Precipitation estimates derived from the Eta model and from TRMM (Tropical Rainfall Measuring Mission) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) remotely sensed data were compared to the precipitation data of the INMET (National Institute of Meteorology) meteorological stations in the south-southeast region of Minas Gerais state, Brazil, in the period between July 2009 and June 2015. Then, information about evapotranspiration (ETR), water deficit (DEF), and water surplus (EXC) was obtained from the precipitation data, using the sequential water balance (SWB) separately for each type of precipitation data (INMET, TRMM, Eta, and CHIRPS). Subsequently, the components of the SWB were comparatively analyzed. The results indicate that all three products overestimate rainfall. The strongest relationships between the INMET data and the estimated data were observed for the TRMM, in terms of precipitation estimates, as well as DEF, EXC, and ETR components. The Eta precipitation estimates are overestimated relative to those from INMET, resulting in underestimation of the water deficit (DEF_ETA) and overestimation of evapotranspiration (ETR_ETA). In general, the CHIRPS data presented a pattern similar to the station data, though statistical analyses were lower than those of the TRMM data.

Http://dx.doi.org/10.5902/1980509815743Este 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 gestão e planejamento da Área Proteção Ambiental Coqueiral, permitindo a indicação de áreas prioritárias para conservação.

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 (R²) 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 R² 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.

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 in loco 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.

The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data collections were performed: one in the dry season and one in the rainy season. A total of 30 plants were marked and georeferenced within the study area. High-resolution images were obtained using a remotely piloted aircraft (RPA) equipped with a multispectral sensor. Leaf water potential was obtained using a Scholander pump. The spatialization and interpolation of the leaf water potential data were performed by geostatistical analysis. The vegetation indices were calculated through the images obtained by the RPA and were used for a regression and correlation analysis, together with the water potential data. The degree of spatial dependence (DSD) obtained by the geostatistical data showed strong spatial dependence for both periods evaluated. In the correlation analysis and linear regression, only the red band showed a significant correlation (39.93%) with an R² of 15.95%. The geostatistical analysis was an important tool for the spatialization of the water potential variable; conversely, the use of vegetation indexes obtained by the RPA was not as efficient in the evaluation of the water conditions of the coffee plants.