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DocumentOther documentWorld Agriculture in a Dynamically-Changing Environment: ifpri's long-term outlook for food and agriculture under additional demand and constraints
Expert Meeting on How to feed the World in 2050
2009Also available in:
No results found.In this paper, we explore the nature of several key drivers of change in food systems, and examine a number possible entry points for policy intervention, in order to determine their effect on food prices and other marketdriven outcomes. Among the drivers of change that we discuss are those of policy-driven growth in biofuel production, which has had a role to play in the rapid increase in food prices, along with other factors. We demonstrate the off-setting impact that supply growth cou ld have on the socio-economic impacts of biofuels, both in terms of price changes, as well as changes in nutrition status. We also look at some evidence that points towards the significant impact that climate change could have on the agriculture and agricultural prices in the future. Combining our quantitative experiments with cited evidence from other studies, we suggest a range of policy interventions that could be instrumental in offsetting the negative impacts of food prices, and hel ping to promote those benefits in situations where they might exist. Among these suggestions, we encourage increased investments in the agricultural sector, so as to reverse the steadily declining growth of research and development spending and change decades of counter-productive agricultural trade and national-level sector policies. -
DocumentOther documentSupporting Sustainable Forest Management through the Global Forest Resources Assessment: Long-Term Strategy (2012-2030) 2015
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No results found.The Global Forest Resources Assessment (FRA) seeks to describe forest area, forest change and selected functions of forests. These assessments ultimately seek to support the expanded application of sustainable forest management, the permanent forest estate and support to the forestry sector by providing reliable information about the world’s forests. The use of forests to help reduce net greenhouse gas emissions through mechanisms such as REDD+ highlights the importance of understanding land us e change in new ways, including net changes in global forest carbon stocks -
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
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