Tierras elegibles para cultivos forestales según el protocolo de Kyoto en dos partidos de la provincia de Buenos Aires, Argentina

Este artículo informa sobre la disponibilidad de tierras para proyectos forestales en el marco del Protocolo de Kyoto (PK), en los partidos de Guaminí y Daireaux, SO de la provincia de Buenos Aires, Argentina. La información es de utilidad para la planificación y el desarrollo de proyectos forestales en el Mecanismo de Desarrollo Limpio (MDL) incluido en PK. Para la cuantificación se emplearon imágenes Landsat de los años 1988, 1989 y 2008 y CBERS-2B del año 2009, la base de datos e imágenes del Inventario Forestal Nacional y los registros de relevamientos de forestaciones realizadas a campo. Se compararon las coberturas de uso del suelo de las imágenes de los años 1989 y 2008. La superficie cubierta con bosque se clasificó como áreas no elegibles y la superficie restante como tierras elegibles que, a su vez, se diferenciaron en: 1) tierras elegibles sin restricciones por la presencia de suelos Udipsament típico y Hapludol éntico donde se obtienen crecimientos forestales promisorios y son áreas que no compiten con el uso agrícola y, 2) tierras elegibles con restricciones al uso forestal como consecuencia de la competencia por otros usos de la tierra o por limitaciones edáficas. Los resultados indican que los partidos de Guaminí y Daireaux poseen una superficie de tierras elegibles de 47.021 ha. La superficie elegible con restricciones es de 314.737 ha en Daireaux y 424.456 ha en Guaminí. Las tierras no elegibles alcanzan una superficie de 8.573 ha.This article reports the amount of land available that can be used only for forestry projects under the Kyoto Protocol (KP), Daireaux and Guaminí districts, Buenos Aires Province, Argentina. The information is valuable to potential investors or public or private operators interested in promoting the development of forestry projects in the Clean Development Mechanism (CDM) PK. We used Landsat 1988, 1989, 2008 and CBERS-2B 2009, the database and images of national forest inventory, surveys of field tree plantations. We compared the coverage of land use on images of 1989 and 2008. Surfaces covered with forests were characterized as non-eligible areas. The remaining area was classified as eligible land. Eligible lands are divided into two subclasses: 1) Eligible land without restriction by the presence of soils Udipsament típico y Hapludol éntico, where growth forest are promising and are areas that do not compete with agricultural use, and 2) Eligible land with forest use restrictions as a result of competition by other land uses or edaphic restrictions. The results indicate that Daireaux and Guaminí Districts have an elegible of 47,021 ha. The eligible areas with restrictions are 314.737 ha in Daireaux and 424.456 ha in Guaminí. The non-eligible area is 8.573 ha.Inst.de SuelosFil: Lupi, Ana Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Ferrere, Paula. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Agencia de Extensión Rural 9 de julio; Argentin

Génesis de suelos en un sector del piedemonte aluvial del Chaco salteño

Se estudia la relación suelo-paisaje, las propiedades físico-químicas y la composición mineralógica de diferentes suelos dentro del cono aluvial del río Del Valle, en el borde occidental del Chaco salteño, en la provincia de Salta, Argentina. Se identificaron seis grupos de suelos con características bien definidas. Los Ustipsamentes y Haplustoles de textura gruesa a media se encuentran principalmente en cordones arenosos, vías de drenaje y planicies del sector proximal del cono; los Argiustoles y Haplustalfes de texturas medias se desarrollan mayormente en las planicies estabilizadas del sector intermedio; mientras que los Haplustertes de textura fina son característicos de los ambientes de bañados del sector intermedio y distal. Los análisis mineralógicos se llevaron a cabo mediante difracción de rayos X (DRX) y medición de la Susceptibilidad Magnética (SM). La DRX del suelo total mostró variaciones en la proporción de cuarzo, feldespatos, muscovita y minerales accesorios entre los perfiles, reflejando la heterogeneidad de sus materiales parentales. La composición mineralógica de la fracción arcilla permitió diferenciar dos tipos de materiales originarios, posiblemente relacionados con distintas áreas de aporte: I) uno rico en esmectitas e illitas donde se desarrollan el Ustipsament, los Haplustoles y el Haplustert; y II) otro con menor proporción de minerales expansibles y dominancia de illita, correspondiente a los horizontes C del Argiustol y el Haplustalf. Por otra parte, las curvas de Susceptibilidad Magnética muestran tendencias opuestas del Vertisol respecto al Argiustol, el Haplustalf y el Haplustol típico, reflejando condiciones físico-químicas diferentes entre estos suelos, mientras que en el Entisol y los Haplustoles énticos, las curvas varían irregularmente de acuerdo con la heterogeneidad de las capas sedimentarias que los conforman. El presente trabajo resume nuevos resultados e interpretaciones acerca de la génesis, composición y distribución de los suelos para una región donde esta información es todavía escasa.The aim of this study was to evaluate the soil-landscape relationships and the physico-chemical and mineralogical composition of different soils within the alluvial fan of Del Valle river, in the western part of the Chaco region in the Salta Province, Argentina. Six groups of soils with distinct characteristics were identified. Medium to coarse-textured Ustipsamments and Haplustolls were found on elongated gently convex sandy accumulations, drainage networks and plains of the proximal section of the alluvial fan. The medium-textured Argiustolls and Haplustalfs are mainly developed on stabilized plains in the intermediate section, while fine-textured Haplusterts characterize swamp environments between intermediate and distal sections of the fan. Mineralogical analyses were performed by X-ray diffractometry (XRD) and Magnetic Susceptibility (MS) measurements. The XRD on total soils samples showed variations in the proportion of quartz, feldspars, muscovite and accessory minerals among the profiles, reflecting the heterogeneity of their parent materials. Moreover, according to the mineralogical composition of the clay fraction, two types of parent materials, possibly related to different source areas, were distinguished: I) one containing high proportions of smectite and illite, where Ustipsamments, Haplustolls and Haplusterts are developed; and II) another one with a lower proportion of expansive minerals and dominance of illite, corresponding to the C horizons of Argiustolls and Haplustalfs. The Magnetic Susceptibility of the Vertisol showed an opposite trend to that of the Argiustoll, Haplustalf and Typic Haplustoll, reflecting different physical-chemical conditions between those soil types, while the MS of the Entisol and Entic Haplustolls varied irregularly with depth in accordance to the heterogeneity of their sedimentary layers. This study summarizes new results and interpretations about the origin, composition and distribution of soils in a region where this information is still scarce.Fil: Moretti, Lucas Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Rodriguez, Dario Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Morras, Hector. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentin

Soil compaction response to wheel traffic in the Rolling Pampas region of Argentina

The present work shows the effects of the different agricultural wheels traffic on the physical properties of a typical Argiudol soil worked under a no-tillage cropping system. Soil compaction produced by traffic was quantified through a series of parameters. These parameters were: a) cone index, b) rut depth and c) soil water content at the traffic moment. A grain chaser, a sprayer, a combine harvester and a tractor equipped with commonly used wheels were tested in the study area. The main results obtained showed that the tyres with the highest inflation pressure and tyre ground pressures produced the highest values of cone index and rut depth. A typical Argiudol soilis not able to constrain topsoil and subsoil compaction when wheeled by tyres with ground pressures greater than 77.6 kPa. This occurs when this soil is worked under a continuous no-tillage cropping system.En el presente trabajo se muestran los efectos del tránsito de diferentes ruedas agrícolas sobre las propiedades físicas de un suelo Argiudol Típico trabajado bajo el sistema de no-labranza. La compactación del suelo producida por el tráfico se cuantificó a través de los parámetros: a) índice de cono, b) profundidad de huella y c) contenido de agua del suelo al momento del tránsito. Se ensayaron carro de granos, cosechadora, pulverizadora y tractor equipados con rodados de uso generalizado en la zona productiva bajo estudio. Los principales resultados obtenidos demostraron que los neumáticos con mayor presión de inflado y presión en el área de contacto rueda/suelo produjeron los mayores valores de índice de cono y profundidad de huella. El suelo Argiudol típico trabajado en forma continua bajo no-labranza no puede limitar la compactación superficial y subsuperficial del suelo cuando es transitado por ruedas con presiones en el área de contacto rueda/suelo mayores a 77.6 kPa.Fil: Contessotto, Enrique Ernesto. Universidad Nacional de Luján; ArgentinaFil: Botta, Guido Fernando. Universidad Nacional de Luján; Argentina.Fil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Luján; ArgentinaFil: Bienvenido, Fernando. Universidad de Almería; EspañaFil: Rivero, David. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina.Fil: Pelizzari, Federico Matías. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina.Fil: Ghelfi, Diego Gabriel. Universidad Nacional de Luján; ArgentinaFil: Nistal, Ayelén Ileana. Universidad Nacional de Luján; Argentin

SISINTAR: Uin paquete para gestionar datos de perfiles de suelo de Argentina

Presentación 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

Argentina: Soil Organic Carbon Sequestration Potential National Map. National Report. Version 1.0. Year: 2021

Soil organic carbon (SOC) is a key factor affecting soil physical fertility, as it improves several soil properties such as infiltration, structural stability, porosity, aeration and structure. It also improves soil chemical fertility since C is part of the soil organic matter, which constitutes the main reservoir of nutrients for crops (nitrogen, sulfur, zinc, among others). SOC is positively correlated with soil microbial biomass that acts on nutrient cycling and metabolization processes of toxic molecules. The total SOC stock in topsoil (0-30cm) is about 19.7 Pg C (FAO-ITPS GSOC map, 2018). Thus, due to the size of the soil carbon pool, even small increments in the net soil C storage may represent a substantial C sink potential. Although agricultural greenhouse gas emissions (GHGs) contribute to an important share of Argentina GHG emissions (135.53 MtCO2eq, 37% of total country GHG emissions; SAyDS, 2019), increasing ASOC stocks through judicious land use and sustainable soil management (SSM) practices may represent an important strategy to reduce and mitigate GHG emissions. In Argentina, the total productive area is about 157 million hectares (INDEC, 2021). Agricultural área (croplands) is about 40 (forty) million hectares, predominantly under no tillage system (91% agricultural area; AAPRESID, 2020). Soybean is the main product (45 million tons in 17 million hectares), followed by corn (44 million tons in 6.3 million hectares), wheat (17 million tons in 6.5 million hectares), barley (4.1 million tons in 0.1 million hectares) and sunflower (2.7 million tons in 1.3 million hectares).The rest of the area (over 124 Million hectares) is occupied with grasslands and shrublands dedicated to livestock production, and other agricultural uses. In the last decade’s agricultural land increased and SOC content decayed. This process of land use change was explained by increasing soybean monoculture and displacing livestock area, reducing SOC content (Lavado & Taboada, 2009). There has been an intense expansion of agriculture at the expense of grasslands, native forests and other natural resources in semiarid, sub-humid and subtropical regions of the country (Volante et al., 2012). Currently, soils of the Chaco-Pampean region exhibit SOC levels between 40-70% of the contents of virgin soils (Alvarez & Steinbach, 2009; Sainz Rozas et al., 2011; Milesi Delaye et al., 2013). Several farming practices may be used to restore or diminish the SOC loss, reduce soil erosion, sequester atmospheric carbon dioxide (CO2 ) and improve the soil quality (Poffenbarger et al., 2020). Among these practices, the inclusion of cover crops (CC) during winter has been postulated as one of the most promising activities (Ruis & Blanco-Canqui, 2017). The inclusion of CC showed average SOC sequestration rates of 0.45 tC/ha/yr (± 0.03), in Argentina (Alvarez et al., 2017; Beltran et al., 2018; Romaniuk et al., 2018). Increasing nutrient availability, crop growth and residue returns by increasing fertilizer use showed an increment of SOC around 0.18 tC/ha/yr (± 0.03) (Duval et al., 2020; Restovich et al., 2019). The inclusion of cycles with perennial pastures in crop rotations showed average SOC sequestration rates of 0.76 tC/ha/yr (± 0.03), exhibiting the greatest potential to increase SOC stocks (Costantini et al., 2016; Gil et al., 2016). Sustainable soil management (SSM) practices (FAO, 2020) such as the above mentioned practices have demonstrated potential to increase SOC stocks in different agricultural systems in Argentina, and thus sequester atmospheric CO2 as SOC to mitigate GHG emissions. However, SOC sequestration from these practices show highly variable sequestration rates, depending on edapho-climatic conditions, land use and management, among other factors. It is therefore relevant to identify which regions, soils, climates and systems have a greater potential to increase SOC stocks, in order to establish priorities for research and implementation of private and public policies. In this Soil organic carbon (SOC) is a key factor affecting soil physical fertility, as it improves several soil properties such as infiltration, structural stability, porosity, aeration and structure. It also improves soil chemical fertility since C is part of the soil organic matter, which constitutes the main reservoir of nutrients for crops (nitrogen, sulfur, zinc, among others). SOC is positively correlated with soil microbial biomass that acts on nutrient cycling and metabolization processes of toxic molecules. The total SOC stock in topsoil (0-30cm) is about 19.7 Pg C (FAO-ITPS GSOC map, 2018). Thus, due to the size of the soil carbon pool, even small increments in the net soil C storage may represent a substantial C sink potential. Although agricultural greenhouse gas emissions (GHGs) contribute to an important share of Argentina GHG emissions (135.53 MtCO2eq, 37% of total country GHG emissions; SAyDS, 2019), increasing ASOC stocks through judicious land use and sustainable soil management (SSM) practices may represent an important strategy to reduce and mitigate GHG emissions. In Argentina, the total productive area is about 157 million hectares (INDEC, 2021). Agricultural área (croplands) is about 40 (forty) million hectares, predominantly under no tillage system (91% agricultural area; AAPRESID, 2020). Soybean is the main product (45 million tons in 17 million hectares), followed by corn (44 million tons in 6.3 million hectares), wheat (17 million tons in 6.5 million hectares), barley (4.1 million tons in 0.1 million hectares) and sunflower (2.7 million tons in 1.3 million hectares).The rest of the area (over 124 Million hectares) is occupied with grasslands and shrublands dedicated to livestock production, and other agricultural uses. In the last decade’s agricultural land increased and SOC content decayed. This process of land use change was explained by increasing soybean monoculture and displacing livestock area, reducing SOC content (Lavado & Taboada, 2009). There has been an intense expansion of agriculture at the expense of grasslands, native forests and other natural resources in semiarid, sub-humid and subtropical regions of the country (Volante et al., 2012). Currently, soils of the Chaco-Pampean region exhibit SOC levels between 40-70% of the contents of virgin soils (Alvarez & Steinbach, 2009; Sainz Rozas et al., 2011; Milesi Delaye et al., 2013). Several farming practices may be used to restore or diminish the SOC loss, reduce soil erosion, sequester atmospheric carbon dioxide (CO2) and improve the soil quality (Poffenbarger et al., 2020). Among these practices, the inclusion of cover crops (CC) during winter has been postulated as one of the most promising activities (Ruis & Blanco-Canqui, 2017). The inclusion of CC showed average SOC sequestration rates of 0.45 tC/ha/yr (± 0.03), in Argentina (Alvarez et al., 2017; Beltran et al., 2018; Romaniuk et al., 2018). Increasing nutrient availability, crop growth and residue returns by increasing fertilizer use showed an increment of SOC around 0.18 tC/ha/yr (± 0.03) (Duval et al., 2020; Restovich et al., 2019). The inclusion of cycles with perennial pastures in crop rotations showed average SOC sequestration rates of 0.76 tC/ha/yr (± 0.03), exhibiting the greatest potential to increase SOC stocks (Costantini et al., 2016; Gil et al., 2016). Sustainable soil management (SSM) practices (FAO, 2020) such as the above mentioned practices have demonstrated potential to increase SOC stocks in different agricultural systems in Argentina, and thus sequester atmospheric CO2 as SOC to mitigate GHG emissions. However, SOC sequestration from these practices show highly variable sequestration rates, depending on edapho-climatic conditions, land use and management, among other factors. It is therefore relevant to identify which regions, soils, climates and systems have a greater potential to increase SOC stocks, in order to establish priorities for research and implementation of private and public policies.Fil: Frolla, Franco Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; 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: Beltran, Marcelo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Peralta, Guillermo Ezequiel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Di Paolo, Luciano E. Global Soil Partnership Secretariat - FAO; ItaliaFil: Rodriguez, Dario Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Pascale Medina, Carla. Food and Agriculture Organization (FAO). Alianza Sudamericana de Suelos; Argentin

Machine learning in space and time for modelling soil organic carbon change

Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data-driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36-year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and static, whereas SOC is dynamic and SOC dynamics are of particular interest to carbon sequestration and land degradation studies. Thus, there is a clear need to extend spatial SOC mapping to space–time SOC mapping. temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m−2 and a root mean squared error of 2.04 kg C m−2. In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large.Fil: Heuvelink, Gerard B.M. ISRIC - World soil information; Holanda. Wageningen University. Soil Geography and Landscape Group; HolandaFil: Angelici, Marcos E. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Poggio, Laura ISRIC - World soil information, Wageningen; HolandaFil: Bai, Zhanguo ISRIC - World soil information, Wageningen, The NetherlandsFil: Batjes, Niels H. ISRIC - World soil information, Wageningen, The NetherlandsFil: an den Bosch, Rik ISRIC - World soil information, Wageningen, The NetherlandsFil: Bossio, Deborah The Nature Conservancy; Estados UnidosFil: Estella, Sergio Vizzuality; EspañaFil: Lehmann, Jhoannes. Cornell University. Soil and Crop Sciences; Estados UnidosFil: Olmedo, Guillermo F. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; ArgentinaFil: Sandermann, Jonathan. Woods Hole Research Center; Estados Unido

Improving Latin American soil information database for digital soil mapping enhances its usability and scalability

Spatial soil databases can help model complex phenomena in which soils are decisive, for example, evaluating agricultural potential or estimating carbon storage capacity. The Soil Information System for Latin America and the Caribbean, SISLAC, is a regional initiative promoted by the FAO's South American Soil Partnership to contribute to the sustainable management of soil. SISLAC includes data coming from 49,084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. However, some problems hinder its usages, such as the quality of the data and its high dimensionality. The objective of this research is twofold. First, to evaluate the quality of SISLAC and its data values and generate a new, improved version that meets the minimum quality requirements to be used by different interests or practical applications. Second, to demonstrate the potential of improved soil profile databases to generate more accurate information on soil properties, by conducting a case study to estimate the spatial variability of the percentage of soil organic carbon using 192 profiles in a 1473 km2 region located in the department of Valle del Cauca, Colombia. The findings show that 15 percent of the existing soil profiles had an inaccurate description of the diagnostic horizons. Further correction of an 4.5 additional percent of existing inconsistencies improved overall data quality. The improved database consists of 41,691 profiles and is available for public use at ttps://doi.org/10.5281/zenodo.6540710 (Díaz-Guadarrama, S. & Guevara, M., 2022). The updated profiles were segmented using algorithms for quantitative pedology to estimate the spatial variability. We generated segments one centimeter thick along with each soil profile data, then the values of these segments were adjusted using a spline-type function to enhance vertical continuity and reliability. Vertical variability was estimated up to 150 cm in-depth, while ordinary kriging predicts horizontal variability at three depth intervals, 0 to 5, 5 to 15, and 15 to 30 cm, at 250 m-spatial resolution, following the standards of the GlobalSoilMap project. Finally, the leave-one-out cross validation provides information for evaluating the kriging model performance, obtaining values for the RMSE index between 1.77% and 1.79% and the R2 index greater than 0.5. The results show the usability of SISLAC database to generate spatial information on soil properties and suggest further efforts to collect a more significant amount of data to guide sustainable soil management.Fil: Diaz Guadamarra, Sergio. Universidad Nacional de Colombia. Facultad de Ciencias Agrarias. Departamento de Agronomía; ColombiaFil: Lizarazo, Iván. Universidad Nacional de Colombia. Facultad de Ciencias Agrarias. Departamento de Agronomía; ColombiaFil: Guevara, Mario. Universidad Nacional Autónoma de México. Campus Juriquilla. Centro de Geociencias; MéxicoFil: Guevara, Mario. Universidad Nacional Autónoma de México.Campus Juriquilla. Centro de Geociencias; México. United States Department of Agriculture. Soil Salinity National Laboratory, Estados UnidosFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Wageningen University. Soil Geography and Landscape Group; Países Bajos. International Soil Reference and Information Centre. World Soil Information; Países BajosFil: Araujo Carrillo, Gustavo A. Corporación Colombiana de Investigación Agropecuaria AGROSAVIA; ColombiaFil: Argeñal, Jainer. Universidad Nacional Autónoma de Honduras. Facultad de Ciencias; Honduras.Fil: Armas, Daphne. Universidad de Almería. Departamento de Agronomía, Edif. CITEIIB, España.Fil: Balsa, Rafael A. Ministerio de Desarrollo Agrario y Riego. Dirección General de Asuntos Ambientales Agrarios, Perú.Fil: Bolivar, Adriana. Instituto Geográfico Agustín Codazzi. Subdirección Agrología; ColombiaFil: Bustamante, Nelson. Servicio Agrícola y Ganadero; Chile.Fil: Dart, Ricardo O. Embrapa Solos; BrasilFil: Dell Acqua, Martín. Ministerio de Ganadería, Agricultura y Pesca. Dirección General de Recursos Naturales; UruguayFil: Lencina, Arnulfo. Universidad Nacional de Asunción. Facultad de Ciencias Agrarias; ParaguayFil: Figueredo, Hernán. Sociedad Boliviana de la Ciencia del Suelo; Bolivia.Fil: Fontes, Fernando. Ministerio de Ganadería, Agricultura y Pesca. Dirección General de Recursos Naturales; UruguayFil: Gutierrez Diaz, Joan S. Aarhus University. Faculty of Science and Technology,.Department of Agroecology; DinamarcaFil: Jiménez, Wilmer. Ministerio de Agricultura y Ganadería; Ecuador.Fil: Rodriguez, Dario Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Tenti Vuegen, Leonardo Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentin

Red Nacional de reconocedores de suelos.

Los relevamientos sistemáticos de suelos en Argentina comenzaron en la década de 1960, en el marco del Plan Mapa de Suelos. Dicho plan, desarrollado y liderado por el INTA, dio impulso a la formación de especialistas y a la producción de cartografía de suelos a diferentes escalas. Sin embargo, a partir del año 2000 las actividades se redujeron notablemente y gran parte de los equipos provinciales formados hasta ese momento se desarticularon. Desde entonces los relevamientos continuaron de manera aislada sólo en aquellas provincias donde se mantuvieron los grupos de trabajo. Este hecho condujo a que actualmente diferentes regiones del país no cuenten con información acerca de las propiedades y distribución de suelos a una escala adecuada para la toma de decisiones. En este contexto, en el 2018 se crea la Red Nacional de Reconocedores de Suelos (RNRS) que organiza las capacidades técnicas y operativas a nivel nacional para dar pronta respuesta a la creciente demanda de cartografía. Se trata de un equipo interinstitucional e interdisciplinario de especialistas distribuidos por todo el país, que realiza tareas de relevamiento, produce y difunde cartografía básica y utilitaria de suelos, ofrece capacitación y genera espacios de discusión y actualización metodológica. A la fecha, la RNRS ha relevado aproximadamente 760.000 ha en el sur de Córdoba, estimando completar durante el presente año el relevamiento del departamento Río Cuarto. Esta estrategia organizacional permitirá avanzar en el mapeo semidetallado de suelos en nuestro país, estableciendo vinculaciones sinérgicas entre profesionales de diferentes instituciones a fin de fortalecer y potenciar los equipos de trabajo en cada región. El motivo de esta contribución es presentar la RNRS, sus objetivos, avances a la fecha y desafíos a futuro, haciendo una breve revisión del estado actual de los relevamientos a escala semidetallada en nuestro país.Fil: Moretti, Lucas M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Rodriguez, Darío M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Altamirano D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Amin, S. Universidad Nacional de Río Cuarto; 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: Babelis, German Claudio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; ArgentinaFil: Becerra, Alejandra Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Bedendo, Dante Julian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; ArgentinaFil: Boldrini, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; AgentinaFil: Bongiovanni, C. Universidad Nacional de Río Cuarto; ArgentinaFil: Bozzer, S. Universidad Nacional de Río Cuarto; ArgentinaFil: Cabrera, A. Universidad Nacional de Río Cuarto; ArgentinaFil: Canale, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; AgentinaFil: Chilano, Y. Universidad Nacional de Río Cuarto; ArgentinaFil: Cholaky, Carmen. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Cisneros; José Manuel. Universidad Nacional de Río Cuarto. Cátedra de Uso y Manejo de Suelos; ArgentinaFil: Colazo, Juan Cruz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; ArgentinaFil: Corigliano, J. Universidad Nacional de Río Cuarto; ArgentinaFil: Degioanni, Américo José. Universidad Nacional Río Cuarto. Facultad de Agronomía y Veterinaria. Departamento de Ecología Agraria; ArgentinaFil: de la Fuente, Juan Carlos Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Escobar, Dardo. Ministerio de Agricultura, Ganadería y Pesca; ArgentinaFil: Faule, L. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Córdoba. ArgentinaFil: Galarza, Carlos Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: González, J. Universidad Nacional de Río Cuarto; ArgentinaFil: Holzmann, R. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Alto Valle; ArgentinaFil: Irigoin, Julieta. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Luján. Departamento Tecnología; ArgentinaFil: Lanfranco, M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: León Giacosa, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Matteio, J.P. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Márquez, C. Gobierno de Córdoba. Ministerio de Agricultura y Ganadería; ArgentinaFil: Marzari, R. Universidad Nacional de Río Cuarto; ArgentinaFil: Mattalia, M.L. Universidad Nacional de Río Cuarto; ArgentinaFil: Morales Poclava, P.C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Muñoz, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: Paladino, Ileana Ruth. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Lomas de Zamora. Facultad de Ciencias Agrarias; ArgentinaFil: Parra, B. Universidad Nacional de Río Cuarto; ArgentinaFil: Pérez, M. Gobierno de Córdoba. Ministerio de Agricultura y Ganadería; ArgentinaFil: Pezzola, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Perucca, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; ArgentinaFil: Porcel de Peralta, R. Gobierno de Córdoba. Ministerio de Agricultura y Ganadería; ArgentinaFil: Renaudeau, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Salustio, M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; ArgentinaFil: Sapino, V. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Tenti Vuegen, L.M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos. ArgentinaFil: Tosolini, R. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Vicondo, M.E. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina. Universidad Nacional de Córdoba. ArgentinaFil: Vizgarra, L.A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Quimili; ArgentinaFil: Ybarra, D.D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Winschel, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Zamora, E. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentin

A multivariate approach for mapping a soil quality index and its uncertainty in southern France

Pedometricians 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. University of Montpellier, LISAH. INRAE, IRD, Montpellier SupAgro, FranciaFIL: 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; Franci

Mapping the soils of an Argentine Pampas region using structural equation modeling

Current digital soil mapping (DSM) methods have limitations. For instance, it is difficult to predict a large number of soil properties simultaneously, while preserving the relationships between them. Another problem is that prevalent prediction models use pedological knowledge in a very crude way only. To tackle these problems, we investigated the use of structural equation modelling (SEM). SEM has its roots in the social sciences and is recently also being used in other scientific disciplines, such as ecology. SEM integrates empirical information with mechanistic knowledge by deriving the model equations from known causal relationships, while estimating the model parameters using the available data. It distinguishes between endogenous and exogenous variables, where, in our application, the first are soil properties and the latter are external soil forming factors (i.e. climate, relief, organisms). We introduce SEM theory and present a case study in which we applied SEM to a 22,900 km2 region in the Argentinian Pampas to map seven key soil properties. In this case study, we started with identifying the main soil forming processes in the study area and assigned for each process the main soil properties affected. Based on this analysis we defined a conceptual soil-landscape model, which was subsequently converted to a SEM graphical model. Finally, we derived the SEM equations and implemented these in the statistical software R using the latent variable analysis (lavaan) package. The model was calibrated using a soil dataset of 320 soil profile data and 12 environmental covariate layers. The outcomes of the model were maps of seven soil properties and a SEM graph that shows the strength of the relationships. Although the accuracy of the maps, based on cross-validation and independent validation, was poor, this paper demonstrates that SEM can be used to explicitly include pedological knowledge in prediction of soil properties and modelling of their interrelationships. It bridges the gap between empirical and mechanistic methods for soil-landscape modelling, and is a tool that can help produce pedologically sound soil maps.Inst.de SuelosFil: Angelini, Marcos Esteban. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC-World Soil Information; Holanda. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Hauvelink, Gerard B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC-World Soil Information; HolandaFil: Morras, Hector. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Kempen, Bas. ISRIC-World Soil Information; Holand