Climate Change Impacts on Crop Risks and Agricultural Production in Finland

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1 Climate Change Impacts on Crop Risks and Agricultural Production in Finland Heikki Lehtonen MTT Agrifood Research Finland / Economics Luutnantintie Helsinki heikki.lehtonen@mtt.fi

2 Outline Introduction Model Data Scenarios on crop yield means and variance crop insurance scheme for winter cereals and oilseeds Impacts on different production lines and land use in agriculture Conclusions

3 Yields of winter wheat (upper) and oilseeds (bottom) in Finland in Yield per hectare, 100 kilo 50,0 45,0 40,0 35,0 30,0 25,0 20,0 15,0 10,0 5,0 0, More frequent winter time damages -Longer growing season -Long-term trend in cereals yields, but little trend in the last 20 years Year -Decreasing yield trend for oilseeds in the longterm -Very clear negative trend in the last years Yield per hectare, 100 kilo 20,0 18,0 16,0 14,0 12,0 10,0 8,0 6,0 4,0 -More frequent problems with insects etc 2,0 0, Year Source: MTT

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5 Does CC contribute in increasing agricultural output in Finland? Change in area under intensive agriculture until 2050 = A1FI = A2 =B1 =B2 HadCM3 climate model; IPCC s SRES-scenarios Source: Berry, P.M., Rounsevell, M.D.A., Harrison, P.A. and Audsley, E. (2006). Assessing the vulnerability of agricultural land use and species to climate change and the role of policy in facilitating adaptation. Environmental Science and Policy, 9,

6 In the literature there are some studies suggesting strong growth in agricultural production in Finland resulting from climate change p. 158, scenario output: One might think that Sweden and Finland would show comparable behaviour. However, studying the climate and thus the crop yields, the south of Sweden is very similar to Denmark and good for agriculture whereas the north is cold and remains quite cold even in the A1FI scenario. Finland although appearing well north of much of Sweden has a much warmer summer and, although barely suitable for winter wheat currently, a huge area of Finland gives higher yields of wheat than the south of Sweden in the A1FI climate. Therefore, whereas the agricultural area in Sweden doubles, the area in Finland increases 10-fold. In the future if arable agriculture becomes very profitable, there will be pressure to change. p. 160, conclusions: Where land is not suitable for other uses at present, it may or may not be protected. Not changing might be good for the environment (is 10% loss of Finnish forests significant?), whereas changing to a substantial exporter of cereals might be good for the Finnish economy. EU-wide this might represent a surplus of cereals. In fact several of the scenarios indicate that although feasible in Finland, low product prices due to the higher level of production, mean that cereals are not grown in Finland. Source: E. Audsley, K.R. Pearn, C. Simota, G. Cojocaru, E. Koutsidou, M.D.A. Rounsevell, M. Trnka, V. Alexandrov What can scenario modelling tell us about future European scale agricultural land use, and what not? Environmental Science and Policy Vol. 9. Issue 2:

7 There are many reasons to be suspicious on such results There are many constraints in achieving potential benefits from CC Climate is not the only natural handicap in Finnish agriculture small size of field plots and their spatial fragmentation! clearing farmland from forest is applicable only in subset of potential cases Policy dependence: Costs were 1,67 times the market revenues in 2006! 1,51 in 2007 increasing production woiuld mean also increasing the volume of subsidies CC imposes increasing input prices as well think about prices of energy and fertilisation ! Agriculture in more productive areas can respond to increasing prices much more efficiently than in Finland (Brazil, South-East Europe )

8 Natural handicaps in Finnish agriculture Low crop yield level per ha The need for winter proof buildings and equipment The seasonal nature of farming work; very short peak periods imply high costs The feeding of livestock long housing period imply high work load per animal compared to outdoor grazing Long distances, small market high per unit transportation costs, long distance to export markets Small size of field parcels, fragmented field parcels and long distances between field parcels

9 How will climate change improve the crop yields? Climate change may help to improve: short growing period and low temperature sum too little rain in early part of the growing season night frosts in early summer which inhibit or prevent growth Climate change fails to improve: poor soil quality too low ph and too little liming poor drainage systems costs of plant protection Costs of winter proof buildings Conclusions of crop scientists: Crop yields may improve 10-25% in the long run (compared to the baseline), depending on the crop

10 Natural handicaps which are only slightly relieved by the climate change The need for winter proof buildings and equipment there will be cold periods in winter, but they are likely to be shorter than before The seasonal nature of farming work slightly longer sowing and harvesting periods The feeding of livestock longer outdoor grazing period

11 Some natural handicaps are not at all relieved by the climate change Long distances, small market 5% population growth up to 2028, then declining without increasing immigration Small size of field parcels, fragmented field parcels and long distances between field parcels can be somewhat improved with or without climate change: new land reforms needed lakes, forests, marsh, rocks etc. preventing large field plots are hard to be eliminated before the next ice age

12 Climate models Climate impact crop response and adaptation measures Liberalization of world trade Finnish national economy Shifts in productivity Production Demand Global economy and agri-food system Trade Finnish food system in global and EU context Population growth Shifts in food consumption patterns MTT s ILMASOPU / economics is responsible for all the analysis in the green box! Climate change impacts on socio-economics take place primarily outside Finland!

13 We have 2 RELATED TARGETS: Evaluate (1) the possible scale of climate change induced changes increase of both mean and variance of crop yields - in agricultural production and use of farm land in Finland (2) the potential and relative competitiveness of the Finnish agriculture in different scenarios of the climate change and in different states of global and European food markets Our study is related to a larger research project (ILMASOPU) which examines the climate change adaptation potential of the Finnish agriculture and food economy. The consortium of MTT, Finnish Meteorological Institute, Finnish Environment Institute and Boreal Plant Breeding Ltd provides comprehensive, regional future estimates of Finnish field and horticultural crop production, competitiveness and environmental effects in changing climate and global markets. Part of ISTO program:

14 Model Risk-averse behaviour in farm planning models: If X is a vector of different activities, the vector of the land use of different crops is (x 1, x 2,,x n ) and P is vector of the prices of different crops (p 1, p 2,,p n ) The model maximizes the utility function: Max u = E[PQ] cx - ΦV[PQ] 1/2, where E[PQ] is the expected profit, c is unit cost of the activity (euros/ha), Φ is a positive risk aversion parameter and V the variance operator.

15 Model (2) This can be written: Max u = P* E[y]X cx - Φ[X ΩX] 1/2, where P* is the expected price, y yield, Ω is covariance matrix between profits of the different activities. The target function u is maximized with resource constraint as matrix A contains the resource use, like availability of land and working hours: AX <= b. If the expected return per hectare is denoted r*= P*E[y], we have: Max u=r*x-cx-φ[x ΩX] 1/2

16 The specified risk terms are added to DREMFIA sector model based on mathematical programming Max u=r*x-cx -Φ[X ΩX]1/2 Optimisation MAX: producer and consumer surplus - annual market equilibrium - different yields and inputs in regions - feed use of animals changes endogenously - constraints on energy, protein and roughage needs of animals - non-linear yield functions t = t + 1 for dairy cows - domestic and imported products are imperfect substitutes - processing activities of milk and sugar - export cost functions Results/Initial values production land use consumption prices imports exports transportation Policy scenarios supports for farmers EU prices Crop yield functions - optimal level of fertilisation Steering module - bounds for land use variables; validated to observed data - trends in consumption - inflation - increase in crop and animal yield potential Model of technology diffusion - endogenous sector level investment and technical change - investments depend on relative profitability and accessibility of each technique - gradual shifts of capital to best performing techniques

17 Structural implications of the research method Objective function is quadratic easy to add mean-variance specifications robust solve however this implies symmetric distributions the research method includes the possibility of large structural changes in agriculture higher yields provide cost savings for all crops and production lines higher crop yields facilitate more rapid concentration of production on large farms in western and south-west Finland facilitates more concentrated production structure also for milk sector in a number of regions in Finland

18 Main areas and support regions C4 Northern Finland C3 Ostrobothnia C2 C2 north. C1 Middle Finland B A Southern Finland

19 Data The variance-covariance matrixes of the crop contribution margins, i.e. margin after annual variable costs, are calculated spring wheat, winter wheat, rye, barley, malt barley, oats, mixed grain, oilseeds and green set-aside The matrices are calculated for each of the 18 production regions in the DREMFIA model. Regional data of crop yields from Product and input prices and agricultural subsidies from the official statistics Use of inputs is consistent with the DREMFIA model specifications (based on various statistical sources) The use of inputs per hectare in different regions is validated to total income and cost calculations of agriculture, farm taxation data and farm level production costs calculations made by rural advisory services

20 Variance-covariance matrix of crop contribution margins in Varsinais-Suomi region (in south-west) Spring wheat S.wheat 7884 Winter wheat W.wheat Rye Rye Barley Malt barley Barley M. barley Oats Oats Mixed grain M. grain Oilseeds Oilseeds Greenfal Aver. margin (eur/ha) Aver.land use (ha) Greenfal

21 Scenarios The baseline: product prices and farm subsidies at 2006 level, inflation of input prices by 1.8% per year, on the average, until The first scenario (C1Y): the same risk aversion parameter as in the baseline but 20% increase of yield for each crop The second scenario (C2): increase of the risk aversion parameter by 100% implying the increase of the total covariance of crop profit margins by 100 % to simulate the increased variability of the yields The third scenario (C2Y): assume both the increase of the total covariance of crop profit margins by 100 % and the increase of crop yields by 20 % The fourth scenario (INS): on the top of the scenario C2 we assume the crop insurance program simulated with the minimum yield level for winter wheat, rye and oilseeds which are the most risky crops in Finland, especially in the northern areas: invest 5.8 million euros in addition to existing the 5.8 million euros of production linked CAP area payments

22 Relative changes in variance-covariance matrix in Varsinais- Suomi region (in south-west) due to the crop insurance Spring wheat Winter wheat Rye Barley Malt barley Oats Mixed grain Oilseeds Greenfal S.wheat 1 W.wheat Rye Barley M. barley Oats M. grain Oilseeds Greenfal

23 Relative changes in variance-covariance matrix in Northern Ostrobothnia region (in north-west) due to the crop insurance Spring wheat Winter wheat Rye Barley Malt barley Oats Mixed grain Oilseeds Greenfal S.wheat 1 W.wheat Rye Barley M. barley Oats M. grain Oilseeds Greenfal

24 Relative change in the expected crop profit margins due to the crop insurance scheme Uusimaa Varsinais- Suomi South. Ostrobothnia North Savo North. Ostrobothnia Sping wheat Winterwheat Rye Barley Malt barley Oats Mixed grain Oilseeds Greenfal

25 Results Country-level changes in total cereal area (1000 ha), milk production (million kg), pigmeat production (million kg) and farm income (million euros) in 2020 Base: C1 C1Y C2 C2Y INS Cereals total Grasslands Set-aside Milk prod Beef Pigmeat Poultry meat Farm income

26 Increasing crop yields in c1y and c2y scenarios boost pigmeat production (million kg) base c1y c2 c2y observed Y1997 Y1999 Y2001 Y2003 Y2005 Y2007 Y2009 Y2011 Y2013 Y2015 Y2017 Y2019 Y1995

27 Country-level land use (1000 ha) in 2020 Baseline C1Y C2 C2Y INS Spring wheat Winter wheat Rye Barley Malt barley Oats Mixed grain Oilseeds Potatoes Cultivated land, total Set-aside Unused land

28 Interpreting the results (1/3) Area under winter wheat and oilseeds did not increase due to specified crop insurance, but only rye area, and areas under feed grains there is a high variance of contribution margins of oilseeds and winter wheat, and a low covariance between feed grains and rye hence the decreased profit variability of rye, winter wheat and oilseeds decreases the total covariance of feed grains as well crop insurance alone may not be sufficient incentive in the case of oilseeds since price risk is also very significant, not only yield risk crop insurance may not be a solution for every crop (as it should not be) Risk aversion and/or increasing yield volatility plays a significant role However our approach of increasing observed yields and variances is problematic from the climate change viewpoint

29 Interpreting the results (2/3) Crop insurance seems to become increasingly effective in maintaining production crop insurance is likely to be less trade and production distorting than crop specific per hectare payments (reported by OECD) crop insurance could provide an incentive or safety net for farmers in developing cultivation practices of those products which potentially benefit most from the CC there is a risk that crop insurance maintains risky or relatively unproductive practices? Major problem: Is it reasonable to expect the observed crop yield / price / income volatility to expand? cross-covariance terms? what will happen to dependence between Finnish crop yields and European crop prices? Is such a dependence relevant? Domestic market structure? if crop yield changes and volatility in CC scenarios can be approximated by biological crop yield models, how do we evaluate future price volatility?

30 Interpreting the results (3/3) Pig sector seems to be relatively most capable of utilising higher yields; most economic benefit may accrue to animal sector, not to crop sector this result is dependent on the level of pigmeat prices in the EU and Russia In the case of some other crops, such as potatoes or oilseeds, whose demand is relatively inelastic, land area may clearly decrease despite increasing yields (even relatively more than the yield increase) increasing yields may not outweigh the implicit cost of high variance and hence some crops remain relatively unprofitable Farm income increases relatively less than crop yields; increased production volumes of pigmeat and some crops depress producer prices high production costs relative to 2006 EU prices inhibits production response higher yields do not eliminate all natural disadvantages

31 How to proceed? Problem: Most of the socio-economic variables affecting Finnish agriculture are determined outside Finland prices of inputs and outputs are cleared on global and EU markets There is a need to follow systematically the most relevant international research projects related food markets and CC MTT has no resources for global scale food market or ecological research -> networking with whom and on which basis? MTT s relative advantage may be interdisciplinarity and the revealed interest of international CC research on northern European agricultrure what are the core competencies where MTT can offer value added in international research projects what are our research tools in socio-economic CC research?

32 How to improve the short-comings: Topdown, bottom-up, or a hybrid model? Traditional CGE models, top-down models, adopt an economy-wide perspective taking into account initial market distortions (failures), linkages between markets and income effects for various economic agents such as private households or the government. The comprehensiveness of endogeneity in economic responses to policy shocks typically goes at the expense of specific sectoral or technological details. As a matter of fact, conventional top-down models have a limited representation of the technology portfolio and lack detail on current and future technological options, relevant for an appropriate assessment of policy proposals. The bottom-up engineering models are partial equilibrium representations of some selected markets, e.g. the energy sector. They feature a large number of discrete technologies to capture process substitution, or efficiency improvements, but omit interaction with the rest of the economy. Hybrid modelling efforts to combine technological explicitness of bottom-up models with the economic comprehensiveness of top-down models?