Effects of site productivity on forest harvest scheduling subject to green-up and maximum area restrictions. Issue 5 (3rd July 2016)

• Record Type: Journal Article
• Authors: Borges, Paulo; Martins, Isabel; Bergseng, Even; Eid, Tron; Gobakken, Terje
• Is Part Of: Scandinavian journal of forest research. Volume 31:Issue 5(2016)
• Page Start: 507
• Page End: 516
• Publication Date: 2016-07-03
• Subjects: Forest management -- mixed integer programming -- spatial optimization -- area restriction model -- growth simulator; Forests and forestry -- Scandinavia -- Periodicals; Forests and forestry -- Periodicals; Forests and forestry -- Research -- Scandinavia -- Periodicals; 634.90948
• DOI: 10.1080/02827581.2015.1089931
• Languages: English
• ISSNs: 0282-7581

Abstract

ABSTRACT: Green-up requirements are of great interest for forests near cities since these forests are commonly used for recreational activities by the local population as well as for commercial forestry activities. We present three formulations to establish green-up requirements, based on a dynamic green-up approach and constructed by means of: (i) a predefined fixed length for the green-up time, (ii) a predefined variable length for the green-up time and (iii) height information produced by the growth simulator. Additionally, restrictions on harvested volume and maximum open areas were applied. All the green-up formulations were applied to five datasets comprising different initial forest conditions regarding age and site index distribution. Results show that higher net present values are obtained by the formulation that allow a predefined variable length for the green-up time and by using the height information from the growth simulator compared to the formulations using a predefined fixed length for the green-up time. The increase in NPV was most pronounced for the old forest datasets and varied between 4.23% and 8.15%. The optimal solution was always found when modeling the green-up requirement using the height information. This formulation also tended to find optimal solutions faster than other formulations.