02834naa a2200265 a 450000100080000000500110000800800410001902200470006002400360010710000170014324501190016026000090027950003040028852017410059265300250233365300180235865300130237665300280238965300190241765300160243670000180245270000150247070000170248577300660250210643022023-08-30       2023    bl uuuu     u00u1 u    #d  a0169-4286 (print); 1573-5095 (electronic).7 a10.1007/s11056-023-09993-72DOI1 aGASPARRI, P.  aPredictive model of stump regrowth in Eucalyptus globulus based on pre-harvest information.h[electronic resource]  c2023  aArticle history: Received 22 February 2023; Accepted 22 July 2023; Published 28 July 2023. -- Correspondence author: Gustavo Balmelli; email: gbalmelli@inia.org.uy -- FUNDING: The study was funded by a scholarship awarded by INIA (National Institute of Agricultural Research) to the first author. --  aABSTRACT.- Eucalyptus species have a great capacity for regeneration after harvest, which allows a second rotation as a coppice crop. The decision whether to manage the next rotation as a coppice crop or to replant depends on the expected economic result of each alternative. The problem that foresters face is the difficulty of predicting the productivity in the next rotation, which will depend largely on the percentage of stumps that resprout. Therefore, the objectives of this work were: (i) to identify the preharvest variables that influence stump regrowth and (ii) to develop a model for predicting the probability of stump regrowth in commercial plantations of Eucalyptus globulus in Uruguay based on pre-harvest information. Thirty-three plots were established in commercial plantations, in which silvicultural management, growth and health status before harvest were recorded, as well as the number of stumps that sprouted after harvest. Significant differences were found in the percentage of resprout for the following variables: genetic material, type of harvest machine, proportion of trees with DBH > 14 cm, proportion of trees with bark cankers, proportion of trees with epicormic shoots, and proportion of trees with apical death. However, the logistic regression model adjusted to predict the probability of regrowth only included as explanatory variables the genetic material, the type of harvesting machine, the proportion of trees with DBH > 14 cm, and the proportion of trees with bark cankers. The use of this model will allow managers of E. globulus plantations to make more informed decisions for the next rotation. © The Author(s), under exclusive licence to Springer Nature B.V. 2023  aBlue gum plantations  aHealth status  aModeling  aSISTEMA FORESTAL - INIA  aStump survival  aTree growth1 aHIRIGOYEN, A.1 aRACHID, C.1 aBALMELLI, G.  tNew Forests, 2023. https://doi.org/10.1007/s11056-023-09993-7