A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees
As various methods for precision inventories, including light detection and ranging (LiDAR), are becoming increasingly common in forestry, planning at the individual-tree level is becoming more viable. In this study, we present a method for finding the optimal thinning times for individual trees fro...
Published in: | Canadian Journal of Forest Research |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Canadian Science Publishing
2019
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1139/cjfr-2019-0053 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfr-2019-0053 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfr-2019-0053 |
id |
crcansciencepubl:10.1139/cjfr-2019-0053 |
---|---|
record_format |
openpolar |
spelling |
crcansciencepubl:10.1139/cjfr-2019-0053 2024-09-09T19:59:41+00:00 A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees Fransson, Peter Franklin, Oskar Lindroos, Ola Nilsson, Urban Brännström, Åke 2019 http://dx.doi.org/10.1139/cjfr-2019-0053 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfr-2019-0053 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfr-2019-0053 en eng Canadian Science Publishing http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining Canadian Journal of Forest Research page 320-331 ISSN 0045-5067 1208-6037 journal-article 2019 crcansciencepubl https://doi.org/10.1139/cjfr-2019-0053 2024-08-01T04:10:01Z As various methods for precision inventories, including light detection and ranging (LiDAR), are becoming increasingly common in forestry, planning at the individual-tree level is becoming more viable. In this study, we present a method for finding the optimal thinning times for individual trees from an economic perspective. The method utilizes a forest growth model based on individual trees that has been fitted to Norway spruce (Picea abies (L.) Karst.) stands in northern Sweden. We find that the optimal management strategy is to thin from above (i.e., harvesting trees that are larger than average). We compare our optimal strategy with a conventional management strategy and find that the optimal strategy results in approximately 20% higher land expectation value. Furthermore, we find that for the optimal strategy, increasing the discount rate will reduce the final harvest age and increase the basal area reduction. Decreasing the cost to initiate a thinning (e.g., machinery-related transportation costs) increases the number of thinnings and delays the first thinning. Article in Journal/Newspaper Northern Sweden Canadian Science Publishing Norway Canadian Journal of Forest Research 320 331 |
institution |
Open Polar |
collection |
Canadian Science Publishing |
op_collection_id |
crcansciencepubl |
language |
English |
description |
As various methods for precision inventories, including light detection and ranging (LiDAR), are becoming increasingly common in forestry, planning at the individual-tree level is becoming more viable. In this study, we present a method for finding the optimal thinning times for individual trees from an economic perspective. The method utilizes a forest growth model based on individual trees that has been fitted to Norway spruce (Picea abies (L.) Karst.) stands in northern Sweden. We find that the optimal management strategy is to thin from above (i.e., harvesting trees that are larger than average). We compare our optimal strategy with a conventional management strategy and find that the optimal strategy results in approximately 20% higher land expectation value. Furthermore, we find that for the optimal strategy, increasing the discount rate will reduce the final harvest age and increase the basal area reduction. Decreasing the cost to initiate a thinning (e.g., machinery-related transportation costs) increases the number of thinnings and delays the first thinning. |
format |
Article in Journal/Newspaper |
author |
Fransson, Peter Franklin, Oskar Lindroos, Ola Nilsson, Urban Brännström, Åke |
spellingShingle |
Fransson, Peter Franklin, Oskar Lindroos, Ola Nilsson, Urban Brännström, Åke A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees |
author_facet |
Fransson, Peter Franklin, Oskar Lindroos, Ola Nilsson, Urban Brännström, Åke |
author_sort |
Fransson, Peter |
title |
A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees |
title_short |
A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees |
title_full |
A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees |
title_fullStr |
A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees |
title_full_unstemmed |
A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees |
title_sort |
simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees |
publisher |
Canadian Science Publishing |
publishDate |
2019 |
url |
http://dx.doi.org/10.1139/cjfr-2019-0053 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfr-2019-0053 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfr-2019-0053 |
geographic |
Norway |
geographic_facet |
Norway |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
Canadian Journal of Forest Research page 320-331 ISSN 0045-5067 1208-6037 |
op_rights |
http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining |
op_doi |
https://doi.org/10.1139/cjfr-2019-0053 |
container_title |
Canadian Journal of Forest Research |
container_start_page |
320 |
op_container_end_page |
331 |
_version_ |
1809930774369009664 |