Econometric ridge regression models of risk-sensitive sunflower yield / Modelos economométricos de regressão de rendimento de girassol sensível ao risco

The article considers econometric ridge regression models of the risk-sensitive sunflower yield on the example of an export-oriented agricultural crop. In particular, we have proved that despite the functional mulcollinearity of the predictors in the sunflower yield model with respect to risk caused by the algorithm peculiarities of the hierarchy analysis methods, the ridge regression procedure makes it possible to obtain its complete specification and provide biased but stable estimates of the forecast parameters in the case of uncertain input variables. It has been substantiated that the rational value of the displacement parameters is expedient to be established using a graphical interpretation of the ridge wake as the border of fast and slow fluctuations in the estimates of the ridge regression coefficients. Econometric models were calculated using SPSS Statistics, Mathcad and FAR-AREA 4.0 software. The empirical basis for forecast calculations was the assessment of trends in sunflower production in all categories of farms in the Rostov region of Russia for the period of 2008-2018. The calculation results of econometric models made it possible to develop three author's scenarios for the sunflower production in the region, namely, inertial, moderate, and optimistic ones that consider the export-oriented strategy of the agro-industrial complex.(AU)

O artigo considera modelos econométricos de regressão de rendimento de girassol sensível ao risco sobre o exemplo de uma cultura agrícola orientada para a exportação. Em particular, provamos que apesar da multicolinearidade funcional dos preditores no modelo de rendimento de girassol com relação ao risco causado pelas peculiaridades dos algoritmos dos métodos de análise hierárquica, o procedimento de regressão de cristas permite obter sua especificação completa e fornecer estimativas tendenciosas, mas estáveis dos parâmetros de previsão no caso de variáveis de entrada incertas. Foi comprovado que o valor racional dos parâmetros de deslocamento é conveniente de ser estabelecido usando uma interpretação gráfica da esteira da crista como fronteira das flutuações rápidas e lentas nas estimativas dos coeficientes de regressão da crista. Os modelos econométricos foram calculados usando o software SPSS Statistics, Mathcad e FAR-AREA 4.0. A base empírica para os cálculos de previsão foi a avaliação das tendências da produção de girassol em todas as categorias de fazendas na região de Rostov na Rússia para o período de 2008-2018. Os resultados dos cálculos dos modelos econométricos permitiram desenvolver três cenários de autor para a produção de girassol na região, a saber, os cenários inercial, moderado e otimista que consideram a estratégia orientada à exportação do complexo agroindustrial.(AU)

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

Soybean crops occupy most areas in Rio Grande do Sul State and are highly dependent on rainfall since most of them are non-irrigated. Rainfall during the harvest period is often insufficient to meet the water demand, making water indicators an important tool for the crops. This study compared two approaches in the parameterization process of TVDI (Temperature-Vegetation Dryness Index) in a subtropical climate region of Brazil. The process used Moderate Resolution Imaging Spectroradiometer (MODIS) images of the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI), with spatial resolutions of 1,000 m and periods of 8-16 d, respectively. The evaporative triangles for the TS/NDVI scatter plots were built either for each image (scene-specific parameterization) or for all images at once (crop-type parameterization). The rainfall data were obtained from meteorological stations located in the study site and the analysis period comprised two contrasting harvests regarding soybean yield (most important crop in the region). The scene-specific parameterization allowed to analyze water status in the study site by inspecting the wet and dry edge of each image and identifying the areas of stress in each one. TVDI crop parameterization showed that the model was able to determine the time and frequency of water stress events during the crop-seasons. TVDI crop-parameterization, therefore, is more consistent for crop monitoring and forecasting purposes.(AU)

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

Soybean crops occupy most areas in Rio Grande do Sul State and are highly dependent on rainfall since most of them are non-irrigated. Rainfall during the harvest period is often insufficient to meet the water demand, making water indicators an important tool for the crops. This study compared two approaches in the parameterization process of TVDI (Temperature-Vegetation Dryness Index) in a subtropical climate region of Brazil. The process used Moderate Resolution Imaging Spectroradiometer (MODIS) images of the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI), with spatial resolutions of 1,000 m and periods of 8-16 d, respectively. The evaporative triangles for the TS/NDVI scatter plots were built either for each image (scene-specific parameterization) or for all images at once (crop-type parameterization). The rainfall data were obtained from meteorological stations located in the study site and the analysis period comprised two contrasting harvests regarding soybean yield (most important crop in the region). The scene-specific parameterization allowed to analyze water status in the study site by inspecting the wet and dry edge of each image and identifying the areas of stress in each one. TVDI crop parameterization showed that the model was able to determine the time and frequency of water stress events during the crop-seasons. TVDI crop-parameterization, therefore, is more consistent for crop monitoring and forecasting purposes.

A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). A individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management.(AU)

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Paraná State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha1 to 0.19 t ha1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha1 to 0.35 t ha1. The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Paraná State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha1 to 0.19 t ha1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha1 to 0.35 t ha1. The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.(AU)

Yield-scaled global warming potential of two irrigation management systems in a highly productive rice system

Water management impacts both methane (CH4) and nitrous oxide (N2O) emissions from rice paddy fields. Although controlled irrigation is one of the most important tools for reducing CH4emission in rice production systems it can also increase N2O emissions and reduce crop yields. Over three years, CH4 and N2O emissions were measured in a rice field in Uruguay under two different irrigation management systems, using static closed chambers: conventional water management (continuous flooding after 30 days of emergence, CF30); and an alternative system (controlled deficit irrigation allowing for wetting and drying, AWDI). AWDI showed mean cumulative CH4 emission values of 98.4 kg CH4 ha1, 55 % lower compared to CF30, while no differences in nitrous oxide emissions were observed between treatments ( p > 0.05). No yield differences between irrigation systems were observed in two of the rice seasons ( p > 0.05) while AWDI promoted yield reduction in one of the seasons ( p 0.05). When rice yield and greenhouse gases (GHG) emissions were considered together, the AWDI irrigation system allowed for lower yield-scaled total global warming potential (GWP). Higher irrigation water productivity was achieved under AWDI in two of the three rice seasons. These findings suggest that AWDI could be an option for reducing GHG emissions and increasing irrigation water productivity. However, AWDI may compromise grain yield in certain years, reflecting the importance of the need for fine tuning of this irrigation strategy and an assessment of the overall tradeoff between relationships in order to promote its adoption by farmers.

Yield-scaled global warming potential of two irrigation management systems in a highly productive rice system

Water management impacts both methane (CH4) and nitrous oxide (N2O) emissions from rice paddy fields. Although controlled irrigation is one of the most important tools for reducing CH4emission in rice production systems it can also increase N2O emissions and reduce crop yields. Over three years, CH4 and N2O emissions were measured in a rice field in Uruguay under two different irrigation management systems, using static closed chambers: conventional water management (continuous flooding after 30 days of emergence, CF30); and an alternative system (controlled deficit irrigation allowing for wetting and drying, AWDI). AWDI showed mean cumulative CH4 emission values of 98.4 kg CH4 ha1, 55 % lower compared to CF30, while no differences in nitrous oxide emissions were observed between treatments ( p > 0.05). No yield differences between irrigation systems were observed in two of the rice seasons ( p > 0.05) while AWDI promoted yield reduction in one of the seasons ( p 0.05). When rice yield and greenhouse gases (GHG) emissions were considered together, the AWDI irrigation system allowed for lower yield-scaled total global warming potential (GWP). Higher irrigation water productivity was achieved under AWDI in two of the three rice seasons. These findings suggest that AWDI could be an option for reducing GHG emissions and increasing irrigation water productivity. However, AWDI may compromise grain yield in certain years, reflecting the importance of the need for fine tuning of this irrigation strategy and an assessment of the overall tradeoff between relationships in order to promote its adoption by farmers.(AU)

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.

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.

Agrometeorological models for 'Valencia' and 'Hamlin' sweet oranges to estimate the number of fruits per plant

The development of models that allow forecasting yield tendencies is important to all sectors of the citrus industry. This work evaluated the influence of meteorological variables in different phases of the crop cycle in order to propose empirical models to estimate the number of fruits per plant (NFP) of 'Valencia' and 'Hamlin' sweet oranges. NFP sampling data from the citrus juice industry of the State of São Paulo, on the total of 15 harvests (1990/91 to 2004/05), classified into three age classes, and meteorological data of maximum and minimum air temperature and rainfall of Limeira, SP, Brazil, were utilized. Correlation coefficients were initially computed between the number of fruits per plant and each meteorological variable used for water balance and variables related to air temperature, in different periods. Linear multiple regression models were fit to describe the empirical relationship between NFP and the subsets of agrometeorological predictors that presented higher correlations in different phases of the crop cycle. The meteorological conditions during the phases of vegetative summer flush, pre-flowering, flowering and beginning of fruit growth influenced the number of fruits per plant. The proposed models presented adequate goodness-of-fit with determination coefficients varying from 0.72 to 0.87.

O desenvolvimento de modelos para previsões de tendências de produtividade é de grande importância para todos os elos da cadeia produtiva de citros. Buscou-se avaliar a influência de variáveis meteorológicas, em diferentes fases do ciclo da cultura, para propor modelos empíricos para estimação do número de frutos por planta em laranjeira 'Valência' e laranjeira 'Hamlin'. Utilizaram-se dados amostrais, provenientes da indústria de suco paulista, de número de frutos por planta (NFP), em três classes de idade, referentes aos valores estimados anuais de produtividade, no total de 15 safras (1990/91 a 2004/05), e dados meteorológicos (temperatura do ar e precipitação pluvial) para o município de Limeira, SP, Brasil. Foram determinados os coeficientes de correlação linear entre NFP e variáveis meteorológicas componentes do balanço hídrico e temperatura, em diferentes períodos. Modelos de regressão linear múltipla foram ajustados para os subconjuntos de variáveis meteorológicas que apresentaram as maiores correlações significativas com o NFP em diferentes fases do ciclo da cultura. As condições meteorológicas durante as fases de crescimento vegetativo de verão, pré-florescimento, florescimento e início de crescimento dos frutos influenciaram a produção de frutos por planta. Os modelos apresentaram boa qualidade de ajuste, com coeficiente de determinação variando de 0,72 a 0,87.

Agrometeorological models for 'Valencia' and 'Hamlin' sweet oranges to estimate the number of fruits per plant

The development of models that allow forecasting yield tendencies is important to all sectors of the citrus industry. This work evaluated the influence of meteorological variables in different phases of the crop cycle in order to propose empirical models to estimate the number of fruits per plant (NFP) of 'Valencia' and 'Hamlin' sweet oranges. NFP sampling data from the citrus juice industry of the State of São Paulo, on the total of 15 harvests (1990/91 to 2004/05), classified into three age classes, and meteorological data of maximum and minimum air temperature and rainfall of Limeira, SP, Brazil, were utilized. Correlation coefficients were initially computed between the number of fruits per plant and each meteorological variable used for water balance and variables related to air temperature, in different periods. Linear multiple regression models were fit to describe the empirical relationship between NFP and the subsets of agrometeorological predictors that presented higher correlations in different phases of the crop cycle. The meteorological conditions during the phases of vegetative summer flush, pre-flowering, flowering and beginning of fruit growth influenced the number of fruits per plant. The proposed models presented adequate goodness-of-fit with determination coefficients varying from 0.72 to 0.87.

O desenvolvimento de modelos para previsões de tendências de produtividade é de grande importância para todos os elos da cadeia produtiva de citros. Buscou-se avaliar a influência de variáveis meteorológicas, em diferentes fases do ciclo da cultura, para propor modelos empíricos para estimação do número de frutos por planta em laranjeira 'Valência' e laranjeira 'Hamlin'. Utilizaram-se dados amostrais, provenientes da indústria de suco paulista, de número de frutos por planta (NFP), em três classes de idade, referentes aos valores estimados anuais de produtividade, no total de 15 safras (1990/91 a 2004/05), e dados meteorológicos (temperatura do ar e precipitação pluvial) para o município de Limeira, SP, Brasil. Foram determinados os coeficientes de correlação linear entre NFP e variáveis meteorológicas componentes do balanço hídrico e temperatura, em diferentes períodos. Modelos de regressão linear múltipla foram ajustados para os subconjuntos de variáveis meteorológicas que apresentaram as maiores correlações significativas com o NFP em diferentes fases do ciclo da cultura. As condições meteorológicas durante as fases de crescimento vegetativo de verão, pré-florescimento, florescimento e início de crescimento dos frutos influenciaram a produção de frutos por planta. Os modelos apresentaram boa qualidade de ajuste, com coeficiente de determinação variando de 0,72 a 0,87.

Spectral variables, growth analysis and yield of sugarcane

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

A informação espectral tem boa relação com variáveis agronômicas e pode contribuir com informações para o monitoramento, acompanhamento e previsão de safras. O presente trabalho descreve a análise multitemporal do comportamento espectral da variedade de cana-de-açúcar SP80-1842 e a relação com variáveis agronômicas como índice de área foliar (IAF), número de perfilhos por metro (NPM), produtividade (TCH) e biomassa total (BMT). Nas safras 2000/2001 e 2001/2002, um talhão comercial, localizada no município de Araras/SP foi monitorado em nove campanhas de coleta de dados radiométricos e agronômicos. O comportamento temporal das variáveis espectrais acompanhou o comportamento das variáveis agronômicas. A banda 4 (B4), o índice de vegetação da razão simples (SR), o índice de vegetação por diferença normalizada (NDVI) e o índice de vegetação ajustado ao solo (SAVI) aumentaram seus valores até o fim da fase de crescimento vegetativo, aproximadamente até os 240 dias após o corte, a partir do qual os valores se estabilizaram e diminuíram em função da entrada da cultura na fase de maturação. A banda 3 (B3) e o índice de vegetação da razão (RVI) tiveram queda em seus valores desde o início do ciclo, com posterior estabilização e aumento em seus valores devido ao aumento da quantidade de palha e da queda da biomassa foliar. As variáveis espectrais B3, RVI, NDVI e SAVI tiveram correlações maiores que 0,79 com as variáveis IAF e BMT e de aproximadamente 0,50 com o NPM. Os melhores modelos de regressão linear múltipla foram os com RVI, IAF e NPM e explicaram 0,97 da variação da TCH e 0,99 da BMT.

Spectral variables, growth analysis and yield of sugarcane

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

A informação espectral tem boa relação com variáveis agronômicas e pode contribuir com informações para o monitoramento, acompanhamento e previsão de safras. O presente trabalho descreve a análise multitemporal do comportamento espectral da variedade de cana-de-açúcar SP80-1842 e a relação com variáveis agronômicas como índice de área foliar (IAF), número de perfilhos por metro (NPM), produtividade (TCH) e biomassa total (BMT). Nas safras 2000/2001 e 2001/2002, um talhão comercial, localizada no município de Araras/SP foi monitorado em nove campanhas de coleta de dados radiométricos e agronômicos. O comportamento temporal das variáveis espectrais acompanhou o comportamento das variáveis agronômicas. A banda 4 (B4), o índice de vegetação da razão simples (SR), o índice de vegetação por diferença normalizada (NDVI) e o índice de vegetação ajustado ao solo (SAVI) aumentaram seus valores até o fim da fase de crescimento vegetativo, aproximadamente até os 240 dias após o corte, a partir do qual os valores se estabilizaram e diminuíram em função da entrada da cultura na fase de maturação. A banda 3 (B3) e o índice de vegetação da razão (RVI) tiveram queda em seus valores desde o início do ciclo, com posterior estabilização e aumento em seus valores devido ao aumento da quantidade de palha e da queda da biomassa foliar. As variáveis espectrais B3, RVI, NDVI e SAVI tiveram correlações maiores que 0,79 com as variáveis IAF e BMT e de aproximadamente 0,50 com o NPM. Os melhores modelos de regressão linear múltipla foram os com RVI, IAF e NPM e explicaram 0,97 da variação da TCH e 0,99 da BMT.