Although seafood is the most highly

POLICYFORUM ECONOMICS Sustainability and Global Seafood Martin D. Smith, 1,2 * Cathy A. Roheim, 3 Larry B. Crowder, 4 Benjamin S. Halpern, 5 Mary Turnipseed, 1 James L. Anderson, 3 Frank Asche, 6 Luis Bourillón, 7 Atle G. Guttormsen, 8 Ahmed Khan, 9 Lisa A. Liguori, 10 Aaron McNevin, 11 Mary I. O Connor, 5 Dale Squires, 12 Peter Tyedmers, 13 Carrie Brownstein, 14 Kristin Carden, 15 Dane H. Klinger, 16 Raphael Sagarin, 17 Kimberly A. Selkoe 5,18 Tight coupling to ecosystems and dependence on common-pool resources threaten fisheries and aquaculture. Although seafood is the most highly traded food internationally, it is an often overlooked component of global food security. It provides essential local food, livelihoods, and export earnings. Although global capture fisheries production is unlikely to increase, aquaculture is growing considerably. Sustaining seafood s contributions to food security hinges on the ability of institutions, particularly in developing countries, to protect and improve ecosystem health in the face of increasing pressures from international trade. Seafood (fish and shellfish harvested from capture fisheries and aquaculture production in marine and freshwater environments) contributes at least 15% of average animal protein consumption to 2.9 billion people and as much as 50% for some small island and West African states ( 1). Seafood is the main source of omega-3 fatty acids that are essential for brain development ( 2) and provides important micronutrients for the poor ( 3). As a source of livelihood, capture fisheries and aquaculture employed 43.5 million people in 2006, and 520 million people relied on income from seafood production ( 1). Seafood is also the most highly traded food commodity internationally ( 1). Fish and shellfish exports from developing countries exceed the value of coffee, rubber, cocoa, tea, tobacco, meat, and rice combined ( 1). Developing countries benefit from this trade by exporting high-valued seafood to developed countries, importing low-valued seafood, and using the surplus value to purchase other goods and services (fig. S1). However, they often lack the institutions necessary to prevent deleterious ecosystem 1 Nicholas School of the Environment, Duke University, Durham, NC 27708, USA. 2 Department of Economics, Duke University, Durham, NC 27708, USA. 3 Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, RI 02881, USA. 4 Center for Marine Conservation, Nicholas School of the Environment, Duke University, Beaufort, NC 28516, USA. 5 National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, Santa Barbara, CA 93101, USA. 6 Department of Industrial Economics, University of Stavanger, Stavanger, 4036, Norway. 7 Comunidad y Biodiversidad, A.C. (COBI), Boulevard Agua Marina 297, Colonia Delicias, Guaymas, Sonora, 85420, México. 8 Department of Economics and Resource Management, Norwegian University of Life Sciences, 1432, Aas, Norway. 9 International Coastal Network, Department of Geography, Memorial University, St. John s, Newfoundland A1B 3X9, Canada. 10 Marine Extension Service, University of Georgia, Brunswick, GA 31520, USA. 11 World Wildlife Fund, Washington, DC 20037, USA. 12 Southwest Fisheries Science Center, La Jolla, CA 92037, USA. 13 School for Resource and Environmental Studies, Dalhousie University, Halifax, Nova Scotia, B3H 3J5 Canada. 14 Whole Foods Market, Austin, TX 78703, USA. 15 Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA. 16 Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA 94305, USA. 17 Institute of the Environment, University of Arizona, Tucson, AZ 85719, USA. 18 Hawaii Institute of Marine Biology, Kaneohe, HI 96744, USA. *Author for correspondence: E-mail: marsmith@duke.edu impacts of seafood production and to sustain trade benefits. Developed countries have a history of these problems, as well, but with less-obvious consequences. Although terrestrial food systems provide protein, support livelihoods, and generate export earnings, two characteristics of fisheries and aquaculture production uniquely threaten food security: tight coupling to ecosystems and dependence on common-pool resources. Fisheries and aquaculture are vulnerable to exogenous shocks to ecosystems such as climate change, but endogenous changes are particularly important. Commonpool fish stocks are often open-access, and fishing effort can push stock levels beyond maximum sustainable yield. In those cases, price increases lead to reduced seafood production ( 4, 5). This scenario does not generally occur in terrestrial food production. Fishing not only reduces target species populations but also can alter marine food webs ( 6) and has cumulative impacts on marine ecosystems ( 7), undermining the productive capacity of fisheries. Ultimately, the total productivity of a capture fishery is limited by the target species ability to reproduce, and poor governance often leads to fish populations being pushed beyond this limit. Aquaculture attempts to decouple fish production from environmental fluctuations by controlling growing conditions, feed input, and disease ( 8, 9). However, poor management can lead to reduced production even when prices rise, partly due to poorly defined property rights in locations where aquaculture is conducted. In estuarine and marine environments, nutrient pollution, farmed fish escapes, disease spread, and the use of capture fish in feed also threaten aquaculture s sustainability ( 10). Consumption is not shared equally among countries ( see the figure on page 785). Levels are high in developed and island countries but low in some developing countries (China and Southeast Asia are notable exceptions). Overlaying net exports, governance, and undernourishment suggests that seafood s contribution as a source of protein and livelihood is precarious. To compare institutional effectiveness across countries, we used an average of four governance indicators developed for the World Bank ( 11) as a proxy. Countries with undernourishment and weak governance often serve as net exporters of seafood to wellnourished countries with strong governance ( see the table on page 786). However, the largest seafood net exporters (China, Norway, and Chile) have neither the weakest governance nor the greatest undernourishment, suggesting that they have some institutional capacity to promote sustainability ( see the figure). At the global scale ( see the table), regions with low undernourishment are net importers of seafood from regions with high undernourishment. In principle, developing countries could consume more seafood simply by exporting less of it. But prevailing conditions in the global seafood market make it advantageous for many countries to be seafood exporters and generate surplus value (fig. S1). A population-weighted average governance score follows the same trend as per capita seafood consumption; regions with more undernourishment tend to have weaker governance ( see the figure and the table). Poor governance ultimately squanders seafood availability, for example, by failing to control overfishing and bycatch, as well as failing to regulate the environmental impacts of aquaculture. Corruption (included in governance) can also prevent export earnings from benefiting the poor. On each continent, the governance index is lower in less-nourished regions. Per capita seafood consumption follows the same pattern, except in Oceania, which has a preponderance of small island nations with abundant seafood sources ( see the table). Asia generates most of the world s net seafood exports from countries with moderate to Downloaded from www.sciencemag.org on February 13, 2010 784 12 FEBRUARY 2010 VOL 327 SCIENCE www.sciencemag.org Published by AAAS

POLICYFORUM severe undernourishment. China, Indonesia, Vietnam, Thailand, Taiwan, India, and Myanmar are large net exporters (>300,000 metric tons) and, with the exception of Taiwan (for which data are unavailable), have moderate to high undernourishment. China illustrates the potential for aquaculture to contribute to food security by expanding export-oriented and domestically consumed aquaculture. This growth contributed to China s recent substantial reduction in undernourishment ( 12). Ninety-two percent of global animal aquaculture production occurs in developing countries, of which 31% is carp that is mostly grown in small Chinese facilities for domestic consumption ( 13). In contrast, Japan is the world s largest net importer (3.82 million metric tons) and has low undernourishment. In Africa, severely undernourished regions, e.g., Namibia and Senegal, are net exporters, but moderately undernourished regions are net importers, e.g., Nigeria ( see the table). Small amounts of exports from Africa also reflect access agreements between countries in West Africa and other regions (mostly Europe and Asia) to exploit their offshore fish stocks. These landings are counted neither as African production nor as African exports, although they come from African waters. The United States and European Union countries are well nourished and among the largest net importers. In contrast, large-scale aquaculture production creates opportunities for countries with all levels of nourishment 0 5 10 15 25 50 180 Per capita consumption (kg/person per year) ND 2.0 1.0 0 1.0 2.0 Governance effectiveness ND <5% 5 15% >15% Percent undernourishment Seafood consumption, governance, and undernourishment. (Top) Apparent per capita edible seafood consumption (2003 to 2005 average kg per year in live weight equivalent) from FAO FishStat Plus ( 13). Edible seafood is from fisheries and aquaculture used for human consumption. Apparent consumption is calculated for each nation by adding total seafood production to total imports and subtracting total exports. Per capita consumption divides apparent consumption by population. (Bottom) Governance by nation is the average of four World Bank indicators (each with a score of 2.5 to 2.5 and averaged for 2003 to 2005): rule of law, control of corruption, governmental effectiveness, and regulatory quality ( 11). Undernourishment categories by nation are FAO s average percentage of the population that is undernourished for 2003 to 2005 ( 12). (low, moderate, and high) to be net exporters, e.g., Vietnam, Chile, and Norway. These data highlight the benefits of the seafood trade but also seafood s precarious role in contributing to food security. Weak governance threatens countries abilities to consume seafood domestically or export it and use the trade system to purchase other foods. Because much of the world s seafood production comes from regions with weak governance, improved governance is essential to sustain or increase seafood s contribution to food security. Two very different histories of fish production in Chile and Mexico illustrate the importance of effective governance. Chile s rugged coastline is well suited to salmon farming. Salmon product ion has been primarily an indirect source of food through earnings and employment. Global trade and lax environmental regulations in Chile facilitated rapid expansion of salmon farming, but currently the industry is experiencing its worst disease crisis ever, an outbreak of infectious salmon anemia. Although 670,000 metric tons were produced in 2008, the prediction is that Chile will produce less than 100,000 metric tons in 2010. The outbreak has been attributed to institutional failure to react to known risks from lake-based smolt production and unvaccinated fish ( 14). Chile s crisis tells a cautionary tale about expanding aquaculture production without effective institutions to protect the environment. The spiny lobster fishery (Panulirus interruptus) along the central west coast of the Baja California peninsula is the largest lobster fishery in Mexico, with ~1600 metric tons captured every year. Ninety percent of the catch is exported live, and the export is critical for local livelihoods and quality of life. There are 500 fishermen organized into nine fishing cooperatives har- Downloaded from www.sciencemag.org on February 13, 2010 www.sciencemag.org SCIENCE VOL 327 12 FEBRUARY 2010 785 Published by AAAS

POLICYFORUM Continent level of undernourishment Percent of world population Seafood net exports (metric tons/year) vesting the resource. Strong comanagement by cooperatives and the federal government has kept the Mexican Baja California lobster fishery from overexpanding to increase shortterm export earnings at the expense of future resource availability ( 15). What policy initiatives can create incentives for better governance and enhance seafood s role in food security? Developing countries rely heavily on common property resource management, in which communities organize themselves to solve the commons problem ( 16, 17). These institutions may fail during rapid change (e.g., new technology) or if they are not buffered from external forces (e.g., international trade) ( 18 20). Thus, developing countries are in a quandary with respect to seafood exports; existing common property institutions are threatened by export-oriented seafood production, and robust rights-based institutions generally require effective governance. Given the high tradability of seafood, trade policy is a natural consideration, and import tariffs theoretically can promote renewable resource sustainability ( 21). But seafood tariffs are likely to violate World Trade Organization (WTO) rules, reduce short-term trade, and fail to differentiate among well-managed and poorly managed fisheries and aquaculture operations. In contrast, private initiatives such as ecolabeling, third-party certification, and direct sourcing have the potential to differentiate among seafood suppliers. Success of these voluntary initiatives may require that consumers are willing to pay a premium for sustainability to cover the costs of investment in sustainable governance (e.g., management), equipment (e.g., fishing gear), and infrastructure (e.g., traceability systems). Whether consumers actually will pay this premium is an open question, which suggests that other funding sources such as direct foreign aid, may be necessary. Aid providers would need to coordinate with WTO to ensure that recipients are not accused of dumping seafood on the global market. Natural resource prices fail to reflect the cost of sustainability in many countries ( 22). In the short run, as producers transition toward environmental stewardship, prices rise for products like shrimp, lobster, Seafood consumption (kg/person per year) Pop. weighted avg. governance World Low 29.3 7,838,123 21.72 0.63 Moderate 31.1 3,387,403 20.05 0.40 High 37.9 3,182,602 9.03 0.51 Africa Low 3.1 73,540 11.09 0.13 Moderate 3.7 935,520 10.71 0.87 High 7.1 289,134 5.57 0.93 Asia Low 6.6 5,462,261 31.89 0.32 Moderate 22.4 3,858,470 24.21 0.36 High 30.0 2,912,576 9.95 0.41 Europe Low 11.3 2,376,047 20.09 0.68 Moderate 0.0 0 High 0.0 0 North America Low 7.0 2,190,357 20.54 1.17 Moderate 0.3 51,508 9.48 0.28 High 0.6 11,711 5.22 0.73 Oceania Low 0.4 90,891 25.69 1.79 Moderate 0.0 91,751 34.14 0.77 High 0.0 0 South America Low 0.9 2,026,111 11.07 0.07 Moderate 4.7 424,210 8.16 0.19 High 0.1 7,397 1.61 0.58 Relation of exports, undernourishment, seafood consumption, and governance. Data were obtained as described in the figure legend. Low, moderate, and high refer to population-weighted averages of countrylevel undernourishment status. They indicate, for each continent, the proportion of the population that lives in countries where <5%, 5 to 15%, and >15%, respectively, of that country s population is undernourished. Undernourishment data are unavailable for countries representing <3% of the population of each continent, with the exception of Oceania (for which 20% of the population lives in countries without data). and salmon. But over the longer term, producers and consumers are better off because seafood supplies and livelihoods are sustainable. Price increases that reward sustainability may also raise prices of low-valued seafood, displacing fish protein from diets of the poorest of the poor in the short term. That is, when the price of the high-value product increases, demand for a substitute low-value product increases, raising its price. Research is needed to determine whether these price increases are large enough to warrant a policy intervention such as direct aid. Finally, bilateral trade between developed and developing countries highlights the importance of governance in developed countries as well. Developing countries import low-valued seafood for consumption, as well as highvalued seafood for processing, from developed countries. Sustaining these contributions to consumption and livelihood requires that developed countries also govern their resources effectively. References and Notes 1. Food and Agriculture Organization of the United Nations (FAO), The State of the World Fisheries and Aquaculture 2008 (FAO, Rome, 2009). 2. J. R. Hibbeln et al., Lancet 369, 578 (2007). 3. N. Roos, M. A. Wahab, C. Chamnan, S. H. Thilsted, J. Nutr. 137, 1106 (2007). 4. P. Copes, Scott. J. Polit. Econ. 17, 69 (1970). 5. H. S. Gordon, J. Polit. Econ. 62, 124 (1954). 6. D. Pauly, V. Christensen, V, J. Dalsgaard, R. Froese, F. Torres Jr., Science 279, 860 (1998). 7. B. S. Halpern et al., Science 319, 948 (2008). 8. J. L. Anderson, Mar. Resour. Econ. 17, 133 (2002). 9. F. Asche, Mar. Resour. Econ. 23, 527 (2008). 10. R. L. Naylor et al., Nature 405, 1017 (2000). 11. D. Kaufman, A. Kraay, M. Mastruzzi, Governance Matters VIII: Aggregate and Individual Governance Indicators 1996 2008 (World Bank Policy Research Working Paper No. 4978, World Bank, Washington, DC, 2009). 12. FAO, Prevalence of Undernourishment in Total Population (FAO, Rome, 2008); www.fao.org/economic/ess/ food-security-statistics/en. 13. FAO, FishStat Plus (2009); www.fao.org/fishery/ statistics/en. 14. F. Asche, H. Hansen, R. Tveteras, S. Tveterås, Mar. Resour. Econ. 24, 405 (2009). 15. L. Bourillón, Biodiversitas-CONABIO 86, 7 (2009). 16. E. Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge Univ. Press, Cambridge, 1990). 17. E. Ostrom Science 325, 419 (2009). 18. B. R. Copeland, M. S. Taylor, Am. Econ. Rev. 99, 725 (2009). 19. T. Dietz, E. Ostrom, P. C. Stern, Science 302, 1907 (2003). 20. J. E. Cinner, S. Aswani, Biol. Conserv. 140, 201 (2007). 21. J. A. Brander, M. S. Taylor, J. Int. Econ. 44, 181 (1998). 22. K. Arrow et al., J. Econ. Perspect. 18, 147 (2004). 23. Supported by the National Center for Ecological Analysis and Synthesis, University of California at Santa Barbara; and the Working Group on Envisioning a Sustainable Global Seafood Market and Restored Marine Ecosystems. Supporting Online Material www.sciencemag.org/cgi/content/full/327/5967/784/dc1 10.1126/science.1185345 Downloaded from www.sciencemag.org on February 13, 2010 786 12 FEBRUARY 2010 VOL 327 SCIENCE www.sciencemag.org Published by AAAS

www.sciencemag.org/cgi/content/full/327/5967/784/dc1 Supporting Online Material for Sustainability and Global Seafood Martin D. Smith,* Cathy A. Roheim, Larry B. Crowder, Benjamin S. Halpern, Mary Turnipseed,James L. Anderson, Frank Asche, Luis Bourillón, Atle G. Guttormsen, Ahmed Khan, Lisa A. Liguori, Aaron McNevin, Mary I. O Connor, Dale Squires, Peter Tyedmers, Carrie Brownstein, Kristin Carden, Dane H. Klinger, Raphael Sagarin, Kimberly A. Selkoe This PDF file includes *Author for correspondence. E-mail: <marsmith@duke.edu> Fig. S1 Tables S1 to S5 References Published 12 February 2010, Science 327, 784 (2009) DOI: 10.1126/science.1185345

Real Price ($/kg) 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Developed export Developed import Developing export Developing import

Fig. S1. Real unit prices of internationally traded seafood by country group. Average price per kilogram, in 2005 constant US$ (adjusted by the U.S. GDP deflator), of aggregated seafood imports and exports for developing and developed countries (S1, S2). Exports of seafood by developing countries have a higher unit value than imports, providing trade benefits to that country group. Real prices of developing country imports are also trending down, which suggests that low-value fish protein is becoming more affordable. Both import and export quantities are large, which suggests that developing countries purchase low-valued seafood with export earnings and have surplus earnings available for other uses. By comparison, developed countries are importing higher-valued seafood than they are exporting. References S1. Food and Agriculture Organization of the United Nations (FAO), FishStat Plus (2009); www.fao.org/fishery/statistics/en. S2. Bureau of Economic Analysis, U.S. Department of Commerce, Table 1.1.4. Price indexes for GDP (2009); www.bea.gov/national/nipaweb/selecttable.asp?selected=y. 1

Table S1. Data in support of Fig. S1. Year Quantity (metric tons) Developed countries Export Import Value Quantity (1000s (metric U.S. $) tons) Value (1000s U.S. $) 1976 5396759 5036248 6470340 7656728 1977 5390986 5963827 6512025 9088283 1978 5823976 7250999 6859194 10904221 1979 6397340 8595993 7550977 13641911 1980 6577464 9368233 7508807 13772382 1981 6679226 9421247 7356125 14081536 1982 6746574 8925249 7874778 14302788 1983 7424729 9191038 8150430 14692326 1984 7970442 9126619 8813388 14867259 1985 8531175 9747245 10205141 16277402 1986 9104815 12657776 10515834 21333173 1987 9331155 15410922 11188381 26988998 1988 10170206 17349376 11884138 30830031 1989 9499902 17236566 12521374 31038946 1990 9865868 20323353 12694640 34675470 1991 9458519 21372180 12421755 37649722 1992 9605105 22026406 12304896 38880155 1993 10633697 21418697 13070393 38339161 1994 11613845 23943528 15357996 44078537 1995 11518376 25789479 15546247 48193602 1996 12353245 26987283 15536906 48335392 1997 12806732 26143116 16479734 47531050 1998 12281788 26277042 16289806 47712111 1999 12520515 27765150 16810752 49544459 2000 13156786 27528355 17666415 50602212 2001 13939186 27972822 18701116 49638499 2002 13890273 29766633 18630451 50855239 2003 14205156 33151718 18863674 55899203 2004 14608611 36955494 19108616 61943487 2005 14918124 40550453 19581915 66165247 2006 14710903 43814801 19630365 72655621 2007 14870371 48619456 19974834 78244752 2

Table S2. Data in support of Fig. S1. Year Quantity (metric tons) Export Value (1000s U.S. $) Developing Quantity (metric tons) Import Value (1000s U.S. $) U.S. GDP Deflator 1976 2524031 2943659 1660945 1185337 35.505 1977 2923129 3695889 1717623 1345426 37.764 1978 3328778 4677710 2050414 1902243 40.413 1979 3673973 5741710 2449834 2392680 43.773 1980 3841555 6146565 2434243 2789795 47.776 1981 3790036 6583801 2735579 3404429 52.281 1982 4301340 6593451 3070565 3640528 55.467 1983 3892543 6672762 2647413 3191293 57.655 1984 4364913 6964599 3004661 3221924 59.823 1985 5347380 7385057 3044468 3213288 61.633 1986 5998572 10090118 3487463 4032443 63.003 1987 6308915 12399010 4003748 4583798 64.763 1988 6418435 13972119 4414614 5778281 66.99 1989 7284217 14484553 4843443 6043260 69.52 1990 7232605 15534343 4705973 5255050 72.213 1991 7785876 17251080 5356051 6268285 74.762 1992 8048405 18005643 5440586 6913114 76.537 1993 9176451 19742667 5944555 6900223 78.222 1994 11172079 23789629 6590769 8053361 79.867 1995 11110039 26464153 6768546 8889474 81.533 1996 11020471 26399654 7139538 9691582 83.083 1997 11554028 27557629 7133948 10000386 84.554 1998 10236129 25350884 6328411 8575117 85.507 1999 11746797 25574800 7532184 9237626 86.766 2000 13352837 28288308 8960731 10601894 88.648 2001 13759560 28660010 9391849 11180050 90.654 2002 13679111 28936309 9657056 11901384 92.113 2003 13978499 31067532 9893976 12790539 94.099 2004 15184468 34939949 11127915 14805130 96.769 2005 16234465 38521244 12312323 16693607 100 2006 16778217 42758569 12441479 18575184 103.263 2007 16848541 45444307 13043721 21177943 106.221 3

Table S3. Data in support of Fig. 1 (2003 2005 averages). World Bank Indicators: Gov_eff, governmental effectiveness; Reg_qual, regulatory quality; Rule_law, rule of law; Cont_corrup, control of corruption. Country Code Gov_eff Reg_qual Rule_law Cont_corrup CHINA CHN 0.08744 0.31392 0.40415 0.53759 NORWAY NOR 2.058354 1.464544 1.937689 1.985013 CHILE CHL 1.233417 1.435745 1.197048 1.322082 INDONESIA IDN 0.48307 0.5865 0.85819 0.91754 VIETNAM VNM 0.402 0.54339 0.42292 0.7401 ICELAND ISL 2.21108 1.672414 2.089567 2.441712 THAILAND THA 0.292865 0.295726 0.08036 0.20025 ARGENTINA ARG 0.16716 0.68519 0.66188 0.39374 TAIWAN TWN 1.195714 1.053794 0.879236 0.832 INDIA IND 0.10024 0.29683 0.110825 0.3448 NEW ZEALAND NZL 1.995173 1.730203 1.862193 2.321469 RUSSIA RUS 0.28193 0.31052 0.86988 0.75026 CANADA CAN 2.067565 1.568381 1.756311 1.907741 MYANMAR MMR 1.50575 2.19468 1.61779 1.56385 NAMIBIA NAM 0.122513 0.151721 0.055064 0.042076 NETHERLANDS NLD 2.04321 1.756142 1.717311 2.032125 ECUADOR ECU 0.84107 0.68485 0.75528 0.81196 MOROCCO MAR 0.15805 0.24391 0.01886 0.12467 PERU PER 0.52424 0.124379 0.65123 0.23595 IRELAND IRL 1.607331 1.624712 1.535855 1.502668 DENMARK DNK 2.222115 1.765316 1.943787 2.260953 SENEGAL SEN 0.18374 0.24653 0.28712 0.24282 MALDIVES MDV 0.221007 0.268119 0.208722 0.11732 PAKISTAN PAK 0.55872 0.73374 0.86825 0.92499 LATVIA LVA 0.63214 1.018262 0.513237 0.317186 SOUTH AFRICA ZAF 0.786539 0.540967 0.110618 0.434124 KOREA, NORTH PRK 1.77382 2.17491 0.98536 1.60324 URUGUAY URY 0.438894 0.290703 0.453173 0.842966 PHILIPPINES PHL 0.17989 0.11989 0.53962 0.5902 MAURITANIA MRT 0.15931 0.09795 0.54425 0.000721 TANZANIA TZA 0.41067 0.4108 0.38058 0.75844 PANAMA PAN 0.009851 0.302446 0.12869 0.32374 PAPUA NEW GUINEA PNG 0.76979 0.75779 0.97288 0.97888 VANUATU VUT 0.62093 0.56502 0.162217 0.31058 YEMEN YEM 0.86374 0.85589 1.12686 0.83345 ESTONIA EST 1.056883 1.397395 0.797978 0.944928 UGANDA UGA 0.52145 0.04374 0.73602 0.79473 BANGLADESH BGD 0.79207 0.96352 0.87378 1.33066 CAMBODIA KHM 0.85851 0.46241 1.21844 1.05462 OMAN OMN 0.426034 0.664689 0.720747 0.653005 VENEZUELA VEN 0.8581 1.14252 1.23217 1.06606 GUYANA GUY 0.33739 0.30255 0.60275 0.50146 KENYA KEN 0.76143 0.25807 0.94133 0.91108 MARSHALL ISLANDS MHL 0.93162 0.69888 0.14142 0.62611 MADAGASCAR MDG 0.37186 0.30525 0.19749 0.00983 4

SOLOMON ISLANDS SLB 1.54804 1.70099 0.91286 0.57817 HONDURAS HND 0.5377 0.44279 0.75874 0.7046 COSTA RICA CRI 0.374811 0.575737 0.599826 0.519259 NICARAGUA NIC 0.71049 0.34677 0.70307 0.4875 NIGER NER 0.77334 0.57644 0.78283 0.89601 MICRONESIA FSM 0.44259 0.10175 0.380373 0.19717 SURINAME SUR 0.08489 0.4988 0.19542 0.250168 BELIZE BLZ 0.080293 0.022404 0.078829 0.19638 NETHERLANDS ANTILLES ANT 1.056991 0.727954 0.904162 0.919959 GUINEA-BISSAU GNB 1.40639 1.04875 1.22483 1.10245 KIRIBATI KIR 0.48788 0.79543 0.449629 0.250733 SIERRA LEONE SLE 1.25753 1.10985 1.19031 0.94207 SOMALIA SOM 2.07277 2.26715 2.1505 1.72862 BAHRAIN BHR 0.438791 0.78887 0.758306 0.639867 SUDAN SDN 1.28448 1.19476 1.55427 1.32445 EL SALVADOR SLV 0.20785 0.043388 0.40214 0.23046 BAHAMAS BHS 1.211488 1.108631 1.322606 1.349685 MOZAMBIQUE MOZ 0.46079 0.46326 0.7873 0.69961 COOK ISLANDS COK 0.04335 0.355451 0.821182 0.183702 NEW CALEDONIA NCL 0.36118 0.028714 0.63576 1.33048 TURKEY TUR 0.13322 0.105515 0.092166 0.10676 TUVALU TUV 0.6661 0.1079 1.136778 0.07477 ERITREA ERI 0.97942 1.56865 0.59185 0.19902 AFGHANISTAN AFG 1.15905 1.68578 1.82568 1.54026 AMERICAN SAMOA ASM 0.005936 0.412203 0.997069 0.775359 ANGUILLA AIA 1.265274 0.981037 1.411633 1.032608 BHUTAN BTN 0.321759 0.28443 0.488729 0.875088 FRENCH GUIANA GUF 0.710496 0.744949 0.787571 0.790402 LESOTHO LSO 0.29662 0.57348 0.15363 0.2281 MARTINIQUE MTQ 0.767471 0.840157 0.931597 0.820442 PUERTO RICO PRI 1.084989 0.980857 0.697015 1.022981 REUNION REU 1.044782 1.123849 1.111443 0.865478 VIRGIN ISLANDS (U.S.) VIR 0.923062 1.159552 1.174984 0.707769 WEST BANK GAZA WBG 1.05998 0.94693 0.37737 0.81546 SAO TOME AND PRINCIPE STP 0.71607 0.77287 0.5375 0.7344 BURUNDI BDI 1.34174 1.22566 1.36628 0.9586 PALAU PCI 0.02215 0.73006 0.46903 0.2976 CAPE VERDE CPV 0.02279 0.2371 0.346104 0.308403 MONGOLIA MNG 0.36669 0.40616 0.02711 0.3645 RWANDA RWA 0.83706 0.70449 0.86515 0.6013 CAYMAN ISLANDS CYM 1.260715 1.38054 1.079878 1.255185 NEPAL NPL 0.68934 0.55839 0.71063 0.51033 DJIBOUTI DJI 0.74607 0.77845 0.82693 0.66674 TURKMENISTAN TKM 1.31847 1.93094 1.35495 1.29548 CHAD TCD 1.02885 0.92695 1.17057 1.22187 ETHIOPIA ETH 0.83975 1.01665 0.73173 0.72518 ST. VINCENT AND THE GRENADINES VCT 0.310518 0.453378 0.709372 0.525382 TAJIKISTAN TJK 1.01654 1.06997 1.00176 1.1015 5

PARAGUAY PRY 0.7847 0.72626 1.04432 1.27876 ST. KITTS AND NEVIS KNA 0.089941 0.439354 0.709372 0.506006 MALAWI MWI 0.70473 0.48528 0.41705 0.80494 TONGA TON 0.58006 0.88433 0.275259 0.78714 COMOROS COM 1.54448 1.51185 0.93229 0.83066 DOMINICA DMA 0.370584 0.678486 0.643942 0.599486 GRENADA GRD 0.171031 0.291472 0.277986 0.653654 ZIMBABWE ZWE 1.14245 2.22581 1.66366 1.31642 CENTRAL AFRICAN REPUBLIC CAF 1.55381 1.29651 1.63233 1.20458 UZBEKISTAN UZB 1.02517 1.62284 1.33713 1.12331 GAMBIA GMB 0.55498 0.43254 0.11109 0.54818 BERMUDA BMU 1.050719 1.372606 1.062933 1.270205 LIBERIA LBR 1.53669 1.7429 1.59228 1.2009 ANTIGUA AND BARBUDA ATG 0.440813 0.573279 0.854941 0.802257 ARMENIA ARM 0.13861 0.065881 0.53059 0.64665 ARUBA ABW 1.24049 0.787459 0.904162 1.190317 GUINEA GIN 0.94845 0.98718 1.12646 0.87204 IRAN IRN 0.61368 1.27536 0.60949 0.43973 QATAR QAT 0.443958 0.309126 0.682655 0.852574 GUATEMALA GTM 0.56945 0.26141 1.0305 0.65149 ST. LUCIA LCA 0.425078 0.471474 0.692583 0.568002 BOTSWANA BWA 0.729432 0.66006 0.641434 1.022337 LITHUANIA LTU 0.81259 1.124349 0.516959 0.343855 AZERBAIJAN AZE 0.71807 0.5649 0.80801 1.02282 LAOS LAO 0.98518 1.2657 1.11358 1.0919 SAMOA SAM 0.218465 0.002528 1.003037 0.114286 MALI MLI 0.52659 0.43919 0.1651 0.41417 GABON GAB 0.65516 0.3297 0.60636 0.60349 ZAMBIA ZMB 0.86518 0.59145 0.59475 0.83799 KYRGYZSTAN KGZ 0.68897 0.41876 0.88076 0.9721 IRAQ IRQ 1.67589 1.57957 1.91629 1.42885 BOLIVIA BOL 0.59563 0.26875 0.66263 0.80536 LIBYA LBY 0.79378 1.37153 0.64098 0.77851 KAZAKHSTAN KAZ 0.62346 0.57253 0.9302 1.03776 SWAZILAND SWZ 0.85422 0.58897 0.84629 0.56758 SEYCHELLES SYC 0.06815 0.51848 0.164306 0.208679 ALBANIA ALB 0.51963 0.29012 0.89941 0.75718 MACEDONIA MKD 0.21405 0.16853 0.38502 0.46608 EQUATORIAL GUINEA GNQ 1.31903 1.36416 1.26954 1.56627 BARBADOS BRB 1.147333 1.091273 1.210634 1.20201 BRUNEI BRN 0.440469 1.046621 0.451981 0.313595 GEORGIA GEO 0.5064 0.61903 0.93629 0.66086 LUXEMBOURG LUX 1.993685 1.884375 1.924807 1.889748 TRINIDAD AND TOBAGO TTO 0.407943 0.684513 0.02339 0.024387 BURKINA FASO BFA 0.60252 0.35589 0.58325 0.09277 TOGO TGO 1.41293 0.78785 1.03987 0.83577 SLOVENIA SVN 1.011825 0.853108 0.86798 0.971251 MEXICO MEX 0.076922 0.377025 0.401 0.2835 CONGO COG 1.33042 1.10569 1.29204 1.01082 BOSNIA-HERZEGOVINA BIH 0.67879 0.43518 0.7038 0.31776 6

TUNISIA TUN 0.447123 0.035086 0.191828 0.284841 CYPRUS CYP 1.077863 1.238682 0.846952 0.874762 HAITI HTI 1.49523 1.20934 1.70869 1.57374 MAURITIUS MUS 0.541628 0.474324 0.891808 0.345171 FIJI FJI 0.2298 0.46102 0.07303 0.32299 ANGOLA AGO 1.11896 1.26734 1.37973 1.25233 KUWAIT KWT 0.32301 0.503838 0.719921 0.849286 BULGARIA BGR 0.105566 0.632502 0.10936 0.097553 MACAO MAC 1.236298 1.257128 1.122491 0.90027 ALGERIA DZA 0.46757 0.5745 0.67592 0.53499 MALTA MLT 1.020988 1.225705 1.4332 1.146606 JORDAN JOR 0.149712 0.275828 0.383943 0.388779 SYRIA SYR 1.01148 0.96758 0.39922 0.61207 LEBANON LBN 0.28011 0.16577 0.33415 0.51484 CROATIA HRV 0.4163 0.464221 0.073248 0.17159 HUNGARY HUN 0.875511 1.116899 0.827263 0.702461 MOLDOVA MDA 0.7336 0.55534 0.6811 0.88409 SERBIA YUG 0.36293 0.54993 0.84315 0.45235 BENIN BEN 0.47941 0.51488 0.57575 0.69737 SLOVAKIA SVK 0.747201 1.068275 0.428024 0.447956 CUBA CUB 0.696 1.53882 1.19066 0.27115 GREECE GRC 0.779998 0.917342 0.801313 0.44871 UNITED ARAB EMIRATES ARE 0.617056 0.693909 0.73253 1.012514 COLOMBIA COL 0.12296 0.0211 0.81877 0.25855 JAMAICA JAM 0.00261 0.211211 0.56386 0.48874 SWEDEN SWE 2.088322 1.650254 1.860089 2.13593 FINLAND FIN 2.186309 1.83087 1.919614 2.406948 DOMINICAN REPUBLIC DOM 0.44356 0.27177 0.60501 0.59361 CZECH REPUBLIC CZE 0.941662 1.069506 0.770096 0.438635 ROMANIA ROM 0.04435 0.071004 0.21568 0.25406 CONGO, Dem. Rep. ZAR 1.65485 1.6639 1.82931 1.47706 AUSTRIA AUT 1.830989 1.530106 1.795339 1.993142 SRI LANKA LKA 0.3031 0.02096 0.017688 0.18775 SWITZERLAND CHE 2.202946 1.563253 1.942638 2.103276 ISRAEL ISR 1.083597 0.866597 0.762371 0.906264 SAUDI ARABIA SAU 0.33199 0.00431 0.185985 0.050668 CAMEROON CMR 0.84044 0.69585 1.07305 1.03285 BELARUS BLR 1.15611 1.52461 1.19883 0.94835 POLAND POL 0.516323 0.715729 0.452605 0.279826 GHANA GHA 0.27996 0.24728 0.17374 0.29095 BRAZIL BRA 0.007554 0.143957 0.35792 0.00189 SINGAPORE SGP 2.18242 1.819879 1.745661 2.265787 MALAYSIA MYS 0.981889 0.553608 0.5283 0.351371 COTE D'IVOIRE CIV 1.21941 0.90098 1.52115 1.16191 BELGIUM BEL 1.88591 1.359497 1.436293 1.468453 EGYPT EGY 0.35941 0.4847 0.02506 0.44781 HONG KONG HKG 1.553987 1.808505 1.40748 1.576171 AUSTRALIA AUS 2.010236 1.648009 1.788008 1.971611 UNITED KINGDOM GBR 1.823161 1.675617 1.643427 1.959829 PORTUGAL PRT 1.123172 1.19867 1.177726 1.183141 7

UKRAINE UKR 0.48552 0.47811 0.75425 0.76784 NIGERIA NGA 0.94513 1.14475 1.48961 1.32565 SPAIN ESP 1.500398 1.270495 1.179611 1.375826 GERMANY DEU 1.577652 1.45593 1.661523 1.896707 ITALY ITA 0.795782 0.986024 0.639599 0.452338 KOREA, SOUTH KOR 0.986223 0.750184 0.768905 0.469554 FRANCE FRA 1.631069 1.15156 1.390566 1.411924 UNITED STATES USA 1.781495 1.510023 1.513535 1.656273 JAPAN JPN 1.260316 1.086334 1.270464 1.178534 8

Table S4. Data in support of Fig. 1 (2003 2005). Amount (metric tons) Code Production Imports Exports Food supply Population (n 10 3 ) CHN 41057387 2118462 5088898 32594444 1282192 NOR 3114745 225864 2077480 228354 4609 CHL 4929663 37126 1284364 365058 16123 IDN 5629869 44809 957610 4668629 223214 VNM 3079637 50435 835826 2128579 83836 ISL 1797667 91307 872855 26679 293 THA 3919042 1274563 2011872 2039855 62565 ARG 933171 34696 715689 252178 38375 TWN 1368890 181161 740786 737434 22688 IND 6176551 9700 544713 5287278 1116961 NZL 638147 35544 464483 108681 4049 RUS 3249055 993301 1411070 2522078 144696 CAN 1272489 578667 962010 766638 31953 MMR 1933452 2428 356153 1150831 47567 NAM 586593 21621 362867 28326 1994 NLD 602530 983519 1296847 316540 16260 ECU 457506 19784 329029 59927 12916 MAR 905986 36569 310538 288910 30156 PER 8380915 62258 304611 545286 26958 IRL 330098 46148 250639 84170 4069 DNK 1052073 991838 1180562 127489 5403 SEN 443104 907 133738 316184 11474 MDV 166657 672 122411 52252 291 PAK 545471 1202 122668 287127 155365 LVA 130759 37947 141481 26986 2316 ZAF 846605 70554 173359 409989 47523 PRK 268700 24105 123367 169420 23510 URY 122084 17773 115653 22886 3325 PHL 2719266 117856 210703 2626371 82869 MRT 196285 698 90896 51754 2882 TZA 348915 760 90391 252736 37512 PAN 218224 19888 100739 35304 3175 PNG 222259 16910 97491 99967 5934 VUT 110002 2033 71157 6445 210 YEM 249139 9018 77453 178305 20484 EST 88980 57072 123570 22491 1348 UGA 350738 673 63503 287908 28038 BGD 2105393 2792 59754 2048138 150517 KHM 373116 4200 55812 321504 13722 OMN 153972 32102 83671 72115 2482 VEN 514005 29270 77994 465159 26260 GUY 57203 1966 31192 28230 739 KEN 132712 11621 38223 105900 34685 MHL 47385 537 23256 653 56 MDG 150246 20566 40760 128468 18138 SLB 33195 436 18119 15512 461 9

HND 38864 8905 26150 21616 6703 CRI 47293 30781 44639 34694 4252 NIC 30135 6490 18402 16650 5394 NER 52488 898 9618 43767 12813 FSM 30316 2731 10825 5122 109 SUR 38000 4444 12103 7668 450 BLZ 15624 875 8431 3668 269 ANT 12779 2897 8326 3971 184 GNB 6184 355 5021 2186 1549 KIR 32391 930 5356 6791 90 SLE 125786 1942 5919 121809 5380 SOM 30000 417 3554 26463 7957 BHR 13280 5349 7661 10959 710 SDN 60933 291 2197 59027 36160 SLV 41497 22546 24448 37793 6576 BHS 11789 4797 6482 9571 319 MOZ 44389 17696 18712 43372 20074 COK 3209 290 836 842 14 NCL 5735 3250 3768 5089 230 TUR 592627 52907 53020 498821 72026 TUV 2156 27 100 416 10 ERI 6040 517 553 6005 4352 AFG 967 0 0 967 24086 ASM 4323 0 0 133 63 AIA 250 0 0 250 12 BTN 300 0 0 300 622 GUF 5485 0 0 5485 187 LSO 46 0 0 46 1966 MTQ 6061 0 0 6061 394 PRI 3000 0 0 3000 3925 REU 3754 0 0 3754 773 VIR 1428 0 0 1428 111 WBG 2088 0 0 2088 3636 STP 3675 9 3 3683 150 BDI 14384 106 9 14481 7573 PCI 1024 147 50 1121 20 CPV 8303 361 249 8414 495 MNG 351 582 325 609 2557 RWA 8275 282 0 8557 9066 CYM 125 364 40 449 45 NPL 39659 477 27 40109 26556 DJI 260 579 16 828 790 TKM 14864 1022 313 15572 4766 TCD 70000 902 2 60235 9807 ETH 9556 918 18 10456 77009 VCT 5384 1106 98 1770 118 TJK 248 1009 0 1258 6469 PRY 23833 1271 249 24856 5793 KNA 445 1170 96 1519 49 MWI 57000 1215 82 58132 12898 10

TON 2669 1591 429 3231 99 COM 14707 1189 2 15895 778 DMA 745 1308 3 2051 68 GRD 2211 2357 586 3987 105 ZWE 15669 3213 1284 17609 13029 CAF 15000 1978 0 16978 4125 UZB 4738 2472 280 6930 26210 GMB 33429 3262 908 35783 1571 BMU 379 2398 31 2543 64 LBR 10358 2855 250 12963 3361 ATG 2704 3022 269 4258 82 ARM 1232 3747 870 4110 3028 ABW 161 3151 224 3088 101 GIN 103587 8596 5656 106526 8836 IRN 481356 23283 19871 439012 68697 QAT 12125 6823 3247 15685 762 GTM 22344 17553 13271 26631 12399 LCA 1461 4331 13 5787 159 BWA 138 4651 68 4718 1815 LTU 155348 115773 110868 133722 3440 AZE 8416 7303 2244 12774 8308 LAO 99067 5099 17 104151 5575 SAM 5273 7081 1809 9486 183 MLI 101008 6650 1320 106337 11269 GAB 45113 10358 4127 51344 1270 ZMB 69917 8880 2140 76671 11272 KGZ 27 6887 85 6885 5154 IRQ 26351 6977 41 33301 27448 BOL 7087 7409 12 14483 9009 LBY 46467 11748 3275 54946 5800 KAZ 30755 51746 42996 40274 15109 SWZ 70 10971 2214 8827 1114 SYC 98602 86611 77486 5247 85 ALB 4893 13559 4382 14060 3134 MKD 1119 9317 117 10318 2030 GNQ 3500 9375 22 12853 473 BRB 2124 10237 374 10631 291 BRN 2877 10604 160 13014 366 GEO 3152 12362 1676 13840 4518 LUX 0 17034 5105 12253 453 TTO 11123 16285 4049 22279 1319 BFA 9005 14053 0 23059 13507 TGO 29170 22559 7224 44606 6072 SVN 2662 22382 6722 18311 1997 MEX 1409112 163919 148129 1098130 103376 COG 55827 16878 856 71849 3530 BIH 8700 19050 2384 25366 3906 TUN 106064 36153 18935 123280 9996 CYP 3951 19970 2408 18389 827 HTI 8133 17968 388 25713 9150 11

MUS 10731 95960 77939 23656 1231 FJI 40554 42171 23435 30622 823 AGO 230515 24857 5557 214069 15637 KWT 4952 20539 230 25261 2616 BGR 11940 28755 8423 32268 7795 MAC 1500 22948 771 23649 467 DZA 127540 26395 2349 151566 32368 MLT 2064 26800 2207 12472 400 JOR 1061 26848 1540 26350 5374 SYR 16773 26763 128 43407 18392 LBN 4648 27017 246 31410 3965 HRV 39797 64548 36294 64197 4538 HUN 19887 34734 5318 49271 10113 MDA 4313 32862 131 37087 3927 YUG 6339 36831 391 42779 10524 BEN 40019 37066 292 76793 8225 SVK 2653 42193 2407 42429 5387 CUB 62008 53792 11365 104413 11245 GRC 194931 153193 109032 233061 11080 ARE 92863 92176 44520 97261 3943 COL 181230 134609 84499 231338 44313 JAM 16134 52713 1376 67471 2665 SWE 277120 499916 442855 264803 8998 FIN 142952 82337 20276 172809 5231 DOM 16117 81656 3568 94205 9325 CZE 24469 102839 22160 105262 10195 ROM 15172 85525 639 96876 21727 ZAR 222965 86361 15 309311 56945 AUT 2687 114421 16792 100305 8252 LKA 244976 124673 17042 352573 19042 CHE 2804 109522 399 111854 7391 ISR 25676 120206 793 145090 6574 SAU 69544 140632 13273 196816 23047 CMR 130044 131112 146 261010 17408 BLR 7469 171113 28731 149793 9847 POL 211715 408293 260413 356020 38246 GHA 395147 237982 81735 558798 22056 BRA 1004589 277950 116085 1099616 184312 SGP 7508 280278 115326 161942 4274 MYS 1450432 453197 286404 1395101 25191 CIV 60300 281888 87563 256477 18281 BEL 27181 521924 289165 258773 10358 EGY 876773 253262 5996 1123851 71556 HKG 166851 506791 220511 437198 6981 AUS 277094 393189 79149 496964 20078 GBR 846741 1131738 791976 1205935 60188 PRT 222121 539043 159894 580035 10470 UKR 250183 442054 35969 656268 47283 NGA 531526 696855 5526 1222855 138005 ESP 1114501 1851361 1058482 1823748 42765 12

DEU 328279 1564713 709692 1180904 82616 ITA 457181 1162234 177778 1410107 58464 KOR 2029027 1523351 473093 2546477 47681 FRA 857065 1769805 546143 2076302 60617 USA 5470080 4268576 1737418 7187519 296842 JPN 5133903 4307256 484792 8077497 127785 13

Table S5. Percentage of people who are undernourished. NA, not available. Code Undernourished (%) Code Undernourished (%) Code Undernourished (%) CHN 9 SLB 9 ETH 46 NOR <5 HND 12 VCT 6 CHL <5 CRI <5 TJK 34 IDN 17 NIC 22 PRY 11 VNM 14 NER 29 KNA 15 ISL <5 FSM NA MWI 29 THA 17 SUR 7 TON NA ARG <5 BLZ <5 COM 52 TWN ANT <5 DMA <5 IND 21 GNB 32 GRD 22 NZL <5 KIR 5 ZWE 40 RUS <5 SLE 47 CAF 43 CAN <5 SOM NA UZB 14 MMR 19 BHR NA GMB 30 NAM 19 SDN 21 BMU 8 NLD <5 SLV 10 LBR 40 ECU 15 BHS 6 ATG 28 MAR <5 MOZ 38 ARM 21 PER 15 COK NA ABW NA IRL <5 NCL 9 GIN 17 DNK <5 TUR <5 IRN <5 SEN 26 TUV NA QAT NA MDV 7 ERI 68 GTM 16 PAK 23 AFG NA LCA 8 LVA <5 ASM NA BWA 26 ZAF <5 AIA NA LTU <5 PRK 32 BTN NA AZE 12 URY <5 GUF NA LAO 19 PHL 16 LSO 15 SAM <5 MRT 8 MTQ NA MLI 11 TZA 35 PRI NA GAB <5 PAN 17 REU NA ZMB 45 PNG VIR NA KGZ <5 VUT 7 WBG 15 IRQ NA YEM 32 STP 5 BOL 22 EST <5 BDI 63 LBY <5 UGA 15 PCI NA KAZ <5 BGD 27 CPV 15 SWZ 18 KHM 26 MNG 29 SYC 9 OMN NA RWA 40 ALB <5 VEN 12 CYM NA MKD <5 GUY 6 NPL 15 GNQ NA KEN 32 DJI 32 BRB <5 MHL NA TKM 6 BRN <5 MDG 37 TCD 39 GEO 13 LUX <5 MDA NA BRA 6 TTO 10 YUG <5 SGP NA 14

BFA 10 BEN 19 BLR <5 TGO 37 SVK <5 POL <5 SVN <5 CUB <5 GHA 9 MEX <5 GRC <5 BRA 6 COG 22 ARE <5 SGP NA BIH <5 COL 10 MYS <5 TUN <5 JAM 5 CIV 14 CYP <5 SWE <5 BEL <5 HTI 58 FIN <5 EGY <5 MUS 6 DOM 21 HKG NA FJI <5 CZE <5 AUS <5 AGO 46 ROM <5 GBR <5 KWT <5 ZAR 76 PRT <5 BGR <5 AUT <5 UKR <5 MAC NA LKA 21 NGA 9 DZA <5 CHE <5 MYS <5 MLT <5 ISR <5 CIV 14 JOR <5 SAU <5 BEL <5 SYR <5 CMR 23 EGY <5 LBN <5 BLR <5 HKG NA HRV <5 POL <5 AUS <5 HUN <5 GHA 9 GBR <5 15