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The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference ...

Infrared region of electromagnetic spectrum has remarkable applications in crop studies. Infrared along with Red band has been used to develop certain vegetation indices. These indices like NDVI, EVI provide important information on any crop physiological stages. The main objective of this research was to discriminate 4 different soybean varieties (BMX Potencia, NA5909, FT Campo Mourao and Don Mario) using non-imaging hyperspectral sensor. The study was conducted in four agricultural areas in the municipality of Deodapolis (MS), Brazil. For spectral analysis, 2400 field samples were taken from soybean leaves by means of FieldSpec 3 JR spectroradiometer in the range from 350 to 2500 nm. The data were evaluated through multivariate analysis with the whole set of spectral curves isolated by blue, green, red and near infrared wavelengths along with the addition of vegetation indices like (Enhanced Vegetation Index - EVI, Normalized Difference Vegetation Index - NDVI, Green Normalized Difference Vegetation Index - GNDVI, Soil-adjusted Vegetation Index - SAVI, Transformed Vegetation Index - TVI and Optimized Soil-Adjusted Vegetation Index - OSAVI). A number of the analysis performed where, discriminant (60 and 80% of the data), simulated discriminant (40 and 20% of data), principal component (PC) and cluster analysis (CA). Discriminant and simulated discriminant analyze presented satisfactory results, with average global hit rates of 99.28 and 98.77%, respectively. The results obtained by PC and CA revealed considerable associations between the evaluated variables and the varieties, which indicated that each variety has a variable that discriminates it more effectively in relation to the others. There was great variation in the sample size (number of leaves) for estimating the mean of variables. However, it was possible to observe that 200 leaves allow to obtain a maximum error of 2% in relation to the mean.

This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. ...

Boron (B) is an essential element whose deficiency results in rapid inhibition in the growth of plants, acting on their meristematic growth. Real-time monitoring of B fertilization in eucalyptus is helpful for guiding precision diagnosis and efficient management of plant boron nutrition. This research hypothesizes that different boron levels alter the reflectance of different wavelengths in eucalyptus. In this context, the objective of this study was to identify spectral ranges that can be used to monitor the boron status in eucalyptus plants. The experiment was carried out in a greenhouse, in which the treatments consisted of increasing boron levels in the form of boric acid (17% of B), whose levels varied from deficit to toxicity. Thus, five treatments were established: no boron, 1, 10, 20, and 40 mg/dm3 of boron. The remote sensing data used were bands, heights, and vegetation indices calculated after obtaining the spectral curves in each treatment. Our findings show that it is possible to accurately distinguish the boron levels in eucalyptus using hyper and multispectral bands. The 350–371 nm spectral range can be used for detecting boron-deficient plants. Plants with adequate boron levels can be identified by using the 426–444 nm, 1811–1910 nm, 1948–2115 nm, and 2124–2208 nm spectral ranges. Finally, the 425–475 nm spectral range can be used to find boron-toxicity plants.

Monitoring soybean areas by remote sensing is extremely useful, especially in Brazil, which has a large territorial extension and where soybean cultivation has spread to all regions of the country. In this sense, the development of remote sensing techniques that enable the quantification and discrimination of soybean areas and now in cultivated cultivar level is of crucial importance for the soybean production chain in Brazil. This study aimed to discriminate soybean cultivars as a function of different hyperspectral bands using the sensor-system MSI-Sentinel-2 (Vis-NIR-SWIR) as a simulation and sample sizes using multivariate statistics to determine if the specific bands of this sensor are capable of performing such discrimination. Four soybean cultivation areas in the Midwest region cultivated with four cultivars (BMX Potencia, NA5909, Don Mario, and FT Campo Mourao) were assessed. Spectral readings from each sample soybean leaf were performed, and a total of 2400 vegetation spectral readings were obtained. Data were composed of 28 bands and 22 reflectance factor height (RID) values for each soybean cultivar. Multivariate statistical analysis was performed to verify the association between soybean cultivars and their relationship with hyperspectral bands, as well as to verify the possibility of cultivar differentiation based on hyperspectral bands. The results obtained demonstrated to be possible discriminate soybean cultivars by using multivariate techniques applied to multi and hyperspectral data. The bands that contributed significantly (>5%) to cultivar differentiation in order of importance were: B26, B27, A17, A21, A20 and A14. Discriminant analysis was efficient in the cultivar classification, and canonical variable analysis revealed bands associated with specific discrimination of each cultivar. Bands that most contributed to cultivar discrimination were also identified for MSI orbital sensor.

Soil management practices for soy cultivation are considered necessary to sustain crop yields, in addition to implementing a synergy between sustainable agricultural practices and reducing environmental impacts. Thus, it is necessary to develop a model that can accurately and quickly distinguish between soil management treatments for soy based on spatial analysis. O objetivo desta pesquisa foi desenvolver uma metodologia aplicada na deteccao dos tipos de Plantios Convencional e Direto, atraves da integracao entre GEOBIA (Geographic Object-Based Image Analysis) e MD (Mineracao de Dados) nas areas identificadas com o cultivo da soja (Glycine max L. Merr.) no estado de Mato Grosso (MT). Para a identificacao das areas cultivadas com a cultura da soja foi utilizado o PCEI (Perpendicular Crop Enhancement), por meio de imagens multiespectrais do sensor MODIS (Moderate-Resolution Imaging Spectroradiometer). O processamento das bandas espectrais e os indices de vegetacao em uma serie temporal do satelite Landsat foram associadas as tecnicas de mineracao de dados e classificacao orientada a objeto devidamente tratados e aplicados uma segmentacao em multiresolucao para definicao dos objetos, com a selecao pre-definida dos alvos, sendo entao gerada uma Arvore de Decisao (AD) pelo algoritmo C4.5/J48 implementado no software Weka®. O algoritmo foi composto por 28 atributos e por 468 amostras de treinamento, representando todas as classes de plantio para obtencao de uma analise matricial de erro e estatistica Kappa. As estatisticas indicaram que a classificacao alcancou uma precisao geral de 95.29% e um coeficiente Kappa de 0.92. Os resultados revelaram que a combinacao das tecnicas GEOBIA e MD sao eficientes e promissoras para o processo de classificacao do tipo de tratamento de solo.

The least limiting water range (LLWR) is widely used as an agronomic indicator of soil physical quality, however few studies have reported its spatial variability, mainly in integrated crop-livestock systems (ICL). This study aimed to evaluate the spatial dependence of LLWR and its limits that establish the minimum physical and water restrictions for plant growth in an Oxisol under ICL systems. In addition, to verify correlations between LLWR limits and soil attributes that are commonly used to evaluate the soil compaction state. A georeferenced sampling mesh of 12 ha and 170 randomly distributed points was installed at an experimental station, which specifically studies five ICL systems and a no-tillage system with soybean and maize successions. The soil was sampled in 0.00–0.10 and 0.10–0.20 m layers for determinations of porosity, penetration resistance (PR), bulk density and LLWR. The results showed that LLWR presented spatial dependence and adjusted to the exponential model of semivariance, was positively correlated to macroporosity and total porosity and negatively correlated to bulk density, PR and microporosity. Among the parameters that compose it, the PR curve presented spatial dependence and explained the main changes in soil caused by management systems.