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Evaluation of growth models for mixed forests used in Swedish and Finnish decision support systems / Jorge Aldea in Forest ecology and management, vol 529 (February-1 2023)  

Public [article]  inForest ecology and management > vol 529 (February-1 2023) . - n° 120721 Titre : Evaluation of growth models for mixed forests used in Swedish and Finnish decision support systems Type de document : Article/Communication Auteurs : Jorge Aldea, Auteur ; Simone Bianchi, Auteur ; Urban Nilsson, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120721 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Betula (genre) [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Suède [Termes IGN] système d'aide à la décision [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Interest in mixed forests is increasing since they could provide higher benefits and positive externalities compared to monocultures, although their management is more complex and silvicultural prescriptions for them are still scarce. Growth simulations are a powerful tool for developing useful guidelines for mixed stands. Heureka and Motti are two decision support systems commonly used for forest management in Sweden and Finland respectively. They were developed mostly with data from pure stands, so how they would perform in mixed stands is currently uncertain. We compiled a large and updated common database of well-replicated experimental research sites and monitoring networks composed by 218 and 1,160 plot-level observations of mixed stands from Sweden and Finland, respectively. We aimed to evaluated the accuracy of Heureka and Motti basal area growth models in those mixed-species stands and to detect any bias in their short-term predictions. Basal area growth simulations (excluding mortality models) were compared to observed stand-level values in a period-wise process with update of the start values in each period. The residual plots were visually examined for different stand mixtures: Norway spruce (Picea abies Karst.)-birch (Betula spp), Scots pine (Pinus sylvestris L.)-birch and Scots pine-Norway spruce. We observed that the basal area growth models in both decision support systems performed quite well for all mixtures regardless of the proportion of species. Motti simulations overestimated growth in Scots pine-Norway spruce mixtures by 0.063 m2·ha−1·year−1 which may be acceptable for practical use. Therefore, we corroborated that both decision support systems can be currently utilized for short-term forest growth simulation of mixed boreal forests. Numéro de notice : A2023-107 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120721 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120721 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102441 [article]

Stand-level mortality models for Nordic boreal forests / Jouni Siipilehto in Silva fennica, vol 54 n° 5 (December 2020)  

Public [article]  inSilva fennica > vol 54 n° 5 (December 2020) . - n° 10414 Titre : Stand-level mortality models for Nordic boreal forests Type de document : Article/Communication Auteurs : Jouni Siipilehto, Auteur ; Micky Allen, Auteur ; Urban Nilsson, Auteur ; Andreas Brunner, Auteur ; et al., Auteur ; Urban Nilsson Année de publication : 2020 Article en page(s) : n° 10414 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] âge du peuplement forestier [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] mortalité [Termes IGN] Norvège [Termes IGN] régression logistique [Termes IGN] Suède [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age. Numéro de notice : A2020-854 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.14214/sf.10414 Date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.14214/sf.10414 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98710 [article]

Model-based investigation on the effects of spatial evenness, and size selection in thinning of Picea abies stands / Peter Fransson in Scandinavian journal of forest research, vol 34 n° 3 (May 2019)  

Public [article]  inScandinavian journal of forest research > vol 34 n° 3 (May 2019) . - pp 189 - 199 Titre : Model-based investigation on the effects of spatial evenness, and size selection in thinning of Picea abies stands Type de document : Article/Communication Auteurs : Peter Fransson, Auteur ; Urban Nilsson, Auteur ; Ola Lindroos, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 189 - 199 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] distribution spatiale [Termes IGN] éclaircie (sylviculture) [Termes IGN] gestion forestière durable [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] peuplement forestier [Termes IGN] Picea abies [Termes IGN] Suède [Termes IGN] surface terrière [Vedettes matières IGN] Sylviculture Résumé : (auteur) Size and spatial distribution of trees are important for forest stand growth, but the extent to which it matters in thinning operations, in terms of wood production and stand economy, has rarely been documented. Here we investigate how the choice of spatial evenness and tree-size distribution of residual trees impacts wood production and stand economy. A spatially explicit individual-based growth model was used, in conjunction with empirical cost functions for harvesting and forwarding, to calculate net production and net present value for different thinning operations in Norway spruce stands in Northern Sweden. The in silico thinning operations were defined by three variables: (1) spatial evenness after thinning, (2) tree size preference for harvesting, and (3) basal area reduction. We found that thinning that increases spatial evenness increases net production and net present value by around 2.0%, compared to the worst case. When changing the spatial evenness in conjunction with size preference we could observe an improvement of the net production and net present value up to 8.0%. The magnitude of impact differed greatly between the stands (from 1.7% to 8.0%) and was highest in the stand with the lowest stem density. Numéro de notice : A2019-376 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1080/02827581.2019.1577914 Date de publication en ligne : 17/02/2019 En ligne : https://doi.org/10.1080/02827581.2019.1577914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93432 [article]