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Austropuccinia psidii Winter, causal agent of myrtle rust, is one of the main pathogens in Eucalyptus plantations in Brazil. We investigated the effects of the pathogen on wood yield and light use efficiency (LUE) in a Eucalyptus grandis x Eucalyptus urophylla clone in a field trial at the end of a five‐year rotation. The study comprised two treatments that are commonly adopted by managers. One treatment with curative fungicide spray (Triadimenol 250 g l−1) seven months after planting and another with five preventive sprays of the same fungicide, from the 1st to 7th month age of saplings. At 5 years old, the control plots attained a mean annual increment of 35 m³ ha−1 yr−1, the curative and preventive treatments were 39 and 47 m³ ha−1 yr−1 (11% and 34% higher, respectively). LUE was also higher when fungicides were applied, 8% and 26% higher in curative and preventive treatments (1.06 and 1.24 g MJ−1) compared with the control plots (0.98 g MJ−1). To confirm the negative effect of rust at a larger scale, we tracked 102 inventory plots (from 1,234 ha) from 2 to 6 years old with the same clone, half infected and half with rust corrective control. Growth difference decreased with time, from an average of 41% at age 2%–5% at age 6. These results highlight the importance of managing myrtle rust in Eucalyptus plantations by selecting a disease‐tolerant genotype or adopting other strategies for disease control. [ABSTRACT FROM AUTHOR]

Eucalyptus plantations around the world have been largely used by the paper industry. Optimizing the management of resources is a common practice in this highly competitive industry and new forest growth models may help to understand the impact of climate change on the decisions of the optimization processes. Current optimized management plans use empirical equations to predict future forest stands growth, and it is currently impractical to replace these empirical equations with physiological models due to data input requirements. In this paper, we present a different approach, by first carrying out a preliminary assessment with the process-based physiological model 3PG to evaluate the growth of Eucalyptus stands under climate change predictions. The information supplied by 3PG was then injected as a modifier in the projected yield that feeds the management plan optimizer allowing the interpretation of climate change impacts on the management plan. Modelling results show that although a general increase of rain with climate change is predicted, the distribution throughout the year will not favor the tree growth. On the contrary, rain will increase when it is less needed (summer) and decrease when it is most needed (winter), decreasing forest stand productivity between 3 and 5%, depending on the region and soil. Evaluation of the current optimized plan that kept constant the relation between wood price/cellulose ton shows a variation in different strategic management options and an overall increase of costs in owned areas between 2 and 4%, and a decrease of cumulated net present value, initially at 15% with later stabilization at 6–8%. This is a basic comparison to observe climate change effects; nevertheless, it provides insights into how the entire decision-making process may change due to a reduction in biomass production under future climate scenarios. This work demonstrates the use of physiological models to extract information that could be merged with existing and already implemented empirical models. The methodology may also be considered a preliminary alternative to the complete replacement of empirical models by physiological models. Our approach allows some insight into forest responses to different future climate conditions, something which empirical models are not designed for. [ABSTRACT FROM AUTHOR]