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Objetivou-se observar a produção e a composição físico-química do leite e as características da curva de lactação de vacas da raça Pantaneira, e selecionar modelos matemáticos de curva de lactação encontrados na literatura. Utilizou-se 10 animais multíparos, em condição de pastoreio rotacionado, com a gramínea Megathyrsus maximum cv. Mombaça. A coleta de dados iniciou-se logo após o parto e durou até o encerramento natural da lactogênese. A ordenha foi realizada manualmente, às 7:00 e às 16:00h, com a presença do bezerro, e a pesagem do leite efetuada diariamente. As curvas de lactação individuais foram estimadas utilizando-se o parâmetro univariável. Para o ajuste de modelos matemáticos de curva de lactação, foi utilizado uma base de dados de 3.960 medidas de produção de leite. Foram comparados oito modelos que têm sido utilizados na literatura para representar curvas de lactação. A lactação de vacas persistiu por 180 dias, com média diária de produção de leite de 4,93 kg. O pico de lactação ocorreu do 35 aos 50º dias. O leite apresentou em média 5,2% de gordura e 3,2% de proteína. O modelo que apresentou o melhor ajuste de curva de lactação foi o modelo de Pollot.

This study aimed to develop an optimal set and size model of bioenergy facilities using a GIS framework. Data from sugarcane plantations were used to create case scenarios. Spatial analysis was accomplished by applying a two-phase approach: identification of suitable zones to locate bioenergy facilities and identification of ideal areas. These analyses led to a location-allocation model, using Maximize Capacitated Coverage option, in order to assign sugarcane supply areas to potential bioenergy facilities. Nine themes, as well their respective weights, generated by AHP, were used to create a visual depiction of potential sites. This layer was reclassified into five categories, using Natural Breaks (Jenks) method. Various scenarios of bioenergy plants installation were simulated. Around 50% of the area is unsuitable for bioenergy plants. A total of 1,737 potential sites were detected for implanting bioenergy plants. The best regions to locate bioenergy plants were peripheral areas. Cumulative biomass transportation distances dramatically reduce as the number of facilities increases. There is a reduction of 31% in total and average distances when comparing a 4-plant scenario with a single plant scenario. Proposed scenarios can support decision making with different numbers of bioenergy plants. Proposed methodology can be adapted to areas of other countries.

FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais O turismo ecológico tem se mostrado capaz de melhorar o entendimento da população acerca dos benefícios da conservação ambiental. Para que isso seja feito, é necessário dar condições adequadas ao visitante, de maneira que a experiência vivida seja a melhor possível. Neste sentido, a gestão de unidades de conservação passa pelo plane-jamento minucioso acerca dos melhores períodos para visitação. Assim, objetivou-se analisar o Índice de Vegetação por Diferença Normalizada para a APA do Rio Pandeiros de modo a contribuir para o planejamento do ecoturismo no local. Foram consideradas imagens do satélite Landsat-8 obtidas mensalmente entre agosto de 2016 e agosto de 2018. Os dados foram processados utilizando-se o software QGis e os resultados foram correlacionados com o regime pluviométrico da região. Foi possível observar uma estreita relação entre a condição da vegetação e a quantidade de chuva ocorrida. Para o período de junho a outubro, há maior concentração de vegetação mais esparsa e para os meses de dezembro até abril há maior concentração de vegetação densa, nos dois períodos analisados. Conclui-se que é possível utilizar o índice de vegetação por diferença normalizada para indicar períodos de visitação turística no local, permitindo um planejamento de viagem que atenda as demandas do visitante em relação às cenas paisagísticas que serão contempladas Ecotourism can be considered as a tool to teach people about environmental conservation. It is necessary to create conditions to visitants in conservation units in order to improve their experience. In this way, the conservation unit management needs to plan the activities carefully, mainly about the visitation periods. This paper evaluates the normalized difference vegetation index for the Environmental Preservation Area of the Pandeiro’s River in order to contribute to the ecotourism planning. It was considered Landsat images obtained monthly since 2016 August to 2019 August. Data were processed by QGIS software and the results were correlated with the monthly precipitation. It was possible to observe that there is a sensible relationship between vegetation condition and amount of rain. For the period between June and October, there is more sparse vegetation and for the months between December and April there is more dense vegetation. Is was possible to conclude that the normalized difference vegetation index can be applied to indicate visitation periods for the local studied.

Over the past few years, many sectors such as energy generation, industry, domestic supply, and agriculture have encountered serious environmental problems due to the lack of rainfall. Thus, the purpose of this paper is to review the current methodology of royalties distribution from Itaipu hydroelectric plant. In the proposed approach, two criteria were considered to establish the division of Itaipu royalties: (1) the relative percentage of the water flow in the generation of electricity and, (2) the relative percentage of the drop in the height of water. 62 hydroelectric plants were evaluated in this study. In 52 plants the water flow was the factor that most contributed to the generation of electricity. In 2013, 346 municipalities benefited the Itaipu royalties. With the proposed methodology, 1,327 municipalities will receive the resource, what would increase the revenue of each of these municipalities by, on average, US$ 87,436.91 per year. The methodology presented herein proposes a reduction in the environmental disparity that now exists in Brazil, through improvement to Government instruments and environmental policies. The distribution of royalties throughout the watershed can provide additional resources to support payment programs for environmental services at the state and municipality levels.

Quantifying fuel load over large areas is essential to support integrated fire management initiatives in fire-prone regions to preserve carbon stock, biodiversity and ecosystem functioning. It also allows a better understanding of global climate regulation as a potential carbon sink or source. Large area assessments usually require data from spaceborne remote sensors, but most of them cannot measure the vertical variability of vegetation structure, which is required for accurately measuring fuel loads and defining management interventions. The recently launched NASA's Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor holds potential to meet this demand. However, its capability for estimating fuel load has yet not been evaluated. In this study, we developed a novel framework and tested machine learning models for predicting multi-layer fuel load in the Brazilian tropical savanna (i.e., Cerrado biome) using GEDI data. First, lidar data were collected using an unnamed aerial vehicle (UAV). The flights were conducted over selected sample plots in distinct Cerrado vegetation formations (i.e., grassland, savanna, forest) where field measurements were conducted to determine the load of surface, herbaceous, shrubs and small trees, woody fuels and the total fuel load. Subsequently, GEDI-like full-waveforms were simulated from the high-density UAV-lidar 3-D point clouds from which vegetation structure metrics were calculated and correlated to field-derived fuel load components using Random Forest models. From these models, we generate fuel load maps for the entire Cerrado using all on-orbit available GEDI data. Overall, the models had better performance for woody fuels and total fuel loads (R2 = 0.88 and 0.71, respectively). For components at the lower stratum, models had moderate to low performance (R2 between 0.15 and 0.46) but still showed reliable results. The presented framework can be extended to other fire-prone regions where accurate measurements of fuel components are needed. We hope this study will contribute to the expansion of spaceborne lidar applications for integrated fire management activities and supporting carbon monitoring initiatives in tropical savannas worldwide.

The major determinants of productivity in Eucalyptus plantations include the site productive capacity, local environmental uniformity, and the tree genetics, which impact their growth potential and competitiveness. While these factors have received a lot of attention individually, less attention has been given to the relative importance of these factors and the interactions between them. We examined these factors and their interactions using eight experiments located along a gradient in site productivity, consisting of 54 clonal genotypes (Eucalyptus urophylla × E. grandis hybrids) at two plant spacings (3 × 3.33 and 3 × 2 m). Stand productivity was quantified as the mean annual increment at age 6 years (m3 ha−1 year−1). Site productive capacity was represented using a site index based on mean dominant height at age 6 years. Environmental uniformity was calculated from within-plot variability in growth of very young stands using residual spatial autocorrelation. The competitive ability of a given phenotype was calculated as the percent loss in potential growth due to neighbor competition. Productivity increased with all variables but site quality had the greatest effect, followed by environmental uniformity and then competitive ability, with corresponding changes in productivity of about 30, 20 and 10 m3 ha−1 year−1, respectively, across the range of the given variable. With higher environmental heterogeneity, competition among genotypes increased, especially at dense spacing, showing that in such situations, highly competitive genotypes may display productivities similar to the less competitive genotypes in uniform environments. Experimental plots distributed across site quality and uniformity gradients provide opportunity for greater genetic differentiation than plots covering more homogeneous environments, reinforcing the recommendation of establishing genetic tests in locations with extreme site qualities. Our work suggests that the G × E interaction is linked to site quality and, to a lesser degree, to the local environmental uniformity. This work shows the value of adopting environmental gradient-based approaches in tree genetic testing and clone recommendation as a way to more accurately match genotypes to specific sites.

Modern society is dependent on energy use in several ways. Due to environmental and strategic issues, biomass is emerging as an alternative for the diversification of energy sources worldwide. Forest biomass is abundant at Minas Gerais, Southeastern Brazil, but its usage for power generation remains a niche pathway yet to be exploited. The geographical planning of biomass energy production is of the utmost importance to guarantee the distribution of cheap and clean energy. This study presents a GIS-based methodology for prospecting forest biomass power plants in the Minas Gerais state, Brazil. First, applying fuzzy logic, suitable zones for biomass activity were identified by combining various environmental and social factors, along with the State road network infrastructure. For the second step, a location-allocation model was generated, which considered only the most suitable areas for plant development, spatial availability of raw materials, and the existing road network. Various scenarios were simulated with different numbers of bioenergy plants, using a capacitated location-allocation model, in order to consume half of the state forest biomass stock. The best locations and the optimum number of power plants were found by minimizing transport distances of raw material to the factories. This proposed methodology could be adapted for use in other countries.

Ecological tourism stands out as an economic activity that can be reconciled with nature conservation. Such activities have been carried out in several conservation units in Brazil. However, for satisfactory implementation, it is necessary to effectively plan the activities to be conducted, places to be visited, and paths to reach those places. In this context, we aimed to assess whether Google Earth software can assist in the expansion of the plan to use these areas for ecotourism, especially the routes that may be taken by tourists within the conservation unit. For this purpose, tourist interest sites were defined in the State Environmental Protection Area of Rio Pandeiros in the northern region of the state of Minas Gerais. The shortest routes between the pairs of locations obtained using Google Earth and QGIS software were evaluated. Additionally, it was necessary to perform vectorization and classification of all roads in the area. The lengths of the paths obtained were compared using a method identity test. The results showed that for most routes, Google Earth showed longer paths than QGIS. It can be concluded that for the purpose of ecotourism, precise planning should consider the vectorization of roads in areas with many rural roads.

The formation of an urban heat island (UHI) is one of the most common impacts of the urbanization process. To mitigate the effects of UHI, the planning of urban forests (e.g., creation of parks, forests and afforestation streets) has been the major tool applied in this context. Thus, the aim of this study is to evaluate the spatial and temporal distribution of heat islands in Vila Velha, ES, Brazil using the mono-window algorithm. The study followed these methodological steps: 1) mapping of urban green areas through a photointerpretation screen; 2) application of the mono-window algorithm to obtain the spatial and temporal patterns of land surface temperature (LST); 3) correlation between LST and the normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI); 4) application of ecological evaluation index. The results showed that the mean values of LST in urban areas were at least 2.34 to 7.19°C higher than undeveloped areas. Moreover, the positive correlation between LST and NDBI showed an amplifying effect of the developed areas for UHI, while areas with a predominance of vegetation attenuated the effect of UHI. Urban centers, clustered in some parts of the city, received the worst ecological assessment index. Finally, the adoption of measures to guide the urban forest planning within urban centers is necessary to mitigate the effect of heat islands and provide thermal comfort in urban areas.

Biomass estimation plays of crucial role in agriculture and agro-based industries. The macauba, Acrocomia aculeata (Jacq.) Lood., ex Mart., is a palm species that has been a focal point for research and development of an alternative biomass-bioenergy crop for the tropics. The macauba fruit components (exocarp, mesocarp, endocarp and seed/kernel) present different constitutional characteristics and their biomass determination, by traditional methods, is labor-consuming. Therefore, the validation of procedures that can streamline this process is relevant, since it can reduce costs and time for both breeding programs and industries. This study tested the efficacy of Artificial Neural Networks (ANN) on biomass prediction of the macauba fruit components by comparing it to the multiple linear regression method. The data used came from fruits collected in 18 localities, distributed throughout the state of Minas Gerais, Brazil. According to their provenance, the matrices were clustered into two groups with the k-means method for posterior ANN cross-validation. Each group was interchangeably used for both training and validation purposes. The ANN was more efficient than multivariate linear model in the predictions of dry weight of the fruit́s four components and oil content of the mesocarp and seed. As for variables related to dry weight, ANN reached 98% predictive accuracy (i.e., 98% accuracy of the value predicted by the network), and for variables related to oil contents, accuracy was around 90%. Additionally, non-invasive measurements of the fruit (i.e., low-cost and low-time measurement variables) were adequate enough to predict most of the variables of interest. These results show the ANN's prediction potential, saving time and efforts for the consolidation of macauba as a crop.

The study of the genotype × environment interaction is a prominent issue, requiring care for recommending improved superior genotypes to certain areas. Experimentally, it is possible to infer that the puzzle of genotype recommendations relies on edaphic and climatic changes over different terrain latitudes and longitudes, and further fluctuates in microenvironments as a result of site variations. Different gene expression is activated or suppressed in accordance with environmental requirements, resulting in phenotypic plasticity of cultivars. The goal of this study was to generate an optimal recommendation of eucalyptus genotypes in a 6846.0 km2 using climatic and geographical information. For this purpose, we used 24 clones unevenly distributed in 988 plots over the area, with planting ages between 2.5 and 6.5 years. The evaluated trait was production, measured in cubic meters of wood per hectare. Our study area typically has a mid-to-low altitude range (0–390 m), rainfall between 599 and 1749 mm, temperature from 22 to 25 °C, and a series of other bioclimatic variables. For statistical analysis, we used the random regression via mixed-effects models (REML/BLUP) combined with logistic growth models. Among the total number of clones, six proved to be the most suitable to maximize volumetric production in the work area. In addition, a change in the recommendation was verified for clones between 2.5 and 6.5 years of age. This study presents an alternate concept of environmental stratification, which to date has been made categorically by evaluating a number of locations. Our study proposes a way to perform quantitative stratum over an entire area, according to the environmental gradient. The optimal selection of genotypes promotes increased wood production without necessarily increasing the planting area, which is extremely desirable because land is a resource that is becoming increasingly scarce. Furthermore, eucalyptus productivity represents an important part of the costs and revenues of the forest enterprise, such that minimal improvements can significantly affect profit margins. The proposed method is easily adapted for use for other crops and domestic animal production.

Increase of water erosion is one of the main problems in watersheds all over the world and directly affects soil and water resources, causing economic and environmental losses. We proposed a combination of Genetic Algorithm and Erosion/Deposition modelling to allocate vegetation zones in order to reduce soil losses within watersheds. In this study, we have used Unit Stream Power Based Erosion Deposition (USPED) model to predict the spatial distribution of erosion and sediment deposition. Based on USPED estimates, Genetic Algorithm optimize site as well size of forest restoration zones in order to reduce soil losses. Proposed approach considered watershed entirely, in a cell-level optimization, to find the best possible arrangement of forest zones. For initial scenario, soil loss estimate was 2.045,55 ton. ha-1. year-1 and after optimization we achieved 52% reduction. Optimized forest zones formed connected and compacted patches, which enable field implementation. Methodology is also applicable to other watersheds around world.

Brazil is one of the leading timber producers in the world. However, in South Brazil, frost events frequently cause damage and reduce yield in forest plantations, a situation predicted to become more common under global change scenarios. This raises the need for low cost and efficient tools, such as machine learning algorithms to improve forecasting of frost risk. This study used machine learning algorithms to create zoning classifications forecasting frost risk for forest plantations located in the south-central region of Rio Grande do Sul State, Brazil. For this, we gathered and processed data from a local geodatabase (i.e. high-spatial-resolution contour lines, hydrography, and forest stands limits) comprising 30 management units with consistent historical data of frost occurrence. Then, we generated possible local-scale predictors of frost occurrence, which included longitude, latitude, elevation, relative altitude, relief orientation, and Euclidean distance from hydrography. We carried out tests of three machine learning classifiers (Random Forest – RF; Support Vector Machine-SVM and Multi-layer Perceptron-MLP) in order to determine which would most accurately predict frost occurrence. We found that RF provided the highest accuracy (> 90%), as well as the smallest percentages of class-specific errors (i.e. commission and omission errors), when compared to SVM and MLP. Latitude was the most important predictor of frost occurrence when using RF. Conversely, MLP performed worst, especially for classifying frost occurrence versus non-occurrence, and therefore had the highest percentage of class-specific errors. Our findings lead us to conclude that RF is the most proficient algorithm for forecasting frost occurrence from local-scale geomorphological data, without the need for high-cost investment in micro-meteorological sensors to monitor climate frost events linking temperature to plant damage. With increasing global climate extreme events, accurate risk zoning is essential for planning strategies of plantation at the landscape scale.

Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models estimating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems.

The construction of forest roads in Brazilian Amazon is costly and has a significant environmental impact. Several practices and principles must be observed to comply with legislation, to preserve the remaining forest, and to ensure sustainable exploitation. Road planning is complex in this context, based on the number of aspects and variables that must be considered. This research aimed to evaluate computational methods' effectiveness in planning forest roads, optimizing resources to reduce damage to the remaining forest, compared to traditional planning methods. The study area was a native forest under a sustainable forest management regime located in municipalities of Terra Santa and Oriximiná, in Pará, in Brazilian Amazon. Data obtained from area made it possible formulate six instances of different sizes. A binary integer linear programming model was used, solved using CPLEX software, and Dijkstra, Bellman-Ford, Dial, and D'Esopo-Pape shortest path algorithm, implemented in C programming language. During processing of instances, the time taken to obtain the solution increased according to size of instance, however, time difference was not significant. Among the evaluated algorithms, the D'Esopo-Pape algorithm showed the best performance. The evaluated methods were effective in obtaining an optimal solution for proposed forest road planning. The solutions obtained using computational methods more effectively considered the restrictions associated with sustainable forest management, in contrast to those derived from the traditional planning by forestry company.

Sustainable production and land use planning are recurring challenges in the agricultural and forestry activities. In this context, a mathematical programming model is proposed that allows for both optimal production planning and its updating, without land exploitation. The proposed model, based on cash flows, maximizes the overall profit and includes two types of decision variables: the first relates to design of new activities, while the second concerns adaptation of existing activities. Land use capability is considered as a model constraint to avoid deleterious land uses. In a simulated property, the proposed model was able to conceive and update production planning, with suitable prescriptions for production and conservation units, besides eliminating all improper land uses. Adaptation prescriptions were essential to meet stipulated revenue demands, idealized as a financial security measurement for the landowner. New production demands were met through an efficient transition from existing (banana, cocoa and passion-fruit) to new activities (banana, cocoa, passion-fruit, lemon and mango). The proposed model can assist in sustainable production at property level, once it can identify opportunities for production intensification while maintaining the integrity of productive and conservation areas. In addition, the model can create and update production planning. Its large scale use may provide environmental service benefits to the overall agricultural landscape.

For countries dependent on hydroelectricity, water scarcity poses a real risk. Hydroelectric plants are among the most vulnerable enterprises to climate change. Investing in the conservation of the hydrographic basin is a solution found by the hydropower sector. Given the importance of the Itaipu plant to the energy matrix of Brazil and Paraguay, the aim of this study is to review the current distribution of royalties from Itaipu, using the hydrographic basin as a of criterion of analysis. Approximately 98.73% of the Itaipu basin is in Brazil. The flow contributes 99% of the total electricity generated there, while the drop height of the water contributes only 1%. Under the current policy, royalties are shared equally between Brazil and Paraguay. In the proposed approach, each country would receive a percentage for their participation in the drop height and water flow in the output of the turbines, which are intrinsic factors for electricity generation. Thus, Brazil would receive 98.35% of the royalties and Paraguay, 1.65%. The inclusion of the hydrographic basin as a criterion for the distribution of royalties will promote more efficient water resource management, since the payment will be distributed throughout the basin of the plant. The methodology can be applied to hydroelectric projects worldwide.

The Atlantic Forest biome is recognized for its biodiversity and is one of the most threatened biomes on the planet, with forest fragmentation increasing due to uncontrolled land use, land occupation, and population growth. The most serious aspect of the forest fragmentation process is the edge effect and the loss of biodiversity. In this context, the aim of this study was to evaluate the dynamics of forest fragmentation and select potential forest fragments with a higher degree of conservation for seed harvesting in the Itapemirim river basin, Espírito Santo State, Brazil. Image classification techniques, forest landscape ecology, and multi-criteria analysis were used to evaluate the evolution of forest fragmentation to develop the landscape metric indexes, and to select potential forest fragments for seed harvesting for the years 1985 and 2013. According to the results, there was a reduction of 2.55% of the occupancy of the fragments in the basin between the years 1985 and 2013. For the years 1985 and 2013, forest fragment units 2 and 3 were spatialized with a high potential for seed harvesting, representing 6.99% and 16.01% of the total fragments, respectively. The methodology used in this study has the potential to be used to support decisions for the selection of potential fragments for seed harvesting because selecting fragments in different environments by their spatial attributes provides a greater degree of conservation, contributing to the protection and conscious management of the forests. The proposed methodology can be adapted to other areas and different biomes of the world.

The growth structure of Eucalyptus plantations is the result of site environment, genetic material, and different types of interaction between neighboring plants. It is well known that sites that are more homogeneous result in greater forest productivity. However, additional factors inherent in the micro-environment or the quality of cuttings can lead to heterogeneous clonal biomass at the end of the rotation cycle. This study of the growth patterns in commercial stands of Eucalyptus clones had two aims: (i) to determine whether environmental heterogeneity causes competition among genetically identical individuals and (ii) to validate the occurrence of intra-genotypic competition, revealing the potential relationship with forest productivity. The present study was developed based on two linear mixed models: a non-genetic model, which accounts for spatial autocorrelation and is used to estimate the effects of competition between neighboring trees into the single clone plots; and a genetic model to infer the nature of the clonal competition. Three hundred and six square plots containing one hundred plants from eight experiments using a randomized block design, with three replications, were evaluated. The experiments were positioned in different environmental conditions by combining two different plant spacings and two altitude elevations. Using the path analysis procedure, we verified that there were significant direct effects of competition according to the proximity of the trees in the plot. In addition, trees that were more distant caused indirect effects of competition through nearby trees. Stands with uniform growth conditions (measured by residual autocorrelation parameters) actually caused higher productivity. The results from the genetic correlations of intra-genotypic competition and productivity showed that the less competitive clones were always less productive, regardless of the experimental condition. The more competitively aggressive clones could optimize their productivity when planted in sites with high residual levels, reaching productivities similar to those of homogeneous stands. This suggests that the implementation of certain silviculture techniques, seeking to increase site uniformity, is less relevant to these clones. The selection and use of these clones might be useful for large companies, because they offer the opportunity to achieve high productivity, and for smaller producers who do not have access to the silvicultural quality used by large companies.

RESUMO O objetivo deste estudo foi avaliar o ganho de peso de novilhos suplementados com probióticos e aminoácidos, por meio da análise de identidade de modelos. Foram utilizados 64 novilhos castrados da raça Canchim e cruzados Canchim x Nelore com 24 ± 1,8 meses de idade e peso corpóreo inicial de 395,4 ± 10,5 kg, mantidos em pastagem de Brachiaria brizantha em sistema de pastejo contínuo. Foram testados quatro tratamentos: Testemunha (Test); Probiótico líquido (ProbL); Probiótico líquido mais aminoácidos em pó (ProbL+AAsPó); e Probiótico líquido mais aminoácido injetável (ProbL+AAsInj), em um delineamento experimental inteiramente casualizado com 16 animais por tratamento. Os tratamentos foram comparados através do Teste de Identidade de Modelos, utilizando o software R. O modelo logístico de três parâmetros (L3p) foi o selecionado para representar o ganho de peso médio diário em função do tempo. As estimativas dos parâmetros do modelo foram significativas em todos os tratamentos, apresentando coeficiente de determinação (R2)elevado e raiz quadrada do erro quadrático médio (RMSE) baixa. De acordo com o teste de identidade de modelos não houve diferença significativa entre os modelos para os tratamentos utilizados, não havendo diferença no ganho de peso dos animais com a utilização de probióticos e/ou de aminoácidos. SUMMARY The goal of this study was to analyze the weight gain of calves supplemented with probiotics and amino acids, through model identity analysis. There were utilized 64 catrated Canchin and Canchin x Nelore calves at 24 ± 1,8 months of age and initial average weight of 395,4 ± 10,5 Kg. The calves were kept on continuous grazing system in a Brachiária brizantha pasture. Four treatments were evaluated: witnesses (test); probiotic liquid (ProbL); probiotic liquid plus amino acids powder (ProbL+AAsPó); and probiotic liquid plus injectable amino acids, in a randomized experimental design with 16 animals by each treatment. The treatments, in turn, were compared trough identity model test, on R software. In order to represent the daily average weight gain over time, the logistic model with three parameters (L3p) was selected. The parameter’s estimates of the model were significant in every treatment, with high coefficient of determination (R2)and low root mean square error (RMSE). There was not significant difference between the models for the treatments, based on the identity model test. Therefore, there was no difference in weight gain by probiotics and/or amino acids utilization.

Although it is easy to list the several factors that influence forest productivity, it is almost impossible to isolate or measure all biotic and abiotic components that influence crop growth and development on a field-scale. Artificial neural networks (ANN) have been widely used to model forest productivity, since habitual modeling is hampered by the inclusion of categorical variables as well as by the large number of independent variables and its complex relationships with the dependent variable. This study aimed to obtain ANN to estimate eucalyptus productivity as a function of environmental variables, genotype and silvicultural practices, and infer about those more important in forest productivity. It was used data from continuous forest inventory, climate, soil analysis and fertilization carried out on 507 eucalyptus stands, composed of different genotypes and spacing. Multiple-layer Perceptron networks were trained to estimate the mean annual increment of eucalyptus stands at six years of age (MAI6), testing different combinations of input variables, number of neurons in the hidden layer, training algorithms, data percentage in training and validation subsets, and activation functions. In the validation, it was obtained ANN with correlation between the estimated and observed MAI6 higher than 85 % and root mean square error less than 15 %. Despite data complexity, ANN made it possible to estimate MAI6 with good precision and to include easily numerous variables, even categorical. Genotype, spacing, edaphic characteristics (clay, organic matter and Cation exchange capacity- CEC), climatic characteristics (rainfall, temperature and water deficit) and fertilization were the predictive variables that most influenced eucalyptus productivity at the end of rotation.

This study aimed to assess the productive potential of teak plantations in the state of Mato Grosso, Midwest Brazil. We modeled the mean annual increment at the age of 12 years (MAI 12 ) as a function of climatic, edaphic and physiographic variables using artificial neural networks (ANN). The ANNs were shown effective in modeling the mean annual increment, despite of some inconsistences found for areas for which part of the environmental information was not available . The greatest proportion of the land covered by our study (62%) presented moderate productivity, ranging from 10.24 m 3 ha -1 year -1 to 28.76 m 3 ha ‑1 year ‑1 MAI 12 . The highest ( MAI 12 > 28.76 m 3 ha -1 year -1 ) and the lowest ( MAI 12 < 10.24 m 3 ha ‑1 year ‑1 ) productivity areas accounted for 18% and 20% of the study area, respectively. Studies comprising a more comprehensive coverage of the teak production areas are encouraged in order to improve estimation accuracy.

This work aimed to map areas of forest fire risk in the state of Mato Grosso, Brazil. Study area is located in Brazilian Midwest region. Defined risk factors were: land-use, vegetation, slope, terrain orientation, proximity to roads and trails, altitude, precipitation, water deficit, evapotranspiration and temperature. Generated risk map was validated by overlapping it with fire occurrence points. It was observed that the highest risks of forest fires in Mato Grosso State are distributed in central and south / southwest regions. The majority of Mato Grosso area possesses high to extreme fire occurrence risk (55.76%). Adopted methodology was accurate in zoning fire risk in the evaluated area. Forest fire risk zoning provides subsidies for planning preventive and forest firefighting actions in the state. This methodology can be applied to other Brazilian or global regions.

In most countries, the loss of biodiversity caused by the fires is worrying. In this sense, the fires detection towers are crucial for rapid identification of fire outbreaks and can also be used in environmental inspection, biodiversity monitoring, telecommunications mechanisms, telemetry and others. Currently the methodologies for allocating fire detection towers over large areas are numerous, complex and non-standardized by government supervisory agencies. Therefore, this study proposes and evaluates different methodologies to best location of points to install fire detection towers considering the topography, risk areas, conservation units and heat spots. Were used Geographic Information Systems (GIS) techniques and unaligned stratified systematic sampling for implementing and evaluating 9 methods for allocating fire detection towers. Among the methods evaluated, the C3 method was chosen, represented by 140 fire detection towers, with coverage of: a) 67% of the study area, b) 73.97% of the areas with high risk, c) 70.41% of the areas with very high risk, d) 70.42% of the conservation units and e) 84.95% of the heat spots in 2014. The proposed methodology can be adapted to areas of other countries.

In the present work a model was developed for the prediction of the oil content of the mesocarp of fruit from the macauba palm, using visible and near infrared spectrometry. Reference values were determined the by Soxhlet method. The model was calibrated using spectral data from the mesocarp of macauba fruit by partial least squares regression, considering nine latent variables. The results of the calibration series were consistent with those of the validation series, registering an The coefficient of determination between the reference method and the developed model, systematic error between the predicted values and the measured values and root mean square error, for calibration and validation with independent data, respectively, equal to 0.8223, -9.2-14 and 5.917 and 0.7760, 7.081 and -0.064. VIS-NIR spectroscopy is a viable tool in the evaluation of genotypes in breeding programs for the macauba palm. No presente trabalho, foi desenvolvido um modelo para a predição do teor de óleo do mesocarpo nos frutos de macaúba, usando a espectrometria do visível e infravermelho próximo. Os valores de referência foram determinados pelo método soxhlet. O modelo foi calibrado usando dados espectrais do mesocarpo dos frutos de macaúba pela regressão por mínimos quadrados parciais, considerando nove variáveis latentes. Os resultados da série da calibração foram consistentes com os da série da validação, registrando o coeficiente de determinação entre o método referencia e o modelo desenvolvido, erro sistemático entre os valores preditos e os valores mensurados e a raiz quadrada do erro médio quadrático entre o método referência e o modelo desenvolvido, de 0,8223, -9,2-14 e 5,917 e de 0,7760, -0,064 e 7,081, para a calibração e validação respectivamente. A espectrometria VIS-NIR é viável como ferramenta de avaliação de genótipos em programas do melhoramento de macaúba.

O objetivo deste trabalho foi analisar o processo de evolucao do uso e ocupacao da terra na bacia hidrografica do Rio Vieira, localizada no municipio de Montes Claros, Minas Gerais. Para tanto, foi realizada uma analise temporal de imagens do satelite TM/LANDSAT5, dos anos 1990, 1995, 2000 e 2005, contemplando as classes tematicas Area urbana, Vegetacao Natural/Reflorestamento, Area agricola/Pastagem e Solo exposto. Fez-se uso das probabilidades de transicao entre as classes em questao, obtidas pela Cadeia de Markov. Na analise da evolucao, projetou-se o possivel comportamento de todas as classes para um horizonte de 100 anos. As projecoes revelaram uma tendencia de reducao na area das atividades agropecuarias e tambem na da classe Vegetacao Natural/Reflorestamento. Observou-se um crescimento pronunciado da Area urbana, quando comparado com o das demais classes. A tendencia de crescimento do Solo exposto, no que tange a degradacao da terra, revela-se preocupante do ponto de vista ambiental.

O objetivo deste trabalho foi mapear e quantificar as áreas de preservação permanente (APPs) de acordo com a Resolução no 303/02 do CONAMA para, então, com base no mapa de uso da terra e cobertura vegetal, identificar e quantificar os eventuais conflitos de uso da terra, com vistas a nortear o cumprimento da lei e promover o uso adequado dos recursos da sub-bacia hidrográfica do rio Camapuã/Brumado, MG. Utilizaram-se uma imagem digital do sensor ETM+/LANDSAT 7, bases cartográficas planialtimétricas do IBGE para a área de estudo, e os softwares Anudem 5.2, Erdas Imagine 9.2 e ArcGIS 9.3. As APPs ocuparam 63.095 ha, representando 57,0% da área total da sub-bacia (110.711,9 ha). Com relação às categorias de APPs, o terço superior da bacia, ao longo das linhas de cumeada, correspondeu a 52,6% dos 63.095 ha; as zonas de proteção às margens dos cursos d'água, a 31,4%; a proteção das nascentes, a 15,6%; e as encostas com declividade superior a 45° e o terço superior de morros totalizaram 0,4%. As áreas de terço superior da bacia, ao longo das linhas de cumeada, foram as mais desrespeitadas (55,9%), seguidas das margens de curso d'água (23,12%), proteção de nascentes (20,83%), encostas com declividade superior a 45° (0,3%) e terço superior de morros (0,1%). Foram identificados 21.939,3 ha de APPs sendo explorados por atividades agropecuárias, o que representa 34,8% do total de APPs. Entretanto, constatou-se que 30.734,3 ha dos 47.616,9 ha situados fora das áreas de APP, i.e., 64,5% das áreas legalmente passíveis de conversão de uso da terra, estavam cobertos por vegetação nativa. Encontraram-se sólidas evidências de que a falta de conhecimento a respeito da espacialização das APPs dentro da propriedade é a principal causa do descumprimento da lei. A delimitação e espacialização das APPs pelo geoprocessamento viabilizam a aplicação da legislação florestal, contribuindo notoriamente para minimizar os conflitos de uso da terra e evitar a degradação dos remanescentes florestais pela formação de corredores ecológicos naturais que mitiguem os efeitos deletérios da fragmentação de habitats. The major bottleneck towards environmental compliance and law enforcement in Brazil is the lack of up-to-date maps accurately depicting the boundaries of protected areas as set by the Forest Code. A detailed study carried out over the Camapuã/Brumado watersheds, based on CONAMA's Act nº 303/2002, was meant to identify and quantify eventual land use legal conflicts. An ETM+/LANDSAT 7 digital image, topographic datasets and the softwares Anudem 5.2, Erdas Imagine 9.2 and ArcGIS 9.3 were used to create visual depictions of both permanent preservation areas (PPA) and land use/land cover. PPAs corresponded to 57,0% of the total area of the watersheds (110.711,9 ha), being 52,6% located on the upper third of hills along the watersheds' divides, 31,4% on riparian zones, 15,6% around springs and their drainage areas and 0,4% combining the areas situated on hillsides with slopes greater than 45° and on the upper third of hills. On one hand, farming activities were identified on 22.000 ha, representing 35% of the protected areas. On the other hand, 31.000 ha, accounting for 65% of the total area that could be legally converted to agricultural lands, were still preserved by native vegetation. These empirical evidences corroborate the hypothesis that the lack of suitable maps showing the limits of protected areas - a responsibility of Brazilian government - forces farmers to operate on the margin of the law, being the primary cause of illegal land use.

Resumo Estudos que objetivam analisar o mercado madeira serrada no Brasil merecem destaque por fornecerem orientacoes sobre sua tendencia. Apos o avanco no uso de paineis de madeira, o mercado sofreu influencias por esse substituto, se fazendo necessarias analises econometricas para dimensionar e planejar a producao, diminuindo riscos e incertezas de comercializacao. Esse mercado e dividido em madeiras de nao coniferas e coniferas, que contribuem atualmente com 62% e 38% do mercado, respectivamente. Com base em uma serie temporal anual da producao brasileira de madeira serrada de nao coniferas e coniferas no periodo de 1961 a 2009, publicada pela Food and Agriculture Organization (FAO, 2011), o presente trabalho objetivou avaliar a metodologia Box & Jenkins ( Box ; Jenkins , 1976) para realizar previsoes da producao desse mercado. Os modelos foram avaliados com base nos criterios de Akaike e Schwarz, na significância dos coeficientes, no principio de parcimonia e no comportamento dos residuos. Pelos resultados, conclui-se que o modelo autorregressivo de media movel (ARIMA) (2,1,1) foi adequado para prever a producao de madeira serrada de nao coniferas, e o modelo ARIMA (1,1,1) para prever a producao de madeira serrada de coniferas. A metodologia pode ser utilizada para previsao desse mercado. Abstract Econometric analysis of sawn timber production in Brazil . Studies that aim to analyze the lumber market in Brazil are noteworthy for providing guidance on its trend. After advance in wood panels using the market has been influenced by this replacement, and it was necessary an econometric analysis to scale and plan production, reducing risks and uncertainties. Such market is divided into non-coniferous woods and conifers, which currently contribute to 62% and 38% of the market, respectively. Based on an annual time series of non-coniferous and coniferous sawn timber Brazilian production in the 1960 to 2009 period, published by the Food and Agriculture Organization (FAO, 2011), the present research aimed to evaluate the methodology Box & Jenkins (BOX; JENKINS, 1976) to forecast the production of this market. The models were evaluated based on the Akaike and Schwarz criteria, at the coefficients significance, at the parsimony principle and at the waste behavior. According to results, the moving average auto-regressive model (ARIMA) (2,1,1) was adequate to predict the non-coniferous sawn timber production and the ARIMA (1,1,1) model to predict the conifers sawn wood production. The methodology can be used for such market prediction . Keywords: L umber production; time series; Box & Jenkins methodology .

As espécies Heilipodus naevulus (Mannerheim, 1836) foi detectado, em 2008, no município de Barão de Cocais, MG, em plantios comerciais de clones de eucalipto, danificando o ponteiro principal e o lateral. Devido ao seu hábito noturno, o seu tamanho reduzido, coloração acinzentada, e , por se abrigar na área de serapilheira durante o dia, a sua detecção é dificultada, sendo a sua presença apenas evidenciada através dos ponteiros tombados. Este trabalho teve como objetivo conhecer a distribuição desta espécie durante o primeiro ano de crescimento clones eucalipto, a fim de fornecer subsídios para a elaboração de um plano de amostragem. O trabalho foi realizado no município de Barão de Cocais, MG, a partir de outubro/2008 a março/2009. Nos dois talhões de avaliação foram estratificados em baixada, encosta e topo. Foram distribuídas 18 porções, de 10 x 10 árvores, sendo nove por talhão, e, três por estrato. Foi adotado como indicador da presença de H.naevulus na planta a presença ponteiro recém danificado. Para o cálculo da distribuição espacial foram empregados a Razão Variância/Média, o Índice de Morisita, o Parâmetro k da distribuição binomial negativa, o Coeficiente de Green, a Distribuição de Poisson e a Lei de potência de Taylor. Ao longo do período de avaliação da presença de H. naevulus, os índices calculados apresentaram um padrão agregado de ocorrência dos danos, indicando que a espécie apresenta um padrão agregado de distribuição, mesmo em baixa incidência. The species Heilipodus naevulus (Mannerheim, 1836) was detected, in 2008, in the municipal district Barão de Cocais, MG, in commercial plantings of eucalypt clones, damaging the main and the lateral pointer. Due to its night habit, its reduced size, grayish coloration, and, for sheltering in the litter area during the day, its detection is hindered, being its presence just evidenced through tumbled pointers. This work aimed to know the distribution of this species during the first year of eucalypt clones growth in order to supply subsidies for elaborating of a sampling plan. The work was carried out in the municipal district from Barão de Cocais, MG, from October/2008 to March/2009. The two plots of evaluation were stratified into lowland, slope and top. Were distributed 18 portions, of 10 x 10 trees, being nine per plot, and three for stratum. It was adopted as indicator of H. naevulus presence in the plant the pointer presence newly damaged. For calculating of the space distribution were used the Variance/Mean Ratio, Morisita Index, Parameter k of the negative binomial distribution, the Green's Coefficient, Poisson's Distribution and the Law of Taylor's potency. Along the evaluation period of the H. naevulus presence, the calculated indexes introduced an aggregated pattern of damage occurrence. That Indicates that the species presents a aggregated pattern distribution, even in low incidence.

Ao longo dos últimos anos, o planejamento eficiente das operações florestais envolvidas no processo de produção da madeira permitiu que o setor conquistasse importantes avanços técnicos, operacionais e, sobretudo, de produtividade. Entretanto, o crescente aumento de custos tem despertado o setor para a necessidade de buscar novas ferramentas, que objetivem a racionalização de recursos e a minimização dos impactos ambientais. Diante deste contexto, esta pesquisa tem por objetivo desenvolver, através de ferramentas de modelagem espacial, um modelo de simulação baseado em agentes (MBA) capaz de analisar a atividade de baldeio de madeira, presente em operações de colheita florestal de eucalipto, com base em critérios pré-estabelecidos que visam otimizar o desempenho de três importantes componentes da atividade de baldeio, ligadas ao principal agente do processo: os tratores florestais forwarders. Estes componentes são: a distância total percorrida; o tempo gasto para execução da atividade; e o consumo de combustível. Os critérios estabelecidos deram origem a dois cenários de execução da atividade. Os dados obtidos por meio das simulações mostraram que no primeiro cenário a distância total percorrida pelos tratores foi, em média 7% menor que no segundo. Aos valores médios de distância percorrida por cada uma das oito configurações de frota testadas, foi aplicado um teste estatístico de comparação de médias, que apontou diferença estatística entre as médias dos dois cenários. O tempo gasto para execução da atividade foi menor no primeiro cenário para as configurações de frota 1, 2 e 3; para as demais o tempo foi menor no segundo cenário. Pôde-se observar também que o tempo médio de execução da atividade com o maior número de tratores, em relação a atividade de menor número chega a ser, em média, 62 % menor para ambos os cenários. Os dados de consumo de combustível indicaram um consumo menor dos tratores do primeiro cenário. Verificou-se também que a cada acréscimo de tratores na frota, houve um incremento médio de 24L no consumo de combustível pelos tratores no primeiro cenário e 25L no segundo. Ocoeficiente de Gini indicou que a desigualdade no trabalho realizado por cada trator é muito pequena e sofre um ligeiro aumento a medida que mais tratores são adicionados ao modelo. Os resultados demostram o potencial da ferramenta para a compressão dos padrões resultantes das interações entre os agentes. Sendo possível analisar e compreender o comportamento das variáveis de interesse dentro do modelo Over the last few years, efficient planning of the forest operations involved in the timber production process has enabled the sector to achieve important technical, operational and, above all, productivity gains. However, the increasing costs have awakened the sector to the need to seek new tools, which aim at rationalizing resources and minimizingenvironmental impacts. In this context, this research aims to develop, through spatial modeling tools, an agent - based simulation model (MBA) capable of analyzing the wood recycling activity, present in eucalypt forest harvesting operations, based on Preestablished criteria aiming to optimize the performance of three important components of the extraction activity, linked to the main agent of the process: forest tractors forwarders.These components are: the total distance traveled; The time spent to execute the activity; And fuel consumption. The established criteria gave rise to two scenarios of execution of the activity. The data obtained through the simulations showed that in the first scenario the total distance traveled by the tractors was, on average, 7% lower than in the second one. The mean values of distance traveled by each of the eight fleet configurations tested were applied to a statistical test comparing means, which showed statistical difference between the means of the two scenarios. The time spent to execute the activity was lower in the first scenario for the fleet configurations 1, 2 and 3; For the others the time was lower in the second scenario. It was also observed that the average time of execution of the activity with the largest number of tractors, in relation to the activity of smaller numbers, is on average 62% lower for both scenarios. The fuel consumption data indicated a lower consumption of the tractors of the first scenario. It was also verified thatwith each addition of tractors in the fleet, there was an average increase of 24L in the fuel consumption by the tractors in the first scenario and 25L in the second. The Gini coefficient indicated that the inequality in work performed by each tractor is very small and increases slightly as more tractors are added to the model. The results demonstrate the potential of the tool for the compression of the patterns resulting from the interactionsbetween the agents. It is possible to analyze and understand the behavior of the variables of interest within the model

O papel dos produtos florestais não madeireiros (PFNMs) como estratégia para melhorar os meios de subsistência das florestas e conservar a biodiversidade ainda é causa de debate, enquanto, uns exploram as maneiras pelas quais os PFNMs podem fazer parte de uma solução para o desenvolvimento e conservação da Amazônia (JARAMILO et al., 2017), outros mostram a necessidade de cautela antes de taxar os PFNMs como salvadores da floresta (PERES et al., 2003). Para os PFNMs temos uma literatura rica em contextos específicos, que revelam a enorme diversidade e complexidade dos sistemas sócio ecológico e econômico, mas, esse contexto específico é de difícil generalização e não permite uma visão ampla de como os dilemas do extrativismo na Amazônia podem ser abordados na escala da paisagem. Atualmente na Amazônia há três principais produtos do extrativismo borracha, castanha e açaí, esse último desde os anos 90 destronou os clássicos do extrativismo vegetal (castanha e borracha), maior produção e renda, e se consagrou como uma bebida energética. Entanto, no Bioma amazônico o açaí é o rei do extrativismo vegetal no estado do Acre quem está no topo da realeza do extrativismo ainda é a castanha, mas o extrativismo do açaí está em ascensão e hoje é o segundo produto do extrativismo vegetal no estado, é uma posição curiosa, já que a população extrativista acreana não possui tradição de coleta desses frutos. Neste trabalho é abordado como o mercado influencia na vida das famílias florestais no estado amazônico do Acre, mapeamos a sócio ecologia dos sistemas extrativistas do açaí e modelamos a ecologia (densidade e produtividade das palmeiras) e a sócio economia (preço, custos e rendas). Para responder se pode o açaí ser parte importante no desenvolvimento socioeconômico das famílias extrativistas acreanas. Os resultados apontam que o açaí não só pode e deve ser parte importante da estratégia de desenvolvimento socioambiental das famílias florestais no Acre, pois o estado possui um território altamente favorável à ocorrência de palmeiras e florestas com alto potencial de produção e renda de açaí The role of non-timber forest products (NTFPs) as a strategy to improve the livelihoods of family forests and conserve biodiversity is still a cause for debate. While some explore ways in which NTFPs can be part of a solution for the development and conservation of the Amazon (JARAMILO et al., 2017), other researchers point to the need for caution before taxing NTFPs as forest saviors (PERES et al., 2003). For NTFPs, we have a literature rich in "specific contexts", which reveal the enormous diversity and complexity of socioeconomic and economic systems, but this specific context is difficult to generalize and does not allow a broad view of how the dilemmas of extractivism in the Amazon can be approached on the landscape scale. Currently in the Amazon there are three main products of extractive rubber, Brazil nuts and açaí, the latter since the 90s dethroned the classics of vegetable extractivism (Brazil nuts and rubber), becoming the holder of greater production and income, it is a popular royalty that was consecrated as an energy drink. However, in the Amazonian Biome, the açaí is the king of the vegetal extractivism, but in the state of Acre who is at the top of the royalty of the extractivism is still the Brazil nuts. However, the açaí extractivism is on the rise and today is the second product of the vegetal extractivism in the state, is a curious position, since the extractive population acreana does not have tradition of collection of these fruits. In this work, we discuss how the market influences the life of forest families in the Amazonian state of Acre; we map the ecology partner of the extractive systems of açaí and model the ecology (palm density and productivity) and the socio economy (price, costs and income). To answer if the açaí can be an important part in the socioeconomic development of the extractive families acreanas. The results indicate that açaí can not only be an important part of the socio-environmental development strategy of the forest families in Acre, because the state has a highly favorable territory to the occurrence of palm trees and forests with high production potential and income of açaí. We indicate a set of measures involving government, research institutions and forest families, so that the exploitation of the açaí reaches its potential.The information treated in this work still light the yellow signal for the governmental initiatives, that must be thought like care so that it does not the transfer of this product from the socioeconomic of forest families to entrepreneurs