Miguel Cooper

Bio: Miguel Cooper is an academic researcher from University of São Paulo. The author has contributed to research in topics: Soil water & Soil structure. The author has an hindex of 21, co-authored 96 publications receiving 1473 citations. Previous affiliations of Miguel Cooper include Escola Superior de Agricultura Luiz de Queiroz & Puerto Rico Department of Agriculture.

Soil structure changes induced by tillage systems

Abstract: Structure represents one of the main soil physical attributes indicators. The soil porous system (SPS) is directly linked to the soil structure. Water retention, movement, root development, gas diffusion and the conditions for all soil biota are related to the SPS. Studies about the influence of tillage systems in the soil structure are important to evaluate their impact in the soil quality. This paper deals with a detailed analysis of changes in the soil structure induced by conventional (CT) and no-tillage (NT) systems. Three different soil depths were studied (0–10, 10–20 and 20–30 cm). Data of the soil water retention curve (SWRC), micromorphologic (impregnated blocks) (2D) and microtomographic (μCT) (3D) analyses were utilized to characterize the SPS. Such analyses enabled the investigation of porous system attributes such as: porosity, pore number and shape, pore size distribution, tortuosity and connectivity. Results from this study show a tri-modal pore size distribution (PSD) at depths 0–10 and 10–20 cm for the soil under CT and a bi-modal PSD for the lower layer (20–30 cm). Regarding the soil under NT, tri-modal PSDs were found at the three depths analyzed. Results based on the micromorphologic analysis (2D) showed that the greatest contribution to areal porosity (AP) is given by pores of round (R) shape for CT (52%: 0–10 cm; 50%: 10–20 cm; 67%: 20–30 cm). Contrary to the results observed for CT, the soil under NT system gave the greatest contribution to AP, for the upper (0–10 cm) and intermediate (10–20 cm) layers, due to the large complex (C) pore types. For the μCT analysis, several types of pores were identified for each soil tillage system. Small differences in the macroporosity (MAP) were observed for the 0–10 and 20–30 cm between CT and NT. A better pore connectivity was found for the 0–10 cm layer under NT.

Micromorphological analysis to characterize structure modifications of soil samples submitted to wetting and drying cycles

Abstract: The physical characteristics of the soil surface are of extreme importance in relation to energy and matter transfer processes between the atmosphere and the soil. Soil internal structure changes can be due to natural or artificial causes and one important natural process is the alternation of wetting and drying (W–D) processes, which induce swelling and shrinking of soil particles, causing modifications in pore size and shape. To study the consequence of these W–D events on possible modifications in pore size distribution, pore number, and pore shape of soil samples collected in metal rings pore image analysis was used. Samples were taken from profiles of three soils of different characteristics, named as Geric Ferralsol (GF), Eutric Nitosol (EN), and Rhodic Ferralsol (RF). Confined volumetric samples (50 cm3) were submitted to none (T0), three (T1), and nine (T2) subsequent W–D cycles. Image cross sections of resin impregnated soil permitted the micrometric and macrometric characterization of changes in soil structure induced by sequences of W–D cycles. Duncan's statistical test indicated that there were significant differences (α = 0.05) among treatments for all soil samples. General conclusions indicate that total pore area increased for all soils after repeated W–D processes, specifically 19.0 to 28.9% for GF, 5.9 to 11.7% for EN, and 13.0 to 17.2% for RF. Main changes of pore diameter occurred in pores larger than 500 μm, and minor changes were observed in the total number of these pores. It is demonstrated that soil samples undergo important changes in their structures after repeated W–D cycles. The information presented here is very important for the evaluation of soil water retention curves and other soil hydric properties, because soil samples used in these procedures are collected in rings and frequently submitted to several W–D cycles.

Validation of the Arya and Paris Water Retention Model for Brazilian Soils

Abstract: The Arya and Paris (AP) model predicts soil water retention curves from soil particle-size distribution (PSD) data based on the similarity between these two functions. The AP model estimates pore radius (r,) from the radius (R i ) of spherical particles by scaling pore length with a parameter a. This paper evaluates the performance of the AP model with representative Brazilian soil types using three constant a values: a = 1.38, 0.938 (literature values), and 0.977, (obtained in the present work); and a α-variable approach, where a is determined as a function of soil water content (θ). The study was performed with 104 soil samples collected in three sites. The soil PSD curves were obtained with an automatic soil particle analyzer based on the attenuation of 7-ray by dispersed soil particles falling in a liquid medium and the soil water retention were measured with tension table and Richard chamber methods. The best mathematical representation of the α = f(θ) relationship was obtained with a first-order exponential decay equation [α = 0.947 + 0.427exp(-θ/0.129)] that provided values of a in the range from 1.37 (θ=0 m 3 m -3 ) to 0.96 (0 = 0.6 m 3 m -3 ). The root mean square deviation values of estimated and measured 0 were 0.062 m 3 m -3 for a = f(θ), 0.073 m 3 m -3 for a = 0.977, 0.080 m 3 m -3 for a = 0.938, and 0.136 m 3 m -3 for a = 1.38. Therefore, for these set of soils the α-variable approach and the constant ones using 0.977 and 0.938 presented the best estimation for the soil water retention relationships.

A National Soil Profile Database for Brazil Available to International Scientists

Abstract: A comprehensive digital soil profile database of Brazil was compiled and is being made available through the Internet. Most of the soil data were obtained from the Radambrasil project and other regional surveys. The database contains information from 5086 profiles distributed over the whole Brazilian territory corresponding to data from 10 034 horizons, each with 31 variables. The variables were chosen to represent different areas of soil science, embracing soil morphological, chemical, mineralogical, and physical attributes. The distribution uniformity of the data was low with sampling densities varying from one profile per 10 000 km 2 to one profile per 1370 km 2 . The access to the database is free and its design allows its use not only by soil scientists but also by those working with agricultural, environmental, and land use issues.

SoilGrids250m: Global gridded soil information based on machine learning

Abstract: This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

Revista Brasileira de Ciência do Solo

Abstract: Resumen en: The aim of this study was a survey of the estimated costs of soil erosion, an issue of fundamental importance in view of the current worldwide discussion...

SoilGrids1km--global soil information based on automated mapping.

Abstract: Background: Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings: We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg21), soil pH, sand, silt and clay fractions (%), bulk density (kg m23), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha21), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance: SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.