Book Description

Comprehending and forecasting planted forests yields are actually a challenge for forest managers, mainly due to combined, and generally misunderstood, influences of space-time weather oscillations, pests, abiotic disturbances and lack of management practices.Aiming to provide future stochastic production estimations for risk assessment and KPIu2019s for current productivity, considering distinctly climate and forest management effects, we developed a framework named GPT (Portuguese acronym for total productivity management), that integrates a soil and climate database (from 1980 up-to-date), a process-based ecophysiological model (an improved version of 3-PG), yield gaps concept, Monte Carlo approach, probabilistic analysis, forest inventory and remote sensing.This framework was applied to a group of Eucalyptus plantation, ranging from 2 to 4 years old, in northeast Sao Paulo, Brazil, that suffered from defoliation and sporadic mortality due to an intense dry period in 2017. Landsat-8 imagery timeseries were used to estimate different levels of defoliation and eventual mortality.Reference productivity (a timeless reference given by the framework) for this plantation was 270 mu00b3/ha (year 6). Optimist and pessimist scenarios (from probabilistic analysis) ranged from 301 to 223 mu00b3/ha. Actual weather conditions impacted negatively the attainable productivity by 5,4%. Defoliation and mortality impacts over future yield varied from -2% up to -20%, considering optimistic and pessimistic future scenarios, with a most likely estimation of -9,1%. This approach was proven to be helpful for forest managers to predict and prevent yield losses, manage risks and take decisions considering the dynamics of forest growth under changing scenarios.