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Risk analysis of climate change on the spatial distribution of sugarcane orange rust (Puccinia kuehnii) is a strategic study for plant protection to minimize future damages. The objective of this work was to evaluate the potential risk of the occurrence of orange rust in Argentina and Brazil under the climate change scenarios. A mapping methodology integrated the data of climate projections and the phytosanitary problem supported by Geographic Information System (GIS). Normal climate (1961-1990) and future climate (2011-2040, 2041-2070, and 2071-2100) from Intergovernmental Panel on Climate Change were considered. The conditions of climatic favorability for the occurrence of the disease were established by means mathematical logic criteria of GIS, based on knowledge of the authors, who incorporated the implicit effects of the interaction of the virulent pathogen, susceptible host, and predisposing environmental characteristics. The favorability for the occurrence of sugarcane orange rust in the main sugarcane producing regions of Argentina and Brazil varied over the months considered of the cultivation cycle. For Argentina, the future climate scenarios predicted a reduction in favorability for the occurrence of sugarcane orange rust from December to February and an increase in April. In Brazil, the climatic favorability decreased from December to March and increased in May. A análise de risco de mudanças climáticas na distribuição espacial da ferrugem alaranjada da cana-de-açúcar (Puccinia kuehnii) é um estudo estratégico para a proteção de plantas a fim de minimizar danos futuros. O objetivo deste trabalho foi avaliar o risco potencial da ocorrência de ferrugem alaranjada na Argentina e no Brasil, sob cenários de mudanças climáticas. A metodologia de mapeamento integrou os dados das projeções climáticas e do problema fitossanitário por meio do Sistema de Informações Geográficas (SIG). Foi considerado o clima normal (1961-1990) e o clima futuro (2011-2040, 2041-2070 e 2071-2100) do Painel Intergovernamental sobre Mudanças Climáticas. As condições de favorabilidade climática para a ocorrência da doença foram estabelecidas por meio de critérios lógicos matemáticos do SIG, baseados no conhecimento dos autores, que incorporaram os efeitos implícitos da interação do patógeno virulento, hospedeiro suscetível e características ambientais predisponentes. A favorabilidade para a ocorrência da ferrugem alaranjada da cana-de-açúcar nas principais regiões produtoras de cana-de-açúcar da Argentina e do Brasil variou ao longo dos meses considerados do ciclo de cultivo. Para a Argentina, os cenários climáticos futuros previram uma redução na favorabilidade para a ocorrência da ferrugem alaranjada da cana-deaçúcar de dezembro a fevereiro e um aumento em abril. No Brasil, a favorabilidade climática diminuiu de dezembro a março e aumentou em maio. El análisis de riesgo del cambio climático sobre la distribución espacial de la roya naranja de la caña de azúcar (Puccinia kuehnii) es un estudio estratégico para la protección vegetal con el fin de minimizar daños futuros. El objetivo de este trabajo fue evaluar el riesgo potencial de ocurrencia de roya naranja en Argentina y Brasil bajo los escenarios de cambio climático. Una metodología de mapeo integró los datos de las proyecciones climáticas y la problemática fitosanitaria apoyada en el Sistema de Información Geográfica (SIG). Se consideró el clima normal (1961-1990) y el clima futuro (2011-2040, 2041-2070 y 2071-2100) del Panel Intergubernamental sobre Cambio Climático. Las condiciones de favorabilidad climática para la ocurrencia de la enfermedad se establecieron mediante criterios lógicos matemáticos de SIG, basados en el conocimiento de los autores, quienes incorporaron los efectos implícitos de la interacción del patógeno virulento, huésped susceptible y características ambientales predisponentes. La favorabilidad para la ocurrencia de la roya naranja de la caña de azúcar en las principales regiones productoras de caña de azúcar de Argentina y Brasil varió a lo largo de los meses considerados del ciclo de cultivo. Para Argentina, los escenarios climáticos futuros pronosticaron una reducción en la favorabilidad para la ocurrencia de la roya naranja de la caña de azúcar de diciembre a febrero y un aumento en abril. En Brasil, la favorabilidad climática disminuyó de diciembre a marzo y aumentó en mayo. Centro de Investigaciones Agropecuarias Fil: Angelotti, Francislene. Embrapa Semi-arid; Brasil Fil: Morales, Cristina Del Carmem. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina Fil: Hamada, Emília. Embrapa Environment; Brasil Fil: Bisonard, Eduardo Matias. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Animal del Chaco Semiárido; Argentina Fil: Ribeiro do Valle Gonçalves, Renata. State University of Campinas (Unicamp); Brasil Fil: Rago, Alejandro Mario. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigaciones Agropecuarias; Argentina

Climate change can cause significant shifts in the occurrence and severity of agricultural diseases, altering the distribution of phytosanitary problems with severe economic, social, and environmental consequences. Despite their importance, these new geographic and temporal distributions of plant diseases still demand scrutiny. In the present study, we prospect the geographic distribution of sugarcane brown rust (Puccinia melanocephala) considering average temperature and relative humidity in the main producing region of the state of São Paulo. The geographic database was structured using GIS with projections of future climate change provided by IPCC. Mathematical logic equations were defined and applied to data of average temperature and relative humidity, resulting in monthly maps of climate favorability for the occurrence of the disease. Prospective maps for three future periods (2011-2040, 2041-2070, and 2071-2100) and two greenhouse gas emission scenarios were compared to the reference period (1961-1990). The analysis considered months from December to May. Our study shows a tendency of decrease in the favorability for the disease in a longer term (2071-2100), more noticeable in the scenario A2 than in B1. These results suggest; however, that this disease demands attention in the management of the producing region for future climate scenarios. Made available in DSpace on 2022-07-11T12:21:07Z (GMT). No. of bitstreams: 1 Prospecting-brown-rust-of-sugarcane-in-Sao-Paulo-2022.pdf: 1163510 bytes, checksum: 5e08a1030e2fce46e6d3620b2535d7f2 (MD5) Previous issue date: 2022

Taking Persimmon (Diospyros kaki L.), Fig (Ficus carica L.) and Papaya (Carica Papaya L) fruits in São Paulo State, southeast of Brazil, as a case of study, we present here new software that was designed to support smallholders to manage their climate risk on the production area. The main idea of this new software named “Brazilian Mapping for Agricultural Zoning System” (BRAMAZOS) is to transform scientific knowledge into useful information for crop climate risk management, indicating the risk of crop failure and which is the limiting meteorological element for the area unsuitability. The software was developed based on user experience design, focusing on the users facilities with a friendly interface. We simulated these fruits climate risk in current and future scenarios of climate change using ETAHadgem ES Regional Climate Models (RCM), which is a downscaling of the Global Climate Model HadGEM2-ES, based on the IPCC 5th Assessment Report (AR5). We observed changes in the climate risk zone distribution for Persimmon and Papaya cultivation, which could lose almost 30% and 9% of the suitable area respectively. On the other hand, fig did not present significant reduction in the climate risk zone. The results presented here suggested that the temperate fruit examples used here seem to be more sensible to the temperature increase and, the tropical example seems to be more precipitation decrease sensitive. We discussed the significance of BRAMAZOS software as tool to support efficient information to climate risk management, providing agroclimatic information that are efficient to assist decision making, increase food security with the intention to reduce the climate impact on smallholders development and resources management issues.

The objective of this work was to simulate the geographical distribution of the incubation period of coffee leaf rust in Coffea arabica, using data of two regional climate models, Eta-HadGEM2-ES and Eta-MIROC5. The scenario of high greenhouse gas emission (RCP 8.5 W m-2) was used for the states of Minas Gerais and São Paulo, Brazil, for current and future climate scenarios. The behavior of six different regression equations for incubation period (IP), available in the literature, was also analyzed as affected by data from the regional climate models. The results indicate the possibility of an increase in the affected area in the studied region, when the IP is less than 19 days, from 0.5% for Eta-MIROC5 to 14.2% for Eta-HadGEM2-ES. The severity of coffee leaf rust in future scenarios should increase in the hottest and wettest months of the year, extending to the driest and coldest months. The potential of rust infection is estimated differently by the studied equations. In higher temperature scenarios, the Kushalappa & Martins equation indicates a very high severity potential. Resumo: O objetivo deste trabalho foi simular a distribuição geográfica do período de incubação da ferrugem do cafeeiro Coffea arabica, com uso de dados de dois modelos climáticos regionais, o Eta-HadGEM2-ES e o Eta-MIROC5. O cenário de alta emissão de gases de efeito estufa (RCP 8,5 W m-2) foi utilizado para os estados de Minas Gerais e São Paulo, para os cenários climáticos atual e futuro. O comportamento de seis diferentes equações de regressão do período de incubação (PI), disponíveis na literatura, também foi analisado em função dos dados dos modelos climáticos regionais. Os resultados indicam possibilidade de aumento de área afetada na região estudada, com PI inferior a 19 dias, de 0,5% para Eta-MIROC5 a 14,2% para Eta-HadGEM2-ES. A severidade da ferrugem do cafeeiro em cenários futuros deverá aumentar nos meses mais quentes e úmidos do ano, estendendo-se para os meses mais secos e frios. O potencial de infecção da ferrugem é estimado de forma diferente pelas equações estudadas. Em cenários de temperaturas mais elevadas, a equação de Kushalappa & Martins indica um potencial muito alto de severidade.

A ocorrência da mancha preta do amendoim está diretamente relacionada aos elementos climáticos. Desta maneira, mapas de distribuição geográfica e temporal baseados em dados do clima contribuem com informações sobre o comportamento de patossistemas frente às mudanças climáticas. O objetivo deste estudo foi avaliar a evolução da favorabilidade climática à ocorrência da mancha preta para as principais regiões produtoras de amendoim da Argentina e do Brasil em função das mudanças climáticas. Na elaboração dos mapas foram considerados os meses de janeiro a março e empregada a metodologia de análise integrada das projeções climáticas e do problema fitossanitário com suporte do geoprocessamento. Foram considerados o clima normal (1961-1990) e o clima futuro (2011-2040, 20412070 e 2071-2100). Tendo em conta as particularidades da doença nas áreas produtoras de cada país, diferentes regras em critérios de lógica matemática de SIG foram aplicadas para a Argentina e o Brasil, pois elas incorporam efeitos diversos da interação das características próprias do patógeno virulento, hospedeiro suscetível e características ambientais. Os cenários climáticos futuros apontam para o aumento da favorabilidade para a ocorrência da mancha preta do amendoim para as principais regiões produtoras da Argentina e do Brasil. The occurrence of peanut black spot is directly related to climatic elements. Thus, maps of geographic and temporal distribution based on climatic data contribute to information on the behavior of pathosystems in the face of climate change. The objective of this study was to evaluate the evolution of climate favorability to the occurrence of black spot for the main peanut producing regions of Argentina and Brazil due to climate change. The months from January to March were considered to the production of the maps and the methodology of integrated analysis of the climate projections and the phytosanitary problem supported by the geoprocessing was used. Normal climate (1961-1990) and future climate (2011-2040, 2041-2070 and 2071-2100) were considered. Taking into account the particularities of the disease in the producing areas of each country, different rules in criteria of mathematical logic of GIS were adopted for Argentina and Brazil as they incorporate the effect of the interaction of the virulent pathogen, susceptible host and environmental characteristics. Future climate scenarios point to an increase in the favorability of peanut black spot occurrence in the main producing areas of Argentina and Brazil. Instituto de Investigación Animal del Chaco Semiárido Fil: Bisonard, Eduardo Matias. Instituto Nacional de Tecnologia Agropecuaria (INTA). Instituto de Investigación Animal del Chaco Semiárido; Argentina Fil: Hamada, Emília. Embrapa Meio Ambiente; Brasil Fil: Angelotti, Francislene. Embrapa Semiárido; Brasil Fil: Ribeiro do Valle Gonçalves, Renata. Universidade Estadual de Campinas. Centro de Pesquisas Meteorológicas e Climáticas Aplicadas a Agricultura; Brasil Fil: Rago, Alejandro Mario. Instituto Nacional de Tecnologia Agropecuaria (INTA). Centro de Investigaciones Agropecuarias; Argentina

This paper discuss how to use the clustering analysis to discover and extract useful information from multi-temporal satellite images with low spatial resolution to improve the agricultural monitoring of sugarcane fields. A large database of satellite images and specific software were used to perform the images pre-processing, time series extraction, clustering method applying and data visualization on several steps throughout the analysis process. The pre-processing phase corresponded to an accurate geometric correction, which is a requirement for applications of time series of satellite images such as the agricultural monitoring. Other steps in the analysis process were accomplished by a graphical interface to improve the interaction with the users. Approach validation was done using NDVI images of sugarcane fields because of their economic importance as source of ethanol and as effective alternative to replace fossil fuels and mitigate greenhouse gases emissions. According to the analysis done, the methodology allowed to identify areas with similar agricultural development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution, such as that from the AVHRR/NOAA sensors, can indeed be satisfactorily used to monitor agricultural crops in regional scale.

Coffee is one of the most important crops in Brazil. Monitoring the crop is necessary to understand future production and a sound understanding of coffee's biophysical properties improves such monitoring. Biophysical properties such as dry biomass can be estimated using remote sensing, including the new generation of high-resolution images (GeoEye-1, for instance). In this study we aim to investigate the relationship between vegetation indices (VI) of high-resolution images (GeoEye-1) and coffee biophysical properties, including dry biomass and carbon. The study also aims at establishing an empirical relationship between remote sensing data (vegetation indices), simple field measurements and dry biomass, allowing calculation of coffee biomass and carbon without resorting to destructive methods. Individual GeoEye-1 satellite's bands (NIR, RED and GREEN) showed significant correlation with biomass, but the best correlation occurred with vegetation index. There is a strong correlation between NDVI, RVI, GNDVI and dry biomass, allowing the estimation of coffee crops' carbon stock. RVI had correlation with plant area index (PAI). The empirical correlation was established and the forecast equation of coffee biomass was created.

The objective of this work was to assess the potential impact of climate change on the spatial distribution of coffee nematodes (races of Meloidogyne incognita) and leaf miner (Leucoptera coffeella), using a Geographic Information System. Assessment of the impacts of climate change on pest infestations and disease epidemics in crops is needed as a basis for revising management practices to minimize crop losses as climatic conditions shift. Future scenarios focused on the decades of the 2020's, 2050's, and 2080's (scenarios A2 and B2) were obtained from five General Circulation Models available on Data Distribution Centre from Intergovernmental Panel on Climate Change. Geographic distribution maps were prepared using models to predict the number of generations of the nematodes and leaf miner. Maps obtained in scenario A2 allowed prediction of an increased infestation of the nematode and of the pest, due to greater number of generations per month, than occurred under the climatological normal from 1961-1990. The number of generations also increased in the B2 scenario, but was lower than in the A2 scenario for both organisms. O objetivo deste trabalho foi avaliar os impactos potenciais da mudança climática sobre a distribuição espacial de nematóides (raças de Meloidogyne incognita) e do bicho-mineiro (Leucoptera coffeella) da cultura de café, com uso de Sistema de Informações Geográficas. O conhecimento dos impactos da mudança climática na ocorrência de pragas e doenças é de grande importância para o setor agrícola, pois permite a elaboração de estratégias para minimizar prejuízos futuros. Cenários futuros centrados nas décadas de 2020, 2050 e 2080 (cenários A2 e B2), foram obtidos a partir de cinco Modelos Climáticos Globais, disponibilizados pelo Centro de Distribuição de Dados do Painel Intergovernamental sobre Mudança do Clima. Por meio de modelos para previsão do número de gerações anuais do nematóide e do bicho-mineiro, foram elaborados mapas de distribuição geográfica. Os mapas obtidos no cenário A2 permitem verificar que poderá haver aumento na infestação, tanto do nematóide quanto da praga, pelo maior número de gerações por mês, quando se compara com a normal climatológica de 1961-1990. No cenário B2, o número de gerações também foi maior que no cenário atual, porém nota-se que é inferior ao observando no cenário A2.

The use of time series of meteorological satellite images, such as the AVHRR/NOAA, and agrometeorological data can be very useful in developing monitoring and forecasting methods for sugarcane crops because they are based on detection changes of space-time behavior. The knowledge about different sugarcane producing areas and climate in a given region is information required to develop models that can be applied simultaneously to several producing municipalities of sugarcane in order to assess the relation between NDVI and WRSI, the estimated productivity and the detection of similarity between the municipalities through distance functions. Thus, the main goal of this paper is to propose numerical models applied to monitor the sugarcane production based on time series of NDVI/AVHRR images and agrometeorological data. The regression method analyzes the relation between a single dependent variable (sugarcane production) and several independent variables (planted area, NDVI, WRSI), that is, use the independent variables whose values are known to predict the values of the selected dependent variable. The models proposed to estimate the sugarcane production using the variables planted area, NDVI and WRSI presented correlation coefficients (R2) around 0.9 and are able to estimate the sugarcane production for the state of Sao Paulo in Brazil.

In the last few years multi-temporal data obtained from remote sensing have been used in several applications with different purposes bringing important and successful results. In particular, time series extracted from NDVI images have become more and more useful to improve agricultural monitoring. Various satellites and sensors with diverse resolutions capture such images allowing the generation of a huge volume of data. The use of multiple sensors for analysis of agricultural fields can be a solution to long-term studies and also in cases when is necessary to use data from a long time or a specific date. Thus, whether we could combine data provided by sensors from different satellite we can probably obtain best results. However, the sensors have different specifications that may lead to erroneous results when working with more than one sensor indiscriminately.

The “food safety” issue has concerned governments from several countries. The accurate monitoring of agriculture have become important specially due to climate change impacts. In this context, the development of new technologies for monitoring are crucial. Finding previously unknown patterns that frequently occur on time series, known as motifs, is a core task to mine the collected data. In this work we present a method that allows a fast and accurate time series motif discovery. From the experiments we can see that our approach is able to efficiently find motifs even when the size of the time series goes longer. We also evaluated our method using real data time series extracted from remote sensing images regarding sugarcane crops. Our proposed method was able to find relevant patterns, as sugarcane cycles and other land covers inside the same area, which are really useful for data analysis.

Resumo: A análise de risco das mudanças climáticas globais sobre doenças de plantas é de grande importância para o setor agrícola, pois permite a avaliação de estratégias de manejo para minimizar prejuízos futuros. O presente trabalho teve por objetivo simular os cenários futuros de epidemias de ferrugem do cafeeiro (Hemileia vastatrix), pela elaboração de mapas de distribuição geográfica usando um modelo que estima o período de incubação do patógeno e dados de três modelos climáticos globais (CSIRO-Mk3.0, INM-CM3.0, and MIROC3.2.medres). O período da normal climatológica de 1961-1990 foi comparado com as décadas de 2020, 2050 e 2080, usando cenários A2 e B1 do IPCC. Mapas foram preparados com resolução espacial de 0,5 X 0,5 graus de latitude e longitude para dez Estados produtores do Brasil. As variáveis climáticas utilizadas foram temperaturas máximas e mínimas mensais. Os mapas obtidos no cenário A2 permitem verificar que há tendência de redução do período de incubação, quando se compara os cenários futuros com o clima de 1961-1990. No cenário B1, também foi observada redução do período, porém nota-se que é inferior ao cenário A2. Abstract: Risk analysis of climate change on plant diseases has great import an ce for agr icul tu re s in ce i t al lows t he eva lu at ion of management strategies to minimize future damages. This work aimed to s imu la te futu re s cena rios of coff ee rus t (Hemil eia vastatrix) epidemics by elaborating geographic distribution maps using a model that estimates the pathogen incubation period and the output from three General Circulation Models (CSIRO-Mk3.0, INM-CM3.0, and MIROC3.2.medres). The climatological normal from 1961-1990 was compared with that of the decades 2020s,2050s and 2080s using scenarios A2 and B1 from the IPCC. Maps were prepared with a spatial resolution of 0.5 × 0.5 degrees of lat itude and longitude for ten producing states in Brazil. The climate variables used were maximum and minimum monthly temperatures. The maps obtained in scenario A2 showed a tendency towards a reduction in the incubation period when future scenarios are compared with the climatological normal from 1961-1990. A reduction in the period was also observed in scenario B1, although smaller than that in scenario A2.

Satellite images time series have been used to study land surface, such as identification of forest, water, urban areas, as well as for meteorological applications. However, for knowledge discovery in large remote sensing databases can be use clustering techniques in multivariate time series. The clustering technique on three-dimensional time series of NDVI, albedo and surface temperature from AVHRR/NOAA satellite images was used, in this study, to map the variability of land use. This approach was suitable to accomplish the temporal analysis of land use. Additionally, this technique can be used to identify and analyze dynamics of land use and cover being useful to support researches in agriculture, even considering low spatial resolution satellite images. The possibility of extracting time series from satellite images, analyzing them through data mining techniques, such as clustering, and visualizing results in geospatial way is an important advance and support to agricultural monitoring tasks.

After extensive and devastating drought in the Sahel, Africa, in the late 60's and early 70's (1968-1973) which resulted in the deaths of thousands of people and millions of animals, the desertification issue has been considered in the international agenda being ultimately relevant for the scientific community and governments in the worldwide. In 1992, the United Nations (UN), through Agenda 21 Chapter 12, defined desertification as ”land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities”. Desertification exacerbates socio-economic problems such as poverty and migration, which mainly affects the most vulnerable people and communities, bringing risk to global food security [1]. According to the UN, the annual economic losses are close to 4 billion dollars, with a cost for recovery of 10 billion dollars per year on a global scale.

We present a review of literature on remote sensing spatial and temporal images resolution applied to agriculture purposes. In this paper, the defi nitions are reviewed and a focus is given on the applications of resolutions in agricultural studies. A few main applications with low, medium and high spatial resolution focus were selected for this review, especially in sugarcane and coffee crops, where remote sensing contributions are traditionally important. The paper starts with an overview of agriculture and remote sensing, focused on the history of spatial and temporal resolutions uses. This section is followed by a review of low spatial and high temporal resolution systems, with important results in sugarcane. In this section studies conclude that sensors like AVHRR/NOAA can significantly contribute to improve sugarcane monitoring, as well as help understanding of development and expansion. After that, we present medium and high spatial resolution systems, especially for coffee crops that have been showing good results in fi ne scale, with detailed information. Studies conclude that satellites like Geoeye and IKONOS can obtain biophysical information of the culture and satellites like Landsat can monitor the coffee growing. Finally the review presents a conclusion with some key recommendations.

Orientadores: Jurandir Zullo Junior, Luciana Alvim Santos Romani Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola Resumo: A produção de cana-de-açúcar no Brasil possui função cada vez mais estratégica na economia do país devido ao interesse da substituição de combustíveis fósseis por fontes de energia renováveis, como o etanol, com o propósito de diminuir as emissões de gases de efeito estufa (GEEs). Nesse sentido, é importante a proposição de soluções inovadoras e tecnologicamente viáveis para auxiliar a geração de modelos mais eficientes, objetivos, precisos, antecipados e adequados ao monitoramento e previsão das safras nacionais. Esse trabalho teve o objetivo principal de desenvolver modelos numéricos que avaliam a tendência da produção de cana-de-açúcar em escala regional utilizando dados agroclimáticos e espectrais de baixa resolução espacial. Áreas produtoras de cana-de-açúcar foram selecionadas e dados de produção, área e produtividade referentes ao período de 2001 a 2010 foram utilizados. As imagens do satélite AVHRR/NOAA foram processadas e corrigidas automaticamente permitindo a obtenção de perfis temporais mensais do índice de vegetação NDVI, temperatura de superfície e albedo. As condições agroclimáticas, ao longo do período de análise, foram descritas pelo índice agroclimático ISNA. As séries de dados foram avaliadas por meio de técnicas de análise de séries temporais multivariadas utilizando os métodos de agrupamentos K-Means, K-Medoids e Clarans. Com estes métodos de agrupamentos, foi possível identificar e mapear áreas de plantio de cana-de-açúcar e sua expansão. Modelos de previsão de produção da cultura foram gerados pelo método de regressão linear múltipla utilizando dados de área plantada e dos índices NDVI e ISNA. A partir dos modelos gerados, foi possível avaliar o comportamento da variação da produção para o modelo regional de mudanças climáticas ETA/CPTEC em um cenário de alta emissão de gases de efeito estufa para o ano de 2020. Ao utilizar satélites de baixa resolução espacial, é mais difícil evidenciar a diferença entre tipos de plantios de cana-de-açúcar, mas neste trabalho, realizado com a técnica de agrupamento de dados, mesmo ocorrendo mistura espectral, foi possível acompanhar a evolução da cultura ao longo das safras identificando regiões com padrões semelhantes. Desta maneira, a análise de agrupamentos pode melhorar a compreensão do desenvolvimento da cana-de-açúcar e sua expansão para outras regiões do país. Os modelos gerados para estimar a produção de cana-de-açúcar em relação aos dados de área plantada e dos índices NDVI e ISNA apresentaram coeficientes de correlação (R2) em torno de 0,9 e conseguiram estimar os valores de produção para todo estado de São Paulo com precisão. Os modelos gerados para anos mais secos, no período de dezembro a março, indicaram que a produção de cana-de-açúcar tende a aumentar em torno de 3,5%. Para anos mais chuvosos, no mesmo período, a produção tende a aumentar em torno de 1,6% no ano de 2020, independentemente da área plantada. Os resultados obtidos são úteis para o planejamento agrícola em escala regional, pois permitem acompanhar o desenvolvimento e a produção da cana-de-açúcar de forma objetiva e sistemática a partir de dados fornecidos por sensores remotos, estações agroclimáticas e modelos de mudanças climáticas Abstract: The sugarcane production in Brazil has increasingly strategic role in the economy due to the interest of replacing fossil fuels with renewable energy sources such as ethanol, in order to reduce emissions of greenhouse gases (GHGs). In this sense, it is important to propose innovative and technologically feasible solutions to assist the generation of more efficient, objective, accurate, anticipated and appropriate models to the monitoring and forecasting of national crop. The main objective of this work was to develop numerical models that assess the trend of sugarcane production on a regional scale using agroclimatic and spectral data of lower spatial resolution. Producing areas of sugarcane were selected and data production, area and yield were used for the period 2001-2010. The images from satellite AVHRR/NOAA were processed and automatically corrected and subsequently were obtained monthly temporal profiles of NDVI vegetation index, surface temperature and albedo. The climatic conditions during the period of analysis, were described by WRSI agroclimatic index. The data sets were analyzed by techniques of multivariate time series analysis using the methods of clusters K-Means, K-Medoids and Clarans. With these clustering methods, it was possible to identify and map the expansion of areas planted with sugarcane. Predictive models of crop production were generated by multiple linear regression analysis using data from cropland and NDVI and WRSI indices. From the generated models, it was possible to evaluate the behavior of the variation in production for regional climate change model ETA/CPTEC in a scenario of high emission of greenhouse gases for the year 2020. It is more difficult to highlight the difference between types of plantings of sugarcane using low spatial resolution satellites, but this work with technical data clustering, even occurring spectral mixture, it was possible to follow the evolution of the culture over the crops identifying regions with similar patterns. Thus, the cluster analysis can improve understanding of the development of sugarcane and its expansion to other regions of the country. The models for estimating the production of sugarcane with the data of planted area and NDVI and WRSI indices showed a correlation coefficient (R2) around 0.9 and were able to estimate the values of production for the entire state of São Paulo accurately. The models for the driest years in the period from December to March, indicated that the production of sugarcane tends to increase around 3.5%. Wettest years in the same period, production tends to increase around 1.6% in 2020, regardless the cultivated area. The results are useful for agricultural planning at the regional scale because they allow to monitor the development and production of sugarcane objectively and systematically from data provided by remote sensors, agroclimatic stations and climate change models Doutorado Planejamento e Desenvolvimento Rural Sustentável Doutora em Engenharia Agrícola

The amount of data generated and stored in many domains has increased in the last years. In remote sensing, this scenario of bursting data is not different. As the volume of satellite images stored in databases grows, the demand for computational algorithms that can handle and analyze this volume of data and extract useful patterns has increased. In this context, the computational support for satellite images data analysis becomes essential. In this work, we present the SITSMining framework, which applies a methodology based on data clustering and classification to extract patterns and information from time series obtained from satellite images. In Brazil, as the agricultural production provides great part of the national resources, the analysis of satellite images is a valuable way to help crops monitoring over seasons, which is an important task to the economy of the country. Thus, we apply the framework to analyze multitemporal satellite images, aiming to help crop monitoring and forecasting of Brazilian agriculture.

According to IPCC, the increase of greenhouse gases emissions (GHG) in atmosphere is causing global warming, and this phenomenon could increase global temperature. In tropical areas of Brazil, the air temperature is supposed to increase from 1.1°C to 6.4°C causing large impacts in agricultures areas, including coffee production regions. The main objective of this paper was quantify the biomass of Arabica coffee trees above-ground (and carbon stock) using the vegetation index NDVI based on a high resolution image (Geoeye-1) and biophysical measures of coffee trees. In addition, the study aimed to establish an empirical relationship between biophysical measures of Arabica coffee trees, remote sensing data and dry biomass. The study was conducted in the south of Minas Gerais, which is the main producing region of Arabica coffee in Brazil. It was conclude that NDVI based on images of high spatial resolution, such as from Geoeye-1 satellite, has a strong correlation with dry biomass and carbon sink, showing that it is possible to estimate the carbon stock of coffee crops using remote sensing data without destructive methods.

This paper presents an analysis of relation between EVI and TRMM data to improve the monitoring of sugarcane production in south-central Brazil. As this region has a deficient network of ground-based meteorological stations, we proposed to use TRMM satellite data in order to complete lack of data. As both data from TRMM and meteorological ground-based stations presented a high correlation, as well as there are cross-correlation between precipitation and vegetation index data, we proposed a formula to estimate EVI values from TRMM precipitation series. Results indicate the potential of using medium spatial resolution satellite in agriculture specially to regional monitoring in a country of continental dimensions such as Brazil.

This paper discusses how to take advantage of clustering techniques to analyze and extract useful information from multi-temporal images of low spatial resolution satellites to monitor the sugarcane expansion. Additionally, we introduce the SatImagExplorer system that was developed to automatically extract time series from a huge volume of remote sensing images as well as provide algorithms of clustering analysis and geospatial visualization. According to experiments accomplished with spectral images of sugarcane fields, this proposed approach can be satisfactorily used in crop monitoring.

CONTEXT: Bluish discoloration and swelling of the scrotum in newborns can arise from a number of diseases, including torsion of the testes, orchitis, scrotal or testicular edema, hydrocele, inguinal hernia, meconium peritonitis, hematocele, testicular tumor and traumatic hematoma. Forty-two cases of scrotal abnormalities as signs of neonatal adrenal hemorrhage were found in the literature. CASE REPORT: We present a case of scrotal hematoma due to adrenal hemorrhage in a newborn. Conservative treatment with clinical follow-up was adopted, with complete resolution within 10 days. The possible differential diagnoses are reviewed and discussed.

Brazil is an important sugar cane producer, which is the main resource for ethanol production, a renewable source of energy. This agricultural commodity is important to the country economy, becoming fundamental to improve models that assist the crops monitoring process. Vegetation indexes originated from remote sensing images and agrometeorological indexes can be combined to represent sugar cane fields in a regional scale. However, finding different regions with similar patterns to classify or analyze their characteristics is a non-trivial task. Accordingly, this paper presents a method to find similar sugar cane fields represented by series of vegetation and agrometeorological indexes. The proposed method combines a weighted distance function with an algorithm to find similar objects. Results were coincident in the most cases with the classification done by experts, finding regions with similar characteristics of climate and productivity. Consequently, this approach can help in decision making processes by agricultural entrepreneurs.

Orientador: Jurandir Zullo Junior Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola Resumo: O Brasil, maior produtor de cana-de-açúcar do mundo, conta com uma posição privilegiada para atender as necessidades mundiais de açúcar e álcool anidro. O país possui regiões produtoras, sendo o estado de São Paulo a maior delas, com safras alternadas que podem garantir a presença do produto nos mercados local e mundial. Isto requer, cada vez mais, a existência de sistemas operacionais que permitam acompanhar o desenvolvimento da cultura ao longo da safra em escala regional. Por outro lado, há um aumento crescente na disponibilidade de dados de sensores remoto que podem ser aplicados no monitoramento da vegetação agrícola, especialmente das que ocupam áreas mais extensas, como é o caso da cana-de-açúcar. Dentro deste contexto, o trabalho teve o objetivo principal de avaliar a correlação existente entre um índice agroclimático (ISNA) e a resposta espectral (NDVI) da cana-de-açúcar registrada nas imagens do satélite AVHRR/NOAA, em regiões produtoras do estado de São Paulo, de 2001 a 2008. As imagens utilizadas foram processadas automaticamente pelo Sistema NAVPRO que gerou imagens corrigidas radiométrica e geometricamente. Com as imagens corrigidas, gerou-se imagens composições de valor máximo de NDVI mensais e perfis temporais de NDVI mensais. As condições agroclimáticas, ao longo do período de análise, foram descritas pelo índice ISNA (Índice de Satisfação das Necessidades de Água). Para calculá-lo, fez-se o balanço hídrico e calculou-se as evapotranspirações real e máxima, em períodos decendiais, quinzenais e mensais. A análise de correlação cruzada entre os dados procurou identificar como as variações climáticas ocorridas de 2001 a 2007 influenciaram a resposta espectral da cana-de-açúcar registrada nas imagens do AVHRR/NOAA. Estas análises identificaram correlações significativas entre o ISNA e o NDVI. Os modelos gerados pelas análises de séries temporais multivariadas foram bem ajustados. Para todos os modelos, o ISNA antecede o NDVI, isto é, o NDVI depende do ISNA em todos os municípios e as previsões feitas ficaram dentro do intervalo de confiança, tornando, assim, os resultados satisfatórios. Os modelos de previsão desenvolvidos permitem utilizar os resultados de safras passadas como base para a estimativa dos parâmetros espectral e agroclimático de safras futuras Abstract: O Brasil, maior produtor de cana-de-açúcar do mundo, conta com uma posição privilegiada para atender as necessidades mundiais de açúcar e álcool anidro. O país possui regiões produtoras, sendo o estado de São Paulo a maior delas, com safras alternadas que podem garantir a presença do produto nos mercados local e mundial. Isto requer, cada vez mais, a existência de sistemas operacionais que permitam acompanhar o desenvolvimento da cultura ao longo da safra em escala regional. Por outro lado, há um aumento crescente na disponibilidade de dados de sensores remoto que podem ser aplicados no monitoramento da vegetação agrícola, especialmente das que ocupam áreas mais extensas, como é o caso da cana-de-açúcar. Dentro deste contexto, o trabalho teve o objetivo principal de avaliar a correlação existente entre um índice agroclimático (ISNA) e a resposta espectral (NDVI) da cana-de-açúcar registrada nas imagens do satélite AVHRR/NOAA, em regiões produtoras do estado de São Paulo, de 2001 a 2008. As imagens utilizadas foram processadas automaticamente pelo Sistema NAVPRO que gerou imagens corrigidas radiométrica e geometricamente. Com as imagens corrigidas, gerou-se imagens composições de valor máximo de NDVI mensais e perfis temporais de NDVI mensais. As condições agroclimáticas, ao longo do período de análise, foram descritas pelo índice ISNA (Índice de Satisfação das Necessidades de Água). Para calculá-lo, fez-se o balanço hídrico e calculou-se as evapotranspirações real e máxima, em períodos decendiais, quinzenais e mensais. A análise de correlação cruzada entre os dados procurou identificar como as variações climáticas ocorridas de 2001 a 2007 influenciaram a resposta espectral da cana-de-açúcar registrada nas imagens do AVHRR/NOAA. Estas análises identificaram correlações significativas entre o ISNA e o NDVI. Os modelos gerados pelas análises de séries temporais multivariadas foram bem ajustados. Para todos os modelos, o ISNA antecede o NDVI, isto é, o NDVI depende do ISNA em todos os municípios e as previsões feitas ficaram dentro do intervalo de confiança, tornando, assim, os resultados satisfatórios. Os modelos de previsão desenvolvidos permitem utilizar os resultados de safras passadas como base para a estimativa dos parâmetros espectral e agroclimático de safras futuras Mestrado Planejamento e Desenvolvimento Rural Sustentável Mestre em Engenharia Agrícola

O conhecimento dos prováveis impactos das mudanças climáticas globais sobre a ocorrência de doenças de plantas é de grande importância para o setor agrícola, pois permite a elaboração de estratégias de controle. O presente trabalho teve por finalidade estudar os possíveis impactos das mudanças climáticas sobre a sigatoka-negra da bananeira, por meio da elaboração de mapas de distribuição da doença confeccionados a partir dos cenários disponibilizados pelo IPCC. Os mapas mostraram que haverá redução da área favorável à doença no país. Tal redução será gradativa para as décadas de 2020, 2050 e 2080 e de forma mais acentuada no cenário A2 que no B2. Apesar disso, extensas áreas ainda continuarão favoráveis à ocorrência da doença, especialmente no período de novembro a abril. The knowledge of the probable impacts of global climatic changes on the occurrence of plant diseases is of great importance for the agricultural sector, since it allows the elaboration of control strategies. The aim of the present paper was to study the probable impacts of climatic changes on black Sigatoka disease of banana, by means of the elaboration of distribution maps of the disease using IPCC scenarios A2 and B2. The maps evidenced that there will be a reduction of the favorable area to the disease in the country. Such reduction will be gradual for the decades of 2020, 2050 and 2080 and higher for A2 scenario than for B2. Despite this, extensive areas will still continue favorable to the occurrence of the disease, especially in the period of November to April.

A ocorrência da mancha preta do amendoim está diretamente relacionada com os elementos climáticos. Desta maneira, a elaboração de mapas de distribuição geográfica e temporal contribui informações sobre o comportamento de patossistemas frente às mudanças climáticas. Assim, o objetivo deste trabalho foi avaliar a evolução da favorabilidade climática à ocorrência da mancha preta para as principais regiões produtoras de amendoim da Argentina e do Brasil em função das mudanças climáticas. Na elaboração dos mapas foram consideradas as principais áreas produtoras de amendoim da Argentina e do Brasil e os meses de janeiro a março. Para a elaboração dos mapas foi empregada a metodologia de análise integrada das projeções climáticas e do problema fitossanitário com suporte do geoprocessamento. Foram considerados o clima normal (1961-1990) e o clima futuro (201-2040, 2041-270 e 2071-2100). Diferentes regras foram adotadas para cada a Argentina e o Brasil, pois elas incorporam as particularidades da doença nas áreas produtoras de cada país, como efeito da interação das características próprias do patógeno virulento, hospedeiro suscetível e ambiente. Os cenários climáticos futuros apontam o aumento da favorabilidade para a ocorrência da mancha preta do amendoim para a Argentina e para o Brasil. Made available in DSpace on 2020-11-11T11:23:37Z (GMT). No. of bitstreams: 1 Evolucao-da-mancha-preta-do-amendoim-nas-principais-regioes-2020.pdf: 1387315 bytes, checksum: 1732127f2cf730d076502b10d472a57c (MD5) Previous issue date: 2020

We present a review of literature on remote sensing spatial and temporal images resolution applied to agriculture purposes. In this paper, the defi nitions are reviewed and a focus is given on the applications of resolutions in agricultural studies. A few main applications with low, medium and high spatial resolution focus were selected for this review, especially in sugarcane and coffee crops, where remote sensing contributions are traditionally important. The paper starts with an overview of agriculture and remote sensing, focused on the history of spatial and temporal resolutions uses. This section is followed by a review of low spatial and high temporal resolution systems, with important results in sugarcane. In this section studies conclude that sensors like AVHRR/NOAA can significantly contribute to improve sugarcane monitoring, as well as help understanding of development and expansion. After that, we present medium and high spatial resolution systems, especially for coffee crops that have been showing good results in fi ne scale, with detailed information. Studies conclude that satellites like Geoeye and IKONOS can obtain biophysical information of the culture and satellites like Landsat can monitor the coffee growing. Finally the review presents a conclusion with some key recommendations. International Issue. Título equivalente: Múltiplas resoluções do sensoriamento remoto aplicado à agricultura: uma revisão.