and K (57%, biotite only), are the first to undergo weathering and the oligoclase crystals are relatively intact within the saprolite with a loss of only 18%. The Ca accumulation can be attributed to the precipitation of CaCO3 from the percolating solution due to the current and/or the paleoclimatic conditions. Overall, the most important losses occur for Si, Mg and Na with -286 x 10(6) mol/ha (62% of the total mass loss), -67 x 10(6) mol/ha (15% of the total mass loss) and -39 x 10(6) mol/ha (9% of the total mass loss), respectively. Al, Fe and K account for 7%, 4% and 3% of the total mass loss, respectively. In the red soil profiles, the open-system mass-transport functions point out that all major elements except Mn are depleted. Most of the oligoclase crystals have broken down with a loss of 90%. The most important losses occur for Si, Na and Mg with -55 x 10(6) mol/ha (47% of the total mass loss), -22 x 10(6) mol/ha (19% of the total mass loss) and -16 x 10(6) mol/ha (14% of the total mass loss), respectively. Ca, Al, K and Fe account for 8%, 6%, 4% and 2% of the total mass loss, respectively. Overall these findings confirm the immaturity of the saprolite at the watershed scale. The soil profiles are more evolved than saprolite but still contain primary minerals that can further undergo weathering and hence consume atmospheric CO2

Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. However, a landscape perspective is needed to understand the relative importance of local and regional factors and land management for the microbial communities and the ecosystem services they provide. In the most comprehensive analysis of spatial patterns of microbial communities to date, we investigated the distribution of functional microbial communities involved in N-cycling and of the total bacterial and crenarchaeal communities over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 × 16 km2 sampling grid. At each sampling site, the abundance of total bacteria, crenarchaea, nitrate reducers, denitrifiers- and ammonia oxidizers were estimated by quantitative PCR and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time, and soil physico-chemical properties to the spatial distribution of the different communities were analyzed by canonical variation partitioning. Our results indicate that 43–85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of microbial communities at the landscape scale. The present study highlights the potential of a spatially explicit approach for microbial ecology to identify the overarching factors driving the spatial heterogeneity of microbial communities even at the landscape scale