1,720,982 research outputs found
Índice de productividad. Partido de Patagones, provincia de Buenos Aires
No full textMapa de Índices de Productividad, a escala 1:250.000 del partido de Patagones, provincia de Buenos Aires, Argentina.Instituto de SuelosFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Tenti Vuegen, Leonardo Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentin
Mapa complementario a Carta de suelos de la República Argentina. Partido de Patagones, provincia de Buenos Aires
No full textMapa de Suelos, a escala 1:250.000 del partido de Patagones, provincia de Buenos Aires, Argentina. Con fondo de Imagen Bing Satellite.Instituto de SuelosFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Tenti Vuegen, Leonardo Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentin
Polygenic vertisols and "Hidden" vertisols of the Paraná River Basin Argentina
No full textVertisols and soils with vertic properties occupy considerable areas in the east of the Pampa Region. Many of thèse Vertisols have particular features that dif- ferentiate them from most of the Vertisols in the world. In the Mesopotamian Pampa, Vertisols have developed from a calcareous and gypsiferous loam, later covered by a thin loess mantle. Here it is considered that Vertisols would have been the domi- nant soils in the different segments of a hilly landscape. Later, under humid climate, and due to the buffering effect of the non-expansive silty loessic surface, clay illu- viation would have been a generalized process across the landscape. At the same time erosion processes took place. The conservation of the thin layer of loessic sedi- ments on the higher slopes resulted in the formation of Vertic Alfisols and few Mollisols, while its erosion in the backslopes caused the exhumation of buried Vertisols. On the other hand, in the High Undulating Pampa, there are no Vertisols but there are Vertic Argiudolls. Two superficial sedimentary levels have been distin- guished here. The lower one is a smectitic loessic sediment, covered by a relatively thick illitic loessic deposit. New studies undertaken in large pits have revealed the existence of diapiric structures in the lower sediment, which remained “hidden” up to the present. Therefore, it can now be considered that Vertisols were also the domi- nant soils here, later buried by the thick loess. In the humid periods, the summit of convex slopes would have been partly eroded, leaving the underlying sinectitic material closer to the surface. Consequently, current Mollisols on top of the land- scape developed vertic properties due to the mixing of inaterials and depending on the greater or lesser proximity to the paleosurface. Therefore, thèse pampean Vertisols and vertic Mollisols and Alfisols can be considered polygenic and related by different degrees of a same process. These advances in the understanding of the landscape and soils together with the quantitative analysis of soil profiles data, appear highly useful to distinguish vertic soils at the series level and improve sur- veying and mapping work.Fil: Morras, Héctor José María. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Bressan, Emiliano Miguel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Tenti Vuegen, Leonardo Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Moretti, Lucas Martín. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Rodriguez, Darío Martín. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentin
Digital soil texture map of Argentina and their relationship to soil - forming factors and processes
No full textSoil texture is determined by the parent material of the soil and the resulting pedogenetic processes. Because the spatial distribution of soil texture governs several soil physical and ofsoil profile database generate textural soil class map for the same soil dpths; and describe the distribution of soil texture in relation to different landscape units. We used 4663 soil texture observations and 64 environmental covariates to represent the soil forming factors (e.g., remote sensing data, climate data of geomorphology map). We modelled clay, silt, and sand at 0-15, 15-30, 30-60 and 60-100 cm, fraction. and empirical relationship with environmental covariates using Random Forest to predict their spatial distribution.
Finally we performance an analysis of uncertainty through repeated cross – validation. We observed model efficiency coefficient (MEC) value between 0.452 and 0.557, with and RMSE between 8.77% and 11.21% for the clay fraction. The MEC for the silt fraction ranged from 0.561 to 0.638 with and RMSE of 10.50% to 12.01% for sand fraction the MEC ranged from 0.587 to 0.640 with RMSE values between 16.19% and 16.76%. The general patterns of uncertainty are consistent with areas of limited data. Our resulys increased the quality, quantity and accessibility of information on soil texture in Argentina by providing new insights into both the distribution of parent materials and the intensity of pedogenetic processes in each region.Fil: Schulz, Guillermo A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Rodriguez, Darío M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Food and Agriculture Organization on the UN Global soil Partnership; Italia. Universidad Nacional de Lujan. Departamento de Ciencias Básicas; ArgentinaFil: Moretti, Lucas M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Olmedo, Guillermo Federico. Food and Agriculture Organization on the UN Global soil Partnership; Italia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; ArgentinaFil: Tenti Vuegen, Leonardo Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Colazo, Juan Cruz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; ArgentinaFil: Guevara, Mario. Universidad Nacional Autónoma de México. Campus Juriquilla. Centro de Geociencias; Méxic
SISINTAR: Uin paquete para gestionar datos de perfiles de suelo de Argentina
Full text linkPresentación en diapositivasEl INTA de Argentina mantiene SISINTA un sistema de información para gestionar datos de perfiles de suelo (información de campo, laboratorio y ubicación). Permite búsquedas por atributos y ubicación, así como la descarga de los datos. El paquete SISINTAR fue desarrollado para permitir el acceso, lectura y manipulación de datos de perfiles de suelo de SISINTA de forma programática, utilizando estándares en el procesamiento, visualización y representación de información de suelos y desde R.Instituto de SuelosFril: Campitelli, Elio. Universidad de Buenos Aires. Centro de investigación del Mar y la Atmósfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de investigación del Mar y la Atmósfera; Argentina.Fil: Corrales, Paola. Universidad de Buenos Aires. Centro de investigación del Mar y la Atmósfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de investigación del Mar y la Atmósfera; Argentina.Fil: Angelini, Marcos Esteban. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO); ItaliaFil: Rodriguez, Darío M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Bellini Saibene, Yanina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentin
A multivariate approach for mapping a soil quality index and its uncertainty in southern France
No full textPedometricians have spent a lot of effort on mapping soil types and basic soil properties. However, end-users typically need a more elaborate soil quality index for land management. Soil quality indices are typically derived from multiple individual soil properties, by evaluating whether specific criteria are met. If this is based on individually mapped soil properties, then an important consequence is that cross-correlations between soil properties are ignored. This makes it impossible to quantify the uncertainties associated with the mapped indices. The objective of this study was to map a soil potential multifunctionality index for agriculture (Agri-SPMI) over a 12 125 km2 study region located along the French Mediterranean coast to help urban planners preserve soils of highest quality. The
index considered the ability of soils to fulfil four functions under five land use scenarios. Each soil function fulfilment for a given scenario was represented by a binary map. The final soil quality index map was the sum of the 20 binary maps. A regression co-kriging model was developed to map the basic soil properties first individually from legacy soil data and spatial soil covariates using a Random Forest algorithm, and next interpolate the residuals using cokriging and the linear model of coregionalisation. The mapping uncertainties of soil properties were propagated by calculating the soil quality index over 300 stochastic simulations of soil properties derived from the linear models of coregionalisation. Results showed a poor prediction accuracy of the quality index, mainly because
some soil properties were poorly predicted (notably available water capacity and coarse fragments) and used in combination with extreme thresholds that defined land suitability. Overall, the uncertainty was correctly quantified because the stochastic simulations reproduced the width of the observed distribution well, but the shapes of the distributions differed considerably from those of the
observations. We envisage some ways for improvement, such as creating probability maps instead of the mean from simulations, and changing the prediction support from point to area.Fil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina.FIL: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Países Bajos. ISRIC – World Soil Information; Países BajosFil: Lagacherie, P. , University of Montpellier, LISAH, INRAE, IRD, Montpellier SupAgro; FranciaFil: Angelini, Marcos Esteban. University of Montpellier, LISAH. INRAE, IRD, Montpellier SupAgro; Franci
Mapeo de suelos afectados por sales en Argentina
Full text linkEl Global Soil Partnership (GSP) de la FAO ha iniciado un proyecto para estimar el área de suelos afectados por sales a escala global, utilizando técnicas de Mapeo Digital de Suelos (MDS). El mapa de suelos afectados por sales de Argentina es la contribución nacional a dicha iniciativa, realizado bajo parámetros consensuados a nivel mundial para establecer los límites de salinidad.Fil: Rodríguez, Darío Martín. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Tenti Vuegen, Leonardo Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Wageningen University. Soil Geography and Landscape Group; Holanda. International Soil Reference and Information Centre. World Soil Information; HolandaFil: Olmedo, Guillermo Federico. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; Argentina. FAO; ItaliaFil: Lavado, Raul Silvio. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de investigaciones en Biociencias Agrícolas y Ambientales; Argentin
Mapping soil carbon sequestration across Argentina and Mexico using roth C.
No full textPresentación en diapositivasInstituto de SuelosFil: Reinoso, Verónica. Secretaria de Agricultura y Desarrollo Rural, México.Fil: Frolla, Franco Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; ArgentinaFil: Ortiz, Sol. Secretaria de Agricultura y Desarrollo Rural; México.Fil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Cerón, Areli. Secretaria de Agricultura y Desarrollo Rural; México.Fil: Beltran, Marcelo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Bunge, Verónica. Secretaria de Agricultura y Desarrollo Rural; México.Fil: Peralta, Guillermo Ezequiel. Global Soil Partnership Secretariat - FAO Rome; ItaliaFil: Di Paolo, Luciano Elías. Global Soil Partnership Secretariat - FAO Rome; ItaliaFil: Rodríguez, Darío Martín. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Velázquez, Juan. Secretaria de Agricultura y Desarrollo Rural; México.Fil: Pascale Medina, Carla. Global Soil Partnership Secretariat - FAO Rome; ItaliaFil: Guevara, Mario. Universidad Autónoma de México. Centro de Geociencias, Méxic
Extrapolation of a structural equation model for digital soil mapping
No full textIn theory, two separate regions with the same soil-forming factors should develop similar soil conditions. This theoretical finding has been used in digital soil mapping (DSM) to extrapolate a model from one area to another, which usually does not work out well. One reason for failure could be that most of these studies used empirical methods. Structural equation modelling (SEM) is a semi-mechanistic technique, which can explicitly include expert knowledge. We therefore hypothesize that SEM models are more suitable for extrapolation than purely empirical models in DSM. The objective of this study was to investigate the extrapolation capability of SEM by comparing different model settings. We applied a SEM model from a previous study in Argentina to a similar soil-landscape in the Great Plains of the United States to predict clay, organic carbon, and cation exchange capacity for three major horizons: A, B, and C. We concluded that system relationships that were well supported by pedological knowledge showed consistent and equal behaviour in both study areas. In addition, a deeper understanding of indicators of soil-forming factors could strengthen conceptual models for extrapolating DSM models. We also found that for model extrapolation, knowledge-based links between system variables are more effective than data-driven links. In particular, model modifications can improve local prediction but harm the predictive power of extrapolation.Instituto de SuelosFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Luján; ArgentinaFil: Kempen, B. ISRIC — World Soil Information; HolandaFil: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; HolandaFil: Temme, Arnaud J.A.M. Kansas State University. Geography Department; Estados UnidosFil: Ransom, Michel D. Kansas State University. Department of Agronomy; Estados Unido
Updating a Physiography-Based Soil Map Using Digital Soil Mapping Techniques
No full textResearch work carried out in Entre-Rios province(Argentina) for mixed land use planning and management in relation to suitable soil conditions required high-resolution soil information at farm level. Basic information was provided by a 1:20000 scale soil map made using physiographic analysis with intensive aerial photo-interpretation of soil-landscape relationships and landscape-||oriented field survey. Continuous productivity - index (PI) classes were predicted from a nu8mber of environmental covariates, mostly DEM derivatives, using regression and geostatistical techniques.The PI land classification was used to adjust the soil-landscape/soil-series interpretation of the existing choropleth soil map by means of correlating discretePI values obtained from a conventional mapping procedure with cpntinuous PI values obtained by soil digital mapping procedures.Instituto de SuelosFil: Bedendo, Dante Julian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; ArgentinaFil: Schulz, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; ArgentinaFil: Olmedo, Guillermo Federico. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; ArgentinaFil: Rodríguez, Darío Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentin
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