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Understanding Europe’s future ability to feed itself within an uncertain climate change and socio economic scenario space


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Title: Understanding Europe’s future ability to feed itself within an uncertain climate change and socio economic scenario space
Authors: Sandars DL, Audsley E, Holman IP
Affiliations: Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK;
Abstract: Europe’s ability to feed its population depends on the balance of agricultural productivity (yields and land suitability) and demand which are affected by future climate and socio-economic change (arising from changing food demand; prices; technology change etc). Land use under 2050 climate change and socio-economic scenarios can be rapidly and systematically quantified with a modelling system that has been developed from meta-models of optimal cropping and crop and forest yields derived from the outputs of the previously developed complex models (Audsley et al; 2015). Profitability of each possible land use is modelled for every soil in every grid across the EU. Land use in a grid is then allocated based on profit thresholds set for intensive agriculture extensive agriculture, managed forest and finally unmanaged forest or unmanaged land. The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. The model iterates until demand is satisfied (or cannot be met at any price). Results are presented as contour plots of key variables. For example, given a 40% increase in population from the baseline socio-economic scenario, adapting by increasing crop yields by 40% will leave a 38% probability that the 2050 future climate will be such that we cannot feed ourselves – considering “all” the possible climate scenarios.
Audsley E, Trnka M, Sabate S, Maspons J, Sanchez A, Sandars D, Balek J, Pearn K (2015) Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation. Climatic Change 128:215–227 DOI 10.1007/s10584-014-1164-6
Presentation type preference: Oral
Session: Economics in modelling climate change and agriculture

Published in: Science
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Understanding Europe’s future ability to feed itself within an uncertain climate change and socio economic scenario space

  1. 1. Understanding Europe’s future ability to feed itself within an uncertain climate change and socio- economic space Daniel Sandars, Eric Audsley, Ian Holman 9th April 2015 MACSUR Conference, Reading University
  2. 2. Urban Crop yields Forestry Flooding Rural land allocation Hydrology Biodiversity Pests & diseases Water use Snow cover Water availability Climate & socio-economic scenarios
  3. 3. The CLIMSAVE Integrated Assessment Platform (IAP) is a web-based tool to enable you to explore climate change from regional to EU scales • Impacts – simulates how climate and socio-economic change may affect urban, flooding, agriculture (arable and grassland), forest, water resources and biodiversity • Vulnerability – identify ‘hot spots’ in Europe • Adaptation – assess how adaptation can reduce impacts • Accessible at under the European Commission Seventh Framework Programme Contract Number: 244031
  4. 4. Climsave Land Allocation • Demand = population + changes in (ruminant meat consumption, non ruminant meat consumption) - imports • Supply = yields + increase (crop breeding, efficiency of irrigation) - land removed for conservation and bioenergy cropping. • Land is apriori allocated to urban then on profit thresholds to arable (350 Eur/ha), dairy grass, extensive grazing, managed forest, unmanaged forest, and finally abandoned. Prices are iterated to supply demand
  5. 5. Method • Rapid and systematic Impact Response Surfaces of keys pairs of variables – Climate: Temperature Increase, Rainfall decrease, CO2 levels – Social: Population increase – Adaptation: Yield increase • Programmatically implemented on the desktop-based CLIMSAVE • CLIMSAVE has 109 continuous and discrete scenario variables producing 171 output variable for each of the 27k 10’ grids
  6. 6. Results
  7. 7. Results
  8. 8. Method Label T Increase, C Rainfall decrease CO2,PPM 0 0 0 350 TRC1 1 -10% 400 TRC2 2 -20% 450 TRC3 3 -30% 500 TRC4 4 -40% 550
  9. 9. Results
  10. 10. Results
  11. 11. Method There are 60 climate scenarios • Five Climate Models (CSMK3 (default), HadGem, CPM4, GFCM21, MPEH5) • Four Emission Scenarios • Three Climate Sensitivities
  12. 12. Results
  13. 13. Discussion • Land comes from some other use – Timber production is likely to be reduced • Imports are held constant – Global population growth might reduced food available to import • Demand is satisfied in terms of calories, but the crops and livestock produced may have changed drastically due to differential response to CO2 etc. – We might not like our new diets • Yield increase –is that a good adaptation to population growth? – Would the research investment succeed?
  14. 14. Conclusions • Rapid runs enable systematic model evaluation over its input space – It is feasible to run other variables • CO2 rising to 550 ppm mitigates the impact of rising temperature and falling rainfall • Population rising by 40% remains a problem • It can help identify discontinuities and non intuitive behaviour