1. Finding efficient management policies for forest plantations through simulation
Models and Simulation
Project 2013

2. Goal: Find a “good” management policy for a forest plantation
Initial decisions
Interventions
Tree model
Plantation – stand model
Optimization by evolution simulation - genetic algorithm 2

3. Tree Model Photosynthesis

4. Photosynthesis Biomass
Water
Temperature
Sunlight
Minerals
Photosynthesis
Biomass
Crown- leaves and branches (LA Leaf Area)
Roots - weight
Trunk- (D diameter at breast height in cm and H Height )

7. Spatial model of the tree crown growth

8. Plantation model-celular automaton

9. Competition Index
Competition in forestry, refers to the action caused by a tree over another and the second prevents normal development and growth. For individual trees, competition is usually estimated by a competition index.

10. Plantation parameters
Area
Time interval
Tree species
Local variation of soil fertility
Tree genetic strength

11. Forest Management decisions
Initial decisions: distance and planting pattern

12. Forest Management actions
Pruning
Thinning
When and which ones?

13. Experimental setting Area: 100m x 100m, distance between 1
0m and 9m with 1 m grid size
Time interval: one year
Tree species:
shade intolerant

14. Neighborhood radio R: 6m
Local variation of soil fertility: (U[0,1]*0.1)
Tree genetic strength: (U[0,1]*0.1)
Daily insolation: N (1,0.2)
Daily rain: N (0.8,0.4)
Daily temperature : N (25,10)
Policy: (pattern, distance, density cell size, density threshold, thinning criteria)
Thinning criteria: random, min D, max D, closest to average D.

15. Results Netlogo simulation of a plantation evolution under a policy
Find a “good” policy with a Genetic Algorithms