Environmental vulnerability assessment of the Doce River basin, southeastern Brazil.

Environmental vulnerability is an important tool to understand the natural and anthropogenic impacts associated with the susceptibility to environmental damage. This study aims to assess the environmental vulnerability of the Doce River basin in Brazil through Multicriteria Decision Analysis based on Geographic Information Systems (GIS-MCDA). Natural factors (slope, elevation, relief dissection, rainfall, pedology, and geology) and anthropogenic factors (distance from urban centers, roads, mining dams, and land use) were used to determine the environmental vulnerability index (EVI). The EVI was classified into five classes, identifying associated land uses. Vulnerability was verified in water resource management units (UGRHs) and municipalities using hot spot analysis. The study employed the water quality index (WQI) to assess the EVI and global sensitivity analysis (GSA) to evaluate the model input parameters that most influence the basin's environmental vulnerability. The results showed that the regions near the middle Doce River were considered environmentally more vulnerable, especially the UGRHs Guandu, Manhuaçu, and Caratinga; and 35.9% of the basin has high and very high vulnerabilities. Hot spot analysis identified regions with low EVI values (cold spot) in the north and northwest, while areas with high values (hot spot) were concentrated mainly in the middle Doce region. Water monitoring stations with the worst WQI values were found in the most environmentally vulnerable areas. The GSA determined that land use and slope were the primary factors influencing the model's response. The results of this study provide valuable information for supporting environmental planning in the Doce River basin.

Soils, Geoenvironments and Ecosystem Services of a Protected Area in Western Brazilian Amazonia.

The Serra do Divisor National Park (SDNP) in the Westernmost Brazilian Amazonia possesses unique Mountain landscapes of sub-andean nature, with high geo-biodiversity and pristine environments, with a potential high contribution in ecosystems services. We studied and mapped the basic geo-environmental units of the main sector of the Park, evaluating soil carbon stocks as a key ecosystem service provided by the Protected Area. For the identification, characterization and mapping of the geoenvironmental units, we integrated pedological, geomorphological and vegetation data obtained by local soil survey and field campaigns, as well as secondary data. Eight geoenvironmental units were identified and mapped, distributed in three main compartments: the Serra do Divisor (SD) the upper Moa River and the medium Moa River. This region presents similar environments to the sub-Andean region, notably the Ceja Forest at the top surface of the SD. Soils at the SD have high organic carbon accumulation, with close association with the nutrient-poor, quartz-rich rocks, and shows organic matter illuviation indicating active podzolization. The SDNP encompasses important ecosystems and services linked with high geo-biodiversity, and high soil carbon stocks, representing a new frontier for scientific research in the only area of transitional sub-andean forested landscape in Brazil.

Assessment, regionalization, and modeling rainfall erosivity over Brazil: Findings from a large national database.

In this study, we used a large national database to assess the rainfall erosivity (RE) patterns in time and space over the Brazilian territory. Thereby, RE and erosivity density (ED) values were obtained for 5166 rainfall gauges. Also, the concentration of the RE throughout the year and the RE's gravity center locations were analyzed. Finally, homogeneous regions regarding RE values were delimited and estimative regression models were established. The results show that Brazil's mean annual RE value is 5620 MJ mm ha-1 h-1 year-1, with considerable spatial variation over the country. The highest RE magnitudes were found for the north region, while the northeast region shows the lowest values. Regarding the RE's distribution throughout the year, in the southern region of Brazil, it is more equitable, while in some spots of the northeastern region, it is irregularly concentrated in specific months. Further analyses revealed that for most of the months, the RE's gravity centers for Brazil are in the Goiás State and that they present a north-south migration pattern throughout the year. Complementarily, the ED magnitudes allowed the identification of high-intensity rainfall spots. Additionally, the Brazilian territory was divided into eleven homogeneous regions regarding the RE patterns and for each defined region, a regression model was established and validated. These models' statistical metrics were considered satisfactory and, thus, can be used to estimate RE values for the whole country using monthly rainfall depths. Finally, all database produced are available for download. Therefore, the values and maps shown in this study are relevant for improving the accuracy of soil loss estimates in Brazil and for the establishment of soil and water conservation planning on a national scale.

Potential of plant species adapted to semi-arid conditions for phytoremediation of contaminated soils.

Physical and chemical remediation techniques used in contaminated areas are expensive and damaging to the soil structure. Biological alternatives, such as phytoremediation, are economical and applicable to large areas. The main limitation of phytoremediation is identifying plants that are both capable of stabilizing and/or absorbing metals from soil and adapted to edaphoclimatic conditions of the contaminated areas. The objective of this study is to evaluate the ability of plant species adapted to Brazilian semi-arid conditions to grow in soils contaminated with Pb. A greenhouse experiment was carried out in a 4 × 5 factorial: four plant species (M. oleifera, P. juliflora, A. peregrina, and U. ruziziensis) and five Pb concentrations in soil (0.0; 0.52; 1.05; 2.10, and 4.20 g kg-1). All species grew at all Pb levels, but only P. juliflora and A. peregrina did not exhibit significant reductions in most growth variables. U. ruziziensis, despite showing reductions in growth variables, was the species with the highest dry matter accumulation in both shoots and roots, in addition to accumulating higher amounts of Pb. We conclude that the species P. juliflora, A. peregrina and U. ruziziensis are more suitable for cultivation in soils containing high levels of Pb.

Modeling of surface sediment concentration in the Doce River basin using satellite remote sensing.

Surface sediment concentration (SSC) is linked to several problems related to water quality and its monitoring is costly because of the required fieldwork and laboratory analyses. Thus, sediment measurements are often sporadic, punctual, and performed during a short period. Orbital remote sensing allows the monitoring of SSC along the river channel permitting continuous and spatial information. This work had two objectives: (1) to model the surface concentration of sediments in the main channel of the Doce river using data from Multispectral Instrument (MSI)/Sentinel 2 and Operational Land Imager (OLI)/Landsat 8 satellite sensors; and (2) to compare different linear modeling approaches to select the best variables for SSC monitoring. For comparison with actual field data, we used mean SSC measurements in 14 sediment gauge stations from 2013 to 2020. Reflectance data of the MSI/Sentinel 2 and OLI/Landsat 8 satellites bands and spectral indices related to the monitoring of water resources were used as explanatory variables. Simple and multiple linear regression models (SLR and MLR), least absolute shrinkage and selection operator (LASSO), and Elastic Net regression were used to predict the SSC. The near-infrared band images from both MSI/Sentinel 2 and OLI/Landsat 8 satellites showed a strong linear relationship with the SSC. Multiple linear regression, LASSO and Elastic Net regressions showed good performance for SSC prediction. Sediment flow maps show an SSC reduction in the Doce river channel in recent years, indicating that part of the material from the Fundão tailings dam rupture may have been transported through sediment resuspension and transport processes.

Acid sulfate soils from Antarctica: genesis and properties along a climatic gradient.

Sulfurization is a pedogenic process that involves pyrite oxidation and strong soil acidification, accounting for the formation of acid sulfate soils. In Antarctica, acid sulfate soils are related to specific parent materials, such as sulfide-bearing andesites in Maritime Antarctica and pyritized sedimentary rocks in James Ross Archipelago. The hypothesis is that the acid sulfate soils of these regions vary according with a climate gradient. The reviewing of current data showed that the acid sulfate soils of warmer and wetter Maritime Antarctica have a greater weathering degree, higher acidity, leaching, phosphorus adsorption capacity, structural development, and well-crystallized iron oxides and kaolinite formation. On the other hand, the sulfurization at the drier region of James Ross Archipelago is counterbalanced by the semiaridity, resulting in lower acidity and higher base contents combined with little morphological and mineralogical evolution besides presence of weatherable minerals in the clay fraction. The sulfurization process interplays with other pedogenic processes, such as the phosphatization in Maritime Antarctica and salinization in James Ross Archipelago. Higher temperatures and soil moisture enhance the pedogenesis, showing that even the Antarctic sulfate soils, which originated from specific parent materials, have their development and characteristics controlled by a clear climatic gradient.

Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence.

Several anthropic activities, especially mining, have contributed to the exacerbation of contents of potentially toxic elements in soils around the world. Mines can release a large amount of direct sources of contaminants into the environment, and even after the mines are no longer being exploited, the environmental liabilities generated may continue to provide contamination risks. Potentially toxic elements (PTEs), when present in the environment, can enter the food chain, promoting serious risks to human health and the ecosystem. Several methods have been used to determine the contents of PTEs in soils, but most are laborious, costly and generate waste. In this study, we use a methodological framework to optimize the prediction of levels of PTEs in soils. We used a total set of 120 soil samples, collected at a depth of 0-10 cm. The covariate database is composed of variables measured by proximal sensors, physical and chemical soil characteristics, and morphometric data derived from a DEM with a spatial resolution of 30 m. Five machine learning algorithms were tested: Random Forests, Cubist, Linear Model, Support Vector Machine and K Nearest Neighbor. In general, the Cubist algorithm produced better results in predicting the contents of Pb, Zn, Ba and Fe compared to the other tested models. For the Al contents, the Support Vector Machine produced the best prediction. For the Cr contents, all models showed low predictive power. The most important covariates in predicting the contents of PTEs varied according to the studied element. However, x-ray fluorescence measurements, textural and morphometric variables stood out for all elements. The methodology structure reported in this study represents an alternative for fast, low-cost prediction of PTEs in soils, in addition to being efficient and economical for monitoring potentially contaminated areas and obtaining quality reference values for soils.

Soil predictors are crucial for modelling vegetation distribution and its responses to climate change.

Bioclimatic envelope models have been extensively used to predict the vegetation dynamics in response to climate changes. However, they are prone to the uncertainties arising from General Circulation Models (GCMs), classification algorithms and predictors, with low-resolution results and little detail at the regional level. Novel research has focused on the improvement of these models through a combination of climate and soil predictors to enhance ecological consistency. In this framework, we aimed to apply a joint edaphoclimatic envelope to predict the current and future vegetation distribution in the semiarid region of Brazil, which encompasses several classes of vegetation in response to the significant environmental heterogeneity. We employed a variety of machine learning algorithms and GCMs under RCP 4.5 and 8.5 scenarios of Coupled Model Intercomparison Project Phase 5 (CMIP5), in 1 km resolution. The combination of climate and soil predictors resulted in higher detail at landscape-scale and better distinction of vegetations with overlapping climatic niches. In forecasts, soil predictors imposed a buffer effect on vegetation dynamics as they reduced shifts driven solely by climatic drift. Our results with the edaphoclimatic approach pointed to an expansion of the dry Caatinga vegetation, ranging from an average of 16% to 24% on RCP 4.5 and RCP8.5 scenarios, respectively. The shift in environmental suitability from forest to open and dry vegetation implies a major loss to biodiversity, as well as compromising the provision of ecosystem services important for maintaining the economy and livelihoods of the world's largest semiarid population. Predicting the most susceptible regions to future climate change is the first step in developing strategies to mitigate impacts in these areas.

Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow.

There are different methods for predicting streamflow, and, recently machine learning has been widely used for this purpose. This technique uses a wide set of covariables in the prediction process that must undergo a selection to increase the precision and stability of the models. Thus, this work aimed to analyze the effect of covariable selection with Recursive Feature Elimination (RFE) and Forward Feature Selection (FFS) in the performance of machine learning models to predict daily streamflow. The study was carried out in the Piranga river basin, located in the State of Minas Gerais, Brazil. The database consisted of an 18-year-old historical series (2000-2017) of streamflow data at the outlet of the basin and the covariables derived from the streamflow of affluent rivers, precipitation, land use and land cover, products from the MODIS sensors, and time. The highly correlated covariables were eliminated and the selection of covariables by the level of importance was carried out by the RFE and FFS methods for the Multivariate Adaptive Regression (EARTH), Multiple Linear Regression (MLR), and Random Forest (RF) models. The data were partitioned into two groups: 75% for training and 25% for validation. The models were run 50 times and had their performance evaluated by the Nash Sutcliffe efficiency coefficient (NSE), Determination coefficient (R2), and Root of Mean Square Error (RMSE). The three models tested showed satisfactory performance with both covariable selection methods, however, all of them proved to be inaccurate for predicting values associated with maximum streamflow events. The use of FFS, in most cases, improved the performance of the models and reduced the number of selected covariables. The use of machine learning to predict daily streamflow proved to be efficient and the use of FFS in the selection of covariables enhanced this efficiency.

Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing.

Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard to estimate ETo and requires several meteorological elements. In developing countries, the number of weather stations is insufficient. Thus, free products of remote sensing with evapotranspiration information must be used for this purpose. In this context, the objective of this study was to estimate monthly ETo from potential evapotranspiration (PET) made available by MOD16 product. In this study, the monthly ETo estimated by Penman-Monteith method was considered as the standard. For this, data from 265 weather station of the National Institute of Meteorology (INMET), spread all over the Brazilian territory, were acquired for the period from 2000 to 2014 (15 years). For these months, monthly PET values from MOD16 product for all Brazil were also downloaded. By using machine learning algorithms and information from WorldClim as covariates, ETo was estimated through images from the MOD16 product. To perform the modeling of ETo, eight regression algorithms were tested: multiple linear regression; random forest; cubist; partial least squares; principal components regression; adaptive forward-backward greedy; generalized boosted regression and generalized linear model by likelihood-based boosting. Data from 2000 to 2012 (13 years) were used for training and data of 2013 and 2014 (2 years) were used to test the models. The PET made available by the MOD16 product showed higher values than those of ETo for different periods and climatic regions of Brazil. However, the MOD16 product showed good correlation with ETo, indicating that it can be used in ETo estimation. All models of machine learning were effective in improving the performance of the metrics evaluated. Cubist was the model that presented the best metrics for r2 (0.91), NSE (0.90) and nRMSE (8.54%) and should be preferred for ETo prediction. MOD16 product is recommended to be used to predict monthly ETo, which opens possibilities for its use in several other studies.

Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil.

This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively. Using PCA, four principal components were selected as indicators of water quality, explaining the cumulative variance of 68% in the rainy season and 65% in the dry season. The HCA grouped the stations into four groups in the rainy season and three groups in the dry season, showing the influence of seasonality on the grouping of stations. Moreover, the HCA made it possible to differentiate water quality stations located in the headwaters of the basin, in the main river channel, and near urban centers. The results obtained through multivariate statistics proved to be important in understanding the current water quality situation in the basin and can be used to improve the management of water resources because the collection and analysis of all parameters in all monitoring stations require greater availability of financial resources.

Machine learning models for streamflow regionalization in a tropical watershed.

This study aims to assess different machine learning approaches for streamflow regionalization in a tropical watershed, analyzing their advantages and limitations, and to point the benefits of using them for water resources management. The algorithms applied were: Random Forest, Earth and linear model. The response variables were the three types of minimum streamflow (Q7.10, Q95 and Q90), besides the long-term average streamflow (Qmld). The database involved 76 environmental covariates related to morphometry, topography, climate, land use and cover, and surface conditions. The elimination of covariates was performed using two processes: Pearson's correlation analysis and importance analysis by Recursive Feature Elimination (RFE). To validate the models, the following statistical metrics were used: Nash-Sutcliffe coefficient (NSE), percent bias (PBIAS), Willmott's index of agreement (d), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and relative error (RE). The linear model was unsatisfactory for all response variables. The results show that nonlinear models performed well, and their covariate of greatest predictive importance was flow equivalent to the precipitated volume, considering the subtraction of an abstraction factor of 750 mm (Peq750). Generally, the Random Forest and Earth models showed similar performances and great ability to predict the minimum streamflow and long-term average streamflow assessed, constituting powerful and promising alternatives for the streamflow regionalization in support to the management and integrated planning of water resources at the level of river basins.

[Walkability and healthy aging: an analytical proposal for small and medium-sized Brazilian cities]. / Caminhabilidade e envelhecimento saudável: uma proposta de análise para cidades brasileiras de pequeno e médio porte.

Evidence has shown that urban environments that discourage walking contribute to functional incapacity in the elderly. Various indices have been proposed to describe an area's walkability, combining different aspects of the built environment that promote (or inhibit) walking. However, due to problems with the quality and availability of data in Brazil, there is no walkability index to date applies to all cities of the country and that has been properly tested in the population. The current study aimed to propose a walkability index based on geographic information systems for a medium-sized city, with open-access data, and to test its association with functional incapacity in the elderly. The study used data from the urban area of a medium-sized Brazilian city to select a parsimonious set of variables through factor analysis. The resulting index was tested for its association with the capacity to perform activities of daily living that require more movement, in 499 elderly, using generalized estimating equations. The resulting walkability index consists of residential density, commercial density, street connectivity, presence of sidewalks, and public lighting. These variables comprised the first factor in the factor analysis, excluding only arborization which was retained in the second factor. The worst walkability score was associated with the highest functional incapacity score. Based on the results and their validation, the study suggests an easily applicable walkability index with great potential for use in action plans to adapt environments.

Caminhabilidade e envelhecimento saudável: uma proposta de análise para cidades brasileiras de pequeno e médio porte / Walkability and healthy aging: an analytical proposal for small and medium-sized Brazilian cities / La posibilidad de pasear y el envejecimiento saludable: una propuesta de análisis para ciudades brasileñas de tamaño pequeño y medio

Há evidências de que ambientes urbanos que desestimulam a caminhada contribuem para a incapacidade funcional de idosos. Vários índices foram propostos para descrever a caminhabilidade de uma área combinando aspectos do ambiente construído que promovem ou inibem a caminhada. No entanto, devido a problemas de qualidade e disponibilidade de dados no Brasil, até o momento não há um índice de caminhabilidade aplicável a todas as cidades do país e devidamente testado na população. O objetivo deste estudo foi propor um índice de caminhabilidade baseado em sistemas de informação geográfica para uma cidade de médio porte, com dados de livre acesso, bem como testar sua associação com a incapacidade funcional em idosos. Foram usados os dados da área urbana de um município de médio porte para selecionar um conjunto parcimonioso de variáveis por meio de análise fatorial. O índice obtido foi testado em relação à sua associação com a capacidade para a realização de atividades de vida diária que requerem maior movimentação, em 499 idosos utilizando equações de estimativas generalizadas. O índice de caminhabilidade resultante foi composto por densidade residencial, densidade comercial, conectividade de ruas, presença de calçadas e iluminação pública. Essas variáveis compuseram o primeiro fator da análise fatorial, excluindo-se apenas a arborização que ficou retida no segundo fator. Verificou-se que o pior escore de caminhabilidade estava associado ao maior escore de incapacidade funcional. Com base nos resultados e na validação deles, o estudo sugere um índice de caminhabilidade facilmente aplicável com grande potencial de uso em planos de ação para adequar os ambientes.

Evidence has shown that urban environments that discourage walking contribute to functional incapacity in the elderly. Various indices have been proposed to describe an area's walkability, combining different aspects of the built environment that promote (or inhibit) walking. However, due to problems with the quality and availability of data in Brazil, there is no walkability index to date applies to all cities of the country and that has been properly tested in the population. The current study aimed to propose a walkability index based on geographic information systems for a medium-sized city, with open-access data, and to test its association with functional incapacity in the elderly. The study used data from the urban area of a medium-sized Brazilian city to select a parsimonious set of variables through factor analysis. The resulting index was tested for its association with the capacity to perform activities of daily living that require more movement, in 499 elderly, using generalized estimating equations. The resulting walkability index consists of residential density, commercial density, street connectivity, presence of sidewalks, and public lighting. These variables comprised the first factor in the factor analysis, excluding only arborization which was retained in the second factor. The worst walkability score was associated with the highest functional incapacity score. Based on the results and their validation, the study suggests an easily applicable walkability index with great potential for use in action plans to adapt environments.

Existen evidencias de que los ambientes urbanos que desestimulan los paseos contribuyen a la incapacidad funcional de los ancianos. Se propusieron varios índices para describir la posibilidad de pasear en un área, combinando aspectos del ambiente construido que promueven o inhiben los paseos. No obstante, debido a problemas de calidad y disponibilidad de datos en Brasil, hasta el momento no existe un índice sobre la posibilidad de pasear, aplicable a todas las ciudades del país, y debidamente probado en la población. El objetivo de este estudio fue proponer un índice sobre la posibilidad de pasear, basado en sistemas de información geográfica para una ciudad de tamaño medio, con datos de libre acceso, así como probar su asociación con la incapacidad funcional en ancianos. Se utilizaron los datos del área urbana de un municipio de tamaño medio para seleccionar un conjunto parsimonioso de variables mediante análisis factorial. El índice obtenido fue probado en 499 ancianos, en lo que se refiere a su asociación con la capacidad para la realización de actividades de vida diaria, que requieren un mayor movimiento, utilizando ecuaciones de estimación generalizadas. El índice resultante sobre la posibilidad de pasear estaba compuesto por: densidad residencial, densidad comercial, conectividad de calles, presencia de aceras e iluminación pública. Estas variables formaron parte del primer factor de análisis factorial, excluyendo solamente la arborización, que quedó fijada en el segundo factor. Se verificó que la peor puntuación sobre la posibilidad de pasear se asoció a la mayor puntuación de incapacidad funcional. En base a los resultados, y a la validación de los mismos, el estudio sugiere un índice sobre la posibilidad de realizar paseos, fácilmente aplicable, con un gran potencial de uso en planes de acción para adecuar los ambientes.

Eficiência de utilização de nutrientes por cultivares de cafeeiro / Nutrients use efficiency by coffee cultivars

Elucidar as diferenças nas exigências nutricionais entre as cultivares é uma forma de obter maior produtividade e otimizar o uso de fertilizantes. Este trabalho teve por objetivo avaliar a eficiência na produção de raiz e parte aérea por unidade absorvida de N, P, K, Ca, Mg, S, B, Cu e Zn em quatro cultivares de cafeeiro arábico ('Acaiá IAC-474-19', 'Icatu Amarelo IAC-3282', 'Rubi Mg(-1)192' e 'Catuaí Vermelho IAC-99'). Para tanto, foi conduzido um experimento em condições de campo, no Campus da Universidade Federal de Viçosa, durante dois anos, em delineamento experimental de blocos casualizados, em arranjo fatorial 4x3, constituído de quatro cultivares e três níveis de adubação (baixo, normal e alto), com quatro repetições. As plantas que constituíram o nível normal receberam adubação baseada na marcha de acúmulo de nutrientes em café arábica. Nos níveis de adubação baixo e alto, as plantas receberam, respectivamente, 0,4 e 1,4 vezes a recomendação de adubação feita para o nível normal. A eficiência de utilização de nutrientes para produção de raízes foi diferenciada entre as cultivares quando houve restrição na quantidade de adubos fornecidos (nível baixo), não havendo diferenças entre elas quando se empregou dose normal e alta de fertilizantes. A eficiência na produção de raízes por unidade de N, P, K, Ca, Mg e S absorvidos foi maior na cultivar 'Acaiá IAC-474-19' e menor na 'Rubi Mg(-1)192'. Conclui-se que a eficiência de utilização de nutrientes para produção de raízes e uso de nutrientes pela parte aérea de cafeeiros foi diferenciada entre cultivares.

To elucidate the differences in the nutritional requirements among the cultivated varieties of plant species is a form of obtaining higher productivity and to optimize the fertilizer use. This work had as objective to evaluate the root and shoot production efficiency of four arabic coffee cultivars ('Acaiá IAC 474-19', 'Icatú Amarelo IAC-3282', 'Rubi Mg(-1)192' and 'Catuaí Vermelho IAC 99') per unit of N, P, K, Ca, Mg, S, B, Cu and Zn absorbed. For this purpose an experiment was carried out in field conditions at the Universidade Federal de Viçosa. The treatments were settled in a 4x3 factorial arrangement (four cultivars and three fertilization levels; low, normal and high) in randomized blocks with four replications. The plants of the normal fertilization level received fertilization based on previously determined coffee plant recruitment. The plants of the levels low and high received, respectively, 0.4 and 1.4 times the normal fertilization doses. The efficiency of production of roots was differentiated among them cultivate when there was restriction in the amount of supplied fertilizers (low level), did not have differences among them when normal and high dose of fertilizers was used. When cultivate in the low fertilization level 'Acaiá IAC 474-19' presented highest efficiency to the production of roots per unit of N, P, K, Ca Mg and S absorbed, while in the some condition 'Rubi Mg(-1)192' presented the smallest. It was concluded that efficiency of utilization of nutrients for root and shoot productions were differentiated among cultivars.