Version 1
: Received: 19 September 2024 / Approved: 19 September 2024 / Online: 19 September 2024 (14:50:24 CEST)
How to cite:
Nobre, S. R.; McDill, M. E.; Rodriguez, L. C. E.; Diaz-Balteiro, L. Reframing Forest Harvest Scheduling Models for Ecosystem Services Management. Preprints2024, 2024091554. https://doi.org/10.20944/preprints202409.1554.v1
Nobre, S. R.; McDill, M. E.; Rodriguez, L. C. E.; Diaz-Balteiro, L. Reframing Forest Harvest Scheduling Models for Ecosystem Services Management. Preprints 2024, 2024091554. https://doi.org/10.20944/preprints202409.1554.v1
Nobre, S. R.; McDill, M. E.; Rodriguez, L. C. E.; Diaz-Balteiro, L. Reframing Forest Harvest Scheduling Models for Ecosystem Services Management. Preprints2024, 2024091554. https://doi.org/10.20944/preprints202409.1554.v1
APA Style
Nobre, S. R., McDill, M. E., Rodriguez, L. C. E., & Diaz-Balteiro, L. (2024). Reframing Forest Harvest Scheduling Models for Ecosystem Services Management. Preprints. https://doi.org/10.20944/preprints202409.1554.v1
Chicago/Turabian Style
Nobre, S. R., Luiz Carlos Estraviz Rodriguez and Luis Diaz-Balteiro. 2024 "Reframing Forest Harvest Scheduling Models for Ecosystem Services Management" Preprints. https://doi.org/10.20944/preprints202409.1554.v1
Abstract
Linear programming models have been used in forest management planning since the 1960s. These models have been formulated in three basic ways: Models I, II, and III, which are defined by the sequences of management unit states represented by the variables. In Model I, variables represent sequences of states from the beginning of the planning horizon to the end. In Model II, variables represent sequences of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, i.e., two states. The objectives of this paper are to clarify the definitions of these model variations and evaluate the advantages and disadvantages of each model. This second objective was achieved by formulating a case study problem using each model type. The case study includes three increasingly complex scenarios, each incorporating additional ecosystem services. Results show that despite having more variables and constraints, Model III requires the least time to formulate due to its less dense parameter matrix. Model II has the shortest solution times, followed closely by Model III, while Model I requires the longest times for both formulation and solution. These results are increasingly apparent in more complex scenarios.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
