Manuscript Click here to download Manuscript: FEEDME_Climatic_Change_txt.docx as:to Dawson, T.P., Perryman, A.N. and Osborne, T., 2014, Modelling impacts of climate change on global food security, Climatic Change, Click Cite here view linked References online: http://link.springer.com/article/10.1007%2Fs10584-014-1277-y (DOI 10.1007/s10584-014-1277-y).
Modelling impacts of climate change on global food security 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
Terence P. Dawson1, Anita H. Perryman2 and Tom M. Osborne3 1. School of the Environment, University of Dundee, Dundee, DD1 4HN, UK. Email:
[email protected], Tel: +44 (0)1382 388064, Fax: +44 (0)1382 388588 2. School of Geography and Environment, University of Southampton University Road, Southampton, SO17 1BJ, UK. 3. National Centre for Atmospheric Research, Walker Institute, University of Reading, Reading, RG6 6BB, UK Abstract The United Nations Food and Agriculture Organization (FAO) estimate that nearly 900 million people on the planet are suffering from chronic hunger. This state of affairs led to the making of the United Nations Millennium Development Goals in 2000, having the first goal to “Eradicate extreme poverty and hunger” with a target to halve the proportion of people who suffer from hunger. However, projections of a rapidly growing population, coupled with global climate change, is expected to have significant negative impacts on food security. To investigate this prospect, a modelling framework was developed under the QUEST-GSI programme, which we have termed FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation). The model uses country-level Food Balance Sheets (FBS) to determine mean calories on a per-capita basis, and a coefficient of variation to account for the degree of inequality in access to food across national populations. Calorific values of individual food items in the FBS of countries were modified by revision of crop yields and population changes under the SRES A1B climate change and social-economic scenarios respectively for 2050, 2085 and 2100. Under a no-climate change scenario, based upon projected changes in population and agricultural land use only, results show that 28-31% (2.5 billion people by 2050) of the global population is at risk of undernourishment if no adaptation or agricultural innovation is made in the intervening years. An additional 21% (1.7 billion people) is at risk of undernourishment by 2050 when climate change is taken into account. However, the model does not account for future trends in technology, improved crop varieties or agricultural trade interventions, although it is clear that all of these adaptation strategies will need to be embraced on a global scale if society is to ensure adequate food supplies for a projected global population of greater than 9 billion people. Key words: Climate change, food security, undernourishment, Introduction About 870 million people, representing one in eight people, or 12.5% of the global population, are estimated to be chronically undernourished (FAO 2012) with the vast majority of these coming from developing countries. These estimates demonstrate that, since 1990, limited progress has been made towards the first of the Millennium
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Development Goals that is to halving world hunger by 2015 (UN, 2000). However, rapidly growing populations, especially in developing countries, coupled with global climate change and recent economic volatility are expected to negatively impact upon food security. With a global population set to increase to about 9 billion by 2050 and crop production already showing signs of decline (Lobell et al 2011), the capacity of the Earth’s resources to meet the growing food demand will be challenging. Global agriculture is currently facing a convergence of pressures from a rapidly growing population, climate change, land availability and degradation, loss of biodiversity and food insecurity. Indeed, the UK Government Chief Scientist, Prof. John Beddington’s “perfect storm” speech calls for a 50% increase in food production to meet global food security needs by 2030 (Beddington, 2009). Population projections to 2100 vary hugely within socio-economic and climate scenarios and is a key input to models of food security. For example, Parry et al. (2005) examined the impacts of climate change and risk of hunger over a range of climate and social-economic scenarios and tested a variety of policy options and adaptation measures. They found that the scenario having the lowest projected population growth in developing countries also showed the largest reduction in risk of hunger for any policy or adaptation measure tested. Food security, defined as: “the availability at all times of adequate world supplies of basic food-stuffs” (UN, 1975), is determined by a complex set of factors including: (1) Availability of staple foods (access to productive land, agricultural production and other market sources); (2) Stability of supplies (based upon future global market trends taking into account the climate, socio-economic and political situation); (3) Access to adequate supplies (derived from price of staple foods and physical access); and (4) biological utilisation of food (using nutritional indicators) (Kaufmann, 2000). In recent years, the dietary energy provision-based methodology, adopted by the Food and Agriculture Organisation (FAO) has become the de-facto standard for rapid assessment of undernourishment as an indicator of food (in)security (Maxwell, 1996). This methodology is derived from the principle that food deprivation is based on a comparison of usual food consumption expressed in terms of dietary energy (calories) with minimum energy requirement norms. The proportion of the population with food consumption below the minimum energy requirement is considered at risk of undernourishment (FAO, 2012). Adaptation of the FAO methodology for use in future scenarios of climate, population and social-economic changes has resulted in a modelling framework, which we have termed FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation) (Figure 1). FEEDME integrates the Food and Agriculture Organisation (FAO) methodological framework for undernourishment calculation (FAO, 2004) with country level statistics from the FAOSTAT database, which have been revised according to baseline and future scenarios developed under QUEST-GSI (Global-Scale Impacts of climate change), a project under the Natural Environment Research Council QUEST programme (Arnell, 2008). Methodology The FEEDME model uses the analysis of undernourishment in the population at the country-level using the FAO measure of food deprivation, referred as the prevalence of undernourishment (FAO, 2004). This approach adopts the measure of food consumption in terms of energy (kcal) with countries having a minimum energy requirement threshold. The proportion of the population with food consumption
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below the minimum energy requirement is considered underfed. Historically, the FAO have prepared annual estimates of national food production, imports, exports and food availability in terms of total mounts, on a per-capita basis and in terms of calorific values for all food commodities, which are compiled into Food Balance Sheets (FBS). The FBS of each country enables the FAO to keep track of food trends, which are used to prepare annual estimates for monitoring progress in food security for the country as a whole in support of the first of the Millennium Development Goals integrating hunger and poverty reduction (UN, 2000). The food balance sheets for 175 countries were downloaded from the FAOSTAT website and reformatted to a standardised structure in Microsoft Excel spreadsheets for automatic manipulation by the FEEDME model. A probability distribution framework is used to estimate of the proportion of the population below minimum level of dietary energy consumption (Equation 1).
P(U) = P( x< MDER ) = ∫ f(x)dx = Fx (MDER)
(Equation 1)
X < MDER
where P(U) is the proportion of undernourished in total population (%); (x) refers to the dietary energy consumption (kcal); MDER is a cut-off point reflecting the minimum energy requirement (kcal); f(x) is the density function of dietary energy consumption; Fx is the cumulative distribution function. Figure 2 illustrates the theoretical distribution of dietary energy consumption for estimating the proportion of population undernourished where f(x) is the proportion of the population corresponding to a lognormal distribution of per capita dietary energy consumption levels (x) represented on the x-axis. The variable x, which is the overall mean daily per person dietary energy consumption, and the coefficient of variation, CV(x) of the density function f(x) can be used to describe the shape of the distribution curve. The FAO has published these values for most countries, available from the FAOSTAT database, although these values can also be calculated from household survey data where look-up tables can be developed based upon household daily per person dietary energy consumption by household income class (see FAO, 2004 for further information). This approach is useful in that x and CV(x) can be generated at community to regional levels to more accurately portray food security at sub-national scales. Estimation of the minimum dietary energy requirement (MDER) is determined by sex and age group structure within a country, and is based upon reference body weights and associated basic metabolic rates using the proportion of each sex and age group (and incorporates a pregnancy allowance based upon birth rate) in the total population. Projecting changes in undernourishment through the 21st century Changes in undernourishment throughout the 21st century are projected using FEEDME by changing the climate and socio-economic drivers according to defined climate and socio-economic scenarios. In this analysis, it is assumed that the world develops following the Special Report on Emissions Scenarios (SRES) A1B emissions and social-economic pathway (Nakicenovic & Swart, 2000), as
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implemented in the global integrated assessment model IMAGE v2.3 (van Vuuren et al., 2007). The IMAGE v2.3 A1 population projection shows a decline in global population following a peak in 2050, and defines a global population slightly less than that under most other projections (such as UN medium-fertility population projections). Agricultural land use change projections for 2050, 2085 and 2100 under the A1B scenarios were extracted from the IMAGE 2.3 model. These projections were selected to be consistent with the population and GDP data from the same source. The change in agricultural land use is largely driven by demand for agricultural products and hence strongly linked to population changes and income growth (Leemans and van den Born, 1994). The model also adopts the following no-climatic assumptions: (i) (ii) (iii) (iv)
national level population age and gender structures remain at 2000 values; income and food inequality Gini coefficients remain constant at 2000 values; minimum dietary energy requirements (MDER) for a country remain constant throughout the 21st century; the volume of food imports to a country remains constant through the 21st century.
These assumptions reflect limitations in the quantitative characterisation of these scenarios. They also avoid any prescription of action by national governments with respect to their respective adaptation strategies. The most critical of these assumptions relate to the total volume of trade. The assumption of no change in food imports, for example, leads to a projected increase in undernourishment over the 21st century in the absence of climate change, simply due to increases in population. The projected output indicator of the model – proportion of undernourished people – is therefore to be interpreted as an indicator of exposure to undernourishment and a measure of the need for some form of adaptation response. For example, when faced with rapid increases in the proportion of people undernourished, this response may take the form of increasing national food production or making changes to international food trade agreements (increasing imports or reducing exports). These responses are difficult to predict and can occur immediately following a crisis as demonstrated in the grain export ban by Russia after drought and wildfires devastated domestic crops in 2010 (Financial Times, Sep 3, 2010). The consequences of such actions can create instability and spikes in food prices that result in global impacts on food security. The effects of climate change are represented within FEEDME by changing national production of each food type, using the results from the General Large Area Model for annual crops (GLAM) to scale the national-level production values of food items in the food balance sheets. GLAM estimates the growth, development and, ultimately, yield of annual crops by combining a water balance model with crop growth parameterizations (Challinor et al 2004) and has been applied to estimate the impacts of climate change at the global scale (Osborne et al 2013). A CO2 fertilisation effect was accounted for within the GLAM crop model using a CO2-dependent factor that was added to GLAM’s biomass accumulation parameterisation. National production values were determined by multiplying simulated yields at each grid cell by the corresponding growing area derived from the 0.5° spatial resolution global datasets of Leff et al., (2004). Projections of change were not available for all the individual food types in a typical food balance sheet, so within the QUEST-GSI project all plant-and vegetable based foods were allocated to one of three reference crop types (Wheat,
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Maize and Soybean) according to their physiological behaviour (Table 1). Depending on their photosynthetic pathways, all plants belong to two major groups, either C3 and C4 types, with most crops (>85%) classified as C3 types (wheat and rice being the two most important cereals on a global scale). However, many developing countries are heavily dependent upon the C4 crops, with maize, millet, sugarcane, and sorghum being economically very important. The changes in crop productivity provided by the crop simulation models assume no improvements in agricultural technology, and assume limited adaptation to climate change by changes in crop duration and/or planting date (Osborne et al 2013). Percentage change in agricultural land using the difference in the baseline (2000) and projected land-cover maps was used to re-scale production of all crops with changed land use affecting each commodity equally. The FEEDME model assumes no changes in dietary requirements as income rises. Although the historical evidence shows that for many countries meat consumption increases as GDP goes up, there is not a strong correlation between GDP and meat consumption at a global scale. In addition, because of the very poor feed conversion ratio (a measure of an animal's efficiency in converting feed mass into increased body mass) of livestock, accounting for increases in meat consumption for any country projected to have high undernourishment levels would result in further undernourishment as grains are diverted to animal feed. Although the FEEDME model does allow for it, no adjustments to meat and aquatic productivity in the food balance sheets were made in this study. For many countries, the contribution of meat and aquatic (fish) food types amounts to a relatively small percentage of the total mean energy budget per capita. For example, even in wealthy nations such as the UK or USA, total consumption of meat and fish accounts for only 12% of total mean intake of calories per capita, whereas in poorer countries, this percentage becomes insignificant. The FEEDME model was run for a total of 175 countries for the year 2000 (baseline) and for climate and population projections in years 2050, 2085 and 2100 using scenarios from several global climate models (GCMs) as well as a noclimate (population and land-cover changes only) setting. Results and discussion In a comparison with the FAO official statistics data on global undernourishment for the period (http://www.fao.org/hunger/en/), the FEEDME model correctly produced national-level prevalence of undernourishment in populations using the mean of three years (2000-2002) of annual food balance sheets data and population figures (Figure 3a), which was adopted as the baseline condition. Results from the GLAM crop simulation models runs showed that for all the major growing areas under the A1B scenario, climate change is projected to have a negative impact on wheat, maize and soybean production (Osborne et al 2013). For example, under the 2050 scenario, wheat, maize and soybean was projected to have an overall mean reduction of up to 40%, 50% and 50% respectively when compared to baseline productions with some variation between the GCMs and regions spatially. The percentage of the population at risk of undernourishment from the FFEDME model for the 2050 time-slice was categorised and mapped on a national-level (Figure 3b). Results show that many regions of the world will have a significantly increased proportion of undernourishment where the baseline map shows no problems currently. In particular, most of South America and Africa, Australia and central Asia will see 50% or more of the population at risk of undernourishment, with some parts of Europe, South-East
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Asia, USA and Russia also seeing an increase in the population at risk of undernourishment. For the two most populous countries, India and China, however, the percentage at risk of undernourishment will decline from current levels. This reflects current strategies for food security by adopting more intensive agricultural production for those two countries. The full data for the 2000, 2050, 2085 and 2100 time slices are presented in a global summary chart with the number of people at risk of undernourishment accrued for the 175 countries (Figure 4). The no-climate change scenarios are solely the results of projected changes in population and agricultural land use. These factors suggest an additional 28-31% of the global population at risk of undernourishment if no adaptation or agricultural innovation is made in the intervening years (2.5 billion people in 2050 and 2085, and around 2.1 billion people in 2100). In practice, agricultural innovation and adaptation has been able to keep up with increasing demand so far (Simon, 1981). For this to continue requires significant progress in agricultural technology, improved crop varieties and implementation; some authors think this is plausible (Ewart et al, 2005) while others disagree (Lobell at el, 2009). There is certainly scope in many countries with current high levels of undernourishment to apply existing agricultural technology to improve yields (Geerts et al. 2009). As the A1B scenario narrative specifies significant catch up in developing countries (infrastructure, markets, access to information, capital and agricultural inputs) it seems highly likely that a significant proportion of the projected increase in risk of undernourishment can be accounted for by agricultural improvements (Nakicenovic & Swart, 2000). The impact of climate change can also be seen in Figure 4 as the additional proportion of the population at risk of undernourishment beyond the no-climate change results. There is only minor variation in the results from the 5 GCMs used for this analysis. For 2050 this averages as a further 21% of the total population of the included countries, for 2085 it is 19% and for 2100, 17%. In absolute terms this represents an additional 1.7 billion people at risk of undernourishment due to climate change alone by 2050 (above the 2.5 billion people at risk due to population growth), 1.3 billion at 2085 and 1.1 billion at 2100. To feed these people will require adaptation over and above that needed to feed those at risk through population increase and agricultural land use change. Achieving this is not without some major technological challenges to overcome. Manipulation of the international trade in agricultural produce can, in many circumstances, provide some relief to those countries who can afford to increase food imports, or who are currently exporting a large quantity of food items that they can scale back or halt completely in the future. Where a national export ban can be applied, this may show a reduction in calorie shortfall but might lead to an unrealistic and highly unbalanced diet. It is also assumed that, on a global scale, there remains sufficient food production to fulfil all country-level import requirements, which may not be the case if the majority of countries need to withhold or scale back their food exports under the extreme scenarios. Many of the smaller nations are highly dependent on imports for their food supply. Many grow one or two globally important (cash) crops for export and import the bulk of the diversity in their diets. As imports are currently kept static in this current analysis, the undernourishment results are directly dependent on population changes. For approximately 25% of the countries included in this study, there was no crop yield change data available. This is because most of these countries do not grow wheat, maize or soybean. Where no crop yield change data was available we have assumed that yields remain unchanged into the future. These countries include most of the small islands and very small nations and
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some arid Middle Eastern countries. Hence this group shows no climate change impacts on undernourishment but only impacts due to population and land use change. However, these are mainly nations with low populations so the affect on the overall (global) absolute impacts are less than this might at first suggest. Because rice was not modelled as a reference crop, the projections for wheat were extrapolated to rice production. The biophysical modelling of wet (paddy) rice yields is strongly dependent upon controlled irrigation conditions, which are usually carefully managed for rice production. This can therefore result in a divergence between estimates of the future production of (rain fed) wheat and rice under climate change for different regions of the world. However, specific studies using wet rice models have also shown declining yields under climate change of an order of magnitude similar to wheat (Tao et al., 2008, Naylor et al., 2007). Conclusion In the absence of continued agricultural innovation and changes in agricultural trade, and based upon population and agricultural land use change only, an additional 28% of global population would be at risk of undernourishment by 2050 – around 2.5 billion people - compared to the current (baseline) status. The impact of climate change increases the proportion of the global population at risk of undernourishment under the SRES A1B scenario by a further 21% for 2050, 19% for 2085 and 17% for 2100. In absolute terms this represents an additional 1.7 billion people at risk of undernourishment due to climate change by 2050, 1.3 billion at 2085 and 1.1 billion at 2100, with the number varying only slightly between climate models. Overall, the results of this study are generally in line with FAO statements that global food production must increase by 70% to feed everybody by 2050, due to population and climate change (FAO, 2009) as well as provide a quantitative corroboration of the UK Chief Scientist, Professor John Beddington’s ‘perfect storm’ dystopia. Acknowledgements This research was funded under the NERC QUEST-GSI project no. NE/E001866/1. References Arnell, N.W., 2008, The global-scale impacts of climate change: QUEST-GSI, website: http://www.met.reading.ac.uk/research/quest-gsi/ (last accessed on 12/12/12). Beddington, J., 2009, Food, energy, water and the climate: A perfect storm of global events? Government Office for Science, London. Available at www.bis.gov.uk/assets/goscience/docs/p/perfect-storm-paper.pdf (last accessed on 12/12/12). Challinor, A.J., T.R. Wheeler, P.Q. Craufurd., J.M. Slingo and D.I.F Grimes, 2004, Design and optimissation of a large-area process-based model for annual crops. Agricultural and Forest Meteorology 124, 99-120. Ewert, F., Rounsevell, M.D.A., Reginster, I., Metzger, M.J. and Leemans, R., 2005, Future scenarios of European agricultural land use I: Estimating changes in crop productivity, Agriculture, Ecosystems and Environment, 107, 101-116.
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FAO, 2012, The State of Food Insecurity in the World 2012, Food and Agriculture Organisation, Rome. Available at http://www.fao.org/publications/sofi/en/ (last accessed on 7/12/12). FAO, 2009, How to Feed the World in 2050, Food and Agriculture Organisation, Rome, pp35. FAO, (2004), FAO methodology for estimating undernourishment, Food and Agriculture Organisation, Rome. Kaufmann, S., 2000, A Selection of Indicators for Food and Nutrition Security Programmes, Food and Agriculture Organisation, Rome. Leemans, R. and van den Born, G.J., 1994, Determining the potential distribution of vegetation, crops and agricultural productivity (p 133-162) in Alcamo, J (ed.) IMAGE 2.0 Integrated modelling of Global Climate Change. Leff, B.. N. Ramankutty and J.A. Foley, 2004, Geographic distribution of major crops across the world. Global Biogeochemical Cycles 18 (1) art. no. GB1009. Lobell, D.B., Schlenker, W. and Costa-Roberts, J., 2011, Climate trends and global crop production since 1980, Science 333, 616-620. Lobell, D.B, K.G. Cassman & C.B. Field, 2009, Crop Yield Gaps: Their Importance, Magnitudes, and Causes, Annual Review of Environment and Resources 2009, 34, 179-204. Maxwell, D., 1996, Measuring Food Insecurity: The Frequency and Severity of ‘Coping Strategies.’, Food Policy 21, 291-303. Nakicenovic, N. and R Swart (eds), 2000, Special Report on Emissions Scenarios: A special report of working group III of the Intergovernmental Panel on Climate Change. IPCC. Naylor R.L., Battisti, D.S., Vimont, D.J., Falcon, W.P. and Burke, M.B., 2007, Assessing risks of climate variability and climate change for Indonesian rice agriculture, Proceedings of the National Academy of Sciences 104, 7752-7757. Osborne T., Rose, G and Wheeler, T., 2013, Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation, Agr. For. Meteor 170, 183-194. Parry, M, Rosenzweig, C. and Livermore, M., 2005, Climate change, global food supply and risk of hunger, Philosophical Transactions of the Royal Society B, 360, 2125-2138. Simon J., 1981, World population growth: an anti-doomsday view, in Menard, S.W and Moen, E.W. (eds) Perspectives on population: an introduction to concepts and issues. Oxford University Press. Oxford. Page 123-128.
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Tao, F., Hayashi, Y., Zhang, Z., Sakamoto, T. and Yokozawa, M., 2008, Global warming, rice production, and water use in China: Developing a probabilistic assessment, Agricultural and Forest Meteorology 148, 94–110. UN, 2000, United Nations General Assembly, United Nations Millennium Declaration, Resolution Adopted by the General Assembly , 18 September 2000, A/RES/55/2. Available at http://www.un.org/millennium/declaration/ares552e.pdf (last accessed on 7/12/12). UN, 1975, Report of the World Food Conference, 5-16 November 1974. United Nations, Rome. Available at http://www2.ohchr.org/english/law/malnutrition.htm (last accessed on 12/12/12). van Vuuren, D.P., den Elzen, M.G.J., Lucas, P.L., Eickhout, B., Strengers, B.J., van Ruijven, B., Wonink, S. & van Houdt, R., 2007, Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Climatic Change 81, 119-159.
Table 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
Table 1. Assignment of Food Balance Sheet commodities to reference crops for projection of production changes Figures Figure 1. Schematic of the FEEDME Model Figure 2. Probability Density Function, f(x) of a dietary energy consumption for a theoretical country where the area under the curve below the Minimum Dietary Energy Requirement (MDER) is the prevalence of undernourished within the population. x is the mean dietary energy consumption for that country. See the text for further details. Figure 3. World maps showing the percentage at risk of undernourishment for the 2000-2002 period baseline (a), and the mean of all GCM models for the 2050 A1B scenario (b). Figure 4. Global summary of percentage of the population considered to be at risk of undernourishment under the A1B scenario for several GCMs.
Table
Table Group
Reference Crop
C4 (cf maize) C3 (cf wheat) C3 (cf soy)
Maize crop yield data Wheat crop yield data Soybean crop yield data
Other
No change
Meat/dairy
Currently assume no change
Aquatic
Currently assume no change Based on dominant production from either sugarcane (C4) or beet (no change) above
Sugars & Sweeteners
FBS commodities: summary Cereals, sugar crops, Vegetable Oils Cereals, Alcoholic Beverages Pulses, Oilcrops, , Vegetable Oils
Starchy roots, sugar crops, treenuts, vegetables, fruits stimulants, spices, miscellaneous Meat, offals - edible, animal fats (inc milk), eggs Fish, seafood; fish oils; aquatic products, other
FBS commodities: individual maize, millet, sorghum, sugarcane, maize germ oil wheat, rice, barley, rye, oats, other cereals, beer Soyabeans, groundnuts, sunflowerseed, rape and mustardseed, cottonseed, sesameseed, other oilcrops, soyabean oil, groundnut oil, sunflowerseed oil, rape and mustard oil, cottonseed oil, sesameseed oil Sugar beet, honey, coconuts, palmkernels, olives, palmkernel oil, palm oil, coconut oil, olive oil, wine, beverages (fermented and alcoholic)
Table 1. Assignment of Food Balance Sheet commodities to reference crops for projection of production changes
Figure
Figures
Climate Change
Crop Model
Land Use & Land Use Change
Socio-Economic Drivers
Fisheries & Marine
Total Population
Global Trade Model
GDP and variation in incomes
Household Budget Surveys
Population (age & gender structure)
FAO Food Balance Sheets Dietary Energy Supply (Calories available per capita)
Coefficient of variation (income Gini and food requirement Gini)
Minimum Dietary Energy Requirements
Proportion of population undernourished Number of people undernourished
Figure 1. Schematic of the FEEDME Model
Figure 2. Probability Density Function, f(x) of a dietary energy consumption for a theoretical country where the area under the curve below the Minimum Dietary Energy Requirement (MDER) is the prevalence of undernourished within the population. x is the mean dietary energy consumption for that country. See the text for further details.
(a)
(b)
Figure 3. World maps showing the percentage at risk of undernourishment for the 2000-2002 period baseline (a), and the mean of all GCM models for the 2050 A1B scenario (b).
% of global population at risk of undernourishment (A1B) 70
60
50 No CC hadcm3
40
echam5
ipsl
30
cgcm31 ccsm30
20
10
0 Baseline
2050
2085
2100
Figure 4. Global summary of percentage of the population considered to be at risk of undernourishment under the A1B
