Large pine weevil (Hylobius abietis) is a serious pest of coniferous plantations throughout Northern Europe including in Ireland, causing significant mortality in replanted trees. Replanted trees are new transplants set on clear fell sites. This mortality results in severe economic losses to foresters (Langstrom and Day, 2004). Development of weevils takes place in the stumps of felled conifer trees and emerging adults feed directly on new transplants on site. Currently, young trees are mainly protected by chemical insecticides applied when weevil attack is anticipated. In Integrated Pest Management (IPM), adequate timing of management actions can help reduce the necessity for pesticides, or the amount used. In the case of pine weevil, forecasting the extent of weevil infestation is centred around the process of stump hacking to estimate numbers of weevils developing there (Teagasc, 2020). The research outlined here adapts an existing UK simulation model developed to determine geographic variation in voltinism of pine weevil under climate change (Wainhouse et al., 2014) to forecast timing of first year emergence of pine weevil in Ireland. The model utilises historical temperature data, derived either from nearest synoptic stations (weighted for distance) or interpolated gridded (Walsh, 2012) to forecast cumulative weevil emergence for specific sites and years for which existing biological data of emerging weevil populations were available. Observed and model simulated emergence patterns were compared, both for an early version of the model and a corrected version (adapted with the machine learning algorithm random forest). Site-specific co-variates that affect the model forecast simulations of weevil emergence were identified. Previous research at Maynooth University (Williams et al., unpublished) had demonstrated that the original implementation of the UK model could be used to predict site-specific patterns of weevil emergence based on data from local weather stations. This project builds on these findings, resulting in the development of the pineR model, incorporating data from additional sites to detect potential site-specific factors of influence, such as elevation, that could be considered to provide more accurate predictions of first year weevil emergence in Ireland. It also lays the groundwork for future work that would potentially incorporate information on weevil population structure in stumps to create an accessible version of the model using the in-stump values and local weather data to forecast timing of weevil emergence from a specific stage. It is envisaged that pineR will ultimately help refine the stump assessment protocol (i.e., stump hacking) currently used by foresters in Ireland.