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; Email: Daniel.sandars@cranfield.ac.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
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Understanding Europe’s future ability to feed itself within an uncertain climate change and socio economic scenario space
- 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. Urban Crop yields Forestry Flooding Rural land allocation Hydrology Biodiversity Pests & diseases Water use Snow cover Water availability Climate & socio-economic scenarios
- 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 www.climsave.eu http://ec.europa.eu/research/fp7/Funded under the European Commission Seventh Framework Programme Contract Number: 244031
- 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. 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
- 7. Results
- 8. Results
- 9. 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
- 10. Results
- 11. Results
- 12. Method There are 60 climate scenarios • Five Climate Models (CSMK3 (default), HadGem, CPM4, GFCM21, MPEH5) • Four Emission Scenarios • Three Climate Sensitivities
- 14. Results
- 15. 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?
- 16. 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
Editor's Notes
- If you have ever tried to understand someone else’s system model source code or its output you will realise that it is non-trivial to understand the behaviour of complicated systems models. Today I am going to show you recent work Colleagues and I have done to use Impact Response Surfaces (IRS) explore model behaviour. I am Daniel Sandars from Cranfield University Abstract 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). 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
- The model that we have looked at is called Climsave and it has been presented to Macsur on several occasions. It consists of numerous interlinked modules and a database all run within a web-based command module. Each of these module is a meta-model of a vastly more complex model. Three meta-modelling techniques have been used, regressions, look-up tables, and neural networks.
- This is what Climsave is and does. It is an Integrate Assessment Platform that for 27k 10 minute grids across Europe looks at cross-sectoral impacts, vulnerabilities and adaptation to Climate Change. It is web-based and any of you can go an use it. It was developed under EU framework 7 funding. For development we also have a deskbased model and it was this that we use to programmatically explore Impact Response Surfaces.
- How Europe feeds itself is determined by the land allocation which is driven by the need to satisfy demand. After the needs for housing are satisfied (using all qualities of soil away from flooding) then best land is given to arable cropping which is where it is most profitable. There is a hierachy of further possible land uses until land is abandoned. This hierarchy is important as if more land is required to house people and feed them due to population growth all other landuses are forced into poorer quality situations. Overall the supply of land is finite. The model improves the profitability of arable cropping in small increments until enough land exceeded the Eur350/ha threshold to meet demand. We do not say how the profitability is obtained be it support payments or market prices and do not consider potential price elasticities.
- To explore Climsave we have chosen key variables representing climate change, social change and adaptation. We implemented the procedure programmatically on the desk-top version. To put it into context Climsave has 109 continuous and discreet scenario input variable producing 171 output variables. Each run takes around two minutes.
- This slide shows the web-based interface to Climsave and show you where the climate and population scenario variables are adjusted and also what the output, in this case Intensive agriculture, looks like across Europe.
- In this plot we are looking at the interaction between Temperature increase and Rainfall decrease showing the additional land to feed Europe rising. However is we take a transect of points from the origin to the top right we can compare to a third variable as shown in the next slide.
- In this chart the third variable is atmospheric CO2 concentration. On the bottom axis we have the transect of increasing land use with temperature increase and rainfall decrease, but what we see is that with CO2 elevation the additional land required decreases and in some cases land is actually releases from food production. If we go on and take a transect from the origin to the top right we can compare a fourth variable.
- But first a reminder of the variable values in each of the points along this new composite axis.
- What we see is that in Europe as a whole the CO2 increases can cancel out the detrimental effects of rising temperature and decreasing rainfall. However, the additional pressure of rising population only adds to the additional demand for agricultural area.
- All other things being equal a 40% large population would need 40% for additional agriculture to feed itself. We have taken this into account in this chart and find that 30-40% more land is needed again to support a 40% larger population. This is due to the fact that at the margin the additional land made available is of a lower quality thus more is needed for a given amount of production. This might not at first have seemed intuitive. One thing this chart show more strongly than any of the others is a degree of discontinuity between points. We don’t know exactly why we see this and will have to investigate it. It maybe due to the facts that the Climsave model involves meta models such as neural networks. It maybe that the points on the X-axis are now very sparse and we need more intermediate points. If we now take Population Increase as the interesting axis we can consider how we might adapt to that with say yield increases
- In addition to temperature, rainfall and climate sensitivity Climsave contains 60 discrete combinations of climate models. If we find a scenario where we are vulnerable and cannot feed Europe then in how many of these 60 would that be true? We want to avoid the situation where there is a headline result that Europe can’t feed itself that overlooks the outcome of the other 59 climate scenarios which maybe very different.
- To put these 60 climate cases into context here we can see the drop down boxes that set them on the IAP interface.
- This chart shows yield increase against population increase and the proportion of all climate model scenarios that we cannot feed Europe. For example is there is population increase and no yield increase then ultimately in all climate scenarios we can’t feed Europe –A strong result. Vice versa and the opposite is true we can feed ourselves in all climate scenarios and equally strong result. In between it is more mixed.