ABSTRACT Maize is a demanding crop that is responsive to nitrogen fertilisation, and meeting its needs is essential to avoid a loss of productivity or environmental contamination. Monitoring nutrient status during crop development is fundamental for optimising nitrogen fertilisation, and using spectral sensors can help detect spatial variability in the field. Based on this premise, the aim of this study was to evaluate the effectiveness of three low-cost sensors in detecting variability in the spectral and agronomic characteristics of the maize cultivar, BM 3066 PRO2®, induced by different N doses (0, 100, 200 and 300 kg ha-1) under two cropping systems, no-tillage and conventional. The study employed a camera with a sensor in the visible region of the spectrum (RGB), a camera in the visible-infrared region (OCN), both mounted on a drone, and a portable chlorophyll meter. Spatial variability was assessed during the crop cycle using the normalised difference vegetation index (NDVI) and chlorophyll index. The results showed that the NDVI(ON) vegetation index from the OCN sensor was more effective at differentiating the treatments than was the NGR vegetation index from the RGB sensor. Furthermore, the chlorophyll b index was better at detecting variations induced by different nitrogen doses, outperforming the vegetation indices obtained by means of aerial images. The airborne sensors under test are more suitable for detecting early spatial macro-variability, while the chlorophyll meter is more effective at assessing the degree of nitrogen deficiency. fertilisation contamination field premise lowcost low cost cultivar 306 PRO2 PRO PRO2® 0, 0 (0 100 20 30 ha1 ha 1 ha-1 systems notillage no tillage conventional RGB, , (RGB) visibleinfrared infrared OCN, (OCN) drone NDVI (NDVI NDVION ON NDVI(ON Furthermore images macrovariability, macrovariability macro variability, macro-variability deficiency ( 10 2 3 ha- (RGB (OCN

ABSTRACT Basin hydrological and morphometric parameters are elements that must be made on a map with sufficient information. This information collected on-site has been limited by public order problems. Basin circularity index (Ci) is among the morphometric indices, which has not been calculated yet for the basins under study. Basins automatic delimitation carried out from Geographic Information Systems (GIS), has allowed its research and management due to efficiency in data processing, ease, and low cost; in addition, increase images and databases availability. The work aim was to evaluate the Digital Elevation Model (DEM) SRTM proposed methodology in the Corporación Autónoma Regional de las Cuencas de los Ríos Negro y Nare (Cornare) jurisdiction, extracting from it the numerical drainage, in order to delimit basins and sub-basins, as well as calculate indices, such as drainage density (Dd) and Ci. The declivity map showed terrain with very soft to very strong surface relief. Basin´s automatic delimitation based on the drainage order ≥6 was the one that came closest to the basin actual characteristics; regarding the sub-basins delimitation, it was the one based on the ≥3 order. Dd and Ci indices indicated that the sub-basins have moderate drainage, runoff level, and infiltration capacity and tend to be more elongated than circular.

RESUMEN Los parámetros hidrológicos y morfométricos de una cuenca constituyen estimaciones que deben realizarse sobre un mapa con suficiente información. La recopilación de esta in situ se ha visto limitada por problemas de orden público. Entre los índices morfométricos de una cuenca se encuentra el índice de circularidad (Ic), que hasta el momento no ha sido calculado para las cuencas en estudio. La delimitación automática de cuencas realizada a partir de Sistemas de Información Geográfica (SIG), ha permitido la investigación y manejo de las mismas debido a la eficiencia en el procesamiento de datos, facilidad y bajo costo, además de la creciente disponibilidad de imágenes y bases de datos. El objetivo de este trabajo fue evaluar la metodología propuesta para utilizar el Modelo Digital de Elevación (MDE) SRTM de la jurisdicción de la Corporación Autónoma Regional de las Cuencas de los Ríos Negro y Nare (Cornare) y extraer de este el drenaje numérico, para delimitar cuencas y subcuencas, así como calcular índices, tales como densidad de drenaje (Dd) e Ic. El mapa de declividad mostró terrenos con relieve muy suave a muy fuerte. La delimitación automática de cuencas con base en el orden de drenaje ≥6 fue la que más se acercó a la realidad; en cuanto a la delimitación de subcuencas, fue aquella con base en el orden ≥3. Los índices Dd e Ic, indicaron que las subcuencas tienen drenaje, nivel de escorrentía y capacidad de infiltración moderados y tienden a ser más alargadas que circulares.

Abstract Following a disruption on November 5, 2015, the Gualaxo do Norte and Carmo Rivers received iron tailings from the Fundão Dam in Mariana, Minas Gerais, which affected the water’s physiochemical attributes. This study aimed to better understand the impact of this event by evaluating water quality variations, comparing results from previous studies and the limits established in the National Environmental Council (Conama), Resolution 357. For six months, water samples were collected monthly at six points, three in the Gualaxo do Norte River and three in the Carmo River. Turbidity, color, and dissolved metal values were recorded for thirty-six samples, and it was determined that the Gualaxo do Norte River had higher contamination levels than those established in the Conama Resolution 357. According to the results, this study recommends the development of specific analyses and a special water quality classification system. 5 2015 Mariana Gerais waters s attributes variations Conama, , (Conama) 357 months points Turbidity color thirtysix thirty system 201 (Conama 35 20 3 2

ABSTRACT The objective of this study was to map the levels of degradation of pastures and its association with chemical properties of the soil and relief. We selected a sub-basin located in the municipality of Viçosa, Minas Gerais, and we mapped the degradations through Ikonos images that were submitted for classification. Soil samples were collected and analyzed chemically; the results were transformed in spatial data using cokriging, based on variables of the digital elevation model. We associated the maps (cokriging) with data from exposure of the hillside, levels of solar radiation and levels of the pastures’ degradation. The pastures were mainly classified in moderate (44%) and very strong (30%) degradation levels. The areas with mild degradation tend to increase when there is a higher sum of bases, cationic exchange capacity and organic matter. The portions facing North and the increase of solar radiation showed more advanced stages of degradation.

ABSTRACT: Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. This paper investigated the performance of eight machine learning algorithms for soil mapping in a tropical mountainous area of an official rural settlement in the Zona da Mata region in Brazil. Morphometric maps generated from a digital elevation model, together with Landsat-8 satellite imagery, and climatic maps, were among the set of covariates to be selected by the Recursive Feature Elimination algorithm to predict soil types using machine learning algorithms. Mapping performance was assessed using the confusion matrix, and the Z-test among the Kappa indexes of the matrices. In a conventional soil survey, the soils described and classified in the Brazilian System of Soil Classification [Argissolos Vermelho-Amarelos Distróficos – PVAd (Acrisols), Cambissolos Háplicos Tb Distróficos - CXbd (Cambisols), Gleissolos Háplicos Háplicos Tb Distróficos - GXbd (Gleysols), Latossolos Amarelos Distróficos - LAd (Xanthic Ferralsos), Latossolos Vermelho-Amarelos Distróficos - LVAd (Rhodic Ferralsols), and Neossolos Litólicos Distróficos - RLd (Neossols)] were grouped into composite mapping units (MU) using the conventional method. The eight algorithms showed similar performance without statistical difference (Kappa 0.42-0.48). The mapping of soils with varying slopes (LAd, LVAd, CXbd) showed lower accuracy, whereas soils on hydromorphic lowlands (GXbd) were classified more accurately. In map algebra, the result was rather satisfactory, with 63-67 % agreement between the conventional soil map and maps produced by machine learning. The areas with the largest disagreement in the DSM occurred in the LAd unit due to subtle color variation in the Latossolos mantle without a clear relation to any environmental variable, highlighting difficulties in DSM regarding hill slope landforms. Model performance was satisfactory, and good agreement with the conventional soil map demonstrates the importance of the DSM as a potential complementary tool for assisting soil mapping in mountainous areas in Brazil for the purpose of land use planning.

ABSTRACT: The pattern of variation in soil and landform properties in relation to environmental covariates are closely related to soil type distribution. The aim of this study was to apply digital soil mapping techniques to analysis of the pattern of soil property variation in relation to environmental covariates under periglacial conditions at Keller Peninsula, Maritime Antarctica. We considered the hypothesis that covariates normally used for environmental correlation elsewhere can be adequately employed in periglacial areas in Maritime Antarctica. For that purpose, 138 soil samples from 47 soil sites were collected for analysis of soil chemical and physical properties. We tested the correlation between soil properties (clay, potassium, sand, organic carbon, and pH) and environmental covariates. The environmental covariates selected were correlated with soil properties according to the terrain attributes of the digital elevation model (DEM). The models evaluated were linear regression, ordinary kriging, and regression kriging. The best performance was obtained using normalized height as a covariate, with an R2 of 0.59 for sand. In contrast, the lowest R2 of 0.15 was obtained for organic carbon, also using the regression kriging method. Overall, results indicate that, despite the predominant periglacial conditions, the environmental covariates normally used for digital terrain mapping of soil properties worldwide can be successfully employed for understanding the main variations in soil properties and soil-forming factors in this region.

Geomorphometric variables are applied in digital soil mapping because of their strong correlation with the disposition and distribution of pedological components of the landscapes. In this research, the relationship between environmental components of tropical hillslope areas in the Rio de Janeiro State, Brazil, artificial neural networks (ANN), and maximum likelihood algorithm (MaxLike) were evaluated with the aid of geoprocessing techniques. ANN and MaxLike were applied to soilscape mapping and the results were compared to the original map. The ANN architectures with seven and five neurons in the hidden layer produced the best classifications when using samples obtained systematically. When random samples were applied, the best neural net architectures were within 22 and 16 neurons in the hidden layer. In conclusion, the ANN can contribute to soilscape surveys, making map delineation faster and less expensive. The digital elevation model (DEM) and its derived attributes can contribute to the understanding of the soil-landscape relationship of tropical hillslope areas; the use of artificial neural networks and MaxLike is feasible for digital soilscape mapping. The systematic sampling method provided a global accuracy of 70 % and 65.9 % for the ANN and the MaxLike, respectively. When the random sampling method was applied, the ANN had a global accuracy of 69.6 %, and the MaxLike had an accuracy of 62.1 %, considering the total study area in relation to the reference map.

A Antártica é o mais perfeito laboratório natural do planeta para estudos de mudanças ambientais. Partindo dessa premissa, este estudo teve como objetivo realizar uma revisão sobre a origem dos sais presentes no continente antártico, sendo pormenorizado as principais formas e fontes salinas que ocorrem. Na literatura, este tema é de grande discussão, sendo relatado que a origem dos sais pode ser devido à precipitação atmosférica, intemperismo de rochas, origem marinha (spray salino), sulfatos biologicamente produzidos ou atividade hidrotermal. De maneira geral, a presente revisão permitiu inferir diversas origens dos sais solúveis em água nos solos da Antártica. Perto da costa, influências marinhas são dominantes, encobrindo quaisquer contribuições do intemperismo das rochas. Mas para o interior, a influência do intemperismo é mais evidente, ao passo que a influência de circulação atmosférica superior torna-se importante também. Ressalta-se também, a importância da contribuição continental no aporte de sais nos períodos glaciais.

The Antarctica is the most perfect natural laboratory to study the planet from environmental changes. From this premise, this study was to conduct a review on the origin of salts in the Antarctic, and detailed the main forms and sources occurring salt. In literature, this topic is of great discussion, and reported that the origin of salts may be due to precipitation, rock weathering, marine (salt spray), biologically produced sulfates or hydrothermal activity. Overall, this review allows us to infer various sources of water-soluble salts in soils of Antarctica. Near the coast, marine influences are dominant, covering up any contributions from weathering of rocks. But for the interior, the influence of weathering is more evident, while the influence of atmospheric circulation becomes more important as well. We also emphasize the importance of the contribution in the continental input of salts in glacial periods.

Em Roraima, a distribuição espacial das populações indígenas identifica um cenário de busca constante de solos capazes de sustentar uma agricultura itinerante. Este trabalho teve como objetivo estabelecer relação entre a compreensão dos solos por parte dos Yanomami da região do médio Catrimani e o Sistema Brasileiro de Classificação de Solos, bem como avaliar o seu tipo de uso em função de análises químicas para diagnóstico da fertilidade do solo. O trabalho foi executado em duas etapas. A primeira consistiu em visitas a oito malocas para estudar os solos. Foram coletadas amostras em trincheiras até 1,50 m de profundidade para análise e classificação dos solos e (em prospecções com o trado) nas profundidades de 0-10 a 10-30 cm, em 21 tipos de uso agrícola, e da área de floresta para análises químicas de fertilidade. A segunda fase foi uma oficina, abordando os sistemas de exploração agrícola, com duração de 20 h. Focalizou-se a discussão sobre a compreensão do meio ambiente (Urihi)(1) e sistemas de produção agrícolas e sobre a importância do uso correto do conhecimento dos solos. Os solos são denominados pelos Yanomami em função das características morfológicas, pelos teores de matéria orgânica e pela presença de minhocas, e da posição na paisagem - a escolha para agricultura é fundamentada nessas características. As práticas agrícolas consistem da derrubada da mata nativa, queima e plantio das culturas em separado. Os solos descritos na área foram: Argissolo Vermelho-Amarelo (Maxita a uuxi wakë axi), Argissolo Amarelo (Maxita a axi), Latossolo Amarelo (Maxita a axi) e Plintossolo (Maxita a axi a maamaxipë). O processo de derruba e queima promove um incremento inicial do teor de Ca e K trocáveis e P assimilável devido à contribuição das cinzas, o que permite a exploração das áreas por um período máximo de três anos.

In Roraima, the spatial distribution of indigenous peoples indicates a background of constant search for soils capable of sustaining shift cultivation. This study aimed to establish a relationship between the understanding of soil by the Yanomami tribe from the mid-Catrimani river region and the Brazilian System of Soil Classification and evaluate the type of agricultural land use according to soil fertility tests, in two steps. The first consisted of visiting eight Indian communities to collect soil samples at 21 sites with different types of agriculture and forests, both from profile soil (depth 0-1.50 m) and pits (depth 0-0.1 m and 0.1-0.3 m) for laboratory analysis. The second step was a workshop addressing the shift cultivation systems (duration 20 h). The discussion focused on the understanding of the environment (Urihi) and agricultural production systems, and on the importance of the correct use of knowledge of the soil and its fertility. Soils are named according to their morphology and position in the landscape by the Yanomami. The choice of agricultural areas is based on these features, aside from the organic matter content and the presence of earthworms. Agricultural practices comprise clearing and burning of the native forest immediately before planting a set of separate crops. The soils described in the area were classified according to the Brazilian taxonomic system as follows: Red Yellow Argisol (Maxita a uuxi wake axi), Yellow Argisol (Maxita a axi) Yellow Latosol (Maxita a axi) and Plintosol (Maxita a axi a maaxipé). Slashing and burning initially increases exchangeable Ca, K and available P due to the contribution of the ashes, which allows an agricultural exploitation of these areas for a maximum period of three years.

O conhecimento das precipitações pluviais máximas diárias e seu período de retorno em uma região são características importantes no dimensionamento de trabalhos de conservação de solos, obras hidráulicas, estradas, etc. No entanto, muitos locais não possuem séries históricas adequadas de precipitação, sendo o uso da interpolação geoestatística uma alternativa para a estimativa de dados visando viabilizar a elaboração de mapas de iso-ocorrência. Nesse contexto, o presente trabalho teve como objetivo avaliar a distribuição das precipitações pluviais máximas diárias anuais para diferentes períodos de retorno para o Estado do Rio de Janeiro por meio de técnicas geoestatísticas. As séries de dados foram provenientes de 119 postos pluviométricos, que apresentavam, em média, 36 anos de observações. Para cada estação foi identificada a chuva média máxima diária anual e, em seguida, foram estimados os eventos associados aos períodos de retorno de 2, 5, 10, 25, 50 e 100 anos por meio da distribuição Gumbel. Com esses conjuntos de dados, foram obtidos os modelos experimentais de semivariogramas. Os resultados revelaram que as chuvas associadas aos seus respectivos períodos de retorno apresentaram dependência espacial, com modelo exponencial e esférico ajustados aos semivariogramas experimentais. A precipitação pluvial máxima média diária anual e os eventos associados aos diferentes períodos de retorno tenderam ser maiores de Norte para Sul e do interior para o litoral no Estado. Entre a Serra do Mar e o litoral observou-se, além do aumento na magnitude dos valores, maior variabilidade espacial à medida que se aumentaram os períodos de retorno. O modelo geoestatístico foi mais errático para maiores períodos de retorno, e para as regiões Norte, Noroeste e da Baixada Litorânea, sendo estes locais recomendados para a instalação de novas estações pluviométricas.

Knowledge of maximum daily rain and its return period in a region is an important tool to soil conservation, hydraulic engineering and preservation of road projects. The objective of this work was to evaluate the spatial variability of maximum annual daily rain considering different return periods, at the Rio de Janeiro State. The data set was composed by historical series of 119 rain gauges, for 36 years of observation. The return periods, estimated by Gumbel distribution, were 2, 5, 10, 25, 50 and 100 years. The spatial variability of the return periods was evaluated by semivariograms. All the return periods presented spatial dependence, with exponential and spherical model fitted to the experimental semivariograms. The parameters of the fitted semivariogram model were very similar; however, it was observed the presence of higher nugget effects for semivariograms of longer return periods. The values of maximum annual daily average rain in all the return periods increased from north to south and from countryside to the coast. In the region between the Serra do Mar range and the coast, besides increasing in magnitude, an increase in the spatial variability of the studied values with increasing return periods was also noticed. This behavior is probably caused by the orographic effect. The interpolated maps were more erratic for higher return periods and at the North, Northeast and Coastal Plain regions, in which the installation of new pluviometric stations are recommended.