Engenharia Agrícola

Climate change can affect the development of soybean cultivation, impacting your productivity. Thus, agrometeorological information is essential in order to improve productivity strategies. The objective of the paper was to analyze the influence and occurrence of seasonality of the following agrometeorological variables on soybean productivity: mean air temperature [TMean] (ºC), accumulated rainfall value [Rain] (mm), global solar radiation [Sr] (MJ m-2 day-1), and potential evapotranspiration [ETp] (mm), in ten-day variations of the the maximum vegetative development date (MVDD), in the 2011/2012 and 2013/2014 harvest years in the state of Paraná. The study was based on spatial distribution of variables, using univariate and bivariate Global Moran’s Indexes, and multivariate clustering analysis. To verify seasonality in the time distribution of the agrometeorological variables in the ten-day variations close to soybean MVDD, we used the circular statistics, through the mean vector ...

A producao agropecuaria brasileira influencia diretamente na economia do pais, sendo que a producao nacional de graos abrange boa parte desse setor. Desta forma, a informacao de area semeada com as principais culturas agricolas tem grande valor no planejamento de acoes logisticas, em politicas publicas ou privadas do mercado de commodities. Uma forma de obter dados mais confiaveis desse setor da economia e com o auxilio do sensoriamento remoto, obtendo dados de maneira mais rapida e eficiente. Entretanto, para se trabalhar com sensoriamento remoto, se torna necessario uma alta capacidade computacional para o processamento dos dados. Para contornar este problema, tem sido utilizado o processamento de dados em nuvem na plataforma Google Earth Engine (GEE), que esta disponivel de forma gratuita aos usuarios e que permite a realizacao de diversas atividades ligadas ao sensoriamento remoto orbital. Assim, o objetivo deste trabalho foi mapear as principais culturas agricolas de verao e de...

RESUMO: O sensoriamento remoto vem sendo largamente utilizado no mundo para se analisar diversos alvos na superfície terrestre, com uma boa precisão e sem a necessidade de se deslocar até a região em estudo. Este trabalho teve como objetivo o mapeamento da cobertura florestal, dos corpos hídricos e das cidades em todo estado do Paraná. Foram utilizadas imagens de satélite do índice de vegetação EVI do sensor Modis/Terra que tem resoluções espacial de 250 m e temporal diária. Para o processamento das imagens utilizou-se os softwares ENVI 4.5, ArcGis 10 e IDL.Para o mapeamento foram usadas imagens entre os anos de 2010 e 2012, gerando-se o perfil espectrotemporal. Como cada alvo tem características diferentes, foi possível, através da análise da série temporal de EVI, mapear os alvos de interesse.Como resultados, obteve-se o mapeamento e a área (hectares) da cobertura florestal, corpos hídricos e cidades por mesorregião do Paraná. Assim, constatou-se que o território Paranaense é coberto por 24,31% de cobertura florestal e 2,46% pelos corpos hídricos e cidades. Além disto, estes resultados serão utilizados na melhoria do mapeamento das culturas agrícolas do estado, evitando-se desta forma, que as áreas permanentes sejam classificadas como áreas cultiváveis. PALAVRAS-CHAVE: séries temporais, modis, índice de vegetação EVI.

Brazilian farming influences directly the worldwide economy. Thus, fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies. Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics. Medium-to-low resolution free orbital images, such as those from Landsat 8 and Sentinel 2, have been used for crop mapping; however, satellite image processing requires high computing power, especially when monitoring vast areas. Therefore, cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis. Thus, our goal was to develop a method to map the two main crops (soybeans and corn) in Paraná, one of the major Brazilian state producers. Landsat-8, Sentinel-2, SRTM+, and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier, to identify cropped areas. A minimum global accuracy of 90% was found for both crops. Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86. This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform.

Brazilian agricultural production directly influences the country's economy, with national grain production covering a large part of this sector. In this way, the information of area sown with the main agricultural crops has great value in the planning of logistic actions, in public or private policies of the commodities market. One method to obtain more reliable data from this sector of the economy is with the aid of remote sensing, obtaining data more quickly and efficiently. However, in order to work with remote sensing, a high computational capacity is required for data processing. To overcome this problem, the use of cloud data processing in the Google Earth Engine (GEE) platform was employed, which is available to users free of charge, has been used to perform various activities related to orbital remote sensing. Thus, the objective of this work was to map the main summer and winter crops in the state of Paraná, for the 2016/2017 and 2017/2018 harvest years, using the GEE platform. For this purpose, images of the OLI and MSI sensors, images of the digital elevation model (SRTM), false color mosaic segmentation of the sensor images and the Naive Bayes Continuous algorithm as classification methods were used. The mapping process was parameterized separately for each of the 39 microregions of the state. Finally, the crop areas (soybean, 1 st and 2 nd maize and winter crops) were quantified by municipality and compared with official data and field data. The accuracy of the summer mapping resulted in Global Accuracy ranging from 87.6% (Maize, 2017/2018) to 96.7% (Soybean, 2016/2017), with a kappa index (K) between 72% (Maize, 2017/2018 crop year) and 91% (Soybean, 2016/2017). The linear correlation (r) between the mapped area and the official area per municipality was 0.92 and the agreement index dr = 0.81 for the soybean crop and for the 1st crop corn yielded r = 0.59 and dr = 0.53. For the mapping of 2 nd crop maize and winter crops, the Global Accuracy varied between 95% (winter crops, 2018 crop year) and 96.7% (Maize 2 nd crop, 2017 crop year), with index kappa (K) between 90% (winter crops, 2018 crop year) and 92% (Maize 2 nd crop, 2017 crop year). Between the mapped area and the official area per municipality, r = 0.95 and dr = 0.83 for maize 2 nd crop and r = 0.78 and dr = 0.76 for winter crops were obtained. The linear (r) correlation between the mapped area and the field data was 0.96 and the agreement index dr = 0.86 for the 1 st crop corn and for the soybean yield r = 0.96 and p = 0.92 for the crop year 2017/2018, r = 0.79 and dr = 0.71 for maize 2 nd crop r = 0.80 and dr = 0.72 for winter crops in the crop year 2017, r = 0.88 and dr = 0.86 for maize crop 2nd crop er = 0.71 and dr = 0.78 for winter crops in the crop year 2018. The mapping of areas with agricultural crops carried out with the GHG platform can be carried out quickly, accurately and efficiently. Through the mappings it is possible to have the spatial distribution of crops per crop plot, as well as the quantification of areas by area of coverage of a company, municipality, microregion, mesoregion and for every state.

The use of effective technologies for the monitoring of agricultural crops should seek methodologies that provide information regarding crop development, preferably before harvesting. The study of the monitoring and/or estimation of areas using vegetation indices derived from multitemporal data from MODIS sensors is being studied in the search for greater objectivity of the generated values. In this context, the objective of this study was to map areas with winter and second-crop corn using EVI/MODIS time series from the Terra and Aqua satellites, for the seasons from 2012 to 2014 in the Paraná state of Brazil. Accuracy analysis of the mappings was performed in spatial resolution images of 30 m (LISS-III and Landsat-8), to identify and validate the masks the crops of interest. The accuracy of the mapping obtained values of global precision 87.5%, 79.5%, and 82.0%, with Kappa index of 0.81, 0.69, and 0.73, in the 2012, 2013, and 2014 harvests, respectively. Comparing with data from t...

Genome-wide analyses carried out in silico revealed that the DNA repeats called enterobacterial repetitive intergenic consensus sequences (ERICs), which are present in several Enterobacteriaceae, are overrepresented in yersiniae. From the alignment of DNA regions from the wholly sequenced Yersinia enterocolitica 8081 and Yersinia pestis CO92 strains, we could establish that ERICs are miniature mobile elements whose insertion leads to duplication of the dinucleotide TA. ERICs feature long terminal inverted repeats (TIRs) and can fold as RNA into hairpin structures. The proximity to coding regions suggests that most Y. enterocolitica ERICs are cotranscribed with flanking genes. Elements which either overlap or are located next to stop codons are preferentially inserted in the same (or B) orientation.

The goal of this study was to produce a map of soybean cultivation areas in west region of Paraná state, through LANDSAT5/TM (Thematic Mapper) images from the 2003/2004 yield, from november 2003 to february 2004. In order to do this, there were made automatic digital classifications, which were based on regions of interest made before, in a study of monitoring that has been made in Cascavel-PR city. With the overlay of the maps generated by the classifications, it was possible to create a map with the soybean area in this region, and then to compare it with official data and selected points in the area; and also prepare a localization reference of soybean areas for the use of AVHRR/NOAA and MODIS images, of the same region, in the future.

A utilização de ferramentas de apoio à decisão em Sistemas de Informação Geográfica (SIG) tem auxiliado em áreas de aplicação como na análise de impacto, aptidão e viabilidade. Este trabalho apresenta um exemplo de aplicação dessas ferramentas para análise de trajeto de um gasoduto interligando uma refinaria a três cidades na região de Campinas, Estado de São Paulo. Como variáveis foram utilizadas as informações contidas em um mapa de uso do solo e um modelo digital do terreno (MDT). O processamento e a análise das informações espaciais foram realizados por meio do SIG IDRISI. Na primeira abordagem usam-se superfícies de custo para determinar o melhor trajeto. O trajeto obtido considerou os valores de menores custos, na maioria áreas de pastagens e com declividade de até 15%. A segunda abordagem baseou-se no método de múltiplos critérios com a ponderação dos fatores distância, declividade e uso do solo. A análise por múltiplos critérios foi bastante influenciada pelo uso do solo. Porém, a declividade e a distância entre as cidades são fatores fundamentais no traçado do gasoduto, pois reflete o menor custo. O trabalho constituiu um subsídio importante para a tomada de decisão sobre a melhor alternativa de traçado do gasoduto, possibilitando uma escolha mais racional e menos subjetiva.