Sensory analysis of specialty coffee from different environmental conditions in the region of Matas de Minas, Minas Gerais, Brazil.

Specialty coffees can be differentiated in various ways, including the environmental conditions in which they are produced and the sensory composition of the drink. This study aimed to evaluate the effect of altitude, slope exposure and fruit color on the sensory attributes of cafes of the region of Matas de Minas. Sampling points were georeferenced in four altitude ranges (< 700 m; 700 < x> 825 m, 825 < x < 950 m and > 950 m) of the coffee crop; two fruit colors of var. Catuaí (yellowand red); and two slope exposures (North-facing and South-facing). Coffee fruit at the cherry stage were processed andsubmitted to sensory analysis. The sensory attributes evaluated were overall perception, clean cup, balance, aftertaste, sweetness, acidity , body and flavor, which made up the final score. The scores were examined by ANOVA and means werecompared by the Tukey test (p < 0.05). From the sensory standpoint, coffee fruits of both colors are similar, as well as the cof fees from both slope exposures when these factors were analyzed separately . However , at higher altitudes, Y ellow Catuaí produces coffees with better sensory quality . Similarly , coffees from North-facing slopes, at higher altitudes produce better quality cup. The altitude is the main factor that interferes with coffee quality in the area. All factors together contribute tothe final quality of the beverage produced in the region of Matas de Minas.

Spatial scale effects of sampling on the interpolation of species distribution models in the southwestern Amazon.

Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.