Random forest for spatialization of daily evapotranspiration (ET) in watersheds in the Atlantic Forest.

The importance of daily data on reference evapotranspiration (ET) has increased in recent years due to its relevance in planning and decision making regarding irrigated agriculture, water production, and forest restoration. Facing the scarcity of this information measured in loco, the study of interpolation methods capable of representing ET becomes important. Therefore, this study aimed to evaluate the adequacy of the Random Forest (RF) method in the spatialization of ET in the watersheds of the Mid-South region of the Espírito Santo State, located within the Atlantic Forest biome, Brazil.

Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes.

Alternative models for the estimation of reference evapotranspiration (ETo) are typically assessed using traditional error metrics, such as root mean square error (RMSE), which may not be sufficient to select the best model for irrigation scheduling purposes. Thus, this study analyzes the performance of the original and calibrated Hargreaves-Samani (HS), Romanenko (ROM) and Jensen-Haise (JH) equations, initially assessed using traditional error metrics, for use in irrigation scheduling, considering the simulation of different irrigation intervals/time scales. Irrigation scheduling was simulated using meteorological data collected in Viçosa-MG and Mocambinho-MG, Brazil.

Prognoses of diameter and height of trees of eucalyptus using artificial intelligence.

Models of individual trees are composed of sub-models that generally estimate competition, mortality, and growth in height and diameter of each tree. They are usually adopted when we want more detailed information to estimate forest multiproduct. In these models, estimates of growth in diameter at 1.