A Spatial-Based Decision Support System for wood harvesting management in mountain areas

In this paper, a spatial-based economic model is proposed with the aim of estimating the most likely harvest cost of a forest block in relation to its particular morphological and operating features. This work, which is based on the classical stumpage price assessment method, presents an economic balance of a forest cut, attained by conducting a cost analysis of each logging phase of the different standard harvesting strategies. The study area is in the North-West of Italy, in the Mount Cotolivier forest compartment, in Oulx, Piedmont. The map of the stand structure, which is included in the Oulx Forest Management Plan, was used to locate blocks (areas considered homogeneous according to the stand structure and forest typology) where silvicultural cuts could be scheduled. The feasibility of the selected logging strategies was mapped considering six conditioning factors, of both a topological and a topographic nature. Their influence was weighted by means of a score assignation and integrated in a Multi-Criteria Decision Making procedure. The scores were mathematically combined to calculate a spatial dependent cost-function (Block Exploitation Aptitude, BEA) in which the suitability of each block to be harvested was mapped through a specific strategy. The obtained BEA was then used to estimate the most suitable productivity rate of the harvests of each block. The unitary costs of the strategies were estimated and then compared to find the most profitable one for each block.This model has proved to be effective in generating objective economic results concerning harvest cuts in productive stands in mountainous areas. The proposed methodology simultaneously takes into account different factors and generates feasibility scenarios, in the space domain, for the considered harvesting strategies. The proposed model represents a prototype on which an operational Decision Support System could be based to assist forest managers over the short-medium term.     

Global food demand,productivity growth,and the scarcity of land and water resources: a spatially explicit mathematical programming approach

In the coming decades, an increasing competition for global land and water resources can be expected, due to rising demand for food and bio‐energy production, biodiversity conservation, and changing production conditions due to climate change. The potential of technological change in agriculture to adapt to these trends is subject to considerable uncertainty. In order to simulate these combined effects in a spatially explicit way, we present a model of agricultural production and its impact on the environment (MAgPIE). MAgPIE is a mathematical programming model covering the most important agricultural crop and livestock production types in 10 economic regions worldwide at a spatial resolution of three by three degrees, i.e., approximately 300 by 300 km at the equator. It takes regional economic conditions as well as spatially explicit data on potential crop yields and land and water constraints into account and derives specific land‐use patterns for each grid cell. Shadow prices for binding constraints can be used to valuate resources for which in many places no markets exist, especially irrigation water. In this article, we describe the model structure and validation. We apply the model to possible future scenarios up to 2055 and derive required rates of technological change (i.e., yield increase) in agricultural production in order to meet future food demand.