Sugarcane yield estimates using time series analysis of spot vegetation images

The current system used in Brazil for sugarcane (Saccharum officinarum L.) crop forecasting relies mainly on subjective information provided by sugar mill technicians and on information about demands of raw agricultural products from industry. This study evaluated the feasibility to estimate the yield at municipality level in São Paulo State, Brazil, using 10-day periods of SPOT Vegetation NDVI images and ECMWF meteorological data. Twenty municipalities and seven cropping seasons were selected between 1999 and 2006. The plant development cycle was divided into four phases, according to the sugarcane physiology, obtaining spectral and meteorological attributes for each phase. The most important attributes were selected and the average yield was classified according to a decision tree. Values obtained from the NDVI time profile from December to January next year enabled to classify yields into three classes: below average, average and above average. The results were more effective for 'average' and 'above average' classes, with 86.5 and 66.7% accuracy respectively. Monitoring sugarcane planted areas using SPOT Vegetation images allowed previous analysis and predictions on the average municipal yield trend.O atual sistema de previsão de safras para a cultura da cana-de-açúcar (Saccharum officinarum L.) usado no Brasil depende, em boa parte, de informações subjetivas, baseadas no conhecimento de técnicos do setor sucroalcooleiro e em informações sobre demanda de insumos na cadeia produtiva. Avaliou-se o uso de imagens decendiais de NDVI do sensor SPOT Vegetation e variáveis meteorológicas do modelo do ECMWF para inferir sobre os dados de produtividade oficiais registrados em municípios e safras previamente selecionados. Foram selecionados 20 municípios e sete safras compreendidas entre o período de 1999 e 2006. O ciclo de desenvolvimento da cultura foi dividido em quatro fases, de acordo com a fisiologia, gerando para cada fase atributos espectrais e meteorológicos. Foram selecionados os atributos mais relevantes para a classificação da produtividade média municipal e, por meio de árvore de decisão, a produtividade média municipal foi classificada. Valores extraídos do perfil temporal do NDVI entre os meses de dezembro e janeiro permitiram classificar a produtividade em três classes: abaixo da média, média e acima da média. Os resultados foram mais efetivos para as classes "média" e "acima da média", com acertos de 86,5 e 66,7%, respectivamente. O monitoramento de áreas canavieiras do estado de São Paulo por meio de imagens SPOT Vegetation permitiu inferir sobre a tendência da produtividade média municipal previamente

Climatic risk of grape downy mildew (Plasmopara viticola) for the State of São Paulo, Brazil

A viticultura brasileira tem apresentado importância crescente nos últimos anos. Em São Paulo, a expressiva produção é destinada basicamente ao consumo in natura e, mais recentemente, podem ser observados esforços de diversas instituições no sentido de revitalizar a viticultura no estado. O míldio (Plasmopara viticola) é uma das principais doenças da cultura no Brasil, com efeitos extremamente danosos à sua produção. No presente trabalho foi estimada a severidade do míldio da videira nas condições climáticas do estado de São Paulo, com base num modelo de estimativa da doença e empregando-se um Sistema de Informações Geográficas - SIG. O estudo considerou os meses de setembro a abril, período em que a doença pode afetar as videiras em desenvolvimento. Os valores básicos de entrada no banco de dados do SIG foram temperatura média e umidade relativa. A duração do período de molhamento foliar foi estimada a partir dos dados de umidade relativa. Com os dados climáticos organizados no SIG, foram calculados e produzidos os mapas de severidade do míldio da videira, aplicando-se o modelo de estimativa. Também foram avaliados três municípios (Jales, Jundiaí e São Miguel Arcanjo), representativos de importantes pólos vitícolas do estado. O método possibilitou caracterizar de forma quantitativa a severidade do míldio da videira, tanto espacialmente, entre as regiões do estado, como temporalmente, ao longo dos meses, diferenciando de forma adequada os municípios estudados.Viticulture in Brazil has been growing in importance in recent years. In the State of São Paulo, a significant percentage of the production is basically destined to in natura consumption and, more recently, much effort has been made by institutions to revitalize the viticulture in the State. Among fungal diseases, the downy mildew (Plasmopara viticola) is one of the main diseases affecting this crop in Brazil, with extreme damage effects on its production. The objective of this study was to estimate the incidence of the downy mildew on grape under the climatic conditions of the State of São Paulo, based on a mathematical model and using Geographical Information System - GIS tools. The study considered the months from September to April, a period in which the downy mildew can affect grapevines under development. Mean temperature and relative humidity were the basic weather data entered in the GIS database. Leaf wetness duration was estimated from relative humidity measurements. Climatic data entered in the GIS were used to calculate and produce maps depicting the severity of the grape downy mildew, through the application of a disease model. Three cities were evaluated (Jales, Jundiaí, and São Miguel Arcanjo), since they represent the main vineyard centers in the State. The adopted methodology permitted quantifying the severity of the grape downy mildew not only in spatial terms, identifying the variability among the different regions of the State, but also in temporal terms, along the months, making an adequate distinction of the studied cities

Spatial and temporal variability of leaf wetness duration in the State of São Paulo, Brazil

A duração do período de molhamento (DPM) foliar é um dos principais fatores para a ocorrência de doenças de planta. No entanto, esse parâmetro climático não é geralmente registrado sistematicamente nas estações meteorológicas, tendo-se como alternativa estimar a DPM por meio de modelos matemáticos. Este trabalho teve como objetivo estimar a DPM para o estado de São Paulo, baseando-se no número de horas com umidade relativa igual ou superior a 90%, e também espacializá-la utilizando as ferramentas de Sistema de Informações Geográficas (SIG). Utilizando resultados diários de umidade relativa de dez estações meteorológicas do estado, de seis anos, foram obtidas para diferentes períodos as equações de ajuste de DPM (horas dia-1) (R² de 0,58 a 0,81) e de número de dias (ND) ao mês com DPM por um período igual ou superior a dez horas consecutivas (R² de 0,57 a 0,75), ambas em função da umidade relativa média. A DPM média e ND média variaram entre as regiões do estado e nos períodos do ano. Os menores valores estimados de DPM e de ND médias anuais foram observados na região oeste do estado e os maiores valores na região litorânea.One of the main factors influencing the occurrence of plant diseases is the leaf wetness duration (LWD). However, this climatic parameter is not generally and systematically recorded at meteorological stations, and the alternative to obtain an estimate for LWD is the use of mathematical models. The objective of this study was to estimate LWD for the State of São Paulo, on the basis of the number of hours with relative humidity equal to or higher than 90%, and also plot them on a map with help of the Geographical Information System (GIS) tool. Using daily relative humidity data from ten meteorological stations of the State, for six years, adjustment equations were obtained for different LWD periods (hours day-1) (R² from 0.58 to 0.81) and of number of days (ND) per month with LWD for a period equal to or higher than ten consecutive hours (R² from 0.57 to 0.75), both as functions of the mean relative humidity. The mean LWD and the mean ND varied among the different regions of the State and different periods of the year. The smallest estimated values of mean annual LWD and ND were observed for the west region of the State, and the highest values for the coastal region

Variabilidade espacial e temporal da duração do período de molhamento foliar no Estado de São Paulo, Brasil

One of the main factors influencing the occurrence of plant diseases is the leaf wetness duration (LWD). However, this climatic parameter is not generally and systematically recorded at meteorological stations, and the alternative to obtain an estimate for LWD is the use of mathematical models. The objective of this study was to estimate LWD for the State of São Paulo, on the basis of the number of hours with relative humidity equal to or higher than 90%, and also plot them on a map with help of the Geographical Information System (GIS) tool. Using daily relative humidity data from ten meteorological stations of the State, for six years, adjustment equations were obtained for different LWD periods (hours day-1) (R² from 0.58 to 0.81) and of number of days (ND) per month with LWD for a period equal to or higher than ten consecutive hours (R² from 0.57 to 0.75), both as functions of the mean relative humidity. The mean LWD and the mean ND varied among the different regions of the State and different periods of the year. The smallest estimated values of mean annual LWD and ND were observed for the west region of the State, and the highest values for the coastal region.A duração do período de molhamento (DPM) foliar é um dos principais fatores para a ocorrência de doenças de planta. No entanto, esse parâmetro climático não é geralmente registrado sistematicamente nas estações meteorológicas, tendo-se como alternativa estimar a DPM por meio de modelos matemáticos. Este trabalho teve como objetivo estimar a DPM para o estado de São Paulo, baseando-se no número de horas com umidade relativa igual ou superior a 90%, e também espacializá-la utilizando as ferramentas de Sistema de Informações Geográficas (SIG). Utilizando resultados diários de umidade relativa de dez estações meteorológicas do estado, de seis anos, foram obtidas para diferentes períodos as equações de ajuste de DPM (horas dia-1) (R² de 0,58 a 0,81) e de número de dias (ND) ao mês com DPM por um período igual ou superior a dez horas consecutivas (R² de 0,57 a 0,75), ambas em função da umidade relativa média. A DPM média e ND média variaram entre as regiões do estado e nos períodos do ano. Os menores valores estimados de DPM e de ND médias anuais foram observados na região oeste do estado e os maiores valores na região litorânea.2631Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost