Eficiência do flutriafol e do flutriafol + tiofanato metílico aplicados com gotas finas ou médias no controle da ferrugem asiática da soja

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Os impactos ambientais no meio físico: erosão e assoreamento na bacia hidrográfica do rio Taquari, MS, em decorrência da pecuária

O aumento da inundação em áreas do baixo curso do rio Taquari, no Pantanal do estado do Mato Grosso do Sul, tem transformado a pecuária desta região numa atividade com baixa rentabilidade, à medida que extensas áreas de campo passaram a ser inundadas vários meses durante o ano a partir da década de 70. A pecuária realizada em campos naturais de regiões úmidas do Pantanal indica que há necessidade de se investigar metodologias apropriadas para avaliação de impacto ambiental, que abordem impactos diretos, indiretos, cumulativos e processos do meio físico que alteram, de maneira prejudicial, o meio ambiente. Supõe-se que a inundação na planície do rio Taquari esteja relacionada com a ocupação antrópica nas áreas de planalto da bacia do rio Taquari. O presente trabalho tem por objetivo avaliar os impactos ambientais na planície de inundação do baixo curso do Taquari, decorrentes da ocupação antrópica da bacia hidrográfica do rio Taquari em sua totalidade, considerando os impactos ambientais causados pela pecuária à medida que se configura como principal atividade econômica da bacia bem como os processos erosivos e de assoreamento no quadro atual do regime de inundações. As etapas de caracterização da área, de análise dos impactos e as propostas de ações mitigadoras, previstas num Estudo de Impacto Ambiental, foram aqui analisadas. Foram utilizadas informações sobre as características do meio físico, biótico e socioeconômico, selecionadas a partir do levantamento dos dados existentes com recorte efetuado para a bacia hidrográfica do rio Taquari. Na maior parte dos temas, este foi um processo de levantamento, ordenamento e recuperação de informações, na escala original de 1:250.000, do Plano de Conservação da Bacia do Alto Paraguai-PCBAP, gerenciado no SPRING. Foram também realizadas viagens de campo para a complementação dos dados e para o levantamento de atividades antrópicas com verificações \"in loco\" da ocorrência de impacto ambiental. A maioria dos dados socioeconômicos compilados para o presente trabalho teve por base os censos agropecuários e demográficos realizados pelo IBGE. Os resultados obtidos demonstram que os impactos ambientais decorrentes da pecuária no planalto interferem no regime de inundação na planície da bacia, o que só foi possível de ser identificado a partir de análises integradas em toda a bacia hidrográfica do rio Taquari. Verificou-se que os métodos de EIA são adequados para identificar os impactos diretos decorrentes da pecuária, mas não são adequados para identificar os processos e seus efeitos cumulativos na extensão da bacia hidrográfica do rio Taquari. Além disto, a abordagem da avaliação ambiental estratégica, como procedimento para análise ambiental em políticas, planos e programas, mostra-se adequada para as análises na BHRT à medida que está centralizada nos efeitos do ambiente sobre as necessidades e oportunidades de desenvolvimento. Contudo, somente a recuperação de danos ambientais, o controle das origens dos impactos no ambiente e um sistema de gestão consciente de seus compromissos podem levar, juntamente com a melhora dos procedimentos técnicos e administrativos para análises ambientais, à uma maior proximidade da sustentabilidade ambiental na BHRT.

EcoGIS – GIS tools for ecosystem approaches to fisheries management

Executive Summary: The EcoGIS project was launched in September 2004 to investigate how Geographic Information Systems (GIS), marine data, and custom analysis tools can better enable fisheries scientists and managers to adopt Ecosystem Approaches to Fisheries Management (EAFM). EcoGIS is a collaborative effort between NOAA’s National Ocean Service (NOS) and National Marine Fisheries Service (NMFS), and four regional Fishery Management Councils. The project has focused on four priority areas: Fishing Catch and Effort Analysis, Area Characterization, Bycatch Analysis, and Habitat Interactions. Of these four functional areas, the project team first focused on developing a working prototype for catch and effort analysis: the Fishery Mapper Tool. This ArcGIS extension creates time-and-area summarized maps of fishing catch and effort from logbook, observer, or fishery-independent survey data sets. Source data may come from Oracle, Microsoft Access, or other file formats. Feedback from beta-testers of the Fishery Mapper was used to debug the prototype, enhance performance, and add features. This report describes the four priority functional areas, the development of the Fishery Mapper tool, and several themes that emerged through the parallel evolution of the EcoGIS project, the concept and implementation of the broader field of Ecosystem Approaches to Management (EAM), data management practices, and other EAM toolsets. In addition, a set of six succinct recommendations are proposed on page 29. One major conclusion from this work is that there is no single “super-tool” to enable Ecosystem Approaches to Management; as such, tools should be developed for specific purposes with attention given to interoperability and automation. Future work should be coordinated with other GIS development projects in order to provide “value added” and minimize duplication of efforts. In addition to custom tools, the development of cross-cutting Regional Ecosystem Spatial Databases will enable access to quality data to support the analyses required by EAM. GIS tools will be useful in developing Integrated Ecosystem Assessments (IEAs) and providing pre- and post-processing capabilities for spatially-explicit ecosystem models. Continued funding will enable the EcoGIS project to develop GIS tools that are immediately applicable to today’s needs. These tools will enable simplified and efficient data query, the ability to visualize data over time, and ways to synthesize multidimensional data from diverse sources. These capabilities will provide new information for analyzing issues from an ecosystem perspective, which will ultimately result in better understanding of fisheries and better support for decision-making. (PDF file contains 45 pages.)

Spatial analysis for spring bloom and nutrient limitation in Xiangxi bay of three Gorges Reservoir

The spatial and temporal dynamics of physical variables, inorganic nutrients and phytoplankton chlorophyll a were investigated in Xiangxi Bay from 23 Feb. to 28 Apr. every six days, including one daily sampling site and one bidaily sampling site. The concentrations of nutrient variables showed ranges of 0.02-3.20 mg/L for dissolved silicate (Si); 0.06-2.40 mg/L for DIN (NH4N + NO2N + NO3N); 0.03-0.56 mg/L for PO4P and 0.22-193.37 mu g/L for chlorophyll a, respectively. The concentration of chlorophyll a and inorganic nutrients were interpolated using GIS techniques. The results indicated that the spring bloom was occurred twice in space during the whole monitoring period (The first one: 26 Feb.-23 Mar.; the second one: 23 Mar.-28 Apr.). The concentration of DIN was always high in the mouth of Xiangxi Bay, and PO4P was high in the upstream of Xiangxi Bay during the whole bloom period. Si seems no obvious difference in space in the beginning of the spring bloom, but showed high heterogeneity in space and time with the development of spring bloom. By comparing the interpolated maps of chlorophyll a and inorganic variables, obvious consumptions of Si and DIN were found when the bloom status was serious. However, no obvious depletion of PO4P was found. Spatial regression analysis could explained most variation of Chl-a except at the begin of the first and second bloom. The result indicated that Si was the factor limiting Chl-a in space before achieved the max area of hypertrophic in the first and second bloom period. When Si was obviously exhausted, DIN became the factor limiting the Chl-a in space. Daily and bidaily monitoring of Site A and B, representing for high DIN: PO4P ratio and low DIN:PO4P ratio, indicated that the concentration of Si was decreased with times at both site A and B, and the dramatically drop of DIN was found in the end monitoring at site B. Multiple stepwise regression analysis indicated that Si was the most important factor affect the development of spring bloom both at site A and B in time series.

Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, Sao Paulo State, Brazil

This paper presents the groundwater favorability mapping on a fractured terrain in the eastern portion of Sao Paulo State, Brazil. Remote sensing, airborne geophysical data, photogeologic interpretation, geologic and geomorphologic maps and geographic information system (GIS) techniques have been used. The results of cross-tabulation between these maps and well yield data allowed groundwater prospective parameters in a fractured-bedrock aquifer. These prospective parameters are the base for the favorability analysis whose principle is based on the knowledge-driven method. The mutticriteria analysis (weighted linear combination) was carried out to give a groundwater favorabitity map, because the prospective parameters have different weights of importance and different classes of each parameter. The groundwater favorability map was tested by cross-tabulation with new well yield data and spring occurrence. The wells with the highest values of productivity, as well as all the springs occurrence are situated in the excellent and good favorabitity mapped areas. It shows good coherence between the prospective parameters and the well yield and the importance of GIS techniques for definition of target areas for detail study and wells location. (c) 2008 Elsevier B.V. All rights reserved.

SIG - SPRING aplicado na determinação da capacidade de uso das terras da microbacia do Ribeirão Pouso Alegre - Jaú (SP)

A população mundial vem sofrendo, cada vez mais, as conseqüências das agressões efetuadas pelo homem ao meio ambiente, principalmente quanto à ocupação e uso inadequado das terras, o que ocasiona empobrecimento e depauperamento do solo, influenciando na qualidade e disponibilidade de água, levando à destruição das reservas florestais. Assim, é necessária a implantação de políticas públicas, que contemplem o desenvolvimento econômico, urbano, rural e social de uma região, preservando os recursos naturais para futuras gerações. A bacia em estudo está localizada entre as coordenadas UTM 764942; 7546214 e 741816; 7534759, com uma área de 14699,7ha. Este trabalho visou definir as classes de capacidade de uso de terra da microbacia do Ribeirão Pouso Alegre - Jaú (SP) através do Sistema de Informações Geográficas - SPRING. A carta de capacidade de uso da terra foi elaborada a partir do cruzamento das cartas clinográfica e de solo, que foram elaboradas pelo SIG Spring, e o cruzamento de dados foi feito através do LEGAL - Linguagem Espacial para Geoprocessamento Algébrico. Os resultados mostraram que a microbacia é constituída essencialmente pelas classes II e III e o SIG-SPRING permitiu através dos seus módulos discriminarem e quantificar as áreas das classes de terras, declive e capacidade de uso da terra rapidamente.

Estudo da vulnerabilidade à erosão com a aplicação da Equação Universal de Perda de Solo na alta bacia hidrográfica do rio Jacaré Pepira, utilizando SIG/SPRING

Pós-graduação em Geociências e Meio Ambiente - IGCE

BEYOND ROOTS ALONE: NOVEL METHODOLOGIES FOR ANALYZING COMPLEX SOIL AND MINIRHIZOTRON IMAGERY USING IMAGE PROCESSING AND GIS TOOLS

Quantifying belowground dynamics is critical to our understanding of plant and ecosystem function and belowground carbon cycling, yet currently available tools for complex belowground image analyses are insufficient. We introduce novel techniques combining digital image processing tools and geographic information systems (GIS) analysis to permit semi-automated analysis of complex root and soil dynamics. We illustrate methodologies with imagery from microcosms, minirhizotrons, and a rhizotron, in upland and peatland soils. We provide guidelines for correct image capture, a method that automatically stitches together numerous minirhizotron images into one seamless image, and image analysis using image segmentation and classification in SPRING or change analysis in ArcMap. These methods facilitate spatial and temporal root and soil interaction studies, providing a framework to expand a more comprehensive understanding of belowground dynamics.

A community-based assessment of the spatiotemporal distribution of influenza in a U.S.-Mexico border county during the spring 2009 novel H1N1 swine-origin influenza A pandemic

This study retrospectively evaluated the spatial and temporal disease patterns associated with influenza-like illness (ILI), positive rapid influenza antigen detection tests (RIDT), and confirmed H1N1 S-OIV cases reported to the Cameron County Department of Health and Human Services between April 26 and May 13, 2009 using the space-time permutation scan statistic software SaTScan in conjunction with geographical information system (GIS) software ArcGIS 9.3. The rate and age-adjusted relative risk of each influenza measure was calculated and a cluster analysis was conducted to determine the geographic regions with statistically higher incidence of disease. A Poisson distribution model was developed to identify the effect that socioeconomic status, population density, and certain population attributes of a census block-group had on that area's frequency of S-OIV confirmed cases over the entire outbreak. Predominant among the spatiotemporal analyses of ILI, RIDT and S-OIV cases in Cameron County is the consistent pattern of a high concentration of cases along the southern border with Mexico. These findings in conjunction with the slight northward space-time shifts of ILI and RIDT cluster centers highlight the southern border as the primary site for public health interventions. Finally, the community-based multiple regression model revealed that three factors—percentage of the population under age 15, average household size, and the number of high school graduates over age 25—were significantly associated with laboratory-confirmed S-OIV in the Lower Rio Grande Valley. Together, these findings underscore the need for community-based surveillance, improve our understanding of the distribution of the burden of influenza within the community, and have implications for vaccination and community outreach initiatives.^

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) and Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic (2004-2012)

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2005

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2006

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2004

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2008

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2009

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.