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Modelling approaches for mixed forests dynamics prognosis. Research gaps and opportunities (CROSBI ID 256526)

Prilog u časopisu | pregledni rad (znanstveni) | međunarodna recenzija

Bravo, Felipe ; Fabrika, Marek ; Ammer, Christain ; Barreiro, Susana ; Bielak, Kamil ; Coll, Lluis ; Fonseca, Teresa ; Kangur, Ahto ; Löf, Magnus ; Merganičová, Katarina et al. Modelling approaches for mixed forests dynamics prognosis. Research gaps and opportunities // Forest Systems, 28 (2019), 1; 2171-9845, 18

Podaci o odgovornosti

Bravo, Felipe ; Fabrika, Marek ; Ammer, Christain ; Barreiro, Susana ; Bielak, Kamil ; Coll, Lluis ; Fonseca, Teresa ; Kangur, Ahto ; Löf, Magnus ; Merganičová, Katarina ; Pach, Maciej ; Pretzsch, Hans ; Stojanović, Dejan ; Schuler, Laura ; Peric, Sanja ; Rötzer, Thomas ; Río, Miren del ; Đodan, Martina ; Bravo-Oviedo, Andres

engleski

Modelling approaches for mixed forests dynamics prognosis. Research gaps and opportunities

(i) Aim of study: Modelling of forest growth and dynamic has been focused mainly in pure stands. Mixed forest management needs systematic procedures to forecast the impact of silvicultural action. The main objective of the present work is to review the update knowledge and development of forest models that can be applied in mixed forests. (ii) Material and methods: Primary research literature has been reviewed to determine the state of the art of forest modelling in mixtures focusing the analysis mainly in temperate forests (iii) Main results: The essential principles for predicting stand growth in mixed forests have been identified. Forest models applicability in mixtures was analysed. Input data, main model components, output and viewers have been presented. Finally model evaluation procedures and main model examples have been described. (iv) Research highlights: Responses to environmental and management conditions are different in mixed forests that in pure. So to insight on mixed forest dynamic foresters need new theoretical frameworks and different approaches and solutions to forecast dynamic of mixtures in order to define sustainable forest management strategies.

Growth ; yield ; dynamic ; empirical ; classification.

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Podaci o izdanju

28 (1)

2019.

2171-9845

18

objavljeno

2171-5068

2171-9845

Povezanost rada

Šumarstvo

Indeksiranost