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Because of the major international issue of intellectual property rights over seed technology, time-efficient methodologies that could characterize and monitor soybean genotypes are increasingly ne...

The aim of this study was to evaluate the use of mining co-products from the Irati (rhythmite and bituminous shale) and Corumbatai Formations (clayey siltstone) as sources of multi-nutrients for the cultivation of Brachiaria ruziziensis. The treatments were arranged within the double factorial scheme (7 × 2), completely randomized with 10 replications and placed into pots inside a greenhouse. Two factors were evaluated: the type of co-product and the soil class (Arenosol and Ferralsol). The mining co-products were applied in their isolated form and in composite (blended) form. At 60 and 140 days after the application of treatments, the ions available in the soil were determined. After that, four Brachiaria ruziziensis plants were cultivated per pot. After 40 days of cultivation, the aerial part of the plants was cut. Afterward, the macronutrients, as well as the production of shoot dry mass of the plants, were determined. The application of co-products, in general, increased the pH and the contents of ions available: K+ (Mehlich-1), Mg2+, Ca2+, P, SO42− and Si. As a highlight, at 140 days, rhythmite was the most efficient co-product in increasing pH, Ca2+ and Mg2+, with increases of 23%, 241% and 722% in Arenosol, respectively. Also at 140 days, bituminous shale increased the contents of SO42− by 72% and 427% in Ferralsol and Arenosol, respectively. Moreover, the accumulation of K, Ca, Mg and S, as well as the production of shoot dry mass of Brachiaria improved with the application of the co-products. In conclusion, the application of mining co-products alters the chemical attributes of both the soil and the plant, and this favors the production of shoot dry mass of Brachiaria, which demonstrates the potential to be a clean environmental solution to soil fertilization.

This study aimed to evaluate the use of spectroradiometry for the determination of soil exchangeable ions after the application of mining coproducts. The treatments were arranged in a double factor...

The development of spectral prediction models for soil attributes has been extensively studied in the last 10 years. However, one of the problems encountered during this period concerns the representativeness of the samples selected for model generation, which are often unable to capture the existing variability in agricultural areas, generating imprecise models. Thus, it is necessary to establish strategies for selecting soil samples, as well as for making them more representative within the model. Considering this, the aim of the present study was to evaluate strategies for soil sample selection and the recalibration of large models using samples from a smaller area, in a process called spiking, and its effect on soil attribute estimations. A total of 425 soil samples were used for the generation of the state models, as well as 200 soil samples from a target site for attribute recalibration and prediction. From these 200 samples, 10 (subset) were selected by different methods for state model recalibration (spiking), and 190 were used in the prediction. Another 5 and 10 copies of the subsets were also used as extra-weight to recalibrate the models. Models spiked with samples located in the center of the spectral space associated with extra-weight (10 copies) showed better accuracy in sand prediction (RPD = 2.20; r2 = 0.80; RMSEP = 71.6 g kg−1). For organic matter, the use of selected samples based on 5 clusters associated with extra-weight (10 copies) slightly improved the RMSEP and RPD in most cases, reaching a maximum value of 6.1 g dm−3 and 1.20, respectively. However, the subsets selected at the target site were not able to indicate the entire variability of the local samples concerning organic matter, damaging the expansion of the recalibrated state models.

Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity to capture the intrinsic differences between agricultural areas and incorporate them in the modeling process. High quality estimates are generally obtained when these models are applied to soil samples displaying characteristics similar to the samples used in their construction. However, low quality predictions are noted when applied to samples from new areas presenting different characteristics. One way to solve this problem is by recalibrating the models using selected samples from the area of interest. Based on this premise, the aim of this study was to use the spiking technique and spiking associated with hybridization to expand prediction models and estimate organic matter content in a target area undergoing different uses and management. A total of 425 soil samples were used for the generation of the state model, as well as 200 samples from a target area to select the subsets (10 samples) used for model recalibration. The spectral readings of the samples were obtained in the laboratory using the ASD FieldSpec 3 Jr. Sensor from 350 to 2500 nm. The spectral curves of the samples were then associated to the soil attributes by means of a partial least squares regression (PLSR). The state model obtained better results when recalibrated with samples selected through a cluster analysis. The use of hybrid spectral curves did not generate significant improvements, presenting estimates, in most cases, lower than the state model applied without recalibration. The use of the isolated spiking technique was more effective in comparison with the spiked and hybridized state models, reaching r2, square root of mean prediction error (RMSEP) and ratio of performance to deviation (RPD) values of 0.43, 4.4 g dm−3, and 1.36, respectively.

Our work aimed to evaluate the use of visible–near-infrared (Vis–NIR) spectroscopy for predicting the production of leaf dry mass (LDM), as well as macro- and micronutrients contents of soybean leaves grown after application of limestone-mining coproducts. The treatments were arranged within a triple factorial scheme (6 × 2 × 2 + 2) and placed into pots in a greenhouse. We evaluated the following factors: type of input (limestone-mining coproducts), input particle size (filler and powder), and soil class (Arenosol and Ferralsol). After inputs incubation, the soybean was sown. Then, 42 days after sowing, we collected the foliar spectra, as well as leaves, for further analysis of the contents of macro- and micronutrients in leaves and production of LDM. We managed to adjust models at the stage of prediction with R2p > 0.50 and RPDp > 1.50 for the variables LDM, P, K, Mg, S, and Zn, with emphasis on the first four, which presented R2p above 0.65. Therefore, we conclude that Vis–NIR spectroscopy has a potential for predicting LDM and the nutrients contents of soybean subjected to the application of limestone-mining coproducts, with advantages such as speed, low cost, and no use of reagents that are toxic to the environment.

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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. ...

Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.

New techniques and improvements are required to quantify soil’s chemical and physical properties on production environment, reducing environmental impacts and minimizing soil analysis time. The aim of this study is to evaluate the possibility to estimate the content of silt, sand, clay, total iron and organic matter in soils formed by different lithologies in Parana State, Brazil, using VIS-NIR spectrum associated with Partial Least Square Regression (PLSR). 200 soil samples were collected in an area formed by Lixisols, Cambisols, Ferralsols, Arenosols and Nitisols in a depths of 0–0.2 and 0.2–0.8 m. Spectral readings were obtained in laboratory by FieldSpec 3 JR sensor. The spectral curves of the samples were correlated to the attributes through PLSR. The results obtained for sand in prediction were better when compared to the other attributes, presenting R2 = 0.90, r = 0.95 and RPD = 2.3. Clay and total iron presented satisfactory results, mainly for RPD values, which were above 2.4. Based on th...

Studies on the spectral behavior of Brazilian soils and their possible applicability to increase productive efficiencies are still scarce, mainly for paddy soils, present in a small part of the territory and yet very important for agricultural production in the country. The objective of work was to obtain parameters for classification of paddy soils using spectral data obtained by diffuse spectroradiometry. The surface and subsurface soil samples were collected by using a 90 m 2 sampling grid with equidistant points. Spectral analysis was performed in a laboratory by using a spectroradiometer with 1 nm resolution at 350–1100 nm and 2 nm resolution at 1100–2500 nm. The evaluation of spectral curves was performed on the basis of four different procedures. The statistical analysis was performed by using the procedures STEPDISC, STEPWISE, and DISCRIM of Statistical Analysis System (SAS) software. The results show that soil discriminations by the spectral data obtained in the laboratory were successful. However, the model that tests the procedure for soil discrimination has low accuracy of 47.79% when compared with other methods.

The aim of this paper was to quantify and evaluate the spatial distribution of soil chemical properties in different grid sampling sizes using descriptive statistics and geostatistics. The study was carried out in Bom Sucesso City, Parana State, Brazil. Using GPS, a 50 x 50 m grid sampling size was set, comprising four samples per hectare. In each grid point positioned on the field, soil samples at a depth of 00 to 0.20m were extracted, resulting in 203 sampling points in a 51.15 ha area. Calcium, magnesium and potassium quantity were determined. Grid sampling size, with lower number of samples per area, were established from the first grid, removing randomly, soil samples, resulting in: 70.8 x 70.8 m (2 samples/ha), 100 x 100 m (1 sample/ha), 141 4 x 141.4 m (1 sample/2 ha), 173.3 x 173.3 m (1 sample/3 ha) and 200 x 200 m (1 sample/4 ha) grid sampling sizes. Each soil chemical properties in each grid sampling size were subjected to descriptive statistical and geostatistical analysis. An experimental semivariogram was developed to understand the spatial dependence structure. The attributes calcium, magnesium and potassium showed spatial correlation in almost all grids sampling, except for the 200 m grid sampling for calcium and magnesium. Key words: Spatial distribution of attributes, spatial dependence, descriptive statistics, geostatistics.

Soil organic matter (SOM) plays an important role in the soil-plant system, increasing the search for faster and non-destructive methods for detecting this attribute. For this purpose, the use of hyperspectral imaging sensors is promising. This study aimed to evaluate the possibility of predicting organic matter through hyperspectral images in the laboratory associated with multivariate regression modeling procedures. There were 384 soil samples collected at eight depths in an Oxisol, in the State of Parana, Brazil. Principal component and linear discriminant analysis were applied to the spectrum to group the sets according to depth. The hyperspectral imaging technique combined with the model's regression coefficient was used to classify the images relative to the content of organic matter. The partial least squares regression (PLSR) model was developed to correlate spectral data from the sensor with SOM contents acquired by the conventional method. The results obtained in the prediction were R2 = 0.75, r = 0.87 and RPD = 2.1. These results suggest that the model generated by the image sensor was able to discriminate the organic matter contents of the soil at different depths by capturing spectral variations.

Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (p ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R2 ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha−1—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R2 equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha−1 (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation.

The understanding of the radiometric response of visible and near-infrared cameras is important for quantitative spectral information obtainment. There is a lack of information regarding the comparison of calibration approaches of near-infrared images of digital cameras, since the Bayer Filter is not usually removed and the obtained NIR image records the infrared radiation in three different spectral bands (red, green and blue). This article aimed at developing reflectance calibration models for UAV-based visible and near-infrared (processed under four different methodologies) digital images acquired under variant altitude and illumination conditions. Reflectance calibration models were developed for red, green, blue and near-infrared spectral bands using UAV-based digital numbers – DNs (acquired in twelve UAV flights under variant altitude and illumination conditions) and laboratory-based reflectance. The variation of DNs among flight conditions represents from 12% to 17%, depending on the spectral band. For the visible bands, all reflectance calibration models presented R2 superior than 0.94. Regarding the near-infrared bands, the grey scale and green channel approaches demonstrated the best results, with R2 between 0.89 and 0.98. The validation of the developed models, assessed by the correlation between reflectance measured in field and the predicted reflectance, revealed strong correlations between the observed and predicted reflectance for the visible bands and for the near-infrared grey scale and green channel approaches, which were found to be more suitable for the calibration of near-infrared digital images into reflectance.

The use of remote sensing is increasing in agriculture and this raises questions about its efficiency over other usual methods. Thus, the purposes of this study were to compare methodologies for soil mapping, using field samplings and spectral data (from laboratory and from a simulated Landsat-TM), and to estimate their correlation. The soil samples were collected in a wetland with a great variety of soil classes. The distribution of soil classes in the maps was based on independence analysis by the Chi-square. Ten soil classes were determined in the study area, 6 in the first category level. The map of laboratory spectral data showed low correlation with conventional data. The map of spectral data that simulated wavelenghts corresponding the spectral bands of Landsat-TM sensor showed the same behavior of the previous map, with lower correlation with the conventional data. Thus, we verified that the mapping of paddy soils with spectral data shows low correlation with conventional data, however, still rather positive. Resumo: A utilização do sensoriamento remoto cresce cada vez mais na agricultura e isso promove questionamentos sobre sua eficiência em relação aos demais métodos usuais. Assim, o objetivo deste estudo foi comparar metodologias de mapeamento de solos, usando amostras de campo e dados espectrais (de laboratório e de uma simulação Landsat-TM) para estimar esta correlação. As amostras de solo utilizadas foram coletadas em região de várzea com grande variedade pedológica. A distribuição das classes de solo nos mapas foi baseada em análise de independência por Qui-quadrado. Foram determinadas 10 classes de solos na área de estudo, sendo seis no primeiro nível categórico. O mapa de solos com dados espectrais de laboratório apresentou baixa correlação com os dados convencionais. O mapa de dados espectrais, simulando os comprimentos de onda correspondentes às bandas do sensor Landsat-TM demonstrou distribuição semelhante ao mapa anterior, com menor correlação ao método convencional. Assim, verifica-se que o mapeamento de solos de várzea com os dados espectrais mostra baixa correlação com os dados convencionais, porém, ainda assim estatisticamente significativo.

Imaging spectroscopy is a proven technology for identifying and mapping the constituents of soils based on their spectral signatures, the variation of their reflectance or emissivity across different wavelengths, in the visible and infrared bands. Despite the great potential for use of hyperspectral images, there is a lack of previous work that examines the hyperspectral imaging sensors and spectroradiometers, especially in controlled laboratory configurations. Besides that, AisaFENIX is the only sensor of this model available for research in Brazil. In our country there is no experience related to this sensor, being a previous and necessary step of the use of the sensor in aero-surveys. We need to understand the operation and behavior of this sensor. In addition, Brazil is a hot and humid country, with no robust weather models that are in keeping with the local need. This work aims to correlate soil data obtained in the laboratory by two different hyperspectral sensors, AisaFENIX and spectroradiometer, in different bands of the electromagnetic spectrum. Soils were sampled in Mandaguacu, Parana, an area of approximately 200 ha, totaling 207 georeferenced samples. The soil reflectance laboratory data were obtained using the Fieldspec 3 Jr spectroradiometer sensor, which collects a spectral range of 350–2.500 nm (vis-SWIR), and the reflectance values of the hyperspectral images were acquired with the AisaFENIX sensor, which collects a spectral range of 350–2.500 nm. The correlation between the sensors was satisfactory (R2 0.97) in the corresponding iron range (500–640 nm), and for the corresponding bands of kaolinite (2.200 nm), gibbsite (2.265 nm), water (1.400 and 1.900 nm), the R2 values found were 0.61, 0.65, 0.63 and 0.64 respectively. The lowest correlation coefficients found were for the bands of organic carbon (600–1.100 nm), goethite (480 nm), hematite (530 nm) and specific goethite (450–480 nm), with R2 values less than 0.41. The t-test showed that the correlations were significant (p

The increasing use of natural resources in a disorderly way has been demanding constant monitoring and ecological-economic zoning. The knowledge on land use and cover allows that measures that guarantee the preservation, maintenance of the environment and space management be appropriate to the reality, since through these factors it is possible to follow the probable environmental impacts and the socioeconomic development of a place in several contexts. The Geographical Information System (GIS) and remote sensing techniques have been applied to land use and land cover mapping. This study aimed to analyze the conversion of land use from different perspectives, concerning geoprocessing techniques, in the southeastern of Roraima State, Brazil, in two distinct periods. In order to verify the land use and cover, two analyses were conducted, using the Spring and TerraView softwares. Great part of the cultivated areas was converted into capoeira, what probably denotes an ending of profitable agriculture, as well as its abandonment caused by the nutritional deficiency of the soil, that became inappropriate for cultivation in the subsequent years. A fuzzy logic would possibly fit well to the types of data analyzed, because the attribute query is overly complex. O crescente aproveitamento dos recursos naturais de forma desordenada tem demandando constante acompanhamento, por meio de fiscalização e zoneamento ecológico-econômico. O conhecimento do uso e cobertura do solo permite que as medidas que garantem a preservação, manutenção do meio ambiente e gestão do espaço sejam adequadas à realidade, já que, por meio destes fatores, é possível acompanhar os prováveis impactos ambientais e o desenvolvimento socioeconômico de um local, nos mais diversos contextos. As técnicas de geoprocessamento e sensoriamento remoto, aliadas ao Sistema de Informação Geográfica (SIG), vêm sendo aplicadas em mapeamento de uso e cobertura do solo. Este estudo objetivou analisar a conversão do uso de ocupação do solo de diferentes ângulos, no que diz respeito às técnicas de geoprocessamento, em duas datas distintas, na porção sudeste do Estado de Roraima. Com o auxílio dos programas computacionais Spring e TerraView, foram realizadas duas análises, para verificação do uso e ocupação do solo. Grande parte das áreas cultivadas foi convertida em capoeira, o que, provavelmente, denota uma não continuidade da agricultura com fins lucrativos, bem como o seu abandono, causado pela deficiência nutricional do solo, que se tornou impróprio para o cultivo, nos anos posteriores. Possivelmente, uma lógica nebulosa (fuzzy) se adequaria bem aos tipos de dados analisados, pois a consulta por atributo apresenta exagerada rigidez de análise.

Iron oxides and 2:1 minerals have a strong influence on the spectral behavior of soils. However, studies that address more deeply the relationship between them and their reflectances are scarce. Thus, the objective of this study was to estimate the relationship between iron oxides and 2:1 minerals with their reflectance factors, and assess whether such a relationship presents a linear behavior. The experimental design was completely randomized with four replicates per treatment. The spectral readings of the samples of hematite, goethite, magnetite and bentonite were performed using the equipment FieldSpec jr 3, which covers the spectral range from 350 to 2500 nm. After the readings, were chosen the intervals that best represented the minerals and performed statistical analysis from the average reflectance factors found in these points. The results showed that the hematite, goethite and magnetite have nonlinear relationship with their reflectance factors, while bentonite has not only linear relationship in the range studied, but in the entire area covered by the equipment. Thus, we conclude that relatively small amounts of iron oxides are able to affect soil reflectance in the same way that amounts close to 100%. With respect to mineral 2:1 concludes that the increase in the proportion of them in a certain class of soil leads to a progressive decrease of the reflectance factor across the entire spectrum-optical, as described for other elements. Os óxidos de ferro e os minerais 2:1 possuem forte influência sobre o comportamento espectral dos solos. No entanto, estudos que abordem de maneira mais profunda a relação entre ambos e suas respectivas reflectâncias são escassos. Desta forma, o objetivo deste trabalho foi de estimar a relação entre óxidos de ferro e minerais 2:1 com seus respectivos fatores de reflectância, e avaliar se tal relação apresenta comportamento linear. O delineamento utilizado foi inteiramente casualizado com 4 repetições por tratamento. As leituras espectrais das amostras de hematita, goethita, magnetita e bentonita foram realizadas por meio do equipamento FieldSpec 3 jr, o qual recobre a faixa espectral de 350 a 2500 nm. Após as leituras, foram escolhidos os intervalos que melhor representavam os minerais e realizaram-se as análises estatísticas a partir dos fatores de reflectância médios encontrados nestes pontos. Os resultados mostraram que a hematita, goethita e magnetita apresentam relação não linear com seus respectivos fatores de refletância, enquanto que a bentonita apresenta relação linear não só no intervalo estudado, mas em toda faixa coberta pelo equipamento. Desta maneira, conclui-se que quantidades relativamente pequenas destes óxidos de ferro são capazes de afetar a reflectância do solo da mesma forma que quantidades próximas de 100%. Com relação aos minerais 2:1 conclui-se que o aumento na proporção dos mesmos em uma determinada classe de solo leva a uma queda progressiva do fator de reflectância em todo o espectro-óptico, assim como descrito para outros elementos.

Soybean has always stood out for its economic importance in the national agricultural scenario with an increase in its cultivation throughout the Brazilian territory. With the increasing use of precision agriculture (PA) is of paramount importance to understand the variability of agronomic variables. Currently, spectral sensors in the ground-level have been used to establish relationships between spectral response and crop growth parameters. This is possible through the reading of vegetation indices such as Normalized Difference Vegetation Index using the green band (GNDVI). This study was conducted between October to 2008 and April to 2009, in Ponta Grossa – PR, with the objective of evaluating the spectral response of soybean through GNDVI and its correlation with some agronomic variables, such as: productivity of grain, productivity of dry mass, plant density and plant height. GNDVI readings were taken on six phenological stages using Crop Circle active optical sensor. Regression analyzes were used to determine which agronomic variables established relationship with the GNDVI. The GNDVI in soybean established significant correlations only with grain and MS productivity in EF V9. Thus, to establish the correlation of GNDVI with these variables it is recommended that the readings are carried out in this EF.

Soybean has always stood out for its economic importance in the national agricultural scenario with an increase in its cultivation throughout the Brazilian territory. With the increasing use of precision agriculture (PA) is of paramount importance to understand the variability of agronomic variables. Currently, spectral sensors in the ground-level have been used to establish relationships between spectral response and crop growth parameters. This is possible through the reading of vegetation indices such as Normalized Difference Vegetation Index using the green band (GNDVI). This study was conducted between October to 2008 and April to 2009, in Ponta Grossa – PR, with the objective of evaluating the spectral response of soybean through GNDVI and its correlation with some agronomic variables, such as: productivity of grain, productivity of dry mass, plant density and plant height. GNDVI readings were taken on six phenological stages using Crop Circle active optical sensor. Regression analyzes were used to determine which agronomic variables established relationship with the GNDVI. The GNDVI in soybean established significant correlations only with grain and MS productivity in EF V9. Thus, to establish the correlation of GNDVI with these variables it is recommended that the readings are carried out in this EF.A soja sempre se destacou pela sua importância econômica no cenário agrícola nacional tendo sido observado aumento do seu cultivo em todo o território brasileiro. Com o uso crescente da agricultura de precisão (AP) é de suma importância entender a variabilidade das variáveis agronômicas. Atualmente, sensores espectrais em nível terrestre vêm sendo utilizados para estabelecer relações entre as respostas espectrais e parâmetros de crescimento da cultura. Isso é possível por meio da leitura dos índices de vegetação, como o Índice de Vegetação da Diferença Normalizada utilizando a banda verde (GNDVI). Este estudo foi conduzido entre outubro de 2008 e abril de 2009, em Ponta Grossa – PR, com o objetivo de avaliar a resposta espectral da cultura da soja por meio do GNDVI e sua correlação com algumas variáveis agronômicas, como: produtividade de grãos, produtividade de massa seca, densidade de plantas e altura de plantas. Leituras do GNDVI foram realizadas em seis estádios fenológicos (EF) utilizando o sensor óptico ativo Crop Circle. Análises de regressão foram utilizadas para determinar quais variáveis agronômicas estabeleciam relação com o GNDVI. O GNDVI na cultura da soja estabeleceu correlações significativas apenas com a produtividade de grãos e a produtividade de MS no EF V9. Assim, para se estabelecer a correlação do GNDVI com essas variáveis recomenda-se que as leituras sejam realizadas nesse EF.

O objetivo deste trabalho foi avaliar a contaminacao da agua provocada pelo uso de agroquimicos na cultura de arroz irrigado em parte da Area de Protecao Ambiental das Ilhas e Varzeas do Rio Parana, no municipio de Querencia do Norte. Esta regiao e caracterizada por uma grande riqueza natural, bem como pela necessidade de producao agricola. Foram realizadas quatro secoes de coletas de agua, sendo duas no verao e duas no inverno, que ocorreram em pontos pre-estabelecidos, nos rios e canais de drenagem em torno das areas de cultivo de arroz, inclusive no rio Ivai que e receptor de aguas destes locais, utilizando-se galoes de 5 litros. Para determinacao da concentracao dos herbicidas, foi utilizada aliquota de 250 mL de cada amostra, com solucao hexano/diclorometano, concentracao do extrato e afericao do volume em acetonitrila. A medicao foi realizada por cromatografia liquida de alta eficiencia com espectrometria de massa sequencial. Os resultados demonstraram presenca de Organofosforados, Metilcarbamatos, Cloroacetamidas, Triazois, Piretroides, Triazinas, Acetamidas e Pirazois. O composto Carbofurano e o mais abundante na area de estudos. A Atrazina e encontrada e determinada como originaria de regioes proximas a area de estudo. Alem da Atrazina, outros compostos nao recomendados para arroz irrigado sao usados nesta regiao, sendo passiveis de acoes legais para punicao.

A utilização de testes de rotina para determinação de atributos físicos e químicos do solo vem sendo adotada há diversos anos. No entanto, devido a problemas de ordem econômica e ambiental, surge a necessidade do desenvolvimento de novos métodos de avaliação. Nesse contexto, insere-se a espectroscopia de reflectância difusa como ferramenta promissora e rápida, capaz de ajudar na determinação desses atributos. Os objetivos deste trabalho foram estudar o comportamento espectral das frações silte, areia e argila e avaliar a possibilidade de quantificação desses atributos a partir de modelos de regressão individuais gerados para cada elemento. Para geração dos modelos, foram utilizadas frações individuais de silte, areia e argila provenientes de duas áreas formadas por materiais distintos. A validação dos modelos foi feita utilizando o fator de reflectância de amostras de solo no estado natural, advindas de uma área no município de Lobato - PR. Os resultados mostraram que foi possível estimar a percentagem de areia nas amostras de solo de Lobato de maneira satisfatória quando utilizados os modelos individuais da usina Cooperval, não sendo obtido o mesmo quando da utilização dos modelos individuais ajustados para a Fazenda Experimental de Iguatemi. Com relação às frações silte e argila, não foi possível estimá-las satisfatoriamente nas amostras de solo de Lobato quando utilizados os modelos individuais. Conclui-se que, embora os resultados não tenham atingido o patamar ideal, a espectrorradiometria difusa mostrou ser uma ferramenta promissora na estimativa dos atributos físicos do solo, podendo num futuro próximo auxiliar ou mesmo substituir os métodos analíticos de laboratório, reduzindo o tempo e o custo das análises. Routine tests to determine physical and chemical soil properties have been used for several years. However, due to economic and environmental problems, new assessment methods must be developed. In this context, diffuse reflectance spectroscopy is a promising and quick tool in the determination of these properties. The objective of this work was to study the spectral behavior of the silt, sand and clay fractions and evaluate the possibility of quantifying these properties with regression models generated for each individual element. To develop the models, individual silt, sand and clay fractions from two areas formed by different materials were used. The models were validated using the reflectance factor of soil samples in their natural state collected in the municipality of Lobato, Paraná. The results showed that it was possible to satisfactorily estimate the percentage of sand in soil samples from Lobato by individual models of the Cooperval production plant, unlike in the case of individual models adjusted for the Experimental Farm of Iguatemi. It was not possible to estimate the silt and clay fraction in the Lobato soil samples satisfactorily by the individual models. It was therefore concluded that although the results had not reached the ideal level, diffuse spectroradiometry proved to be a promising tool for the estimation of soil physical properties; in the near future this technique may support or even replace the analytical laboratory methods, reducing the time and cost spent on the analyses.

O objetivo deste trabalho foi avaliar algumas propriedades fisicas do solo utilizando o georadar associado a equacao CRIM, assim como, sua correlacao com os metodos laboratoriais. Foram obtidas amostras de solos deformadas e indeformadas em dois transectos, ate a profundidade de 0,5 m para determinacao da umidade volumetrica e da porosidade total do solo por metodos laboratoriais. Nestes mesmos locais e profundidades foram feitas investigacoes com o georadar para determinacao da constante dieletrica do solo (k) a qual foi aplicada a equacao semi-empirica CRIM para determinacao de porosidade total e posteriormente umidade volumetrica. A analise estatistica realizada por meio do (“teste t”) mostrou a existencia de diferenca significativa ao nivel de 5% entre os resultados de porosidade total e umidade volumetrica obtidos por metodos laboratoriais e geofisicos, sendo estes, influenciados pela elevada quantidade de argila e de potassio em solucao, proveniente da adubacao e aplicacao de vinhaca. A correlacao obtida entre os parâmetros estimados com o GPR e os conseguidos com as analises laboratoriais foi baixa, (r = 0,58 para porosidade total e r = 0,59 para umidade volumetrica), mostrando a dificuldade em se determinar propriedades fisicas por meio de tais tecnicas geofisicas.