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Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

The region of Areiópolis in São Paulo State is one of the major sugarcane producers in the world, and chemical management is the basis of the system making its production viable. Thus, the proposed methods for precision agriculture can be evaluated as an alternative for environment protection and can aid the search for greater productivity at the same time. The main objective of the present work was to compare the precision agriculture (PA) and traditional agriculture (TA) management systems and to highlight their distinctions, such as differences in grid sampling, production variation, plant failure and costs. Two experiments were set up, and the soil fertilizers were applied by corrective application methods to 16-ha lots using the average general fertility rate (GFR). The PA method had the highest productivity volume for conversion of green matter to sugar in the 4.0-ha plots. As the size of the PA plots decreased, the costs of soil analyses increased with potassium and lime analyses being the most expensive. The PA plots had more suitable grid sampling in terms of productivity, and the cost/benefit ratio was 4.0-ha. In general, the final cost was higher in the PA system compared to the TA system. The present results provide information to help select the better system between these techniques to manage tropical soils.

The aim of this study was to develop and test a method to determine and discriminate soil classes in the state of São Paulo, Brazil, based on spectral data obtained via Landsat satellite imagery. Satellite reflectance images were extracted from 185 spectral reading points, and discriminant equations were obtained to establish each soil class within the studied area. Sixteen soil classes were analyzed, and discriminant equations that comprised TM5/Landsat sensor bands 1, 2, 3, 4, 5, and 7 were established. The results showed that this methodology could effectively identify individual soil classes using discriminant analyses of the spectral data obtained from the surface. Success rates of > 40% were achieved for 14 of the 16 evaluated soil classes when applying the satellite image data. When the 10 soil classes containing the largest number of minimum cartographic areas were used, the hit rate increased to > 50%, for seven soil classes with a global hit rate of 52%. When the soil classes were grouped based on their parent materials, the hit rate increased to 70%. Thus, we concluded that the spectral method for soil classification was efficient.O objetivo deste trabalho foi desenvolver e testar um método para a determinação da classe de solo e sua separabilidade na paisagem dos solos presentes em uma área de estudo localizada no estado de São Paulo. Um conjunto de equações discriminantes foi obtido utilizando-se o sistema SAS que permitiu estabelecer a classe de solo na área de estudo. Foram analisadas 16 classes de solos as quais foram estabelecidas equações discriminantes compostas pelas bandas 1, 2, 3, 4, 5, e 7 do sensor TM5/ Landsat. As leituras espectrais foram realizadas em 185 pontos da área de estudo, donde se extraiu a reflectância da imagem. Classes de solos podem ser individualizadas por meio de análise discriminante utilizando-se informações sobre seu comportamento espectral obtida pela metodologia apresentada. A análise discriminante apresentou índices de acerto acima de 40% dentro da classe de solo avaliada, para 14 das 16 classes de solos. Utilizando-se as dez classes com maior número de áreas mínimas cartografadas o acerto, dentro a classe, foi maior que 50% para sete classes de solos, com acerto global estabelecido em 52%. Quando se agrupou as classes de solos em função do seu material de origem, o acerto passou para 70%.

Soil fertility variability management is one of the pioneering and important areas in which Precision Agriculture has been commercially applied. Consequently, the objective of this work was to predict soil chemical attributes through spectral responses. The 1,000 ha study area used for this report was located in Uberlândia, Minas Gerais State, Brazil. Thirty sampling points were established, at which the soil was collected at 3 different depths. The samples were chemically and physically analyzed and the radiometric data obtained in the 400 – 2500 nm range. Multiple regression equations were generated for sum of bases, cation exchange capacity, base saturation, aluminum saturation, pH, P, K, Ca, Mg, Al, and H, all using 60 soil samples. H, Al, m%, and pH were found to have R2 values less than 0.50. Equations with an R2 > 0.50 for the other attributes were tested for the 30 unknown soil samples, and the estimated values were obtained. These values were then compared with those determined by conventional analysis. The coefficients of correlation were higher than 50% for all attributes except P and V%. Results indicated that determining chemical attributes with models that are specific for the region is feasible. Uma das primeiras e mais importantes áreas nas quais a Agricultura de Precisão está sendo comercialmente aplicada é o manejo da variabilidade da fertilidade do solo. Desta forma, o objetivo deste trabalho foi predizer o teor dos atributos químicos do solo através da sua resposta espectral. A área de estudo de 1000 ha localiza-se em Uberlândia, Estado de Minas Gerais. Estabeleceu-se 30 pontos de amostragem nos quais o solo foi coletado em 3 profundidades. As amostras foram analisadas química e fisicamente e, os dados radiométricos obtidos com um sensor em laboratório na faixa de 400 – 2500 nm. Equações de regressão múltipla foram geradas para soma de bases, CTC, saturação por bases, saturação por alumínio, pH, P, K, Ca, Mg, Al e H usando 60 amostras de solo. H, Al, m% e pH obtiveram R2 < 0,50. As equações com R2 > 0,50 para os outros atributos foram testadas para as 30 amostras de solo desconhecidas obtendo-se os valores estimados. Posteriormente, estes valores foram comparados com aqueles determinados pela análise convencional. Os coeficientes de correlação foram maiores que 50% para todos os atributos exceto P e V%. Os resultados indicam que é possível determinar atributos químicos com modelos específicos para a região.

O objetivo deste trabalho foi integrar, em uma base de dados geoespacial, informações a respeito da distribuição dos solos na paisagem, bem como de seus atributos dentro de uma Reserva Particular do Patrimônio Natural (RPPN), situada no município de Lobato, Estado do Paraná. Todas as características de aspecto físico e morfológico obtidas foram incorporadas em um Sistema de Informações Geográficas (SIG). Diante dos dados das análises e do estudo de sua distribuição espacial, as unidades fisiográficas passaram a constituir unidades resultando na produção de mapas.The objective of this study was to integrate, into a geospatial database, information regarding the distribution of soils inthe landscape, as well as their attributes inside a Natural Heritage Private Reserve (RPNP) located in the city of Lobato. All physical and morphologic characteristics were incorporated into a Geographical Information System (GIS). Based on the analyzed data and the study oftheir spatial distribution, these physiographic units were eventually mapped.

Systematic, physically based acquisition of information regarding soils is required to meet increasing demand in agricultural and environmental systems. The objective of this work is to evaluate the use of multiple endmember spectral mixture analysis (MESMA) for mapping soil attributes within ASTER imagery. A total of 184 georeferenced soil samples were collected from Rafard, São Paulo State, Brazil. These points were overlain on the satellite image to collect spectral data. The laboratory and image information were then arranged and prepared by clustering samples into classes based on the following soil attributes: texture, organic matter, base saturation (V%), CEC and total iron. Following this classification, mean spectral curves were generated for each attribute class. Spectral curves were used as endmembers for the generation of maps using MESMA. Maps of the same attributes were also generated using geostatistical analyses. Based on the two generated maps, a cross-tabulation was used to evaluate the accuracy of MESMA for mapping soil attributes. Agreement was high for maps of the texture, organic matter, CEC and total iron. We conclude that the methodology used in this work was efficient for mapping soil attributes.

O objetivo deste trabalho foi integrar, em uma base de dados geoespacial, informações a respeito da distribuição dos solos na paisagem, bem como de seus atributos dentro de uma Reserva Particular do Patrimônio Natural (RPPN), situada no município de Lobato, Estado do Paraná. Todas as características de aspecto físico e morfológico obtidas foram incorporadas em um Sistema de Informações Geográficas (SIG). Diante dos dados das análises e do estudo de sua distribuição espacial, as unidades fisiográficas passaram a constituir unidades resultando na produção de mapas.