Testing mechanistic modeling to assess impacts of biomass removal

Abstract

The impacts that stand treatments have on carbon, nitrogen and water cycles is a key question relating the sustainability of forests to individual forest management decisions. While the response of the above ground carbon cycle may be relatively easy to measure and is well understood, e.g. volume growth response due to thinning, the impact of stand treatment and different harvesting scenarios on the water, and nutritional status of the remaining trees and site as well as on the below ground carbon and nitrogen cycle is much more difficult to assess due to the difficulty of obtaining reliable data. An alternative that eliminates the need for detailed site-specific data is to use existing models that were explicitly designed for studying biogeochemical processes. In this paper we test and evaluate the applicability of a species-specific mechanistic biogeochemical model to assess the impact of different harvesting scenarios after thinning. Data are available from 36 Norway spruce stands covering three regions each with three harvesting scenarios after thinning and four replications. The different harvesting scenarios applied are: (1) whole tree harvest, (2) whole tree harvest after one vegetation period, i.e. without needles, and (3) commercial stem wood harvest. The modeling results suggest that the variant 3 exhibited the highest growth efficiency, a measure of net carbon uptake or growth, versus all other harvesting methods. This is confirmed by the available field observations. Nitrogen, an indicator of nutrient supply, behaved similarly. The important exception was nitrogen plant uptake, which was lower immediately after thinning on the sites where the lowest amount of biomass was removed. Significant relationships exist between observed versus modeled stand volume and volume growth suggesting that mechanistic modeling is a suitable diagnostic tool for analyzing the impacts of different management practices and thus, such models can be efficiently used to enhance silvicultural decision-making.