Hyperspectral sensors and regression analysis have been used to analyze the most important spectral ranges for biophysical parameters of target crops, aiding in management decision-making. This study aimed to analyze the spectral response of Brachiaria brizantha cv. Marandú leaves to increasing rates of urea fertilization and predict leaf nitrogen content (LNC). Four rates of urea fertilization (0, 25, 50, and 75 kg of N ha -1 ) were applied. Eight leaves were collected per plot seven times at monthly intervals and subjected to hyperspectral analysis. Leaf spectral responses differed statistically within the visible region, particularly at 550 nm (green). The regression models achieved moderate to good R² values (0.53 to 0.83) for predicting LNC and identified important wavelengths in the red edge region (715 to 720 nm). These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. Marandú crops at different fertilization levels.

SP. mht@usp.br 4 Professor do Centro de Energia Nuclear na Agricultura, CENA/USP, Piracicaba -SP. qdjvlier@usp.br Artigo recebido em 13/09/2017 e aceito em 29/03/2018 R E S U M O Diversos fatores atuam nas condições climáticas de uma região, sendo esses classificados de acordo com sua microescala, topoescala ou macroescala. Entretanto, o fato de Cuba e o Brasil possuírem características climáticas semelhantes, e que as bacias selecionadas para o estudo fiquem na mesma faixa latitudinal, não significa que possuirão igual comportamento erosivo das precipitações. Visando aprimorar o conhecimento sobre a erosividade das chuvas em climas tropicais, este trabalho objetivou caracterizar o potencial erosivo das precipitações nas bacias do Rio Mogi Guaçu (Hemisfério Sul, Brasil) e do Rio Cuyaguateje (Hemisfério Norte, Cuba), facilitando a comparação entre climas aparentemente muito semelhantes. Para desenvolver a pesquisa foram utilizados dados de um período de 20 anos (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999) para cada bacia, sendo dados pluviográficos para a bacia cubana e pluviométricos para a bacia brasileira. Em adição, modelos de erosividade como o fator R de RUSLE e o Índice Modificado de Fournier foram utilizados para determinar a mesma. Os resultados mostram que a bacia do Rio Cuyaguateje apresenta consideravelmente maior erosividade anual variando entre 8294.6 a 18467.0 MJ mm ha -1 h -1 ano -1 , enquanto a bacia do Rio Mogi Guaçu possui erosividade anual variando de 3542.1 a 4461.5 MJ mm ha -1 h -1 ano -1 , demostrando que apesar de ambas bacias possuírem acumulados de precipitações anuais semelhantes, a bacia cubana pode ser considerada mais energizada quando se trata de precipitações. Palavras-chave: Comparações do Clima, Chuva, EI30, Índice de Fournier Modificado. Rainfall erosivity in tropical ecosystems: is there difference within a same climate zone? A B S T R A C TSeveral factors are involved in the formation of climatic conditions within a given region which may be classified according to the microscale, mesoscale, and macroscale effects. Although Cuba and Brazil belong to the same climatic range, and the study basins selected are in the same latitude, it does not mean they have the same pattern in rainfall erosivity. Aiming to enrich the knowledge of rainfall erosivity in tropical climates, the objective of this study is to characterize and compare the erosivity potential of rainfall in the Cuyaguateje (Northern Hemisphere, Cuba) and Mogi Guaçu (Southern Hemisphere, Brazil) basins; areas which have very similar climates. The research considered a 20-year period (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999), using the pluviographic and pluviometric data for the Cuban and Brazilian basins, respectively. Erosivity potential was calculated according to the R-factor (from RUSLE) and Modified Fournier Index models. The results showed that Cuyaguateje river b...

Hyperspectral analysis of Brachiaria brizantha cv. Marandú leaves under contrasting nitrogen levels Remote sensing is a set of techniques that can help to monitor pasture quality. The object of this study is to analyze the spectral response from Brachiaria brizantha cv. Marandú leaves, under contrasting nitrogen levels, to differentiate and predict leaf nitrogen content. The treatments were set in a Randomized Block Design, composed of four blocks and four treatments, totaling 16 plots. Increasing doses of urea fertilization were used: 0, 25, 50, 75 kg N/ha/mowing. During the experiment, 7 data collections were performed, and 8 leaves per plot were extracted for each data collection. These leaves were submitted to hyperspectral data extraction and subsequent chemical analysis to quantify the nitrogen content. When analyzing the spectral pattern of the leaves, statistical differences among samples with different nitrogen levels were noticeable in the visible range of the spectrum in all the collections, with emphasis on the 550 nm region (green). Through linear discriminant analysis (LDA), performed for each collection, the generated centroids by the samples of each nitrogen level presented significant differences, except for LD1 in collections 6 and 7, which did not present a distinction between treatments of 50 and 75 kg of N/ha/mowing, and LD2 in collection 5 that did not distinguish between treatments of 0 and 50 kg of N/ha/mowing. The partial least square regression (PLSR) method generated reasonable to good values of R² (0.53 to 0.83) for the prediction of leaf nitrogen content, where the wavelengths with the highest coefficient in these models are in the red edge region of the spectrum (715 to 720 nm). Finally, when testing the performance of some Vegetation Indexes from literature, collections 4, 6 and 7 presented good determination coefficients (R²) ranging from 0.65 to 0.73; a common feature in the indexes that best estimate the nitrogen content is the presence of wavelengths from the red edge region of the spectrum.