Difference between revisions of "GAYA"
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System name: GAYA-JLP | System name: GAYA-JLP | ||
| + | |||
| + | Full name: Norwegian long range forest management planning model (NABUURS et PÄIVINEN 1996). | ||
=== Brief overview === | === Brief overview === | ||
Swedish DSS composed of the GAYA stand simulator and the J mathematical programming tool. | Swedish DSS composed of the GAYA stand simulator and the J mathematical programming tool. | ||
| − | [[Category: | + | [[Category:Finished articles]] |
[[Category:Decision support system]] | [[Category:Decision support system]] | ||
[[Category:Swedish DSS]] | [[Category:Swedish DSS]] | ||
| + | [[Category:Picea abies]] | ||
| + | [[Category:Pinus sylvestris]] | ||
| + | [[Category:Betula spp.]] | ||
| + | [[Category:Stand level]] | ||
| + | [[Category:Linear programming]] | ||
| + | [[Category:Economic evaluation]] | ||
| + | [[Category:Yield prediction]] | ||
| + | [[Category:Wood quality]] | ||
| + | [[Category:Fertilization]] | ||
| + | [[Category:Carbon sequestration]] | ||
| + | [[Category:Biomass estimation]] | ||
| + | [[Category:Ecological classification]] | ||
| + | [[Category:DOS]] | ||
| + | [[Category:Windows Client OS]] | ||
| + | [[Category:OS/2]] | ||
| + | [[Category:Tactical planning]] | ||
| + | [[Category:Command line interface]] | ||
__TOC__ | __TOC__ | ||
| + | |||
| + | === Scope of the system === | ||
| + | GAYA-JLP is used both for analyse long range forest management planning at forest level based on information for individual forest stands, and for analyses based on (aggregated) sample plot data for the national forest inventory. | ||
| + | |||
| + | It has two main modules the [[#Forest models|stand simulator]] and the [[#Decision-making processes and models|decision model]]. | ||
=== System origin === | === System origin === | ||
| − | * GAYA used simulations to describe silvicultural regimes in single sample plots, stands or strata, and employed linear programming (LP) to settle management strategies on a forest level. Further development has taken place since 1990 and the model, today called GAYA-JLP, is now a comprehensive tool for long-term analyses<ref>EID, T. et K. HOBBELSTAD (2000): AVVIRK-2000: A Large-scale Forestry Scenario Model for Long-term Investment, Income and Harvest Analyses. ‘’Scand. J. For. Res.’’ 15: 472-482.</ref>. | + | * GAYA model used simulations to describe silvicultural regimes in single sample plots, stands or strata, and employed [[:Category:Linear programming|linear programming]] (LP) to settle management strategies on a forest level. Further development has taken place since 1990 and the model, today called GAYA-JLP, is now a comprehensive tool for long-term analyses<ref>EID, T. et K. HOBBELSTAD (2000): AVVIRK-2000: A Large-scale Forestry Scenario Model for Long-term Investment, Income and Harvest Analyses. ‘’Scand. J. For. Res.’’ 15: 472-482.</ref>. |
| + | * GAYA-JLP was developed for long-term economic analysis of forest production in Norway. | ||
| + | * It was used for research and education at NSK (NABUURS et PÄIVINEN 1996). | ||
| − | === Related systems === | + | === Support for specific issues === |
| − | + | It provides support for forest management planning, through harvesting scheduling, economic evaluation, CO<sub>2</sub>-flow evaluation, yield prediction, and its optimization. | |
| − | * | + | |
| + | === Support for specific thematic areas of a problem type === | ||
| + | * Silvicultural | ||
| + | * Certification | ||
| + | * Conservation | ||
| + | * Development choices / land use zoning | ||
| + | * Policy/intervention alternatives | ||
| + | |||
| + | === Related systems === | ||
| + | It has a strong resemblance to the Finish [[MELA]] model. | ||
| + | |||
| + | |||
| + | == Data and data models == | ||
| + | |||
| + | === Typical spatial extent of application === | ||
| + | Large scale model using stand level growth model. | ||
| + | |||
| + | It is used both for analyse long range forest management planning at forest level based on information for individual forest stands, and for analyses based on (aggregated) sample plot data for the national forest inventory. | ||
| + | |||
| + | === Forest data input === | ||
| + | There are a total of 28 state variables defined for the stand. They are divided between species, biologically, and economically related variables. | ||
| + | * Species is defined by tree number, basal are, height, age and volume. | ||
| + | * Stand is further defined by area, site index, and e.g. earlier treatments in the rotation. | ||
| + | * Economical stand related variables are the terrain transportation distance, several difficulty parameters related to felling operations and a wood quality parameter. | ||
| + | |||
| + | === Type of information input from user === | ||
| + | The following control variables can be user-specified in decision making: | ||
| + | * basal area of the removal as a percentage of total basal area; | ||
| + | * basal area in the remaining stand; | ||
| + | * number of trees in the removal; | ||
| + | * number of trees in the remaining stand; | ||
| + | * diameter ratio between removed trees and remaining stand; | ||
| + | * type of fertiliser; | ||
| + | * intensity of fertiliser. | ||
| + | Within the optimization package JLP some constraints have also to be defined, such as non-declining flow of wood, maximisation of NPV, etc. | ||
| + | |||
| + | |||
| + | == Models == | ||
| + | |||
| + | === Forest models === | ||
| + | GAYA is the simulator module. It simulates alternative forest management treatment schedules for each calculation unit. A rotation is divided in two periods, the regeneration and the thinning phase. Within the former, the development cannot be manipulated; it is endogenously defined; only the length of the period can be user defined. During the latter, up to three species per stand can be simulated, projecting its development separately. Development is simulated based on regression functions in terms of diameter, basal area, height, spacing and natural mortality. The time dimension of the calculation, the treatment unit, is user controlled, it is a discrete number of time period of uniform length of 5 or 10 years. Cost and revenues for each management unit are also calculated. | ||
| + | |||
| + | |||
| + | == Decision Support == | ||
| + | |||
| + | === Definition of management interventions === | ||
| + | Silvicultural treatments, fertilization, regeneration methods, thinnings, harvesting. | ||
| + | |||
| + | === Typical temporal scale of application === | ||
| + | Tactical planning. | ||
| + | |||
| + | === Decision-making processes and models === | ||
| + | The implemented optimization module to solve the planning problem is the [[JLP]]-package. It uses [[:Category:Linear programming|linear programming]]. | ||
| + | |||
| + | |||
| + | == Output == | ||
| + | |||
| + | === Types of outputs === | ||
| + | Outputs are displayed in tables, showing nearly 40 defined variables for each time period. They are divided in periodic and non-periodic variables. | ||
| + | |||
| + | Periodic variables | ||
| + | :Treatment undertaken in the period. | ||
| + | :For stand after treatment: total tree number, standing volume by species, basal area, dominant height, age, and NAI (Net Area Increment). | ||
| + | :Removal: total tree number, volume for each species., basal area, mean diameter, sawwood and pulpwood proportions, price and cost information (including haulage costs), cash flow, total biomass, and net CO<sub>2</sub> flow. | ||
| + | |||
| + | Non-periodic variables | ||
| + | :For the treatment schedule: NPV at time zero, ending inventory value (EIV; NPV at the end of the last period). NPV and EIV of CO<sub>2</sub>-flow, total NPV in case CO<sub>2</sub>-flow is priced. | ||
| + | :Variables for the calculation unit: number of schedules, area, site, vegetation type, altitude, rentability’’’, slope, and a GIS-specified treatment code. | ||
| + | |||
| + | === Spatial analysis capabilities === | ||
| + | Spatial constraints such as adjacency cannot be taken into account. However, a GIS integration implementation is operative. | ||
| + | |||
| + | |||
| + | == System == | ||
| + | |||
| + | === System requirements === | ||
| + | Operating Systems: DOS (5.O or above), OS/2 (2.1 or above) or Windows (3.1 or above). | ||
| + | |||
| + | === Architecture and major DSS components === | ||
| + | As it was said before, the GAYA-JLP system is divided in two modules, the simulation model GAYA, and the optimization module using JLP. | ||
| + | |||
| + | === Usage === | ||
| + | It was used at research and educational level. | ||
| + | |||
| + | === Computational limitations === | ||
| + | The GAYA-JLP system has solved problems with 200,000 decision variables and 8,000 stands (NABUURS et PÄIVINEN 1996). | ||
| + | |||
| + | === User interface === | ||
| + | Command line interface. Building all the management schedules is very laborious, especially when large areas and detailed outputs are wanted. | ||
| Line 24: | Line 138: | ||
=== Cited references === | === Cited references === | ||
<references/> | <references/> | ||
| + | |||
| + | === External resources === | ||
| + | * NABUURS G.J. et R. PÄIVINEN (1996): ''Large Scale Forestry Scenario Models - a compilation and review''. European Forest Institute Working Paper No. 10. Joensuu, Finland. | ||
Revision as of 13:38, 30 September 2009
General System description
System name: GAYA-JLP
Full name: Norwegian long range forest management planning model (NABUURS et PÄIVINEN 1996).
Brief overview
Swedish DSS composed of the GAYA stand simulator and the J mathematical programming tool.
Contents
Scope of the system
GAYA-JLP is used both for analyse long range forest management planning at forest level based on information for individual forest stands, and for analyses based on (aggregated) sample plot data for the national forest inventory.
It has two main modules the stand simulator and the decision model.
System origin
- GAYA model used simulations to describe silvicultural regimes in single sample plots, stands or strata, and employed linear programming (LP) to settle management strategies on a forest level. Further development has taken place since 1990 and the model, today called GAYA-JLP, is now a comprehensive tool for long-term analyses[1].
- GAYA-JLP was developed for long-term economic analysis of forest production in Norway.
- It was used for research and education at NSK (NABUURS et PÄIVINEN 1996).
Support for specific issues
It provides support for forest management planning, through harvesting scheduling, economic evaluation, CO2-flow evaluation, yield prediction, and its optimization.
Support for specific thematic areas of a problem type
- Silvicultural
- Certification
- Conservation
- Development choices / land use zoning
- Policy/intervention alternatives
Related systems
It has a strong resemblance to the Finish MELA model.
Data and data models
Typical spatial extent of application
Large scale model using stand level growth model.
It is used both for analyse long range forest management planning at forest level based on information for individual forest stands, and for analyses based on (aggregated) sample plot data for the national forest inventory.
Forest data input
There are a total of 28 state variables defined for the stand. They are divided between species, biologically, and economically related variables.
- Species is defined by tree number, basal are, height, age and volume.
- Stand is further defined by area, site index, and e.g. earlier treatments in the rotation.
- Economical stand related variables are the terrain transportation distance, several difficulty parameters related to felling operations and a wood quality parameter.
Type of information input from user
The following control variables can be user-specified in decision making:
- basal area of the removal as a percentage of total basal area;
- basal area in the remaining stand;
- number of trees in the removal;
- number of trees in the remaining stand;
- diameter ratio between removed trees and remaining stand;
- type of fertiliser;
- intensity of fertiliser.
Within the optimization package JLP some constraints have also to be defined, such as non-declining flow of wood, maximisation of NPV, etc.
Models
Forest models
GAYA is the simulator module. It simulates alternative forest management treatment schedules for each calculation unit. A rotation is divided in two periods, the regeneration and the thinning phase. Within the former, the development cannot be manipulated; it is endogenously defined; only the length of the period can be user defined. During the latter, up to three species per stand can be simulated, projecting its development separately. Development is simulated based on regression functions in terms of diameter, basal area, height, spacing and natural mortality. The time dimension of the calculation, the treatment unit, is user controlled, it is a discrete number of time period of uniform length of 5 or 10 years. Cost and revenues for each management unit are also calculated.
Decision Support
Definition of management interventions
Silvicultural treatments, fertilization, regeneration methods, thinnings, harvesting.
Typical temporal scale of application
Tactical planning.
Decision-making processes and models
The implemented optimization module to solve the planning problem is the JLP-package. It uses linear programming.
Output
Types of outputs
Outputs are displayed in tables, showing nearly 40 defined variables for each time period. They are divided in periodic and non-periodic variables.
Periodic variables
- Treatment undertaken in the period.
- For stand after treatment: total tree number, standing volume by species, basal area, dominant height, age, and NAI (Net Area Increment).
- Removal: total tree number, volume for each species., basal area, mean diameter, sawwood and pulpwood proportions, price and cost information (including haulage costs), cash flow, total biomass, and net CO2 flow.
Non-periodic variables
- For the treatment schedule: NPV at time zero, ending inventory value (EIV; NPV at the end of the last period). NPV and EIV of CO2-flow, total NPV in case CO2-flow is priced.
- Variables for the calculation unit: number of schedules, area, site, vegetation type, altitude, rentability’’’, slope, and a GIS-specified treatment code.
Spatial analysis capabilities
Spatial constraints such as adjacency cannot be taken into account. However, a GIS integration implementation is operative.
System
System requirements
Operating Systems: DOS (5.O or above), OS/2 (2.1 or above) or Windows (3.1 or above).
Architecture and major DSS components
As it was said before, the GAYA-JLP system is divided in two modules, the simulation model GAYA, and the optimization module using JLP.
Usage
It was used at research and educational level.
Computational limitations
The GAYA-JLP system has solved problems with 200,000 decision variables and 8,000 stands (NABUURS et PÄIVINEN 1996).
User interface
Command line interface. Building all the management schedules is very laborious, especially when large areas and detailed outputs are wanted.
References
Cited references
- ↑ EID, T. et K. HOBBELSTAD (2000): AVVIRK-2000: A Large-scale Forestry Scenario Model for Long-term Investment, Income and Harvest Analyses. ‘’Scand. J. For. Res.’’ 15: 472-482.
External resources
- NABUURS G.J. et R. PÄIVINEN (1996): Large Scale Forestry Scenario Models - a compilation and review. European Forest Institute Working Paper No. 10. Joensuu, Finland.
