Long term drivers of food markets variability and uncertainty

Transcription

1 Understanding and coping with food markets volatility towards more stable World and EU food systems. POLICY BRIEFING No 02 February 2014 Objectives 1. We examine the main uncertainties surrounding medium and long term projections for agrifood markets. 2. By means of separate model runs with different sets of stochastic variables we identify major sources of uncertainty. 3. The consequences of the biofuel mandates and of higher price levels on market uncertainty are analysed by running stochastically alternative scenarios to the baseline. 3. By means of a bioeconomic approach, we explore the influence of climate change on agrifood market projections for 2030, providing both a global analysis and a regionalised evaluation within the EU. Long term drivers of food markets variability and uncertainty Retrospective analysis of price volatility drivers in agro-food markets over the last 10 years shows a heterogeneous picture. However, it identifies exchange rate volatility, impact of low stocks, weather shocks and oil prices among the main drivers. 1 The findings call for caution when designing policies to cope with price volatility, connoting according to FAO two principal concepts: variability and uncertainty. In particular when looking into potential future developments, possible changes of identified past drivers and potentially new drivers of volatility must be considered essential for better understanding the complexity of commodity markets price level, variability and uncertainty. Agro-economic models can simulate possible future developments through baselines (in this case we look at the time horizons up to 2023 (medium term) 2 and up to 2030 (long term) 3 ) and scenarios of drivers such as climate change. They also allow the study of future market uncertainty, a concept related to price volatility. In this context, the present briefing focuses and it is structured on the following questions: Which factors could play an important role for future price uncertainty? Which commodities should we be looking at? Do higher prices imply higher volatility? 1 See f. ex. ULYSSES scientific paper No. 1 (Volatility analysis: causation impacts in retrospect ( ) and preparing for the future), and policy briefing No. 1 (Food price volatility drivers in retrospect) 2 European Commission (2013), Prospect for Agricultural Markets and Income in the EU ; OECD-FAO Agricultural; Outlook 2013, OECD. 3 JRC (2014) CAPRI Long-term Climate Change Scenario Analysis: The AgMIP Approach 1

2 Uncertainties The major sources of input uncertainty analyzed include macroeconomic variables and crop and milk yields in the medium term, as well as climate change impacts in the long term. Are biofuels a source of uncertainty? What could be the impact of climate change on prices in the next 15 years? Sources of uncertainty analysed Two approaches are applied for the analysis of long term drivers of market uncertainty: (i) partial stochastics, and (ii) scenario analysis. With partial stochastics, the main sources of systematic uncertainty around the market drivers in a baseline scenario are considered 4. The European Commission's baseline from , and the model AGLINK-COSIMO, are used. The scenario analysis investigates consequences of climate change scenarios on agricultural markets combining the agro-economic model CAPRI and the biophysical modelling platform BIOMA. For the partial stochastic analysis, the selection of which variables to treat stochastically is motivated by two considerations, namely the need to cover the major sources of uncertainty for agricultural markets whilst keeping the analysis simple enough to be able to identify the main contributors of uncertainty in each market. In total, 40 country-specific macroeconomic variables, and 79 country and commodity-specific yields, are treated as uncertain. In the presentation of the uncertainty of the model output variables we use the coefficient of variation in the last year of the 10 years projection period (CV 2023 ). With the scenario analysis, we explore how EU and world market projections will be affected by climate change in the Future crop yield developments under climate change are subject to considerable uncertainty, particularly with regard to climate projections, the magnitude of the carbon fertilization effect and likely adoption of adaptation strategies. To account for such uncertainties, several simulation scenarios have been investigated (see page 7). High price uncertainty for some agricultural commodities The partial stochastic analysis revealed particularly high world market price uncertainty for oilseeds and biofuels; high uncertainty for pork, protein meals, vegetable oils, coarse grains, raw sugar and wheat; and less uncertainty for rice, poultry and dairy products. 4 For detailed information on the sources of uncertainty considered and on the methodology see Ulysses' scientific paper 2 Production and crop roots (causes?) of volatility measures including partial stochastic simulations of yields and macroeconomic variables 5 European Commission (2013), Prospect for Agricultural Markets and Income in the EU Accessible at 2

3 Wheat Wheat Wheat CV % 16% 14% 12% 1 8% 6% 4% 2% FIGURE 1. Coefficient of variation of world market prices in Macroeconomic variables with a higher impact on price uncertainty than yields Figure 2 Figure 2 shows the price uncertainty generated through different partial stochastic scenarios. The partial stochastic scenarios denote which stochastic variables are applied. 'Comb.': denotes the combined (all variables); 'Macro': all macroeconomic variables; 'Yield': all stochastic yield variables. Several factors play a role in the determination of world market price uncertainties. These can be summarized in (i) the shocks introduced with stochastic variables, (ii) their effect on large world market players, and (iii) the simulated reaction of the market actors to price changes. CV Comb. Macro Yield FIGURE 2. Coefficient of variation of world market prices by partial stochastic scenarios in

4 Wheat Wheat Wheat Wheat Even though the macroeconomic variables present stronger effects on world market prices, the uncertainty around yields has a significant impact for oilseeds, wheat, coarse grains and biodiesel. For oilseeds a major source of uncertainty is soybean yield in Argentina (CV of yield errors based on data, CV error, of 2). For wheat the large countries with high yield uncertainty are Ukraine (CV error : 24%), Kazakhstan (CV error : 25%), and Australia (CV error : 34%); for coarse grains, Australian barley (CV error : 3), maize in the new EU Member States (CV error : 25%), and Argentinian barley and maize (CV error of 16% and 11% respectively). However, if larger producers have a dominant position in world markets as, for example. the US on maize, then lower CV error may also have a significant effect on world market price uncertainty. From a policy perspective, the uncertainty attached to yields cannot be reduced. Nonetheless, the identification of these sources allows policy makers and stakeholders to design and apply risk management measures. All macroeconomic variables have significant effects but in different markets Figure 3 Figure 3 shows the price uncertainty generated through different partial stochastic scenarios with only the macroeconomic data. The scenarios denote which stochastic variables are applied. Since the macroeconomic variables present such a strong effect (see Figure 2) additional macroeconomic partial stochastic scenarios were run in order to identify the major sources of uncertainty. CV Crude oil price Exchange rate GDP CPI and GDPD FIGURE 3. Coefficient of variation of world market prices by macroeconomic partial stochastic scenarios in Figure 3 shows that the price uncertainty generated with the macroeconomic partial scenarios is only a fraction of the uncertainty generated when the macroeconomic variables are run together (see Figure 2). However, note that the total effect on price uncertainty, when all macroeconomic variables are run together, is not equal to the sum of the individual effects obtained for each scenario. When all variables are run together, the individual effects may be partially offset by the impact of the other variables.

5 Key results from Figure 3 include: (i) For oilseeds, the strongest medium term drivers of price uncertainty are uncertainty in crude oil prices and in exchange rates; mainly because production costs depend strongly on the energy prices and prices of goods which are traded in international markets. (ii) For biofuels, the strongest macroeconomic driver is the crude oil price. This is a result of close relationship between biofuels and fossil fuels such as gasoline and diesel. Distance 10th-90th percetile in 2023 (iii) For wheat and coarse grains, all macroeconomic variables have similar impacts on price uncertainty with slightly stronger effects for crude oil price and exchange rates. The crude oil prices affect the market through changes on production costs (energy, fertilizers) and the exchange rate through changes on the competitiveness of exports of the corresponding country (exposed to exchange rate changes). (iv) For pork, the most important drivers appear to come from the uncertainty on the demand side (GDP and CPI), which is particularly high in BRIC countries. Higher price levels result in stronger absolute uncertainty With the purpose of investigating the link between price level and price uncertainty, the partial stochastic scenario applying all stochastic variables for the baseline is compared with a scenario of higher wheat price levels CV High wheat price 0 High wheat price FIGURE 4. Relative (left graph) and absolute (right graph, in EUR) consequences on wheat price uncertainty of higher baseline prices levels. The left panel in Figure 4 shows almost no change of the CV in the case of high wheat price, suggesting that higher prices do not necessarily result in higher uncertainty. However, the right panel shows that, in absolute terms, the distance between the 10 th and 90 th percentile of the price distribution for the last year of simulation is larger. 5

6 No mandate No mandate No mandate No mandate Price (USD/tonne) The implication of expected stronger absolute changes has not been fully addressed in the literature but it could have consequences in terms of expected inflation as well as influence of market signals to financial investors. It is suggested that deeper research on this issue should be carried out. Abolishment of the biofuels mandates results in higher uncertainty for biodiesel Figure 5 The biofuel mandates are mechanisms which seem to preclude the transmission of crude oil price uncertainty to biofuel prices. This is especially true when the price level is not competitive and substitution can occur. Biofuel mandates are domestic policies which link the consumption of fossil-fuels and biofuels. With the removal of the mandates in the EU, US, Brazil, Argentina, Australia, and Canada, an adjustment of biofuel prices is observed. This is particularly noticeable for biodiesel (see Figure 5) CV % 2 15% 1 5% Crude oil Crude oil FIGURE 5. Consequences of the abolishment of the biofuel mandates on prices levels (left graph) and on price uncertainty (right graph). Considering an oil price of 115 USD/barrel, bioethanol world market prices are more competitive than crude oil and biodiesel prices. Thus, removing the mandates has a minor impact on the consumption of bioethanol, as well as on its price level and uncertainty. For biodiesel, with the abolishment of the mandates, the incentive for consuming at mandate-levels disappears. Also, production and consumption decrease, which can be the result of a substitution effect between bioethanol and biodiesel due to the relative prices. Although for both, bioethanol and biodiesel, price uncertainty increases without mandates, for biodiesel the increase is higher. This occurs because biodiesel is not as competitive as bioethanol and is, thus, more sensitive to changes on relative prices. The effect of the abolishment of the mandates on the uncertainty of cereal prices is marginal since the consequences on the uncertainty of bioethanol world market prices are also small. Furthermore, the increase of uncertainty of biodiesel world market prices is not enough to see significant consequences on the uncertainty of grain markets. 6

7 High uncertainty in future crop yield developments under climate change Figure 6 Compared to the baseline, potential yields are expected to increase in Europe under full carbon fertilization. Overall, yield changes are more positive (less negative) for the Echam realization (mild) than for the Hadley (warm). Future crop yield developments are subject to considerable uncertainty, particularly with regard to climate projections and the degree of carbon fertilization effect. To account for such uncertainties, we analyse the IPCC emission scenario A1b 6 for the 2030 horizon under several simulation scenarios that differ in (1) the climate projection, Hadley realization (warm) or Echam realization (mild); and (2) the influence of CO 2 effects. 15% 1 5% -5% -1-15% Potential yield (% change) Wheat Maize Barley Rye SugarBeet Potato FieldBeans Rapeseed Sunflower Echam-CO2 Hadley-CO2 Echam-noCO2 Hadley-noCO2 FIGURE 6. Percentage change in potential yields (EU average) Figure 7 On the contrary, water limited yields are expected to decrease for most crops and European regions, even under scenarios with full carbon fertilization. Biophysical simulations performed at 25km grid resolution provide changes in both potential and water-limited yields for nine of the most widely grown crops in the EU. Water limited yield (% change) 1 5% -5% -1-15% -2-25% Wheat Maize Barley Rye SugarBeet Potato FieldBeans Rapeseed Sunflower Echam-CO2 Hadley-CO2 Echam-noCO2 Hadley-noCO2 FIGURE 7. Percentage change in water limited yields (EU average) For non-eu regions, climate induced changes on crop yields were based on previous studies. Yield changes are less positive (or more negative) for EU regions and the Mediterranean compared to global yield effects. 6 A1b (rapid economic growth, convergence across regions and balanced emphasis across all energy sources) is one of the most widely studied scenarios (IPCC 2007) 7

8 Mixed findings in impacts of climate change on agricultural production and prices Figure 8 Global production would increase for most crops under full CO 2 fertilization and decrease in the unlikely scenarios without CO 2 effects. Increase in exogenous crop yields would be counterbalanced by decrease in crop prices and vice versa, leading to interregional adjustments in production, consumption and trade. 6% 4% 2% -2% -4% -6% Production (% change) -8% Wheat Maize Barley Rye Potato Pulses Rapeseed Sunflower Echam-CO2 Hadley-CO2 Echam-noCO2 Hadley-noCO2 FIGURE 8. Effects on global production (% change compared to baseline) Figure 9 Increase (decrease) in productivity will drive prices down (up). Focusing at scenarios with CO 2 effects, stronger production increases in the Echam realization (compared to the Hadley) result in higher price decreases. Producer price (% change) 2 15% 1 5% -5% -1-15% -2 Wheat Maize Barley Rye Potato Pulses Rapeseed Sunflower Echam-CO2 Hadley-CO2 Echam-noCO2 Hadley-noCO2 FIGURE 9. Effects on world producer prices (% change compared to baseline). Climate change induces variation in wheat and maize prices from -13% to +15% depending on the simulation scenario. While the influence of CO 2 fertilization is generally acknowledged, and hence the scenarios without CO 2 are unlikely, there is considerable uncertainty on the degree of this CO 2 effect. While we consider climate change effects on average yields, the contribution of climate change to yield variability has not been addressed and could notably influence simulation results. 8

9 General negative consequences of climate change on Europe's agricultural production Figure 10 Stronger production increases at global level compared to the EU drives down EU crop prices, amplifying the effects of climate change in Europe. With carbon fertilisation, global yield effects are more positive than EUwide effects, but important regional differences exist. Global production increases in the range of % result in global price decreases in the range of 1-16%. As a result, EU crop prices decrease resulting in negative impacts on production for most crops. 15% 1 5% Production (% change) -5% -1-15% Wheat Maize Barley Rye Potato Pulses Rapeseed Sunflower Echam-CO2 Hadley-CO2 Echam-noCO2 Hadley-noCO2 FIGURE 10. Effects on EU production (% change compared to baseline) Climate change affects regional self-sufficiency in food production Diverging regional impacts of climate change in agricultural productivity leads to adjustments in production, consumption and trade flows. Particularly for internationally traded products, regional self-sufficiency rates 7 will be affected, Echam-CO2 Hadley-CO2 Echam-noCO2 Hadley-noCO2 European Union Europe, Non-EU FIGURE 11. Wheat self-sufficiency indicator under the analyzed scenarios Compared to the baseline, wheat self-sufficiency will decrease in Europe, Africa and South America, if carbon fertilization is accounted for. 7 Self-sufficiency in production is an indicator of import dependency, defined as the ratio of production to domestic consumption 9 Africa North America Middle & South America Asia Australia & New Zealand

10 Conclusions and outlook Long term trends in food supply and demand are driven by environmental and socio-economic drivers, as well as by agricultural, trade, energy and other policies. Understanding how uncertainties linked to these drivers can influence future agricultural markets is crucial in facing future food security challenges. By using agro-economic simulation models that consider the systematic uncertainties around market divers, we confirm the findings from retrospective analysis, namely that, in particular, crude oil prices and exchange rates are main sources of price variability, but also changes on other variables such as GDP and CPI (consumption), yields (weather), biofuel mandates, or price levels are also important uncertainty factors. Furthermore, it has been shown that the magnitude of the implications on price uncertainty differs strongly depending on the market. Commodities presenting high world market price uncertainty are oilseeds (and its derivatives), biofuels, pork, wheat, coarse grains, and raw sugar. With partial stochastic simulations two further factors, additional to the systematic uncertainties, were analysed: higher price levels and biofuel policies. First, higher price levels resulted in higher price uncertainty in absolute terms. Second, biofuel mandates appear to be reducing uncertainty by setting strict consumption obligations. Without them the strong uncertainty around crude oil prices is partially transmitted particularly to biodiesel, since it is less competitive than bioethanol. Uncertainty in environmental drivers, in particular climate change, is analysed by means of a bioeconomic scenario analysis. The biophysical and economic impacts of climate change have been assessed, providing both a global analysis and a regionalised evaluation within the EU. Results indicate stronger negative production effects in the EU compared to the rest of the world but with important regional differences. 10

11 Authors and affiliation Marco Artavia, JRC Joint Research Centre European Commission, Spain Maria Blanco, Department of Agricultural Economics and Social Sciences, Technical University of Madrid, Spain Sergio Rene Araujo Enciso, JRC Joint Research Centre European Commission, Spain Fabien Ramos, JRC Joint Research Centre European Commission, Italy Robert M'Barek, JRC Joint Research Centre European Commission, Spain Emiliano Magrini, JRC Joint Research Centre European Commission, Spain Ben Van Doorslaer, JRC Joint Research Centre European Commission, Spain Ayca Donmez, JRC Joint Research Centre European Commission, Spain 11