Las respuestas políticas a la llamada cuestión mapuche en Argentina y Chile desde 1990

El objetivo del presente estudio es el de explicar las correspondencias políticas a los reclamos de las organizaciones mapuche por los estados argentino y chileno. Desde la esfera de la ciencia política, se aplicará el método comparativo para estudiar las décadas de 1990 y 2000 haciendo hincapié en las respuestas institucionales dadas a las demandas indígenas en aquellos países. Se intentará contestar las preguntas de ¿cuál es la “cuestión mapuche” y en qué se diferencia de sus propias demandas?; ¿cómo comprender las múltiples dimensiones de esta cuestión?; ¿cómo respondieron los estados mencionados a los reclamos de los indígenas? y ¿cómo se incorporan los mapuche al juego de las organizaciones internacionales desde los años 90

Diplodon CF. Colhuapiensis (Bivalvia, Hyriidae) in the Santa Cruz Formation (Early-Middle Miocene) at the Río Santa Cruz, Patagonia, Argentina. Stratigraphic and paleoenvironmental considerations

La Formación Santa Cruz (Mioceno Temprano–Medio), es una de las unidades sedimentarias más extendidas de la Patagonia argentina. Esta unidad contiene una gran abundancia y diversidad taxonómica de vertebrados fósiles, especialmente de mamíferos. De esta manera, la información paleoecológica y paleoambiental proviene principalmente del análisis de la asociación de vertebrados, como también la evidencia icnológica y paleobotánica. El registro de bivalvos de agua dulce asignados a la especie Diplodon cf. colhuapiensis Ihering, 1903 de la Formación Santa Cruz, recolectado en la localidad Barrancas Blancas (Estancia Santa Lucía), cerca del Río Santa Cruz, permite inferir las condiciones paleoambientales de la depositación de sedimentos de los niveles portadores de los especímenes. Teniendo en cuenta estos restos, proponemos que el material de Diplodon originalmente asignado al “piso Sehuenense” (piso sehuenense de F. Ameghino), podrían provenir del Mioceno Temprano–Medio de la Formación Santa Cruz. En este sentido, el registro de Diplodon cf. colhuapiensis confirma la existencia de una población establecida de moluscos de la familia Hyriidae en los niveles medio-altos de la Formación Santa Cruz. La presencia de bivalvos de agua dulce, probablemente habitando canales fluviales, sugiere la existencia de cursos de agua bien desarrollados en el ambiente depositacional de la unidad. La identificación del género en la Formación Santa Cruz valida su presencia en el Mioceno Temprano y extiende su distribución sur a la latitud actual del Río Santa Cruz (~ 50o S).The Santa Cruz Formation (Early-Middle Miocene) is one of the most widespread sedimentary units of the Argentine Patagonia. This unit contains an abundant and taxonomically diverse fossil vertebrate fauna, especially in mammals. Thus, the paleoecological and paleoenvironmental information derives mainly from the analysis of the vertebrate assemblages, as well as from the ichnological and paleobotanical evidence. The record of freshwater bivalves assigned to the species Diplodon cf. colhuapiensis Ihering, 1903 from the Santa Cruz Formation, collected in the locality of Barrancas Blancas (Estancia Santa Lucía), at Río Santa Cruz, allows us to infer the particular paleoenvironmental conditions setting during the deposition of the bearing levels. Considering this record, we propose that Diplodon, which was originally assigned to the “Sehuenense stage” (piso sehuenense of F. Ameghino), could have come from the Early-Middle Miocene of the Santa Cruz Formation. In this sense, the specimens referred to Diplodon cf. colhuapiensis suggest the existence of an established community of Hyriidae mollusks at the upper-middle levels of the Santa Cruz Formation. The presence of freshwater bivalves suggests that the depositional environment of this unit included the existence of water courses. The identification of the genus in the Santa Cruz Formation validates its presence in the Early Miocene and extends its southern distribution to the latitude of Río Santa Cruz (~ 50ºS).Fil: Pérez, Leandro Martín. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. División Paleozoología Invertebrados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Cuitiño, José Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Geología y Paleontología; ArgentinaFil: Varela, Augusto Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Geológicas. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Investigaciones Geológicas; ArgentinaFil: Muñoz, Nahuel Antu. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. División Paleontología Vertebrados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log–log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.Sociedad Argentina de Informática e Investigación Operativ

Diplodon cf. Colhuapiensis (bivalvia, hyriidae) en la Formación Santa Cruz (Mioceno Temprano–Medio) en el Río Santa Cruz, Patagonia, Argentina: consideraciones estratigráficas y paleoambientales

The Santa Cruz Formation (Early–Middle Miocene) is one of the most widespread sedimentary units of the Argentine Patagonia. This unit contains an abundant and taxonomically diverse fossil vertebrate fauna, especially in mammals. Thus, the paleoecological and paleoenvironmental information derives mainly from the analysis of the vertebrate assemblages, as well as from the ichnological and paleobotanical evidence. The record of freshwater bivalves assigned to the species Diplodon cf. colhuapiensis Ihering, 1903 from the Santa Cruz Formation, collected in the locality of Barrancas Blancas (Estancia Santa Lucía), at Río Santa Cruz, allows us to infer the particular paleoenvironmental conditions setting during the deposition of the bearing levels. Considering this record, we propose that Diplodon, which was originally assigned to the “Sehuenense stage” (piso sehuenense of F. Ameghino), could have come from the Early–Middle Miocene of the Santa Cruz Formation. In this sense, the specimens referred to Diplodon cf. colhuapiensis suggest the existence of an established community of Hyriidae mollusks at the upper-middle levels of the Santa Cruz Formation. The presence of freshwater bivalves suggests that the depositional environment of this unit included the existence of water courses. The identification of the genus in the Santa Cruz Formation validates its presence in the Early Miocene and extends its southern distribution to the latitude of Río Santa Cruz (~ 50º S).La Formación Santa Cruz (Mioceno Temprano–Medio), es una de las unidades sedimentarias más extendidas de la Patagonia argentina. Esta unidad contiene una gran abundancia y diversidad taxonómica de vertebrados fósiles, especialmente de mamíferos. De esta manera, la información paleoecológica y paleoambiental proviene principalmente del análisis de la asociación de vertebrados, como también la evidencia icnológica y paleobotánica. El registro de bivalvos de agua dulce asignados a la especie Diplodon cf. colhuapiensis Ihering, 1903 de la Formación Santa Cruz, recolectado en la localidad Barrancas Blancas (Estancia Santa Lucía), cerca del Río Santa Cruz, permite inferir las condiciones paleoambientales de la depositación de sedimentos de los niveles portadores de los especímenes. Teniendo en cuenta estos restos, proponemos que el material de Diplodon originalmente asignado al “piso Sehuenense” (piso sehuenense de F. Ameghino), podrían provenir del Mioceno Temprano–Medio de la Formación Santa Cruz. En este sentido, el registro de Diplodon cf. colhuapiensis confirma la existencia de una población establecida de moluscos de la familia Hyriidae en los niveles medio-altos de la Formación Santa Cruz. La presencia de bivalvos de agua dulce, probablemente habitando canales fluviales, sugiere la existencia de cursos de agua bien desarrollados en el ambiente depositacional de la unidad. La identificación del género en la Formación Santa Cruz valida su presencia en el Mioceno Temprano y extiende su distribución sur a la latitud actual del Río Santa Cruz (~ 50º S).Facultad de Ciencias Naturales y Muse

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

ROS-Scavenging Enzymes as an Antioxidant Response to High Concentration of Anthracene in the Liverwort <i>Marchantia polymorpha</i> L.

Marchantia polymorpha L. responds to environmental changes using a myriad set of physiological responses, some unique to the lineage related to the lack of a vascular- and root-system. This study investigates the physiological response of M. polymorpha to high doses of anthracene analysing the antioxidant enzymes and their relationship with the photosynthetic processes, as well as their transcriptomic response. We found an anthracene dose-dependent response reducing plant biomass and associated to an alteration of the ultrastructure of a 23.6% of chloroplasts. Despite a reduction in total thallus-chlorophyll of 31.6% of Chl a and 38.4% of Chl b, this was not accompanied by a significant change in the net photosynthesis rate and maximum quantum efficiency (Fv/Fm). However, we found an increase in the activity of main ROS-detoxifying enzymes of 34.09% of peroxidase and 692% of ascorbate peroxidase, supported at transcriptional level with the upregulation of ROS-related detoxifying responses. Finally, we found that M. polymorpha tolerated anthracene-stress under the lowest concentration used and can suffer physiological alterations under higher concentrations tested related to the accumulation of anthracene within plant tissues. Our results show that M. polymorpha under PAH stress condition activated two complementary physiological responses including the activation of antioxidant mechanisms and the accumulation of the pollutant within plant tissues to mitigate the damage to the photosynthetic apparatus

El proyecto Cinturón Verde y la implementación de políticas públicas para la generación de un periurbano sustentable en el Área Metropolitana de Rosario

El siguiente trabajo busca reconstruir la génesis del Proyecto Cinturón Verde Rosario (PCVR) y su situación actual en relación a un abordaje más integral de la problemática alimentaria para el Área Metropolitana de Rosario (AMR). El PCVR representa una política pública que busca promover y transformar la producción periurbana hacia formas más sostenibles, escalando hacia un abordaje metropolitano en la producción de alimentos. Representa, por ello, un excelente caso para indagar en las problemáticas sobre el diseño e implementación de políticas públicas que promueven sistemas agroalimentarios metropolitanos más sostenibles e inclusivos.Instituto de Prospectiva y Políticas PúblicasFil: Martínez, Lisandro Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Prospectiva y Políticas Públicas; ArgentinaFil: Costa, Mariano. Municipalidad de Rosario. Secretaría Economía Social; ArgentinaFil: Varela, Facundo.Municipalidad de Rosario. Secretaría Economía Social; ArgentinaFil: Porzio, Graciela. Municipalidad de Rosario. Secretaría Economía Social; ArgentinaFil: Mariatti, Agustín. Gobierno de Santa Fe. Programa Provincial Producción Sustentable de Alimentos en Periurbanos; ArgentinaFil: Battiston, Andrea. Municipalidad de Rosario. Secretaria Ambiente y Espacio Público. Subsecretaria Medio Ambiente; ArgentinaFil: Paz, Nicolás. Municipalidad de Rosario. Secretaría Salud, Instituto del Alimento; ArgentinaFil: Pérez Casella, Yanina. Municipalidad de Rosario. Secretaría Producción, Empleo e Innovación; ArgentinaFil: Budai, Natalia. Municipalidad de Rosario. Secretaría Producción, Empleo e Innovación; ArgentinaFil: Martínez, Nahuel. Municipalidad de Rosario. Secretaría Producción, Empleo e Innovación; ArgentinaFil: Terrile, Raúl. Municipalidad de Rosario. Secretaría Producción, Empleo e Innovación; Argentin

Forecasting maize yield at field scale based on high-resolution satellite imagery

Estimating maize (Zea mays L.) yields at the field level is of great interest to farmers, service dealers, and policy-makers. The main objectives of this study were to: i) provide guidelines on data selection for building yield forecasting models using Sentinel-2 imagery; ii) compare different statistical techniques and vegetation indices (VIs) during model building; and iii) perform spatial and temporal validation to see if empirical models could be applied to other regions or when models' coefficients should be updated. Data analysis was divided into four steps: i) data acquisition and preparation; ii) selection of training data; iii) building of forecasting models; and iv) spatial and temporal validation. Analysis was performed using yield data collected from 19 maize fields located in Brazil (2016 and 2017) and in the United States (2016), and normalized vegetation indices (NDVI, green NDVI and red edge NDVI) derived from Sentinel-2. Main outcomes from this study were: i) data selection impacted yield forecast model and fields with narrow yield variability and/or with skewed data distribution should be avoided; ii) models considering spatial correlation of residuals outperformed Ordinary least squares (OLS) regression; iii) red edge NDVI was most frequently retained into the model compared with the other VIs; and iv) model prediction power was more sensitive to yield data frequency distribution than to the geographical distance or years. Thus, this study provided guidelines to build more accurate maize yield forecasting models, but also established limitations for up-scaling, from farm-level to county, district, and state-scales.Publicado originalmente en: Rai A. Schwalbert, Telmo J.C. Amado, Luciana Nieto, Sebastian Varela, Geomar M. Corassa, Tiago A.N. Horbe, Charles W. Rice, Nahuel R. Peralta, Ignacio A. Ciampitti. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystem Engineering. 171: 179–192 DOI: https://doi.org/10.1016/j.biosystemseng.2018.04.020Sociedad Argentina de Informática e Investigación Operativ