Cuál es la situación de la Ley de Bosques en la Región Chaqueña a diez años de su sanción? : revisar su pasado para discutir su futuro

Aguiar, Martín Roberto. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART). Buenos Aires, Argentina.Mastrángelo, Matías. Universidad Nacional de Mar del Plata (UNMDP). Facultad de Ciencias Agrarias. Grupo de Agroecosistemas y Paisajes Rurales, Buenos Aires, Argentina.García Collazo, María Agustina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información. Buenos Aires, Argentina.Camba Sans, Gonzalo Hernán. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART). Buenos Aires, Argentina.Mosso, Clara Emilia. Universidad de Buenos Aires. Facultad de Agronomía. Licenciatura. Buenos Aires, Argentina.Vallejos, María. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART). Buenos Aires, Argentina.Paruelo, José María. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART). Buenos Aires, Argentina.Staiano, Luciana. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART). Buenos Aires, Argentina.Texeira, Marcos. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART). Buenos Aires, Argentina.Verón, Santiago R. Instituto Nacional de Tecnología Agropecuaria (INTA).Instituto de Clima y Aguas. Buenos Aires, Argentina.400-417En un complejo escenario ambiental, productivo y socioeconómico, el 28 de noviembre de 2007 fue sancionada en Argentina la Ley Nacional Nº 26.331 de “Presupuestos Mínimos de Protección Ambiental de los Bosques Nativos" (conocida como "Ley de bosques") con el propósito de proteger los bosques nativos a escala nacional. En este artículo nos proponemos realizar una síntesis crítica de la información disponible acerca de esta ley a diez años de su sanción, con una aproximación que toma en cuenta aspectos ambientales, económicos y sociales. Caracterizamos el desempeño de esta ley en la Región Chaqueña en cuanto a diferentes dimensiones, identificamos sus principales desafíos y describimos una serie de propuestas que desde el sector de Ciencia y Técnica pueden contribuir a su (re)diseño e implementación en el contexto de las actualizaciones de los Ordenamientos Territoriales de Bosques Nativos provinciales. Para ello, integramos información disponible proveniente de distintas fuentes, tales como normativas (nacionales y provinciales), literatura científica, informes de organismos estatales y de ONG y artículos periodísticos. La Ley de Bosques instaló en la opinión pública de nuestro país la problemática vinculada a la pérdida de bosques nativos y se ha posicionado como el principal instrumento de política forestal nacional para su protección. Si bien hubo una reducción en las tasas de deforestación en la región Chaqueña, no existen evidencias certeras de que esta reducción se deba a su aplicación. La Ley de Bosques en la Región Chaqueña presenta una serie de desafíos para mejorar su desempeño en cuanto a su efectividad, equidad y legitimidad social. En este trabajo se presentan diez observaciones que emergen de la revisión realizada. Por otro lado, se esbozan una serie de propuestas de investigación y acción en torno a la ley vinculadas a esas observaciones

Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed

Cropland maps derived from satellite imagery have become a common source of information to estimate food production, support land use policies, and measure the environmental impacts of agriculture. Cropland classification models are typically calibrated with data collected from roadside surveys which enable the sampling of large areas at a relatively low cost. However, there is a risk of providing biased data as environmental and management gradients may not be fully captured from road networks, thereby violating the assumption of representativeness of calibration data. Despite being widely adopted, the potential biases of roadside sampling have so far not been thoroughly addressed. In this study, we looked for evidence of these biases by comparing three sampling strategies: Random sampling, Roadside sampling, and Transect sampling – a spatially constrained variant of Roadside sampling. In these three strategies, non-cropland data are randomly distributed as they can be photo-interpreted. Based on reference maps at 30 m in four study sites, we followed a Monte Carlo approach to generate multiple realizations of each sampling strategy for ten sample sizes. The effect of the sampling strategy was then assessed in terms of representativeness of the data set collected and accuracy of the resulting maps. Results showed that data sets obtained from Roadside sampling were significantly less representative than those obtained from Random sampling but the resulting maps were only marginally less accurate (2% difference). Transect sampling captured systematically less variability than Random or Roadside sampling which led to differences in accuracy as large as 15%. The effect of sample size on accuracy varied across sites but generally leveled off after reaching 3000 pixels. Augmenting the size of Transect samples improved the classification accuracy but not sufficiently to match the performance of the other sampling strategies. Finally, we found that Random and Roadside training sets with similar representativeness yield comparable accuracy. Therefore, we conclude that roadside sampling can be a viable source of training data for cropland mapping if the range of environmental and management gradients is surveyed. This underlines the importance of survey planning to identify those routes that capture most variability808293COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão tem2014/50715-9The authors received funding from the CSIRO Future Science Platform “GrainCast”; the SIGMA project (Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM; FP7-ENV-2013 no. 603719); the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES); the project “Characterizing And Predicting Biomass Production In Sugarcane And Eucalyptus Plantations In Brazil” (FAPESP-Microsoft Research 2014/50715-9); the CESOSO project (TOSCA program Grant of the French Space Agency, CNES

A case study of strategies for fostering international, interdisciplinary research

Bringing together and successfully managing a highly interdisciplinary (ID) research team of socioeconomic, biophysical, and engineering scientists is highly challenging, particularly when that team includes 20 scientists and students across six countries. This paper reports on the results of evaluating the success of such a team as it studies the socioecological impacts of bioenergy development across the Americas. We find that the team has succeeded according to several different metrics. We demonstrate that the literature on accelerated sustainability transitions and small group team creation, development, and management holds valuable lessons for the success of ID teams.Fil: Halvorsen, K. E.. Michigan Technological University; Estados UnidosFil: Knowlton, J. L.. Michigan Technological University; Estados UnidosFil: Mayer, A. S.. Michigan Technological University; Estados UnidosFil: Phifer, C. C.. Michigan Technological University; Estados UnidosFil: Martins, T.. Universidade Federal do Rio de Janeiro; BrasilFil: Pischke, E. C.. Michigan Technological University; Estados UnidosFil: Propato, Tamara Sofía. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; ArgentinaFil: Cavigliaso, P.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: García, C.. Universidad Nacional Autónoma de México; MéxicoFil: Chiappe, M.. Universidad de la República; UruguayFil: Eastmond, A.. Universidad Nacional Autónoma de Yucatán; MéxicoFil: Licata, Julian Andres. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Kuhlberg, M.. Laurentian University; CanadáFil: Medeiros, R.. Universidade Federal do Rio de Janeiro; BrasilFil: Picasso, V.. Universidad de la República; UruguayFil: Mendez, G.. Universidad Nacional Autónoma de Yucatán; MéxicoFil: Primo, P.. Universidad de la República; UruguayFil: Frado, A.. Laurentian University; CanadáFil: Verón, Santiago Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; ArgentinaFil: Dunn , J. L.. Michigan Technological University; Estados Unido

A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

Abstract The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performanc