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ArticleNew findings on loblolly pine plantations from long-term experimental field studies
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Pine plantations in the southern US have been among the most intensively managed forests in the world. Their productivity has been enhanced by intensive silvicultural treatments over the past 60 years, and our knowledge about their treatment response has been expanded through long-term large-scale experimental studies. The analysis of nine long-term loblolly pine (Pinus taeda L.) field trials resulted in several new findings. For loblolly pine in the southern US, there exist maximum productivity and maximum response to silvicultural practices. The maximum response was inversely proportional to the base site quality. The maximum stand basal area (BA) and maximum stand density index (SDI) were redefined for individual stands. The average maximum stand BA and maximum SDI were 46.2 m2 ha-1 and 1002 tph, respectively, and both showed significant variation (30.2– 61.7 m2 ha-1 and 600–1410 tph, respectively). Stand aboveground net primary production (ANPP) generally increased with increasing site quality, due to increased stand foliage biomass in the early stage, and mainly due to increased growth efficiency in the late stages of stand development. More intensive silvicultural treatments increased foliage biomass, thus increased ANPP at early ages; thereafter silvicultural intensity did not affect foliage biomass, ANPP, and growth efficiency. The trend of early age increases in both foliage biomass and ANPP resulting from increased planting density did not hold true with stand development. Keywords: pine plantation; maximum stand density index; maximum stand basal area; maximum response; intensive management ID: 3606017 -
ArticleCoupling machine learning and forest simulations to promote the applicability of long-term forest projections under climate change perspectives
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Projecting forest dynamics is the foundation for sound decision support in adaptive forest management. However, due to their complexity, many forest modeling techniques addressing global changes in terrestrial ecosystems are limited to scientific applications. Integrating conventional research and artificial intelligence technologies has the potential to bridge research and practical use. In this study, we propose a Machine Learning (ML) framework that facilitates the implementation of long-term forest projections under climate change scenarios. Our approach combines ML and forest simulations based on process-based models to project forest dynamics. The goal is to leverage the complementary strength of process-based and state-of-the-art ML models to improve predictions at a reduced computational cost. We use environmental data and periodic field measurements at a national scale to train ML models to predict forest growth. By integrating process-based simulations we investigate how the additional variables can improve the prediction accuracy. The proposed hybrid ML framework identifies forest dynamics processes and drivers across spatial and temporal scales, contributing at many levels to the climate change adaptation: from increasing awareness of the climate-induced hazards to enhancing education and assisting in sustainable natural resource management and planning. Keywords: adaptive forest management, climate change, forest growth modelling, machine learning ID: 3623078 -
ArticleLessons learned from the ASEAN-Korea green partnership in the restoration and sustainable management of degraded forest ecosystems for the Philippines
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.This paper presents the lessons learned from the Republic of Korea financed ASEAN-KOREA Environmental Cooperation Project (AKECOP) about two decades of experience in creative collaboration in research and human resource development in the restoration and sustainable management of degraded forest ecosystems in the ASEAN region. The Philippines and nine other participating ASEAN member states (AMS) have learned valuable lessons from the various innovative collaboration strategies employed by the Project. Participatory project management was adopted to ensure project relevance, quality, reliability and equitability and maximum impact. Research and development in-country and cross-cultural collaborative modes were tried and found to be effective strategies in enhancing research outputs and in facilitating research process learning experiences. Cross country exchange visits to locally successful restoration sites were found to be effective schemes for information exchange and technology transfer. And needs-oriented short training programs and graduate studies at the MSc and PhD level were effective strategies in building a pool of high- level expertise in forest restoration and SFM. However, the most valuable lesson from the Project's experience in creative collaboration is that SUCCESS in smaller collaborative initiatives such as AKECO P can inspire the creation of larger institutional partnerships such as Asian Forest Cooperation Organization (AFoCO). As a way forward, the participating AMS have arrived at a consensus that AKECOP should continue to serve as a platform for regional collaboration in SFM considering its notable part achievements. In line with the ASEAN "GREEN VISION" and its journey towards regional integration, AKECOP is envisaged to re-align its innovative strategies to the changing needs and challenges in the sustainable management of a healthy and productive forest ecosystems in a new world order threatened by the scourge of the COVID 19 crisis. Keywords: creative collaboration, cross-country visits, green vision, participatory planning, shared vision,thematic research ID:3486661
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