Effect of root and butt rot uncertainty on optimal harvest schedules and expected incomes at the stand level

• Key message Root and rot (RBR) caused byHeterobasidion parviporumNiemelä & Korhonen andHeterobasidion annosum(Fr.) Bref. damages Fennoscandian spruce stands. In case the rot infection and its severity are unknown, the mere risk of infection should seldom affect the harvest timing. When it does, the gains by harvesting earlier are minimal. • Context It has been suggested that stands infected by RBR should be harvested earlier than the healthy ones. Yet, we must decide on harvest timing decisions without reliable information on the infection. • Aims We studied if harvesting earlier pays off under RBR uncertainty. • Methods We structured the uncertainty with a decision tree and calculated the optimal rotations based on expected net present values. We compared rotation lengths to those of healthy stands and calculated gains from earlier harvesting. • Results The inclusion of RBR-related uncertainty in the model changed the rotation length of only 14–23% of the stands. The average reduction was 1.3–4.7 years. Yet, the gain from harvesting earlier was too low to be considered. • Conclusion In the absence of information on the extent and severity of RBR, it seldom pays off to advance harvests. The value growth in healthy trees tends to compensate for the value reduction due to rot.

Key Plant, Key Pests: Oak (Quercus spp.)

This series of Key Plant, Key Pests publications is designed for Florida gardeners, horticulturalists, and landscape professionals to help identify common pests associated with common Florida flora. This new 9-page publication provides information and general management recommendations for borers, caterpillars, insect-induced galls, twig girdlers, oak leaf blister, root and butt rot, Tubakia leaf spot, mistletoe, psocids, lace bugs, woolly aphids, powdery mildew, Spanish and ball moss, and lichens. Written by Juanita Popenoe, Caroline R. Warwick, Adam G. Dale, and Alfred Huo, and published by the UF/IFAS Environmental Horticulture Department.https://edis.ifas.ufl.edu/ep596

The use of ultrasound velocity and damping for the detection of internal structural defects in standing trees of European beech and Norway spruce

Field measurements were carried out to assess the feasibility of ultrasound velocity and damping for the non-invasive testing of standing trees. A total of 87 trees of European beech (Fagus sylvaticaL.) and 68 trees of Norway spruce (Picea abiesKarst.) were measured in the field, felled and assessed individually for the presence of red heartwood or butt rot. The field assessment of these internal structural defects at the stump level (SL) of the trees was compared with the ultrasound measurements recorded at two tree heights [(i.e. at the SL and 0.5 m above the stump level (ASL)] and in several directions, all perpendicular (PP) to the tree stem. Lower ultrasound velocity and higher damping were found in both species with the presence of internal defects in both the radial and tangential directions of the tree stem. The diameter at breast height (DBH) had a varying effect on both ultrasound velocity and damping. A binary logistic regression was used to test the potential of ultrasound velocity and damping to predict the presence of internal defects. Both the approaches offer similar levels of prediction accuracy (0.72 and 0.76 in beech, and 0.83 and 0.82 in spruce). Due to the significant reduction in measuring time when using ultrasound damping only, this principle is recommended for the detection of red heartwood in beech trees and butt rot in spruce trees.

Detection and classification of Root and Butt-Rot (RBR) in Stumps of Norway Spruce Using RGB Images and Machine Learning

Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting, and therewith on the individual forest owner and collectively on the forest and wood processing industries. An accurate recording of the presence of RBR during timber harvesting would enable a mapping of the location and extent of the problem, providing a basis for evaluating spread in a climate anticipated to enhance pathogenic growth in the future. Therefore, a system to automatically identify and detect the presence of RBR would constitute an important contribution to addressing the problem without increasing workload complexity for the machine operator. In this study, we developed and evaluated an approach based on RGB images to automatically detect tree stumps and classify them as to the absence or presence of rot. Furthermore, since knowledge of the extent of RBR is valuable in categorizing logs, we also classify stumps into three classes of infestation; rot = 0%, 0% < rot < 50% and rot ≥ 50%. In this work we used deep-learning approaches and conventional machine-learning algorithms for detection and classification tasks. The results showed that tree stumps were detected with precision rate of 95% and recall of 80%. Using only the correct output (TP) of the stump detector, stumps without and with RBR were correctly classified with accuracy of 83.5% and 77.5%, respectively. Classifying rot into three classes resulted in 79.4%, 72.4%, and 74.1% accuracy for stumps with rot = 0%, 0% < rot < 50%, and rot ≥ 50%, respectively. With some modifications, the developed algorithm could be used either during the harvesting operation to detect RBR regions on the tree stumps or as an RBR detector for post-harvest assessment of tree stumps and logs.