The aim of this work was to assess the geographic distribution of coffee quality in Minas Gerais state, Brazil, and to study its interactions with chemical and environmental factors. Correlations between environmental factors, chemical compounds and sensory quality of participants of the Minas Coffee Quality Contest were made through Principal Component Analysis and Biplot Graphics. The results showed discriminations of high and low scores as a result of environmental variables, demonstrating a strong influence of temperature, rainfall, altitude and latitude on the quality of the coffees studied. In addition to the environmental characteristics, the chemical compounds trigonelline, caffeine, and especially the acid-5-cafeiolquinic were also relevant in discriminating the scores obtained through sensory analysis. This work is an initial indication of the factors that determine the quality of coffees produced in Minas Gerais.

RESUMOEste trabalho analisou a fragmentação florestal da Área de Proteção Ambiental Coqueiral, que está localizada no município de Coqueiral, região Sul do estado de Minas Gerais. O objetivo foi avaliar a fragmentação florestal da área de estudo, a partir de métricas da paisagem, bem como elaborar modelos de simulação da paisagem, no intuito de fornecer cenários futuros de restauração ecológica, e compará-los com a situação atual da paisagem. A análise do uso e ocupação da terra foi obtida por meio de técnicas de Sistemas de Informação Geográfica e Sensoriamento Remoto, a partir de uma imagem (SPOTMAP) do satélite SPOT 5. A análise da fragmentação florestal foi realizada utilizando o software FRAGSTATS, para calcular as métricas da paisagem mensurando parâmetros como: área, perímetro, forma, conectividade dos fragmentos. Para as simulações da paisagem foram criados buffers de 1 e 5 m no entorno de todos os remanescentes florestais da área de estudo, bem como a recuperação virtual das áreas de preservação permanente. A análise da fragmentação da paisagem mostrou que a vegetação natural está distribuída em 360 fragmentos, sendo 137 deles menores que 1 ha. Os modelos de simulação da paisagem mostraram que a área de vegetação aumentou de 1943,13 ha para 2299,02 ha na simulação em que as APPs foram reflorestadas (Vegetação natural/APPs restauradas = VA). O tamanho médio dos fragmentos nesta mesma simulação aumentou em relação à paisagem atual, passando de 7,66 m para 15,75 m. A paisagem VA mostrou um menor valor de forma (1,93), indicando que a forma dos fragmentos nesta simulação foi mais simples, o que é importante do ponto de vista da conservação, pois diminui o efeito de borda nos fragmentos. Os valores de isolamento não apresentaram diferença considerável nas simulações: 38,9 m (VN); 40,64 m (VB1); 42,89 m (VB5) e 39,75 m (VA), indicando um baixo isolamento dos fragmentos, mesmo na paisagem atual. O índice de conectividade foi alto (acima de 99%) para todas as simulações, indicando que as paisagens apresentam elevada conectividade estrutural. Estes dados são relevantes subsídios para a tomada de decisão e para

The present study was carried out to analyze chemical descriptors present in the raw coffee bean and to establish an association of these descriptors with the sensorial quality of the coffee beverage, based on expressions resulting from the interactions of coffee genotype, environment, and processing. The chemical descriptors caffeine, trigonelline, sucrose, and isomers of chlorogenic acid (3-CQA, 4-CQA, and 5-CQA), were analyzed through the use of high performance liquid chromatography (HPLC). Trained and qualified cuppers, certified as judges of specialty coffees, carried out the sensorial analysis using the methodology proposed by the Specialty Coffee Association of America (SCAA). Based on the cultivation environment, altitude and the genotype, it was possible to associate the chemical composition of the raw coffee bean with the coffee beverage sensorial quality. Yellow Bourbon cultivated above 1,200 m of altitude present higher contents of trigonelline and 3-CQA in the raw beans as well as high sensorial quality in the beverage.

The goals of this research were to analyze the fragmentation of the Atlantic Forest and to create landscape management scenarios for the municipality of Carmo de Minas, MG, Brazil. We used landscape metrics to analyze the fragmentation process of the study area, which was historically exploited for agropastoral activities. Future scenarios were modeled to represent the potential restoration of the environment based on the behavior of the natural vegetation units. The natural vegetation in the study area is highly fragmented, and the environmental integrity of its remnants is severely threatened. The management scenarios showed how the restored natural units behave in the landscape as well as the isolation and connectivity between them.Using these models, future dynamics of the landscape can be predicted. Two important actions for the conservation of the remaining natural vegetation were identified: the maintenance of secondary forest and the restoration of permanent preservation areas.

<p>The objective of this study was to identify meteorological variables related to the sensorial quality of the coffees from Mantiqueira region in Minas Gerais.  Meteorological conditions are strongly related to the coffee’s sensorial characteristics, however, there aren’t many studies quantifying this relation. Air temperature and rainfall data were collected and spatialized for regional analysis. These were associated to the 2007 through 2011 coffees’ beverage scores. The region is stratified according to relief characteristics. The bigger frequency of high scores occurred on the region’s central-south, where coffee cultivation is performed above 900 m altitude. For the <em>in loco </em>study, meteorological data and coffee samples were collected in selected pilot areas. Coffee crops were selected in three altitude ranges: below 1000 m, between 1000 and 1200 m, and over 1200 m. Above 1000 m the meteorological variable that presented the biggest variation was the air temperature. Above 1000 m  the smallest thermal amplitude occurred, which provided superior quality coffees. The study demonstrates the importance of the meteorological variable characterization aiming to identify locations with greater vocation to the specialty coffees production.</p>

The south of Minas Gerais, Brazil stands out among various regions through its capacity for production of specialty coffees. Its potential, manifested through being one of the most award-winning Brazilian regions in recent years, has been recognized by the Cup of Excellence (COE). With the evident relationship between product quality and the environment in mind, the need arises for scientific studies to provide a foundation for discrimination of product origin, creating new methods for combating possible fraud. The aim of this study was to evaluate the use of carbon and nitrogen isotopes in discrimination of production environments of specialty coffees from the Serra da Mantiqueira of Minas Gerais by means of the discriminant model. Coffee samples were composed of ripe yellow and red fruits collected manually at altitudes below 1,000 m, from 1,000 to 1,200 m and above 1,200 m. The yellow and red fruits were subjected to dry processing and wet processing, with five replications. A total of 119 samples were used for discrimination of specialty coffee production environments by means of stable isotopes and statistical modeling. The model generated had an accuracy rate of 89% in discrimination of environments and was composed of the isotope variables of δ 15 N, δ 13 C, %C, %N, δD, δ 18 O (meteoric water) and sensory analysis scores. In addition, for the first time, discrimination of environments on a local geographic scale, within a single municipality, was proposed and successfully concluded. This shows that isotope analysis is an effective method in verifying geographic origin for specialty coffees.

Com este trabalho, objetivou-se parametrizar e testar um modelo de regressão linear múltipla aplicado sobre os componentes principais mais significativos obtidos de séries de produtividades da cultura do café, representativas de três municípios da região Sul do Estado de Minas Gerais, tomando-se por base o modelo de Stewart et al. (1976), porém se acrescentando novas variáveis representadas por elementos agrometeorológicos, além das penalizações hídricas para os quatro trimestres do ciclo agrícola (julho a junho) da cultura. Tendo em vista ser o número de observações inferior à quantidade de variáveis, recorreu-se à análise multivariada de componentes principais para reduzir a dimensão do conjunto dessas variáveis. A análise de regressão linear múltipla foi aplicada nos três primeiros componentes principais. Os resultados dos testes apresentaram erros relativos percentuais das estimativas bastante discrepantes, ocorrendo tendência de superestimarem as produtividades; contudo, verificou-se que as estimativas pelo modelo tenderam a apresentar comportamento similar ao dos dados observados.

Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (Ψ W) throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the Ψ W by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R 2) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R 2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the Ψ W estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate Ψ W from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.