| 摘要 |
The objective of this project is to study quantitatively the feedback processes linking pollinators, plant diversity and crop yields in the framework of climate and land use changes. The response of agricultural yields to climate change is critically dependent on these feedbacks that until now remain largely unexplored. In order to fill this gap, the project will focus on studying interactions between three main sectors: biodiversity/nature conservation, forestry and agriculture. Within agriculture, the emphasis will be put on three sub-sectors: fruit crops, food/fodder crops and energy crops. The project team will use diverse types of crop and vegetation models to estimate the impacts of climate change on each studied sector in several case study regions in Europe. The study will be undertaken with local stakeholders, who will identify most relevant topics to be addressed by the consortium. The interdependencies between the sectors will be analysed through the dynamics of land use and land cover on the one hand and dynamics of pollinator communities on the other hand.
Pollinator decline, fruit crop damage, and more generally, climate change impacts on crop yields are problems of increasing concern among stakeholders. The project will be shaped along the major problems identified in each studied region by local stakeholders.
The project is undertaken by a multidisciplinary research network, involving three traditionally separated scientific communities: (1) climate modellers producing climate scenarios, (2) climate impact modellers analysing the impacts of climate change on the environment and the ecosystems, and (3) human geographers and social scientists, simulating land use change and analysing climate change impacts on the society. This association allows studying feedbacks which remain largely unexplored so far. Project outcomes will contribute to the next IPCC scientific assessments and to the Copernicus Climate Change (C3S) Services.
PIK will be supporting this project by applying the LPJmL Model and by projecting agricultural yields under various climate scenarios. Detailed weather data such as temperature, precipitation and radiation will be used to drive the dynamic vegetation, hydrology and crop model LPJmL at PIK for projections of yields of fruit trees, important arable crops and grasslands.
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