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Linear programming approach for optimal forest plantation
Univ Guilan, Sowmeh Sara, Iran.
Univ Guilan, Sowmeh Sara, Iran.
Univ Guilan, Sowmeh Sara, Iran.
2017 (English)In: Journal of Forestry Research, ISSN 1007-662X, E-ISSN 1993-0607, Vol. 28, no 2, p. 299-307Article in journal (Refereed) Published
Abstract [en]

The aim of this research was to identify species suitable for plantation. We first identified species for potentially suitable for plantation based on ecological capabilities regarding soil properties. We determined the area of plantation for different species based on ecological capabilities. Then, we collected relevant data such as growth patterns of different species, labor requirements for plantation and plantation cost. A linear programming model and two integer linear programming models were used for optimization. The appropriate species based on ecological capabilities were ash, elm, maple, oak and bald cypress. A linear programming model was used based on ecological capabilities classification to determine the land area of different species for plantation. Then, two integer linear programming models were employed to select the species for plantation. We set ecological properties unequal for all of the species in the first run of the integer programming model. Two groups were classified: group one included maple and ash; group two included bald cypress, oak and elm. The second integer programming model assumed equal ecological properties for all the species. Results of linear programming showed that maple and bald cypress were appropriate for plantation at the site and their plantation areas should be 151.3 and 355.3 ha, respectively. Results of the first integer linear programming model showed that maple and bald cypress would be economically profitable for plantation. The results of the second integer linear programming model showed that only bald cypress would be appropriate for plantation.

Place, publisher, year, edition, pages
2017. Vol. 28, no 2, p. 299-307
National Category
Economics
Identifiers
URN: urn:nbn:se:miun:diva-37459DOI: 10.1007/s11676-016-0318-yISI: 000395076000010OAI: oai:DiVA.org:miun-37459DiVA, id: diva2:1358072
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-07Bibliographically approved

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Mohammadi Limaei, Soleiman

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  • apa
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  • nn-NO
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  • sv-SE
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