Caracterización de rotaciones de cultivos y su impacto en el rendimiento y funcionamiento de sistemas agrícolas

Tesis presentada para optar al título de Doctor en Área Ciencias Agropecuarias, de la Universidad de Buenos Aires, en 2023El análisis de las rotaciones de cultivos a escala regional requiere información histórica a nivel de lote que considere las diferentes condiciones presentes en el área. Esta información ha sido de escasa y dispersa en Argentina durante los últimos años, a pesar de existir herramientas que permitían obtenerla. Es posible a partir de sensores remotos la identificación de los cultivos sembrados en un área y la estimación del crecimiento y rendimiento. El objetivo de esta tesis fue la caracterización de las secuencias de cultivos a lo largo de una serie de campañas agrícolas y la evaluación de su impacto a nivel productivo y ambiental. Se generaron mapas de cultivos a lo largo de siete campañas agrícolas en un área piloto de la Pampa Ondulada donde se analizó la ocurrencia de casos de monocultivo y rotación en relación a variables humanas y ambientales. Se desarrolló un modelo de estimación de biomasa en soja como indicador del estado y rendimiento del cultivo. Se evaluó el efecto de diferentes secuencias de cultivos en la producción de biomasa de lotes de soja y en el carbono orgánico del suelo. A nivel nacional se analizaron las secuencias de cultivos a lo largo de tres campañas. Los casos de monocultivo estuvieron asociados a parcelas catastrales pequeñas comúnmente asignadas a arrendamiento y se agruparon en cercanías a centros de acopio para transporte e industrialización. Se observó un efecto positivo y significativo en biomasa de soja para índices relacionados con el número de períodos con rotación y con la proporción de gramíneas, mientras que se identificaron efectos negativos y significativos con la proporción de soja de primera y con la intensidad de siembra. El análisis espacial de secuencias de cultivos permite identificar controles de su distribución y proponer estrategias que favorezcan la implementación de buenas prácticas agrícolas.Crop rotation analysis at regional level requires the availability of information about planting history at field level that considers the different conditions in the area. This kind of information has been scarse and dispersed in Argentina during last years despite the availability of tools to generate it. Through remote sensing it is possible to identify crops planted in an area and the estimation of crop growing and yield. The objective of this thesis was to characterize crop sequences along a serie of growing seasons and to evaluate its impact in productivity and environment. Seven crop type maps were generated along consecutive growing seasons in a pilot site in Pampa Ondulada where the occurrence of monoculture and rotation was analyzed in relation to human and environmental variables. In addition, a model for the estimation of soybean biomass as a proxy of crop condition and yield was developed. The effect of different crop sequences was evaluated in relation to soybean biomass production and soil organic carbon. At national level, crop sequences were analyzed along three growing seasons. The occurrence of monoculture was associated to small cadastral units were positive and significant effect on soybean biomass was observed for indices related to the number of rotation periods and the proportion of cereals; while negative and significant effects were found for the number of periods with monoculture, the total number of periods with early soybean and with planting intensity. Spatial analysis of crop sequences allows to identify controls of its occurrence and to develop tools that promote the implementation of good agricultural practices.Fil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentin

Spatially variable hidrologic impact and biomass production tradeoffs associated with Eucaliptus ( E. Grandis) cultivation for biofuel production in Entre Ríos, Argentina

Climate change and energy security promote using renewable sources of energy such as biofuels. High woody biomass production achieved from short-rotation intensive plantations is a strategy that is increasing in many parts of the world. However, broad expansion of bioenergy feedstock production may have significant environmental consequences. This study investigates the watershed-scale hydrological impacts of Eucalyptus (E. grandis) plantations for energy production in a humid subtropical watershed in Entre Rios province, Argentina. A Soil and Water Assessment Tool (SWAT) model was calibrated and validated for streamflow, leaf area index (LAI), and biomass production cycles. The model was used to simulate various Eucalyptus plantation scenarios that followed physically based rules for land use conversion (in various extents and locations in the watershed) to study hydrological effects, biomass production, and the green water footprint of energy production. SWAT simulations indicated that the most limiting factor for plant growth was shallow soils causing sea sonal water stress. This resulted in a wide range of biomass productivity throughout the watershed. An optimization algorithm was developed to find the best location for Eucalyptus development regarding highest productivity with least water impact. E. grandis plantations had higher evapotranspiration rates compared to existing terres trial land cover classes; therefore, intensive land use conversion to E. grandis caused a decline in streamflow, with January through March being the most affected months. October was the least-affected month hydrologically, since high rainfall rates over came the canopy interception and higher ET rates of E. grandis in this month. Results indicate that, on average, producing 1 kg of biomass in this region uses 0.8 m3 of water, and the green water footprint of producing 1 m3 fuel is approximately 2150 m3 water, or 57 m3 water per GJ of energy, which is lower than reported values for wood based ethanol, sugar cane ethanol, and soybean biodiesel.Fil: Heidari, Azad. Michigan Technological University. Department of Civil and Environmental Engineering; Estados UnidosFil: Watkins Jr, David. Michigan Technological University. Department of Civil and Environmental Engineering; Estados UnidosFil: Mayer, Alex. Michigan Technological University. Department of Civil and Environmental Engineering; Estados UnidosFil: Propato, Tamara Sofía. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Verón, Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentin

Recent land use and land cover change dinamics in the Gran Chaco Americano

Land transformation is one of the most significant human changes on the Earth’s surface processes. Therefore, land use land cover time series are a key input for environmental monitoring, natural resources management, territorial planning enforcement at national scale. We here capitalize from the MapBiomas initiative to characterize land use land cover (LULC) change in the Gran Chaco between 2010 and 2017. Specifically we sought to a) quantify annual changes in the main LULC classes; b) identify the main LULC transitions and c) relate these transitions to current land use policies. Within the MapBiomas project, Landsat based annual maps depicting natural woody vegetation, natural herbaceous vegetation, dispersed natural vegetation, cropland, pastures, bare areas and water. We used Random Forest machine learning algorithms trained by samples produced by visual interpretation of high resolution images. Annual overall accuracy ranged from 0,73 to 0,74. Our results showed that, between 2010 and 2017, agriculture and pasture lands increased ca. 3.7 Mha while natural forestry decreased by 2.3 Mha. Transitions from forests to agriculture accounted for 1.14% of the overall deforestation while 86% was associated to pastures and natural herbaceous vegetation. In Argentina, forest loss occurred primarily (39%) on areas non considered by the territorial planning Law, followed by medium (33%), high (19%) and low (9%) conservation priority classes. These results illustrate the potential contribution of remote sensing to characterize complex human environmental interactions occurring over extended areas and timeframes.Fil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: de Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, S.R. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Arevalos, F. Asociación Guyra Paraguay; ParaguayFil: Volante, J.N. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Mosciaro, M.J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentin

Detección de outliers en muestras de entrenamiento generadas mediante interpretación visual

Las clasificaciones supervisadas son procesos extremadamente sensibles a la calidad de las muestras utilizadas. La presencia de outliers en las muestras de entrenamiento suele ser una fuente de error muy frecuente. El objetivo de este trabajo es presentar una metodología de detección de outliers con Isolation Forest, en muestras recolectadas mediante interpretación visual de imágenes satelitales generadas por el Proyecto MapBiomas Pampa Trinacional. Isolation Forest, el algoritmo no supervisado utilizado puede detectar anomalías directamente basándose en el concepto de aislamiento sin utilizar ninguna métrica. La metodología consiste en la identificación de outliers (preparación de muestras, modelado y definición del umbral) y la validación del método. El modelado permite etiquetar de manera automática cada muestra como outlier o normal a partir del score. Se logró verificar los píxeles de la muestra señalada como outlier y tipificar el error en 6 categorías. Los resultados muestran una cantidad decreciente de outliers a lo largo del periodo analizado. Los años con mayor cantidad de outliers tienen una correspondencia con los años de menor disponibilidad de imágenes para la construcción de los mosaicos y contribuciones importantes del tipo Error del Mosaico. La clase con mayor porcentaje de error fue Bosque cerrado (14.7%) y los tipos de errores con mayor proporción fueron Clase Mal Asignada (20.39%) y Borde (19.57%). La metodología propuesta permitió el mejoramiento de muestras obtenidas mediante interpretación visual de imágenes satelitales de manera automática con un 80% de acierto.Sociedad Argentina de Informática e Investigación Operativ

Which pixel is a forest? Tree crown delineation using VHR images to estimate tree cover in landsat based classification

Determining the percentage of tree crown cover is extremely important to establish in advance which forest types can be classified with high resolution sensors such as Landsat. This paper describes the determination of a tree crown coverage threshold to define whether a pixel is classified as a forest or not. The methodology consists in the comparison of forest/non-forest classifications generated from Landsat images with tree crown cover maps obtained from PlanetScope very high resolution images, considering those pixels that exceed a given canopy cover threshold (eg. 5-10-15-...90-95-100%) as forest. The canopy coverage threshold was the one that minimized the difference between the Landsat classification and the maps generated from Planet images.Fil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires; Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Ferraina, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires; Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Propato, T. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires; Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gomez Taffarel, María Cielo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Dieguez, Hernán. Universidad de Buenos Aires; Facultad de Agronomía; Argentin

Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America

The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.EEA SaltaFil: Graesser, Jordan. Boston University. Department of Earth and Environment; Estados UnidosFil: Stanimirova, Radost. Boston University. Department of Earth and Environment; Estados UnidosFil: Tarrio, Katelyn. Boston University. Department of Earth and Environment; Estados UnidosFil: Copati, Esteban J. Bolsa de Cereales (Buenos Aires); ArgentinaFil: Volante, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Verón, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Elena, Hernan Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Friedl, Mark A. Boston University. Department of Earth and Environment; Estados Unido

First Large Extent and High Resolution Cropland and Crop Type Map of Argentina

Trabajo presentado al 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22–26 March 2020, Santiago, Chile.The availability of spatially explicit information about agricultural crops for large regions in Argentina is scarce. In particular, due to temporal dynamics of agricultural production (i.e. changes in planted crops from year to year) and spectral similarities among herbaceous crops it is difficult to generate crop type maps from remote sensing. Large regions with marked climatic variations, like the main agricultural areas of Argentina, represent an additional challenge. Here we generated a map based on supervised classifications using field samples along 14 agricultural zones. Best classification accuracies were obtained by combining seasonal indices (year, summer and winter), with indices that describe the temporal dynamics of vegetation. Accuracy was increased at regions with high and balanced number of samples and with longer growing seasons. The map allows to identify areas with clusters of one, two or three crops and to characterize areas with different spatial distribution between cropland and no cropland areas.EEA Salta.Fil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Veron, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos; Argentina.Fil: Banchero, Santiago. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Mosciaro, Maria Jesus. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Propato, Tamara. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Ferraina, Antonela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gomez Taffarel, Maria Cielo. Actividad privada; Argentina.Fil: Dacunto, Luciana. Actividad privada; Argentina.Fil: Franzoni, Agustin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Volante, Jose Norberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentin

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.Instituto de Clima y AguaFil: Waldner, François. Université catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; BelgicaFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Veron, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Zhang, Miao. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Wu, Bingfang. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Plotnikov, Dmitry. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Bartalev, Sergey. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Lavreniuk, Mykola. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Skakun, Sergii. Space Research Institute NAS and SSA. Department of Space Information Technologies; Ucrania. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Kussul, Nataliia. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Le Maire, Guerric. UMR Eco&Sols, CIRAD; Francia. Empresa Brasileira de Pesquisa Agropecuária. Meio Ambiante; BrasilFil: Dupuy, Stéphane. Centre de Coopération Internationale en Recherche Agronomique pour le Développement. Territoires, Environnement, Télédétection et Information Spatiale; FranciaFil: Jarvis, Ian. Agriculture and Agri-Food Canada. Science and Technology Branch. Agri-Climate, Geomatics and Earth Observation; CanadáFil: Defourny, Pierre. Université Catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; Belgic