Diagnose nutricional da cana-de-açúcar em campos dos Goytacazes (RJ)

Reis, Ricardo Andrade; Monnerat, Pedro Henrique

doi:10.1590/s0100-06832002000200010

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…The municipality of Campos dos Goytacazes is located in the North Fluminense region of the state, and the basin where the municipality is inserted is responsible for more than 80% of the petroleum production of the country, and also stands out as a center of the sugarcane crop (Miranda et al, 2010;Reis Junior and Monnerat, 2002). Cordeiro belongs to the Serrana region, which is responsible for a large part of the production of vegetables in the state.…”

Section: Studied Localitiesmentioning

confidence: 99%

Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series

Coutinho

Silva

Madeira

et al. 2018

Rev. bras. meteorol.

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This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values.

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Section: Studied Localitiesmentioning

confidence: 99%

Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series

Coutinho

Silva

Madeira

et al. 2018

Rev. bras. meteorol.

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“…Comparisons between the DRIS and CND diagnostic methods are relatively extensive in the literature (Parent & Dafir, 1992;Parent, Cambouris, & Muhawenimana, 1994;Khiari, Parent, & Tremblay, 2001;Urano et al, 2007;Serra et al, 2010;Camacho, Silveira, Camargo, & Natale, 2012;Politi et al, 2013). However, few studies have compared these diagnoses in sugarcane (Reis Junior & Monnerat, 2002;Santos et al, 2013a). Sugarcane is grown in different regions in Brazil and the world, and optimal nutrient contents for high yields are strongly influenced by different growing conditions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Nutrient Balance in Sugarcane Using DRIS and CND Methods

Calheiros¹,

Freire²,

Filho³

et al. 2018

JAS

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Many methods of nutritional diagnosis present discordant reports. It is necessary to study how these diagnoses relate to agricultural productivity and nutrient balance for a more efficient nutritional monitoring of the crops. This study had two objectives: (1) evaluate and compare Diagnosis and Recommendation Integrated System (DRIS) and Compositional Nutrient Diagnosis (CND) methods for nutritional diagnosis of sugarcane cultivated in the Northeast of Brazil; (2) establish standards, identify and hierarchize nutritional limitations. The database consisted of 183 samples, in which 31 were in areas with high productivity (³ 80 Mg ha-1) and 152 of areas with low productivity (< 80 Mg ha-1). Sugarcane leaves were collected and contents of N, P, K, Ca, Mg, S, Fe, Zn, Cu, Mn and B were determined. The DRIS indexes were calculated by methods DRIS-Beaufils, DRIS-Jones, DRIS-Elwali and Gascho, M-DRIS Beaufils, M-DRIS Jones, and the indexes CND too were calculated. The DRIS-Beaufils, DRIS-Jones, M-DRIS Beaufils and M-DRIS Jones methods tended to agree on the nutritional diagnosis of sugarcane. The nutritional diagnosis of the CND method interpreted by the Potencial Fertilization Response (PFR) was different from the DRIS methods for N and Mn nutrients. The M-DRIS Beaufils and M-DRIS Jones methods showed a higher correlation with nutrient contents. However, there was no significant correlation between agricultural productivity and nutrient balance index mean (NBIm), suggesting that other factors influenced sugarcane production more than nutritional factors. The nutritional diagnosis methods identified excessive fertilization with N and limitations of Ca, Mg, K, S, Mn, Cu, Zn and B in sugarcane in the Northeast of Brazil.

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“…A Região Norte fluminense, especialmente o Município de Campos dos Goytacazes, se destaca um polo da cultura da canade-açúcar (Reis Júnior & Monnerat, 2002). Apesar da atividade canavieira fluminense não ter acompanhado o crescimento nacional do setor, ela continua sendo um dos principais suportes da agricultura do Estado do Rio de Janeiro, quer na geração de receita ou de empregos diretos e indiretos (Oliveira, 2005).…”

Section: Introductionunclassified

Variações do albedo, NDVI e SAVI durante um ciclo da cana-de-açúcar no Norte Fluminense

Esteves

Sousa

Mendonça

et al. 2012

Agraria

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RESUMOO albedo é um parâmetro relevante no balanço de radiação de uma superfície e bastante utilizado em modelos climáticos e agrometeorológicos, tais como estimativas do fluxo de vapor d'água e do saldo de radiação, dentre outros; entretanto, este parâmetro nem sempre está disponível. O objetivo deste estudo foi apresentar as variações do albedo, do Normalized Difference Vegetation Index (NDVI) e do Soil Adjusted Vegetation Index (SAVI) durante o ciclo da cultura da cana-de-açúcar; para tal, instalou-se uma estação micrometeorológica em uma área de 13 ha, em cultivo comercial da Fazenda Partido. O cultivo foi acompanhado periodicamente aos 85, 102, 128, 149, 174, 194, 215, 235, 255 e 280 dias após o corte (DAC) para obtenção da matéria seca e do índice de área foliar (IAF). O albedo foi obtido a partir dos dados gerados pelos piranômetros, instalados na estação. O NDVI e o SAVI foram obtidos durante obtidos o ciclo da cultura, foram obtidos a partir de imagens Moderate Resolution Imaging Spectroradiometer (MODIS) da plataforma Terra, sendo todas as rotinas computacionais necessárias executadas através do programa Erdas 8.7. As variações do albedo foram afetadas pelas condições hídricas do período. De maneira geral, o albedo acompanhou as variações do IAF. O NDVI e o SAVI foram influenciados pelas condições hídricas do período mas acompanharam as variações da interação IAF-albedo. Palavras-chave: balanço de radiação, IAF; índices de vegetação Variations of albedo, SAVI and NDVI during one cycle of sugarcane in the Norte Fluminense ABSTRACTThe albedo is an important parameter in the radiation balance of a surface and is commonly used in climate models and crop weather, such as estimates of the flow of water vapor and net radiation, among others, however, this parameter is not always available. The aim of this study was to show the variations of albedo, Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) during the growing cycle of sugarcane. For the purpose a micrometeorological station was installed in an area of 13 ha in commercial cultivation of Farm Partido. The crop was regularly monitored at 85, 102, 128, 149, 174, 194, 215, 235, 255 and 280 days after cutting (DAC), to obtain the total dry weight and leaf area index (LAI). The albedo was obtained from the data generated by pyramometers, installed at the station. The SAVI and NDVI during the crop cycle were obtained from images Moderate Resolution Imaging Spectroradiometer (MODIS), Terra platform, with all the necessary computational algorithms implemented by the program Erdas 8.7. The albedo variations followed LAI. The NDVI and SAVI were influenced by water conditions of the period, but generally followed the variations of the LAI-albedo interaction.

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Diagnose nutricional da cana-de-açúcar em campos dos Goytacazes (RJ)

Cited by 12 publications

References 9 publications

Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series

Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series

Assessment of Nutrient Balance in Sugarcane Using DRIS and CND Methods

Variações do albedo, NDVI e SAVI durante um ciclo da cana-de-açúcar no Norte Fluminense

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