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ArticleJournal 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 -
ArticleJournal articleForest succession by space and time based on climate and landuse changes
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.This research predicted the transition of forest structure by analyzing changes in the dominant vegetation and spatial distribution based on climate and land use changes. The research region involves the mountainous and city vicinity located in Okcheon-gun, Korea. Climate change detailing was carried out until 2100 by employing the SSP2-4.5 scenario and the MaxEnt model was used to predict the land cover change. The data stemming from the above were applied to the Landis-II model. The analysis of forest changes was performed based on the years 2050 and 2100 that showed the most dramatic prediction results of climate changes. Comparing to 2020, the mean minimum temperature fell down by 0.45°C in 2050 and increased by about 0.96°C in 2100. The mean maximum temperature increased by about 0.31°C in 2050 and about 1.96°C in 2100. In the prediction of land cover change, mountainous region exhibited a decreased tendency of agricultural lands in 2050 and 2100, and region city vicinity showed a decrease in residential lands, demonstrating very small land cover changes of the forest in both regions. As for the predicted vegetation change, both regions showed a decrease in the dominant area of Pinus densiflora, Pinus Koraiensis, and Pinus rigida, on the other hand, showing an increase in the dominant area of Quercus serrate, Quercus variabilis, and Quercus aliena. In conclusion, the future forest vegetation of two regions showed a decreased tendency in the alien species that could not reproduce under natural conditions, tree species that grow in cold climate regions, and the reforestaion species that were planted due to a necessity of human beings, whereas the area of Quercus species, which are mainly distributed to a relatively warm climate, increased. Therefore, in order to determine tree species for restoration where interfered nature and area that need logging, it should be decided based on the predicted vegetation change in a given area to maximize the forest function. Keywords: Forest transition; Climate change; Landuse change; LANDIS-II; Sustainable forest management ID: 3621835 -
ArticleJournal articleForest landscapes restoration measures as a cost effective solution for climate change mitigation and adaptation in India
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Global deforestation and forest degradation have led to massive loss of biodiversity. Hence, it is important not only to protect but also restore the forest ecosystems. Forest biodiversity protection, biomass production and climate change mitigation and adaptation are important key motivation for forest restoration. Tree-based landscape restoration is a widely accepted cost-effective measure to combat climate change. India’s commitment of Nationally Determined Contribution to the Paris Agreement is to sequester additional 2.5 to 3 billion tons CO2 equivalent by 2030 through increased forest and tree cover and this ties in with the Bonn Challenge commitment to restore 21 mha of deforested and degraded lands by 2030 (now increased to 26 mha during UNCCD COP 14 meeting held in Sept. 2019) as well as the SDGs. This commitment can only be met if existing forests are protected and improved and tree cover is extended by 25 to 30 mha. The main objectives of the Forest Landscape Restoration (FLR) in India is to reverse the process of degradation of forests & improve its productive potential, improve the regeneration of native flora & enrich the biodiversity, and enhance biomass production, carbon stocks & incomes of the rural households. For a successful FLR works in India, focus needs to be on proactive involvement of communities and local people, better coordination among the various government agencies and departments for effective implementation of project activities, robust Institutional mechanism, and continuous fund flow and support to sustain the activities and keep the restored areas intact. Here, we present how India can achieve the NDC and Bonn Challenge through forest landscape restoration. Potential to increase forest and tree cover and the carbon sequestration that can be achieved has been discussed. This will support planning for landscape restoration through the past and on-going initiatives which identifies different types of interventions implemented. Keywords: Deforestation and forest degradation; Climate change; Biodiversity conservation; Research; Landscape management ID: 3469382
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Book (series)FlagshipThe State of Food Security and Nutrition in the World 2023
Urbanization, agrifood systems transformation and healthy diets across the rural–urban continuum
2023This report provides an update on global progress towards the targets of ending hunger (SDG Target 2.1) and all forms of malnutrition (SDG Target 2.2) and estimates on the number of people who are unable to afford a healthy diet. Since its 2017 edition, this report has repeatedly highlighted that the intensification and interaction of conflict, climate extremes and economic slowdowns and downturns, combined with highly unaffordable nutritious foods and growing inequality, are pushing us off track to meet the SDG 2 targets. However, other important megatrends must also be factored into the analysis to fully understand the challenges and opportunities for meeting the SDG 2 targets. One such megatrend, and the focus of this year’s report, is urbanization. New evidence shows that food purchases in some countries are no longer high only among urban households but also among rural households. Consumption of highly processed foods is also increasing in peri-urban and rural areas of some countries. These changes are affecting people’s food security and nutrition in ways that differ depending on where they live across the rural–urban continuum. This timely and relevant theme is aligned with the United Nations General Assembly-endorsed New Urban Agenda, and the report provides recommendations on the policies, investments and actions needed to address the challenges of agrifood systems transformation under urbanization and to enable opportunities for ensuring access to affordable healthy diets for everyone. -
BookletCorporate general interestEmissions due to agriculture
Global, regional and country trends 2000–2018
2021Also available in:
No results found.The FAOSTAT emissions database is composed of several data domains covering the categories of the IPCC Agriculture, Forestry and Other Land Use (AFOLU) sector of the national GHG inventory. Energy use in agriculture is additionally included as relevant to emissions from agriculture as an economic production sector under the ISIC A statistical classification, though recognizing that, in terms of IPCC, they are instead part of the Energy sector of the national GHG inventory. FAO emissions estimates are available over the period 1961–2018 for agriculture production processes from crop and livestock activities. Land use emissions and removals are generally available only for the period 1990–2019. This analytical brief focuses on overall trends over the period 2000–2018. -
BookletHigh-profileFAO Strategy on Climate Change 2022–2031 2022The FAO Strategy on Climate Change 2022–2031 was endorsed by FAO Council in June 2022. This new strategy replaces the previous strategy from 2017 to better FAO's climate action with the Strategic Framework 2022-2031, and other FAO strategies that have been developed since then. The Strategy was elaborated following an inclusive process of consultation with FAO Members, FAO staff from headquarters and decentralized offices, as well as external partners. It articulates FAO's vision for agrifood systems by 2050, around three main pillars of action: at global and regional level, at country level, and at local level. The Strategy also encourages key guiding principles for action, such as science and innovation, inclusiveness, partnerships, and access to finance.