Multiple linear regression can efficiently forecast crop yields related to climatic conditions. We can forecast coffee yield at least 5 mo prior to harvesting. An increase in T during vegetative growth was inversely proportional to coffee yield. Coffee yield in southern Minas Gerais is controlled by all meteorological elements. Coffee yield in Cerrado Mineiro is controlled by hydric conditions. Some forecasting techniques have been tested with crop models using various statistical analyses for generating future scenarios of yield (Y). Forecasting, however, can be achieved by simply using regression analysis and carefully selecting independent variables (IVs) with time displacement relative to the dependent variable. The early forecasting of Y is the vanguard of agronomic modeling, promoting improvements in planning, allowing more rational strategic decisions, and increasing food and economic security. Climatic variables are the most important factors controlling the yield and quality of coffee (Coffea arabica L.). We calibrated and tested agrometeorological models to forecast the annual Y of coffee for six traditional producing regions in the state of Minas Gerais, Brazil. We used multiple linear regressions, selecting IVs to maximize the period between the forecast of Y and the harvest for each locality. The IVs were monthly meteorological variables from 1997 to 2014: air temperature, rainfall, potential evapotranspiration, soil water storage, water deficit, and water surplus. The IVs were selected by testing all possible combinations in the domain and avoiding multicollinearity. The agrometeorological models were accurate for all regions, and the earliest forecasts were 6 and 5 mo before harvest for the producing locations of Guaxupé and Coromandel, respectively. The models for yield forecasting for Guaxupé included the water deficit in July and October and July precipitation for the high‐yield season and the water deficit in April and September and October precipitation for the low‐yield season. The models for yield forecasting for Coromandel included the November water surplus and February and September precipitation for the high‐yield season and precipitation for January, April, and October for the low‐yield season.

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This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28 th , 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by ExpectationMaximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.KEYWORDS: crop monitoring, spectral behavior, management, orbital remote sensing. Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants DISCRIMINAÇÃO DE DIFERENTES ESTADOS DE PLANTIOS DE CAFÉ PÓS--COLHEITA, POR MEIO DA TÉCNICA DE MINERAÇÃO DE DADOS E PERFIS ESPECTRAIS RESUMO:Eng.

This study aimed to develop a warning system platform for coffee rust incidence fifteen days in advance, as well as validating and regionalizing multiple linear regression models based on meteorological variables. The models developed by Pinto were validated in five counties. Experiments were set up in a randomized block design with five treatments and five replications. The experimental plot had six lines with 20 central plants of useful area. Assessments of coffee rust incidence were carried out fortnightly. The data collected from automatic stations were adjusted in new multiple linear regression models (MLRM) for five counties. Meteorological variables were lagged concerning disease assessment dates. After the adjustments, two models were selected and calculated for five counties, later there was an expansion to include ten more counties and 35 properties to validate these models. The result showed that the adjusted models of 15–30 days before rust incidence for Carmo do Rio Claro and Nova Resende counties were promising. These models were the best at forecasting disease 15 days in advance. With these models and the geoinformation systems, the warning platform and interface will be improved in the coffee grower region of the south and savannas of the Minas Gerais State, Brazil.

O tema da pesquisa é erosões urbanas, tendo como área de aplicação da metodologia a bacia do córrego Cesários. Os objetivos do trabalho tiveram dois focos principais, um que busca as causas dos processos erosivos e seus fatores constitutivos e o outro que se preocupa com as consequências para a população. Como principais resultados tem-se que as erosões ocorrem por uma combinação de fatores naturais e antrópicos, considerando-se ainda que as causas naturais forma potencializadas pela ocupação da bacia. As principais consequências relacionam-se a perdas de solo e a prejuízos econômicos. Conclui-se que há para bacia do córrego Cesários uma tendência de ocorrência de processos erosivos na baixa vertente, o que provoca assoreamento da drenagem e consequentemente contribui, inclusive, para as inundações.