238 research outputs found
Comparison of Pruning Regimes for Stone Pine (Pinus pinea L.) Using a Functional- Structural Plant Model
Functional-Structural Plant Models (FSPM) are becoming important tools for modeling the structure and growth of plants, including complex organisms like trees. These models combine the advantages of empirical, mechanistic, and structural models to simulate the growth of individual plant structures (branches, buds, leaves, etc.). This approach enables realistic evaluation of the plant’s response– including changes in structure and growth to different environmental conditions. We demonstrate the potential use of these models to evaluate individual tree growth under different management regimes (pruning). The data used in this study was obtained from 3-D measurements taken with a FASTRAK Polhemus digitizer, with specific attention given to bud creation and branching. Each branch segment was analyzed to estimate its age, enabling us to document annual structural changes. We use the XL programming language and a GroIMP environment to simulate and compare different pruning scenarios
Optimal Regeneration Regime under Continuous Crown Cover Requirements in Cork Oak Woodlands
In the present work the cork oak tree spatial growth simulator CORKFITS is used to create candidate scenarios for generating a large set of regeneration regimes combining both time and intensity factors with the individual tree spatial information. An optimal regeneration regime under continuous crown cover requirements is sought by applying a dynamic programming algorithm. It is shown that the crown cover constraint influences the total cork production potential in a negative way. The target cover constraint of 50% decreases the cork production by 66% from the potential in 40 years in our mature plot, and approximately 43% in our young plot. Higher crown cover constraint of 70% decreases the potential cork production approximately by 54% in the mature plot and does not have any influence on the younger plot. The observed losses in cork production in relation with the potential with the crown cover constrains need to be compensated economically by the higher availability of growing space for the grazing and livestock part of the montado/dehesa production system
The Connectivities of Leaf Graphs of 2-Connected Graphs
AbstractGiven a connected graph G, denote by V the family of all the spanning trees of G. Define an adjacency relation in V as follows: the spanning trees t and t′ are said to be adjacent if for some vertex u∈V, t−u is connected and coincides with t′−u. The resultant graph G is called the leaf graph of G. The purpose of this paper is to show that if G is 2-connected with minimal degree δ, then G is (2δ−2)-connected
土地集約化のための離散最適化モデリング
Open House, ISM in Tachikawa, 2015.6.19統計数理研究所オープンハウス(立川)、H27.6.19ポスター発
空間的非線形評価を伴う受粉サービスに対する離散最適化
Open House, ISM in National Center of Sciences Building, 2019.6.05統計数理研究所オープンハウス(学術総合センター)、R1.6.5ポスター発
離散最適化による最適保護区連結ネットワークの探求
Open House, ISM in Tachikawa, 2018.6.15統計数理研究所オープンハウス(立川)、H30.6.15ポスター発
激変する自然環境下の持続的森林資源管理に向けて
Open House, ISM in Tachikawa, 2011.7.14統計数理研究所オープンハウス(立川)、H23.7.14ポスター発
On geometric independency trees for points in the plane
AbstractA plane spanning tree is a tree drawn in the plane so that its edges are closed straight-line segments and no two edges intersect internally, and no three of its vertices are collinear. In this paper, we present several results on a plane spanning tree T such that the graph obtained from T by adding a line segment between any two end-vertices of T is self-intersecting
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Economic analysis of integrating spatial concerns into harvest scheduling
An analytical algorithm to formulate a sparser set of adjacency constraints than the conventional algorithm is proposed. Utilizing matrix algebra applied to an adjacency matrix, constraints are systematically and efficiently derived by the proposed algorithm. The proposed algorithm is proved to provide a true set of adjacency constraints in the sense that no adjacent harvests can occur. A heuristic technique to solve a spatially constrained area-based harvest scheduling problem with even-flow
constraints is proposed. The technique combines the modified random search technique, the modified binary search method, and the PATH algorithm. Partitioning the multiperiod scheduling problem, period by period using the PATH algorithm, the objective at each period is respecified by minimizing harvest flow fluctuation from the lower bound of the harvest flow level, and the feasibility of a solution at each period is expanded to both the current and the following periods. The modified random search technique is applied to generate a feasible solution at each period. The modified binary search method is used to obtain an optimal or appropriate even-flow level. By using the proposed heuristic technique, the cost evaluation of implementing various spatial restrictions on riparian zone planning is presented
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An efficient optimizing model for determining thinning regime and rotation age using the stand projection system growth simulator
The PATH algorithm, which is the efficient dynamic
programming algorithm developed by Paredes and Brodie
(1987) was interpreted from a different point of view.
This modification of the PATH algorithm by the calculus of
variations vastly diminished the calculation task and
memory required to store optimal stands at each stage.
Using the PATH algorithm, a new dynamic programming
model called Stand Optimization System (SOS) was
developed. The system was incorporated into a growth
simulator constructed by Arney (1985). This model
optimizes timing, intensity, and type of thinning as well
as rotation age based on either physical units (basal
area, cubic feet, merchantable cubic feet, merchantable
board feet), present net worth, or soil expectation value.
An economic analysis with Stand Optimization System
was performed so as to evaluate the impacts of interest
rate, quality premium and type of thinning.
Finally, further limitation of optimality on the PATH
algorithm and the relationship between the Lagrange
multiplier and the decision variable were discussed
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