Coffee is a global commodity, with Brazil being a major producer, particularly in the Minas Gerais state. This study applied machine learning to predict the Arabica coffee yield in the region, analyzing two groups of cultivars (G1 and G2) using data from 1993 to 2020. The Factor Analysis of Mixed Data (FAMD) was employed to explore the relationships between climatic factors, management practices, and the coffee yield. Four machine learning models, such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost (XGB), and Support Vector Machines (SVM) were calibrated and evaluated for yield prediction. The FAMD revealed complex interactions among variables, requiring four principal components to explain approximately 64.6% of the total variance. Management practices, such as the planting density and pruning, had a stronger influence on G1 cultivars, while G2 cultivars were more sensitive to climatic conditions, particularly the air temperature. Among the machine learning models, RF and XGB performed best in the yield estimation, whereas MLR and SVM were less effective, particularly for values above 60 bags ha⁻¹ (1 bag = 60 kg). These findings underscore the variability in the yield across cultivars and demonstrate the potential of machine learning to guide tailored management strategies for different coffee cultivars.

The aim of this work was to evaluate the effect of thermal time methods and to identify the phyllochron and photoperiod response in seedlings of two forest species: Corymbia citriodora and Eucalyptus urophylla. A field experiment was installed under a completely randomized design, in a 2 × 11 factorial scheme, with two forest species and eleven sowing dates (E). Phyllochron is influenced by thermal time methods. The best thermal time method is the one that considers the base and optimum temperature and compares them with daily extreme temperatures. In addition, the influence of photoperiod and air temperature on phyllochron values was also verified. The lowest value occurred in E1 (Corymbia citriodora: 24.6 and Eucalyptus urophylla: 13.2 °C day leaf^-1) and the highest in E10 (Corymbia citriodora: 46.4 °C day leaf^-1) and E7 (Eucalyptus urophylla: 29.2 °C day leaf^-1), suggesting that the phyllochron is influenced by sowing dates.