RISING TO THE CHALLENGE:
Changing Course to Feed the World in 2050
ActionAid USA
Report
October 2013
COVER PHOTO: Leaders of the Tuzamure Agaseke cooperative in Karongi, Rwanda
perform a traditional dance. This cooperative ...
3
CONTENTS
Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
EXECUTIVE SUMMARY
Since the 2007-8 food price crisis, alarms have sounded
regarding our ability to feed a growing populati...
5
n	 Biofuels – Biofuels expansion is a relatively recent
phenomenon that has been poorly captured by 	
most economic mode...
INTRODUCTION
“Model outputs should not be misinterpreted as
forecasts with well-defined confidence intervals.
Rather they ar...
7
We proceed on a cautionary note. Estimates of our
ability to “feed the world” rely primarily on global
estimates of supp...
well-understood and can be reliably modeled. He
concurs that yield growth is likely to keep up with
demand growth, which w...
Obviously, these are not trivial shortcomings. Both
suggest that FAO projections err on the side of
overestimating food av...
previous 25 years. This “trend growth” scenario for
developing countries estimates that extreme poverty
worldwide in 2050 ...
adequate attention to consumption data and patterns.
Van Dijk highlights promising new efforts within the EU’s
Foodsecure ...
modeling framework based on the FAO/IIASA Agro-Eco-
logical Zone (AEZ) model and the IIASA World Food
System model, Fische...
Intercomparison and Improvement Project (AgMIP) how-
ever (see text box below) assumes trend growth through
2030, which re...
14
Climate Change
Climate change is one of the most difficult variables to
model due to the layered uncertainties associat...
15
IFPRI researchers ran a series of climate scenarios
using their IMPACT model. As with Fischer, the baseline
is perfect ...
16
extent to which developing countries adopt Western
diets high in animal protein, the sustainability of con-
tinued reli...
17
Here we highlight a few such efforts.
The United Nations’ Millennium Ecosystem Assessment
(MA) examines four scenarios,...
18
BOX 5 — Biophysical Modeling: Water Management
The Comprehensive Assessment of Water Management in Agriculture (CAWMA) ...
Development) used its Agribiom model in their Agri-
monde foresight study, comparing a business-as-usual
scenario (high-gr...
20
CONCLUSIONS
While this review does not represent an exhaustive
assessment of the literature, it should at least provide...
21
xiii
	See Jules Pretty’s work on the sustainable intensification of
African agriculture, for example, which synthesizes...
RECOMMENDATIONS
The review above leads us to the following recommen-
dations regarding future modeling to help policymaker...
And failing to raise productivity and food security in
the regions that have historically been left behind
will have dire ...
1
	 Reilly, Michael and Dirk Willenbockel (2010). “Managing uncertainty: a review of food system scenario analysis and mod...
21	
van Dijk, Michiel (2012). A review of global scenario exercises for food security analysis: Assumptions and results. F...
26
39	
Ausubel, Jesse H., Iddo K. Wernick and Paul E. Waggoner (2012). “Peak Farmland and the Prospect for Land Sparing.” ...
ACKNOWLEDGMENTS
This report was written by Timothy A. Wise and Kristin Sundell, with
support from Marie Brill.
It is adapt...
ActionAid USA
1420 K Street, NW, Suite 900
Washington, DC 20005
www.actionaidusa.org
(202) 835-1240
info@actionaid.org
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Feed the world in 2050

  1. 1. RISING TO THE CHALLENGE: Changing Course to Feed the World in 2050 ActionAid USA Report October 2013
  2. 2. COVER PHOTO: Leaders of the Tuzamure Agaseke cooperative in Karongi, Rwanda perform a traditional dance. This cooperative has received support and training from ActionAid in organic farming, human rights, co-op management, and community participation in decision making. Co-op members, most of whom are genocide widows, grow corn and supply maize flour to local markets.
  3. 3. 3 CONTENTS Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 FAO Modeling: Strengths, Weaknesses, and Misinterpretations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 The Challenge and Utility of Long-Term Economic Modeling — Box 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Common Themes, Questionable Assumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Foresight Modeling: Types, Uses, and Limitations — Box 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Scenario Modeling for Policy Formulation: Gaps and Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Biofuels Expansion.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Climate Change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 The Agriculture Modeling Improvement Project (AgMIP) — Box 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Modeling Broader Questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Biophysical Modeling: Land Use — Box 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Biophysical Modeling: Water Management — Box 5.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Recommendations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Future Research Needs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Endnotes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
  4. 4. EXECUTIVE SUMMARY Since the 2007-8 food price crisis, alarms have sounded regarding our ability to feed a growing population in 2050. Some warn that we need to double food production; others estimate that food production must increase by 60-70%. All feed the alarmist notion that global hunger is the result of flagging food production amid looming resource constraints. The misguided policy prescriptions that follow typically call for the expansion of industrial- scale agricultural development, ignoring the true threats to our global food supply: biofuels expansion, inadequate investment in climate-resilient agriculture, lagging support for small-scale and women food producers, and the massive loss of food to spoilage and waste. Most of the recent warnings derive from a group of economic modeling studies that were recently reviewed by researchers at Tufts University’s Global Development and Environment Institute. In their assessment, the Tufts researchers found that many of the public pronounce- ments calling for a doubling of global food production EXECUTIVE SUMMARY by 2050 are based on outdated or flawed economic forecasting and misleading characterizations of this research. More reliable estimates of current supply, productivity, and demand trends — assuming business- as-usual policies — suggest both the need and the capacity to increase agricultural production by 60% over 2005-7 levels by 2050. This is a far cry from doubling food production. In fact, the failure to distinguish food production from agricultural production obscures the largest single contributor to recent food price spikes: the massive expansion of agricultural biofuel production. Rather than fueling alarmist agricultural productivism, the utility of food security forecasts should be to help decision-makers identify policies that are contributing to high and volatile prices, food insecurity, and looming resource constraints on agricultural production as well as changes that could alleviate these impacts. Most economic forecasting fails to adequately incorporate several key variables:2050 4 RISING TO THE CHALLENGE: Changing Course to Feed the World in 2050 ActionAid USA Report October 2013
  5. 5. 5 n Biofuels – Biofuels expansion is a relatively recent phenomenon that has been poorly captured by most economic modeling to date. Few models adequately account for current trends, with some underestimating business-as-usual expansion by 100%. With national mandates and targets signifi- cantly driving biofuels expansion, updated forecasts are urgently needed to help policymakers assess the food security implications of current policies. Those policies are incontrovertibly resulting in rising and more volatile food prices, with up to 40% of recent price increases in agricultural commodities attributable to biofuels expansion. Those policies are projected to divert as much 13% of cereal production from needed food production by 2030. n Inadequate and poorly targeted agricultural investment – Agricultural investment is key to increasing food production. Whereas many projections stress the importance of agricultural productivity growth, few models assess different priorities for agricultural research and investment. A growing consensus supports increased invest- ment in climate-resilient food production, focusing on small-scale producers in food-insecure parts of the world. Yet most research, private and public, focuses on large-scale, input-intensive agricultural development. So too does most investment, driven by private sector-led projects, such as the “New Alliance for Food Security and Nutrition” initiated by the G8. n Food waste and spoilage – One-third of global food production fails to nourish anyone. In industrial- ized countries, wasteful consumption patterns result in tremendous losses, while in developing countries poor infrastructure means high rates of spoilage before food makes it to market. Most current forecasts ignore the possibility that measures could be taken to address this problem, assuming continued waste of food at current rates. This assumption alone puts alarmist calls for increased food production into question. n Climate change – We are only just beginning to understand the implications of climate change for agriculture and food security. These impacts, plagued by multiple layers of uncertainty, are poorly incorporated into most economic forecasts. With the outcome of international climate negotiations uncertain, urgent attention is needed to mitigate industrial agriculture’s tremendous contribution to global warming and help developing country food producers to adapt to a changing climate. In all of these areas, policymakers need forecasts to help them interrogate established policies and practices that need to change, such as consumption patterns, energy policies, unfair distribution and access, land use, and investment priorities. Meanwhile, a growing body of experience at the local and regional levels, demonstrates the lasting value of investments in smallholder farming and sustainable agricultural methods. Strategic policy changes and investments in this area can scale-up successful approaches and expand them to regions where they are most appropriate and most needed, especially in regions where food security is tenuous despite high agricultural potential. This report reviews the economic forecasting on which most of the alarmist 2050 pronouncements are based, presents alternative modeling that can add useful insights, and identifies areas in which further research can guide policymakers to change failing business-as- usual policies. This much is clear: hunger, now and in the future, is less a matter of inadequate production than inequitable access to food and food-producing resources; and a singular focus on increasing production is misguided as we simultaneously waste one-third of the food that is produced and pursue a course to devote another 13% of cereals to feeding our cars instead of our people.
  6. 6. INTRODUCTION “Model outputs should not be misinterpreted as forecasts with well-defined confidence intervals. Rather they are meant to provide quantified in- sights about the complex interactions in a highly interdependent system and the potential general size order of effects, which cannot be obtained by qualitative and theoretical reasoning alone.”1 Alarm bells sounded in 2008 as global agricultural commodity prices doubled and pundits forecasted a world unable to feed itself. With the global population expected to surpass nine billion in 2050, the heads of the UN’s Food and Agriculture Organization (FAO) and World Food Program (WFP) called for a doubling of global food production by 2050 to meet rising demand from a growing population expected to consume more meat as well as from the rapidly growing demand for bioenergy crops.2 “With almost 80 million more people to feed each year, agriculture can’t keep up with the escalating food demand,” warned Frank Rijsberman, head of the Consultative Group on International Agricultural Research (CGIAR). “FAO estimates that we have to double food production by 2050 to feed the expected 9 billion people, knowing that one billion people are already going to bed hungry every day.”3 Will world food needs finally outstrip productive capacity as Thomas Malthus warned in his famous 1798 treatise, An essay on the principle of population?4 His predictions, from what amounts to one of the first global modeling studies on the world’s ability to feed its growing popula- tion, have been widely discredited. But current resource constraints, exacerbated by uncertainties over climate change, have revived questions about the ability of society and the planet to feed our growing population. In actuality, the FAO’s expert team of agricultural modelers hadn’t really called for a doubling of food production by 2050. Rather, the agency’s models had indicated the need to increase overall agricultural production — including food production — by 70% from 2005/07 to 2050.5 A 2012 update of these estimates brought the figure down to 60% for the same period. As the modeling team noted before the 2012 Rio +20 summit, “We need to improve people’s access to food in their communities, increase production by 60 per cent by 2050, drastically reduce huge losses and waste of food and manage our natural resources sustainably, so that it flourishes for future generations.”6 This report assesses the evidence for these and other claims about feeding the world in 2050. A cottage industry has developed around this question, with studies diverging widely in their assessments and prescriptions. These estimates matter precisely because they drive both public discourse and public policy. Are we facing a Malthusian future of food scarcity, or a “limits to growth” scenario in which the carrying capacity of the planet reaches exhaustion? More importantly, how well do various estimates of global demand and supply incorporate the uncertainties occasioned by changing economic and environmental trends, from climate change to biofuels expansion, from slowing growth in agricultural productivity to rising meat demand from a growing global middle class? Although future-oriented “foresight” modeling often relies on “business-as-usual” scenarios that treat current practices as inevitable, the best use of such modeling is to guide policymakers in changing precisely these practices. How well does current 2050 modeling help policymakers assess and address the drivers of unsus- tainably high agricultural prices through public policies, including the sustainable use of resources? We hope this report will serve as a “user’s guide” to much of the existing modeling. (For a more detailed assessment, see the recent Tufts University paper on the subject.i ) We begin by tracing the origins of the FAO’s recent asser- tions, explaining the reliability and limitations of their modeling efforts. Then, we take a step back to examine the difficulties inherent in such long-range modeling, identify some useful modeling typologies, and present some of the more interesting alternative approaches. In the following section, we look at scenario modeling that has been done on key drivers of 2050 forecasts, namely biofuels expansion and climate change. We conclude with some observations on the strengths and limitations of 2050 modeling to date and suggest areas in which this modeling can make the most useful contributions to decision-making. 6 i “Can We Feed the World in 2050? A Scoping Paper to Assess the Evidence,” by Timothy A. Wise, GDAE Working Paper No. 13-04, September 2013. http://www.ase.tufts.edu/gdae/policy_research/ FeedWorld2050.html
  7. 7. 7 We proceed on a cautionary note. Estimates of our ability to “feed the world” rely primarily on global estimates of supply and demand, yet ecosystems, agricultural production, and hunger occur at local and regional levels. As a result, global estimates of “our” ability to feed “the world” quickly break downii , begging the more important questions of how food systems develop across widely differing landscapes, societies, and levels of economic development, and how equitably the food is distributed. In the end, “the world” is not fed in aggregate, and there is no collective “we” doing the feeding. FAO MODELING: STRENGTHS, WEAKNESSES, AND MISINTERPRETATIONS The most widely quoted figures on agricultural supply and demand in 2050 come from the FAO’s efforts to gauge future food demand. FAO estimates following the 2007-8 food price spikes, which suggested the need to double food production by 2050, were the basis for international alarms about our ability to feed the world. While the 100% figure is still cited by some policymakers, the FAO’s later estimate that a 70% increase in agricultural production is needed seems to have taken hold as the most commonly cited figure. The FAO’s most recent update in June 2012, which lowered this figure to 60%, is generally recognized as the best official estimate, though the 70% figure remains widely cited in government circles and in the media. How reliable are these estimates? A number of mis- conceptions about the nature of such modeling have im- plications for any effort to assess likely outcomes under different environmental or policy scenarios. (See Box 1) First, the FAO is very clear that it is not answering the question, “How can we feed the world in 2050?” Rather, it is answering a more straightforward question: “Will world production increase enough to meet projected demand?” The FAO’s answer to this question is yes. And their estimates should offer some reassurance — with very important caveats — to those who would sound alarms over our ability to produce enough food to feed the global population in 2050. Why are these findings reassuring? 1. Much of the data has been updated to more recent base years (2005-7) and the modelers have incorporated recent and improved estimates of food demand, as well as land and water resources. The shift from 70% to 60% reflects less a change in estimated demand than it does an updated figure, reflecting actual production in the 2005-7 base year period. 2. They are based on widely accepted (though still uncertain) population projections (9.15 billion people by 2050iii ), well-grounded estimates of economic growth (average global GDP growth of 1.36%/year), and an expected growth in demand which incorporates the expected shift in developing countries to more meat-based diets (all agricultural commodities, all uses, 1.1% annual growth). 3. Estimates of agricultural yield growth are moderate but consistent with historical trends (1.1% per year), and not based on unrealistic assumptions regarding productivity improvements. 4. The FAO does not assume an implausible conversion of land to agricultural uses, a problem in some modeling studies. Instead they assume that 70 million hectares are converted by 2050, a 9% increase. 5. The FAO validates its projections against data for more recent years and against the FAO-OECD ten-year projections to 2020.7 Thus, the latest FAO estimates provide substantial reassurance that, with the right policies, global agriculture is capable of meeting projected demand for both food and non-food uses in 2050. Agricultural economist Thomas Hertel generally agrees with this overall assessment.8 He notes that income growth and changing demand are relatively ii The FAO study assigns particular importance to this point: “ ... examining the issue of food insecurity by means of global variables (e.g. can the world produce all the food needed for everyone to be well-fed?) is largely devoid of meaning” and “In conclusion, the issue whether food insecurity will be eliminated by the end of the century is clouded in uncertainty, no matter that from the standpoint of global production potential there should be no insurmountable constraints” (Alexandratos, Nikos and Jelle Bruinsma (2012). World agriculture towards 2030/2050: the 2012 revision. ESA Working paper. Rome, Food and Agriculture Organization: 20-21) iii Population growth rates are anticipated to vary widely depending on the country. Alexandratos et al (ibid) notes that the majority of countries whose population growth is expected to be fast in the future are those showing inadequate food consumption and high levels of undernourishment, mostly in sub-Saharan Africa.
  8. 8. well-understood and can be reliably modeled. He concurs that yield growth is likely to keep up with demand growth, which will slow. He points out that there is greater potential for productivity gains on rain- fed land, where yields are often below 50% of their po- tential, than on irrigated land.9 He cites studies showing that bringing such currently cultivated lands up to their potential production, using existing technology, would generate increases in 2050 of 60% for wheat, 50% for maize, 40% for rice, and 20% for soybeans.10 Most such lands are in less developed regions of developing coun- tries, presenting both an obstacle (resources) and an opportunity (reducing hunger and poverty). Hertel and the FAO also agree on two very important wild cards, neither of which is adequately reflected in most modeling to date: bioenergy production and climate change. n Biofuels expansion – To arrive at its 60% estimate, the FAO assumes sufficient biofuel expansion to meet existing mandates through 2020, then no further expansion beyond that. This is both unrealistic and, from the perspective of policymakers, unhelpful. Current estimates project first generation biofuel demand in 2030 at double the FAO’s assumed levels.11 n Climate change – The FAO modelers openly acknowledge their inability to incorporate the impacts of climate change on agricultural production. Even with perfect mitigation today we would see measurable climate change by 2050. In light of this fact, FAO projections are clearly in need of significant adjustment. As the authors themselves acknowledge, “In principle, a scenario that assumes no climate change has no place in the array of scenarios to be examined.”12 8 BOX 1 — The challenge and utility of long-term economic modeling The studies and analysis in the FAO’s collection22 are a good representation of the economic modeling that has generated some of the most widely cited estimates of food needs in 2050. Alexandratos’s observations highlight the challenges inherent in such long-term modeling and the sensitivity of the results to assumptions about variables of great uncertainty, be they economic, environmental, or policy-related. Despite their significance, such assumptions are often made explicit only in technical annexes, and sometimes not at all. The results, on the other hand, are generally presented with a high degree of certainty, after which they are repeated as definitive by policymakers and the media. Such has been the case with the studies estimating agricultural production and demand to 2050. Indeed, Michael Reilly of the UK’s Government Office for Science and Dirk Willenbockel of the Institute of Development Studies at the University of Sussex, in their excellent overview of food system modeling, warn of this precise problem. “Model outputs should not be misinterpreted as forecasts with well-defined confidence intervals. Rather they are meant to provide quantified insights about the complex interactions in a highly interdependent system and the potential general size order of effects, which cannot be obtained by qualitative and theoretical reasoning alone.”23 Reilly and Willenbockel point out that food system modeling requires the analyst to coordinate the mapping of one uncertain system — agricultural production and consumption — with another — ecosystems. Both systems are characterized by gaps in data and knowledge, limited confidence in predicting the future from past trends, and likely instability of future systems behavior. One set of uncertainties compounds the other, leaving, logically, a virtually unlimited range of possible outcomes. This is true of the “known unknowns,” such as the extent to which rising CO2 levels will have some positive effects on agricultural production (CO2 fertilization) or the rate of agricultural productivity growth. Add in “unknown unknowns,” such as extreme but low-probability climate events, and it becomes clear that the forecasting potential for long-range modeling is extremely limited. Still, even though long-range modeling shouldn’t be used as a “crystal ball,” such efforts have a great deal to offer those struggling to identify the best way forward. At best, they can challenge the “mental maps” of policymakers by drawing out the plausible real-world implications of business-as-usual policies and alternative approaches. They can also assess the relative importance of various drivers of change.
  9. 9. Obviously, these are not trivial shortcomings. Both suggest that FAO projections err on the side of overestimating food availability in 2050, as both trends imply negative impacts on global supply. Common Themes, Questionable Assumptions The impact of biofuels and climate change were among the issues addressed at a 2009 expert meeting convened by the FAO to assess the implications of the food price crisis for the world’s ability to meet future food needs. The resulting papers were later published as a book, Looking Ahead in World Food and Agriculture: Perspectives to 2050.iv Interestingly, a common finding indicated that future food prices will not be as high as recent experience suggests. Price is the key indicator in most economic models, as it gauges the balance between supply and demand — higher prices suggest food shortages while lower prices suggest agricultural surpluses. The models discussed at the 2009 FAO meeting generally projected price levels lower than the “post-surge” prices of recent years, and in line with “pre-surge” (2003-5) price levels through 2030, then rising by 2050 to about 30% above pre-surge pric- es (but still well below current post-surge price levels). These findings are in stark contrast to prevailing charac- terizations of food prices as permanently high and rising.13 FAO researcher Nicos Alexandratos identifies some of the factors that drive agricultural modeling results, demonstrating that these models are extremely sensitive to a few key assumptions. One is population growth — the greater the projected population, the greater the projected demand for agricultural products. FAO uses a 2050 estimate of 9.15 billion people, which is based on the middle path among three United Nations population scenarios, from the 2008 revision of UN population estimates.v Modeling results are also extremely sensitive to assumptions about agricultural productivity growth. Over a 40-year time horizon (2010-2050) each 0.1% change in the assumed growth rate produces a 4% change in total output in 2050; an additional 1% per year adds 40%. Within an economic model therefore, one can dramatically affect the outcome through extremely small upward adjustments to the assumed productivity growth rate. Growth assumptions vary widely among the different models and projections. Some researchers estimate that we will need an annual average yield growth of 1.25% to meet global food needs in 2050.14 The latest FAO projections, on the other hand, estimate that the world average cereals yield would need to grow at just 0.7% per year to meet 2050 demand.15 The World Bank’s model, by contrast, is exceedingly optimistic, projecting 2.1% annual growth in agricultural productivity as a result of technological innovation. This is well beyond historical trends, and nearly twice the productivity growth rate assumed by the FAO (and most others). Such optimistic assumptions can mask a range of dangers. Most significantly, they assume that resource constraints such as climate change, land use, and water availability, will be addressed through innovation. This may turn out to be true in some cases, but incorporating such implicit assumptions into an agricultural model can skew the results dramatically. Not surprisingly, World Bank projections suggest that we will face few constraints on feeding the world in 2050. Modeling results are also very sensitive to economic growth assumptions, with faster economic growth increasing demand at a faster rate but also increasing incomes and reducing poverty. World Bank modeling tends to be optimistic in this area as well, assuming an average annual growth rate to 2050 of 1.6% for high- income countries and 5.2% for developing countries.16 Professor Evan Hillebrand, who served as an economic analyst and modeler for over 30 years at the CIA, offers a more sober assessment, contrasting this “market first” high-growth scenario, with a scenario that assumes developing countries each grow at the rates of the 9 iv The volume includes some valuable updates on the 2009 papers and added material and analysis, including a comparison of the main models and an assessment of their differences and relative strengths and limitations. As such, it represents one of the more comprehensive efforts to gather and assess the results from a range of researchers and models. Conforti, Piero (2011). Looking Ahead in World Food and Agriculture: Perspectives to 2050. Rome, Italy, Food and Agriculture Organization. v Unfortunately, population assumptions are not uniform across models, or even within the same modeling exercise, making results very difficult to compare. One IPCC scenario, for example, assumes a 2050 population of 11.3 billion, dramatically increasing projected food demands.
  10. 10. previous 25 years. This “trend growth” scenario for developing countries estimates that extreme poverty worldwide in 2050 will be 20% rather than the 2.6% projected by the “market first” model. Contrasting these scenarios highlights just how variable regional performance is likely to be. Under the “trend growth” scenario, 53% of Sub-Saharan Africa is projected to live in extreme poverty by 2050 with 78% below the $2.50/ day poverty line (a higher percentage than in 2005), versus 12% under the more optimistic scenario.17 Professor Isobel Tomlinson, of the University of London’s Department of Development Studies, offers a useful critique of the “new productivism” fueled by economic models that foresee looming shortfalls in global produc- tion. As Tomlinson points out, “Increasing production on such a scale was never intended as a normative goal of policy and, secondly, to do so would exacerbate many of the existing problems with the current global food system”.18 She warns of prior ideological commitments to a framing of the food security issue that defines food security as an issue of production rather than access and utilization. Tomlinson also notes that the measure of food security used in many studies is based on per capita food consumption in calories, derived from estimates of availability. Such food availability projections allow for broad trend estimates but they neglect demand-side issues such as food waste, not to mention unequal access and distribution.19 This methodology feeds inaccurate perceptions that economic growth and increases in agricultural production will by themselves lead to reductions in food insecurity.20 Analyst Michiel Van Dijk of the Dutch Agricultural Economics Institute (LEI) agrees, pointing out that most modeling fails to give 10 BOX 2 — Foresight Modeling: Types, uses, and limitations Reilly and Willenbockel provide a useful typology of foresight, or future oriented modeling, distinguishing between projections, exploratory scenarios, and normative scenarios. Projections include efforts like the FAO’s and most of the other studies reviewed thus far. They include baseline models, which attempt to lay out what will happen if we stay on our current path, as well as “what if” scenario modeling in which one or two factors (such as climate change impacts or biofuels expansion) are altered to assess their significance. Exploratory scenarios introduce of a more complex and related set of changes that represent various possible pathways for societies. These pathways include what the authors call “strategic” scenarios, such as the Millennium Ecosystem Assessment, an in-depth evaluation of the consequences of ecosystem change for human well-being. As these evocative scenario names suggest ­— Global Orchestration, TechnoGarden, Order from Strength, Adapting Mosaic — exploratory scenarios attempt to model the implications of a complex group of interrelated factors, rather than one isolated variable. Normative scenarios take this approach a step further by starting with a range of possible paths and modeling the implications of each of them for key parameters, from water and land use to climate mitigation and food production. Within this category, “transformative” modeling is the most ambitious, as the researchers define a desired future and model what it would take to get there. One of the more comprehensive such efforts is the Agrimonde project, which is discussed below. Each of these modeling approaches offers valuable insights. Normative modeling may be the most provocative as it tends to highlight the distance between our present path and an optimal one in light of a particular goal. Similarly, “strategic” modeling charts different paths, futures, and implications, offering sometimes stark contrasts between distinct trajectories. This information is invaluable as societies struggle to confront complex challenges and respect the carrying capacity of the planet. However in both approaches, the complexity of the changes being modeled makes it difficult to determine the impact of any one discreet factor. For better or worse, the fact is that policymakers are usually only able to address particular factors in isolation. Reilly and Willenbockel cite a small body of literature suggesting very limited policy impacts as a result of such modeling.27
  11. 11. adequate attention to consumption data and patterns. Van Dijk highlights promising new efforts within the EU’s Foodsecure Project that incorporate detailed house- hold survey data on consumption into such modeling.21 Tomlinson, in turn, recommends stepping outside such economic models to learn from efforts that take into ac- count concerns for health, equity, and the environment and model alternatives to “productivist” solutions. SCENARIO MODELING FOR POLICY FORMULATION: GAPS AND CHALLENGES Modeling that begins with a narrowly defined question (such as “what if” scenario modeling in which just one factor is altered to gauge the impact), can be more directly useful to policymakers than larger, more complex models (see text box 2). If the baseline established by the model is transparent, realistic, and considers the uncertainties inherent in such long-range efforts, the introduction of a limited set of plausible policy or parameter changes can identify a range of achievable impacts. These, in turn, can inform policy- makers as they consider discreet alternatives. Some of the factors that drive estimates of 2050 food supply and demand have been modeled better than others. While uncertainties plague attempts to estimate agricultural productivity growth, historical trends do offer reliable baselines for assessing future growth, and public policies can demonstrably increase growth rates. A wide range of scenario studies illustrate the importance and effectiveness of policies that foster productivity growth, and many of these are potentially useful to policymakers.vi Two factors in particular — biofuels expansion and climate change — have thus far been poorly incorporated into most foresight modeling on meeting future food needs. Here we examine some of the reasons for this shortcoming and review some of the more significant efforts to project the impacts of various biofuel and climate scenarios. Where climate change presents modelers with a daunting range of uncertainties, it is easier to project the impact of biofuels expansion on food production and availability. Biofuels expansion It is surprising that there has not been more careful modeling of the impacts of biofuels expansion on 2050 food production despite widespread agreement that it is an important factor. Unlike the case of climate change, the key variables are well-defined. The impacts in the case of first-generation biofuels, in terms of crop diver- sion and land use, are direct. Policies promoting biofuels production and use, such as consumption mandates, are clear and in place. And the trajectory, at least over the period of the established mandates (10-12 years), is well specified. Variables ripe for modeling include: changes to biofuels policies; energy prices; and the timing, scale, and impact of second generation biofuels as they are developed and come onto the market. One reason many of the 2050 models take inadequate account of biofuel expansion is the difficulty of incorpo- rating such a recent phenomenon into scenario mod- eling that necessarily relies on base years that predate the biofuels boom. Many 2050 models have a base year of 2000, and even the relatively updated models, with 2005 base years, do not capture much of the recent surge in biofuel production. To the extent such models fail to account for biofuel expansion in their scenario ex- ercises, their results will be of limited use to policymak- ers concerned with biofuels’ impact on food security. In any case, the results of 2050 modeling will be driven to a significant extent by the assumptions modelers make about biofuels expansion. For example, the FAO in its 2012 update incorporated more recent estimates, but assumed expansion only to 2019, with no further expansion to 2050. Simultaneously, they acknowledged that this would likely underestimate biofuel demand, which they correctly note will be driven significantly by oil prices. Although it is flawed and quite out of date, the detailed effort by Günther Fischer of the International Institute for Applied Systems Analysis (IIASA), presents an example of the kind of “what if” scenario modeling that policy- makers require to evaluate biofuel expansion. Using a 11 vi For a more extensive discussion of agricultural productivity scenario modeling, see the Tufts University study. “Can We Feed the World in 2050? A Scoping Paper to Assess the Evidence,” by Timothy A. Wise, GDAE Working Paper No. 13-04, September 2013. http://www.ase.tufts.edu/gdae/policy_research/Feed- World2050.html
  12. 12. modeling framework based on the FAO/IIASA Agro-Eco- logical Zone (AEZ) model and the IIASA World Food System model, Fischer models a series of biofuels expansion scenarios in addition to several climate change scenarios. The results rely on base year data from 2000 and incorporate additional data only up to 2008, but still speak to the consequences of today’s policy and pathway choices. The table below summarizes the key scenarios and results.28 (See Table 1) The results are instructive, providing estimates of food price increases (an economic measure of supply in relation to demand), hunger, and agricultural land use change. To summarize: n The so-called WEO-V1 scenario, which uses 2008 International Energy Agency projections for de- mand, shows that even moderate additional biofuel demand over 2008 levels raises food commodity prices 7%, increases those at risk of hunger by 21 million people, and requires a 21% increase in cultivated land, even with early and gradual deployment of second generation biofuels. n If advanced biofuels are not available until 2030 (WEO-V2), prices increase 11%, hunger risks rise to 42 million, and there is a 29% increase in cultivated land. In other words, delays in the deployment of advanced biofuels have serious implications. n Scenario TAR-V1 uses what we now know are more realistic estimates of demand, in light of current mandates and targets. This raises projected biofuel demand by 100%, with dramatic results — 20% food price increases, 136 million at risk of hunger, 48% increase in cultivated land. n Only the rapid deployment of advanced biofuels (TAR-V3) mitigates these impacts, reducing price impacts to only 9%, with 74 million additional people at risk of hunger, and a 29% increase in cultivated land. n The four sensitivity analyses (SNS V1-V4) show the increasing impact on agricultural prices of different expansion scenarios for first-generation biofuels, from 4% in the low case to 35% in the high case. Note that based on current estimates, the “medium” scenario (SNS-V2) may well be the closest to our current path, suggesting a 13% increase in food prices attributable to biofuels expansion by 2050. This modeling is not perfect, of course. Because the base year is outdated, it makes more sense to use the results to gauge the price impacts of different scenarios in relation to one another, rather than in relation to the base year.vii Still, it is easy to see how this sort of “what if” scenario modeling can offer policymakers a clearer picture of the impacts of their policies. As Fischer’s work indi- cates, in order to feed the world affordably by 2050 we must speed up the development and deployment of advanced biofuels and/or slow demand for first gener- ation biofuels by reducing mandates. Otherwise we are putting undue pressure on low-income consumers (via price hikes) and on the environment (via land and other resource demands). It is surprising that more up-to-date modeling has not been carried out on an issue that is so important to future food security. Table 2 below shows just how outdated most biofuels modeling assumptions are in comparison to the best current estimates, namely the World Energy Outlook (WEO)’s 2012 projections for first generation biofuels use. The FAO estimate, which relies on FAO-OECD projections to 2020, but then holds biofuels use constant through 2050, is less than half of WEO’s current projections for 2030. The International Food Policy Research Institute (IFPRI)’s IMPACT model uses old base year assumptions and assumes minimal growth. World Bank projections do not explicitly account for growing biofuel use. The ongoing Agricultural Model 12 vii The FAO’s Alexandratos points out that the model overestimates consumption levels overall, which raises demand in relation to supply. Land is modeled inadequately, with additional demand translating too readily into additional cultivated land, and with little accounting for the land needs of second generation biofuels. He also suggests that the model does not adequately account for supply responses by farmers to higher agricultural prices, nor the impact of oil prices on biofuel demand. Notably, only rain-fed land is included in this modeling run. Alexandratos, Nikos (2011). Critical evaluation of selected projections. Looking Ahead in World Food and Agriculture: Perspectives to 2050. P. Conforti. Rome, Italy, Food and Agriculture Organization: 465-508. viii Unfortunately, thus far AgMIP has provided new modeling only to estimate the implications of faster deployment of second- generation biofuels.Lotze-Campen, Hermann, Martin von Lampe, Page Kyle, Shinichiro Fujimori, Petr Havlik, Hans van Meijl, Tomoko Hasegawa, A. Popp, Christoph Schmitz, Andrzej Tabeau, Hugo Valin, Dirk Willenbockel and M. Wise (2013). “Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison.” Agricultural Economics forthcoming.
  13. 13. Intercomparison and Improvement Project (AgMIP) how- ever (see text box below) assumes trend growth through 2030, which results in estimates comparable to WEO’s.viii As the table shows, of the IIASA scenarios modeled by Fischer, his worst-case scenario (TAR-V1) seems the closest to business-as-usual projections, at least through 2030. Fisher’s TAR-V1 scenario assumes the fulfillment of mandates and the slow deployment of advanced biofuels, but as explained above Fischer’s modeling still needs improvement to be useful. Updating Fischer’s base year and modeling parameters, address- ing the shortcomings noted by Alexandratos in footnote 9 above, and adjusting scenarios to reflect current policy considerations would yield results that could enable policymakers to better manage biofuel expansion to avoid negative impacts on food security. TABLE 1 — Modeling Biofuel Scenarios TABLE 2 — Projected Cereal Use for 1st Generation Biofuels (millions tons) 13 MODEL SCENARIO 2010 2020 2035 2050 WEO 20121 Current Projections 148 239 421 na REVIEWED MODELS 2008 2020 2030 2050 FAO Alexandratos (2012) 65 (2005) 182 182 182 IMPACT2 Msangi (2011) Baseline 20 75 110 110 World Bank3 van der Mensbrugghe (2011) n/a n/a n/a n/a AgMIP4 baseline, 1st generation 148 273 397 397 IIASA FAO-REF-01 (2008 levels) 83 83 83 83 WEO-V1 83 181 206 246 WEO-V2 83 192 258 376 TAR-V1 83 327 437 446 TAR-V3 83 238 272 262 1 2010 actuals from FAO-OECD (2012); WEO 2012 projected growth rate to 2020, 2035 2 Drawn from Alexandratos (2011), pg. 48 3 Numbers not currently available; Author assumes fulfillment of mandates as of 2009 4 2010 actuals from FAO-OECD (2012); AgMIP growth rate for other years (Lotze-Campen 2013) SCENARIO NAME DESCRIPTION FAO-REF-01 Freezes expansion at 2008 levels¹ WEO-V1 IEA 2008 projections; 2nd generation from 2015 7 21 million 21 WEO-V2 WEO-V1, no 2nd gen. until 2030: all demand to 2030 from 1st gen. 11 42 million 29 TAR-V1 WEO-V1, but mandates fulfilled by 2020: doubles demand for 1st gen. 20 136 million 48 TAR-V3 TAR-V1, but quick 2nd gen: 33% global demand in 2020, 50% in 2030 9 74 million 29 SNS-V1 Scenarios based on share 1st gen. in transport fuels 2020, 2030, 2050: Low: 2%, 2.5% and 3% 4 - - SNS-V2 Medium: 4%, 5% and 6%. 13 - - SNS-V3 High: 6%, 7.5% and 9% 23 - - SNS-V4 Very high: 8%, 10% and 12% 35 - - ¹ Ref scenario: Price Index (1990 = 100): 115; Risk of hunger: 458 million; Cultivated land: 1.7 billion ha ² Relative to reference scenario of no agricultural crops used for biofuel production Source: Fischer (2011) LAND USE CHANGE 2050(%)² CHANGE IN 2050 PRICE INDEX (%) ADDED PEOPLE AT RISK OF HUNGER IN 2030
  14. 14. 14 Climate Change Climate change is one of the most difficult variables to model due to the layered uncertainties associated with climate change and its impacts on agriculture: n There is uncertainty about the extent and timing of climate change likely to result from current levels of emissions. n Projections to 2050 and beyond must account for uncertainties related to the extent and pace of mitigation going forward, including whether global policy changes will reduce emissions and future impacts. n The impact of temperature changes on the earth’s ecosystems is only imperfectly understood. n There is great uncertainty about the impacts of temperature and ecosystem changes on agricultural production. n It is difficult to predict with any certainty what adaptation measures will be taken and how effective they will be. n Impacts are expected to worsen over time, making the 2050 time horizon too short to gauge the full extent of climate risks. Below we highlight scenario modeling that exemplifies the state of the art while demonstrating the profound difficulties involved in dealing with so many uncertainties.ix As with scenario modeling for other key variables, some research is intended less to forecast possible outcomes than to demonstrate the importance of the issue through quantitative methods. Dirk Willenbockel relies on an impressive range of external estimates gauging the productivity impacts of climate change to 2030, by region and crop.29 His assumptions are pessimistic, based on high temperature changes and sensitivities of crops to warming and low CO2 fertilization effects. In comparison to his reference scenario, price increases are significantly higher in 2030 for the main grains (about 140% versus roughly 90%), with corn experiencing the largest increases. Notably, the impacts are especially severe in Sub-Saharan Africa.30 Interestingly, Willenbockel includes a scenario of successful climate change adaptation in Sub-Saharan Africa which assumes that adaptation measures restore agricultural productivity levels in this region to those in his non-climate-change reference scenario. Price increases in this scenario are significantly moderated, even with the assumed failure of mitigation or adaptation efforts in other parts of the world. He concludes from this that investment in regional climate adaptation would have significant benefits for agricultural production and food security.31 Günther Fischer incorporates into the IIASA World Food Systems model two different climate modelsx for running scenarios with and without CO2 fertilization. Fischer runs his scenarios out to 2080, which is useful given the likelihood of rising climate change impacts over time, but this model is limited in that it examines the impact of climate change on rain-fed, but not irrigated, land.32 Fischer draws three conclusions from his scenario runs: 1. The regional impact of climate change is significant, posing threats to future food production. 2. There could be some improvements in rain-fed production if there is a positive CO2 fertilization effect and if farmers are able to adapt to changing climates. 3. The post-2050 period is particularly worrisome, as modeling projects increasingly negative and rapid impacts on production in most regions. Fischer also combines both biofuels and climate change impacts in the modeling scenarios outlined in the biofuels section above. If his most pessimistic scenario (TAR-V1) is indeed the closest to the path we’re now following, and if there is limited CO2 fertilization to moderate the impact of climate change, the prospects are grim. In this scenario, Fischer’s modeling shows price impacts on cereals growing from 49% in 2020, to 53% in 2050 and expanding to 87% in 2080, though it is worth noting that his baseline for comparison assumes no climate change and little biofuel expansion. ix See Wright, Julia (2010). Feeding Nine Billion in a Low Emissions Economy: Challenging, but Possible: A Review of the Literature for the Overseas Development Institute. Oxford, UK, Oxfam International, Overseas Development Institute. Wright provides an extensive review of the literature on climate mitigation and the potential impacts of climate change on our ability to feed the world in 2050. She finds few studies that address the intersection of these two questions, but a good deal on each individually. x HadCM3 and CSIRO models using IPCC A2 emissions pathways in the modeling.
  15. 15. 15 IFPRI researchers ran a series of climate scenarios using their IMPACT model. As with Fischer, the baseline is perfect mitigation, i.e., no change in the climate from 2010 forward. These simulations allow researchers to estimate the economic and social impacts of four climate change scenarios of increasing intensity, defined as increasingly hotter and wetter. Using their “middle-of- the-road” assumptions of GDP and population growth from 2010 to 2050, IFPRI modelers find that prices for the three main grain staples would rise between 20 and 32% on average in comparison to the baseline case of no climate change. As one would expect, the most intense climate change scenario generates price increases 25-30% higher than that. Using their “middle of the road” assumptions, the researchers go on to estimate that within their four climate change scenarios, the number of malnourished children in 2050 increases by 8.5%-10.3%.33 The authors also use the IFPRI model to simulate an extended drought in South Asia from 2030 through 2035 — a frequently forecasted impact of climate change. This simulation provides an opportunity to examine aspects of climate change that are difficult for long-run modeling to predict — rising temperature variability and extreme weather events. The results include sharp price increases for all three major grains during the drought years and a dramatic increase in the number of malnourished children. Other researchers used IIASA’s Global Biosphere Management Model (GLOBIOM) to assess the food security impacts of yield uncertainty due to climate change. They conclude that high levels of uncertainty about yields increase the need for decision-makers to plan for overproduction, a feasible but potentially costly practice. Their modeling also highlights the importance of reducing trade barriers to allow agricultural products to flow from surplus to deficit regions and expanding irrigation to help stabilize yields and increase production. Finally, they note the value of expanding global storage capacity for basic grains in order to reduce vulnerabil- ity to the short-term yield variations expected to come with climate change. This is one of the few mentions we found in the 2050 literature of the importance of food reserves to global food security.34 The Agricultural Modeling Improvement Project (AgMIP), housed at Columbia University’s Earth Institute Center for Climate Systems Research, is directly confronting the wide variability in climate-related modeling simula- tions. (See text box 3) This important work aspires to harmonize models by introducing common parameters and assumptions and imposing common scenarios. The initial set of comparisons shows negative impacts on food security across nearly all the included models. This is a noteworthy change from previous estimates of climate impacts on agriculture, some of which suggested production gains in many regions. The authors of a forthcoming article on the AgMIP climate modeling in Agricultural Economics, emphasize that “assumptions that are typically buried in technical reports can have significant effects on highly visible output measures.” Due to the scale of these often invisible impacts, they propose that the effects of underlying modeling assumptions should be a high priority for further research and call for more reliable agricultural data so that crop models can be fine-tuned.35 Researchers affiliated with Tufts University and the Stockholm Environment Institute warn that many of the existing 2050 scenarios fail to reflect the most recent climate science, which suggests greater disruptions to agriculture due to climate change.36 Of course, 2050 scenarios fail by definition to reflect climate change impacts in the second half of the century, when the effects of a changing climate are projected to worsen significantly. Those who have extended their projections to 2080 and beyond warn of escalating impacts on agriculture after 2050.37 MODELING BROADER QUESTIONS Of course the set of questions policymakers face as they contemplate the long-range future is too broad to be answered solely through examination of policy choices related to agricultural productivity, biofuels expansion, and climate change. As a matter of necessity, approaches to answering these questions go well beyond economic modeling to include biophysical modeling of key natural resources. (See Box 4 on land use and Box 5 on water use) Key concerns within this broader sphere include the
  16. 16. 16 extent to which developing countries adopt Western diets high in animal protein, the sustainability of con- tinued reliance on large quantities of fossil-fuel-based inputs on large monoculture farms, the potential positive impact of reduced inequality in access to food, and the impacts of high price volatility on agricultural production and distribution. Such questions do not lend themselves easily to global quantitative modeling, precisely because the range of variables is so high. Still, quantitative projections remain important and modelers have utilized so-called “exploratory scenarios” in an attempt to quantify alternative paths. These exploratory scenarios chart broad societal pathways, quantifying some of the implications, and highlighting the key drivers of change, and they can be useful in shifting or informing our “mental maps” of a particular issue. The applicability of future efforts will benefit from a project currently underway through the Intergovernmental Panel on Climate Change (IPCC) to develop a coherent set of scenarios — Shared Socio- economic Pathways (SSPs) — that can be used in a consistent way by researchers to generate comparable assessments while pursuing multi-faceted exploratory modeling.xii BOX 3 — The Agriculture Modeling Improvement Project (AgMIP) A forthcoming special issue of Agricultural Economics makes an important contribution to long-range global agricultural modeling. The issue presents analysis from the Agriculture Modeling Improvement Project (AgMIP), a collaborative effort to improve modeling, particularly as it relates to key uncertainties such as climate change and bioenergy production. The issue compares results from ten different models by harmonizing some of their key assumptions regarding population growth, GDP growth, agricultural productivity growth, energy prices, and base years before introducing alternative socio-economic, climate change, and bioenergy scenarios.xi The primary goal is to improve modeling by identifying important differences in assumptions, but the comparison offers a rich set of results across a range of economic models. While the simulations are not necessarily grounded in the most realistic scenarios, they permit a number of important conclusions about climate change and economic modeling. These conclusions and findings, summarized in the overview chapter that was generously made available for this review, include: 1. The underlying assumptions of each model in the AgMIP comparison, account for significant differences in modeling results. Controlling for these differences narrowed the range of results considerably, reinforcing the importance of consistency and transparency in modeling assumptions. 2. All of the controlled variables significantly impact results well out into the future and all present important uncertainties that cannot be resolved conclusively. A scenario changing assumptions from “middle of the road” to a higher population, lower GDP growth scenario demonstrates just how bad the outcome could potentially be for production, consumption, and prices. If population in developing countries grows 11% more by 2050 and GDP growth is over 30% lower due to slowed annual growth rates over time, global per capita calorie consumption will be 6-10% lower on average, with even worse impacts in poorer regions. 3. In contrast to many earlier estimates, climate change scenarios show clear negative impacts on yields at the global level. Estimated price impacts ranged from +2% to +79%. Per capita calorie availability declined across the globe. The authors conclude that scenario modeling is critical to good policy and investment decisions and call for improved exchange and dialogue between decision makers and modelers. xi Models participating in this comparison include: Asia-Pacific Integrated Model (AIM) from the Japanese National Institute for Environmental Studies; ENVISAGE, based on the World Bank’s LINKAGE model, now housed at the FAO; Emissions Prediction and Policy Analysis (EPPA) from MIT; Global Trade and Environ- ment Model (GTEM) from the Australian institute ABARES; Future Agricultural Resources Model (FARM) from the USDA; Modular Applied GeNeral Equilibrium Tool (MAGNET) from Wageningen University; Global Change Assessment Model (GCAM) from the Pacific Northwest National Laboratory; Global Biosphere Optimization Model (GLOBIOM) from IIASA; IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT); Model for Agricultural Production and its Impact on the Environment (MAgPIE) from the Potsdam Institute for Climate Impact Research. xii van Dijk, Michiel (2012). A review of global scenario exercises for food security analysis: Assumptions and results. Food Secure Working paper. Hague, Food Secure. See, too, a detailed literature review on the evidence base for climate mitigation from organic agriculture: Azeez, G. (2009). Soil Carbon and Organic Farming. Bristol, The Soil Association.
  17. 17. 17 Here we highlight a few such efforts. The United Nations’ Millennium Ecosystem Assessment (MA) examines four scenarios, summarized below: 1. “Global Orchestration” – is characterized by global trade liberalization and cooperation as agriculture moves toward large-scale industrial production, with limited environmental management. The result is high global per capita calorie availability, with a 40% decrease in child hunger. But environmental damage is significant, including the projected loss of 50% of Sub-Saharan Africa’s forested land. 2. “TechnoGarden” – involves high technological development, low trade and investment barriers, and better environmental management in the global North. Private investment transforms developing country agriculture through intensification, dramat- ically increasing production in Sub-Saharan Africa. Food production and hunger levels are similar to scenario one. 3. “Adapting Mosaic” – includes greater local and regional diversity of development paths, as WTO negotiations and global climate talks fail. The results vary widely from region to region, and food produc- tion globally is much lower than in the previous two scenarios. 4. “Order from Strength” – features high trade bar- riers, limited global cooperation, and little attention to ecosystem management. Weak agricultural investment drives extensive cropland expansion, and climate change contributes to hunger and mass migration in Africa. Food output grows very slowly to 2050, as child malnutrition increases. For the UN researchers, these modeling results highlight the dangers of “reactive” versus “proactive” environmen- tal management and of fractured versus coordinated global approaches to trade and economic cooperation. In a similarly ambitious exercise, researchers at the French organization CIRAD (Agricultural Research for BOX 4 — Biophysical Modeling: Land Use In most economic models, available agricultural land is identified through biophysical modeling. New land is projected to be converted to agricultural uses to achieve equilibrium between supply and demand. In the case of the FAO model, 70 million hectares of additional agricultural land is required by 2050, an increase of 9%. Assumptions about land availability are key to the outcomes for other variables as well. A modeling constraint on the expansion of agricultural land will produce greater supply and demand imbalances and higher agricultural prices, for example. Scientists from the Rockefeller University’s Program for the Human Environment employ a land-use model to project the pressures on uncultivated lands from the demands of a growing population and changing diets. These researchers optimistically suggest that we may be nearing the point of “peak farmland,” meaning that so-called “land sparing” — the extent to which uncultivated land remains in forest or other states — could actually increase by 2050. According to their calculations, existing resources could be adequate to fulfill future food needs. However as the researchers themselves note, this finding could potentially be invalidated by the “wild card” of global biofuels production. Faculty from Chalmers University of Technology in Sweden incorporate FAO economic modeling into biophysical scenarios to project the impacts on land use. These modelers start with the FAO baseline (assuming that by 2030 global agricultural area will expand from the current 5.1 billion ha to 5.4 billion ha) and proceed to model the land use impacts of three ”what if” scenarios. Faster growth in livestock production efficiency (basically feed efficiency) is projected to reduce land demands from 5.4 billion to 4.8 billion ha. A 20% shift from beef to less land-intensive pork and poultry reduces demand further, to 4.4 billion ha. Finally, a broader shift to diets relying less on meat and reductions in food waste in the world’s wealthier regions reduce land use by an additional 15%. Other researchers have used global food demand estimates to model the land use implications of intensification versus extensification scenarios. Using this approach, modelers from the University of Minnesota’s Department of Ecology, Evolution, and Behavior find that meeting global food demand by bringing more land into production (extensification) would mean one billion more hectares of land cleared by 2050, with high levels of greenhouse gas emissions and nitrogen use. By contrast, moderate intensification of production in low-productivity regions would reduce land demands by 80%, cut emissions by two-thirds, and slightly reduce global nitrogen use.
  18. 18. 18 BOX 5 — Biophysical Modeling: Water Management The Comprehensive Assessment of Water Management in Agriculture (CAWMA) is a recent attempt to integrate biophysical and economic information for policy-relevant scenario assessment. This model relies on the International Food Policy Research Institute’s IMPACT model to estimate food demand and supply and the International Water Management Institute’s WATERSIM model to simulate the same for water. The modelers start with estimated crop demand to 2050 and estimate that water use for such production will increase by 70%-90% under business-as-usual policies. They proceed to model five “what if” scenarios, to assess the impact of different approaches to water resource investment. This detailed set of controlled simulations allows policymakers to evaluate the likely impacts of different approaches to a narrowly defined policy challenge. Notably, climate change is not included in this simulation, making the results less relevant as forecasts but more relevant to policymakers, as the results of different approaches are more easily compared. The five scenarios are: 1. “Rain-fed Optimistic” – This scenario assumes no increase to 2050 in irrigated crop production, the development focus instead being improved water management by poor, rural smallholders. The scenario is optimistic because it assumes that 80% of yield gaps — the gap between current productivity and potential productivity — are closed. As a result, water availability is adequate to meet global agricultural demand. 2. “Rain-fed Pessimistic” – Only 20% of rain-fed yield gaps are closed, necessitating a 53% increase in rain-fed cropland to meet food needs. This has high environmental costs, and many countries have to increase their food imports significantly. Food insecurity increases, to the highest level of any of the scenarios considered. 3. “Irrigation Expansion” – This scenario assumes large investments in irrigation, particularly in Sub-Saharan Africa and Asia, to reduce food import dependence. The cost is high — US$400 billion — and the 33% increase in irrigated area supplies less than one-quarter of the expected rise in food demand. Food security improves for many, but this approach increases pressure on freshwater resources, more than doubling the number of people suffering water scarcity to 2.6 billion in 2050. 4. “Irrigation Yield Improvement” – This simulation prioritizes improved efficiency in the use of land and water. The results include a 75%-80% reduction in yield gaps and a 9% global expansion of irrigated cropland, meeting half the additional demand for food by 2050. It is not inexpensive — US$300 billion — and 32% of freshwater resources are diverted to agriculture. 5. “Trade” – A final scenario examines the outcome when much of the rising demand for food is met by trade, with relatively water-abundant grain producers — USA, Canada, Argentina, etc. — exporting greater volumes to water-deficient countries. This scenario reduces water stresses, but many obstacles make it unlikely, including the high price of food imports for less developed countries, the energy use required by international trade, and government wariness of excessive import dependence. The study concludes with a normative scenario designed to optimize water-resource investment, responding to the limitations highlighted by each of the five simulations discussed above. Recognizing that different strategies will work best in different regions, the authors model a portfolio approach. In Sub-Saharan Africa, for example, a dual approach is followed, including significant expansion of irrigation for cash crops alongside efforts to achieve water-efficiency gains for smallholders on rain-fed lands. Overall, the simulation projects yield increases of 58% for cereals on rain-fed lands, while irrigated agricultural yields rise 55% based on 31% and 38% increases in water efficiency respectively. Strong regulations limit environmental impacts, intensification limits demand for additional agricultural lands, and there is only a 13% increase in freshwater use for agricultural production in 2050.
  19. 19. Development) used its Agribiom model in their Agri- monde foresight study, comparing a business-as-usual scenario (high-growth industrial agriculture derived from the MA’s Global Orchestration scenario) to one based on equity and agro-ecological intensification of agricultural production. In both, it is assumed that the world produc- es enough food, the questions modeled are “how” and “at what cost?” For the Agrimonde project, diets of 3,000 kilocalories are assumed for all people in all countries. The research- ers find that the world can meet future food needs even as yield growth slows due to a shift away from indus- trial agriculture and toward smaller scale farms and agro-ecological practices. This exercise clarifies some of the perhaps-undesirable impacts of such a path. To achieve this hypothetical future, there would need to be a 39% increase in cropland to make up for low yield growth, though as researchers point out, this can come largely from pasture as the world relies less on meat- based diets. They also project a stunning 740% increase in global food trade, as surplus regions supply deficit regions. (There is no accounting for how food-deficit re- gions would afford such a dramatic increase in imports.) A modeling effort led by the Institute of Social Ecology at Vienna’s Klagenfurt University presents an extensive attempt to model alternative scenarios. Like Agribi- om, this model is not economic, based instead on the supply and demand for available biomass from crop and grazing land. Researchers use FAO estimates for their baselines on population, economic growth, agricultural productivity, and land use. Climate change and bioen- ergy scenarios are modeled, as are the impacts of four different diets, from “western high meat” to relatively low meat-based diets. Livestock models range from inten- sive to humane to organic. Land use for crops is varied from the FAO’s baseline of a 9% increase up to19%. The result is an impressive but somewhat dizzying array of 72 distinct scenarios. The Klagenfurt researchers synthesize their results to assess a “wholly organic” future, as well as an inter- mediate scenario. They conclude that the present path (high meat, industrial production) is feasible only with increased conversion of new crop and pasture land (20% instead of the FAO’s baseline of 9%) alongside intensification of production. The low-meat, organic sce- nario was also found to be feasible, with a similar level of farmland expansion (for grains for human consumption in place of grazing for meat production). The impacts of climate change would depend a great deal on the unknown effects of CO2 fertilization. The International Food Policy Research Institute in turn has modeled the impact of a 50% reduction in meat demand to 2050, in developed countries as well as in China and Brazil. Not surprisingly, the results show significant reductions in agricultural prices (higher supply relative to demand) and in food insecurity, with the changes in China and Brazil having the greatest impact. 19
  20. 20. 20 CONCLUSIONS While this review does not represent an exhaustive assessment of the literature, it should at least provide a basic understanding of the primary sources for many of the widely quoted estimates of global food needs in 2050, alongside some possible alternatives. Our review suggests that there is much to be gained from careful modeling based on reliable data that attempts to integrate biophysical and economic trends and relationships. At the same time, a careful review of current modeling literature cannot fail to highlight the high level of uncertainty in biophysical and economic data and the sensitivity of long-range results to small variations in basic assumptions that together make any such modeling projections highly speculative. Harkening back to Reilly and Willenbockel’s words from the introduction, modeling outcomes should not be taken as forecasts. But unfortunately, that is often how they are presented, usually through no fault of the modelers themselves. This much is clear — the path we are currently on does not lead to a Malthusian future, and. there is no need for the alarmist “productivism” often occasioned by decontextualized and overly simplified presentations of modeling outcomes. Warnings of future scarcity often fuel misguided campaigns for the expansion of large-scale, resource intensive, corporate-controlled agriculture, as exemplified by the G8’s New Alliance for Food Security and Nutrition. They drive large-scale land acquisitions and smallholder dispossession in the name of looming food scarcity and the expansion of input-intensive industrial farming, despite the resource constraints that prompted 400 scientific and social science experts from around the world to call that development path into question through the International Assessment of Agricultural Science and Technology for Development (IAASTD). The FAO’s 2012 call for a 60% increase in agricultural supply should be viewed as a starting point for discussion, not a looming threat. This significantly reduced projection should allay some of the worst fears that population growth and changing diets will necessarily overwhelm our ability to produce food. At the same time consideration of the FAO’s findings should lead us to consider and accommodate the uncertainties inherent in all such projections alongside known factors — particularly biofuels expansion and climate change — that have not yet been adequately considered and addressed. The forthcoming overview of the AgMIP project in Agricultural Economics, concludes with a strong call for modeling that utilizes a range of assumptions and pres- ents a range of results, as a way to maximize its useful- ness to policymakers. “Exploring the outcomes from a range of plausible drivers is essential, not least as these drivers in part depend on decisions on public policies and private investments.”47 In other words, the most useful drivers to model are those that are most susceptible to policy intervention. Beyond the limited predictive value of economic modeling, a key purpose of such research should be to illuminate the conse- quences of policy and behavioral change. Biofuels scenario modeling, for example, has not received the attention it deserves given the potential impacts on food production and resource use, as well as the prominent role of public policies in promoting (or potentially dis- couraging) such use. Perhaps even more striking is the absence of the potential impacts of reductions in food loss and waste, which currently prevent an estimated one-third of food from nourishing anyone.48 The FAO is actively campaigning on this issue, focusing on simple and achievable solutions, such as improved storage and infrastructure in developing countries and standards and public education to reduce retail and consumer waste in developed countries. Surely those who warn of looming food shortages can develop clear scenario modeling to estimate the potential impact of, for example, a ten percentage point reduction in food loss by 2050. Under such a scenario, the FAO’s baseline estimate of 2050 agricultural needs might drop again, from 60% to 50%. Some researchers have even estimated that reducing loss and waste to currently achievable levels could cut losses in half and provide enough additional food for one billion people.49 While modeling can be invaluable in evaluating specific policy options, such as biofuels mandates or steps to reduce food waste, its utility is less certain when it comes to broader systemic issues or develop- ment paths, such as whether to pursue large-scale, high-input agriculture or foster smaller scale, low-input
  21. 21. 21 xiii See Jules Pretty’s work on the sustainable intensification of African agriculture, for example, which synthesizes a wide range of field experience involving thousands of farmers and millions of hectares of land across a wide range of countries: Pretty, Jules , Camilla Toulmin and Stella Williams (2011). “Sustainable Intensi- fication in African Agriculture.” International Journal of Agricultural Sustainability 9(1). systems. Current modeling on the question of “feeding the world” tends to be biased by assumptions about basic aspects of our food and agricultural systems that urgently need to change: consumption patterns, unfair distribution and access, land use, trade structures, and investment priorities, for example. In this context, modeling a shift to agro-ecological systems can yield global scale results driven by other underlying assump- tions — dramatic increases in land use because of as- sumed lower yields from organic agriculture for example, or vast expansion of trade in agricultural commodities to compensate for assumed slower productivity growth. A growing body of experience and evidence at local and regional levels demonstrates the long-term value of investments in smallholder farming and sustainable agricultural methods. Policymakers would do well to examine these on-the-ground experiences as a basis for policy changes and investments that can scale-up successful approaches in regions where they are most appropriate and necessary, regions in which food security is tenuous despite high agricultural potential.xiii As a general rule, productive engagement with affected communities and policymakers in developing countries should guide future-oriented “foresight” research, grounding such modeling exercises in real-world experiences and developing-country priorities. In a broad critique of predominant social science research on food systems Thompson and Scoones of the UK-based Institute for Development Studies emphasize this point, calling for multi-disciplinary approaches that look beyond economic growth, focus on sustainability, and rely on participatory research.50 Only modeling that genuinely reflects and responds to the lived experience of the world’s most food-insecure communities will enable policymakers, advocates and the communities themselves to work toward a future where all have enough.
  22. 22. RECOMMENDATIONS The review above leads us to the following recommen- dations regarding future modeling to help policymakers address the challenges that lie ahead: n Global estimates are useful, but national and regional figures are far more instructive, as are local and regional strategies. As noted earlier, there is no “we” who feed “the world.” Global adequacy of projected food supplies can hide a plethora of shortages and injustices at the regional, national, and local levels. This dynamic will worsen as climate change affects agricultural systems, as many of the world’s poorest regions are expected to be hit the hardest. While trade will address a significant portion of local shortfalls, it would be a mistake not to assess regional food production capacities and focus on closing yield gaps, as many researchers have suggested. n Climate change poses a particular challenge for modelers. The AgMIP effort is an important attempt to improve modeling and increase its value for assessing climate change. Uncertainties will inevitably remain of course, and it is incumbent on researchers to present their results transparently, especially the sources and levels of uncertainty. Moreover, basic modeling parameters must be established so that FAO and other widely quoted studies on feeding the world in 2050 account for climate change. n Biofuels expansion deserves more focused attention from modelers. First generation biofuels compete directly and indirectly for crops and resources, exerting upward pressure on food prices. Second generation biofuels should exert less pressure, but uncertainties remain. Meanwhile, most economic models to date have failed to take into account the likely and possible pathways for first generation biofuel expansion. To the extent that biofuels markets are driven by consumption man- dates and other public policies, it is incumbent on modelers to represent the costs of such policies in their long-range projections. To date, they haven’t. Fischer’s excellent scenario work with the IIASA model is out of date, and subsequent efforts rely on unfounded assumptions that have biofuels expanding more slowly than is now likely. AgMIP’s attention to the impact of advanced biofuels after 2030 is help- ful, but it begs the more important question facing policymakers: Can we feed the world if we are simultaneously feeding our vehicles from the same crops and lands? n Other contributors to supply and demand imbalances deserve attention. This need not come from economic modelers, but it could. Reductions in food waste — farm-to-market as well as at the retail and consumer levels — can have a direct impact on food accessibility and supply. An estimated one-third of all food is lost or wasted.51 Obviously, reducing such losses would make a great deal more food available for consumption while reducing resource use. It is striking that the principal economic models reviewed here do not model scenarios of food-loss reduction, which in develop- ing countries could come from improvements in storage and other infrastructure to reduce post-harvest and processing losses. This is, in itself, a valuable development goal. n Investment in developing country agricultural productivity is a top priority. We have seen how sensitive future food supplies are to increases and decreases in agricultural productivity. While it would be helpful if modelers could improve the quality and consistency of the data they use, we do not need to wait for their projections to invest in developing country agriculture. Supporting the world’s small- holder farmers — who produce most of the food in developing countries while simultaneously consti- tuting most of the world’s hungry people — is the single biggest opportunity to reduce hunger and increase food security worldwide. Public investment is particularly critical in reaching this community as services for smallholders are less likely to be viewed as profitable by the private sector. n The priorities that should guide those investments are less a modeling question than a contextual public policy question. As the CAWMA project on water investments suggests (Box 5), a portfolio approach tailored to the specific needs of a given region is the best way to maximize productivity within given resource constraints. The same will be true for other agricultural investments. 22
  23. 23. And failing to raise productivity and food security in the regions that have historically been left behind will have dire consequences for the world’s poor, as indicated by the normative modeling used by the Agrimonde and Millennium Ecosystem Assessment projects. n Scaling up proven strategies is the best path to food security. We have seen how alarmist projections can lead to reflexive investments in large-scale industrial agriculture, the G8’s New Alliance initiative being the most prominent example of late. Yet long-term resource constraints will affect many of the fossil-fuel-based inputs on which such systems depend. Global long-range modeling provides less guidance in such matters than do case studies detailing proven strategies for the sustainable intensification of food production. n 2050 is too short a time horizon to assess long-term sustainability. As noted earlier, climate change and related resource constraints will have increasing impacts over the course of the century. Most models project far greater disruption to agricultural production after 2050 than before. Policymakers need to recognize that 2050 estimates are not the end of the story and may be overly optimistic. n Long-range economic modeling generally minimizes the impacts of volatility, as supply and demand resolve within the models. Yet we anticipate a future of increased volatility in the weather, affecting agricultural systems, and in agricultural markets, thanks to thin reserves and increasing financial speculation. More work is needed to incorporate volatility into global long-range economic modeling.52 Future Research Needs n Biofuels scenarios, including public policy analysis: Research needs to catch up to the latest developments in global biofuels markets, including careful modeling of biofuels expansion scenarios and, to the extent possible, analysis of the impacts of consumption mandates and other public policies. Such research is important because, on the one hand, such policies may undermine food security. On the other hand, energy markets may ultimately prove decisive in determining the expansion trajectories for biofuels. In this case, we may need to consider a different set of national and international policies to regulate the impact of energy prices on food security. n Better integration of energy scenarios into agricultural modeling: The long-term energy outlook has implications far beyond biofuel expansion. It is directly relevant to the economic viability of high-input agriculture, reliant as it is on fossil-fuel-based inputs. If these inputs are expected to rise in cost (or decline in availability), developing countries should be wary of agricultural develop- ment paths that increase their reliance on them. Is IAASTD right that business-as-usual is not an option, and if so what are the alternatives?seareeds n Climate change and its impacts on agriculture: Building on AgMIP’s valuable initiative and the scientific community’s ongoing efforts to improve our understanding, additional research on regional and crop-specific impacts is needed, particularly in the developing world. Adaptation needs and strategies are local, and must be guided by a sound local agricultural and economic analysis. As a matter of necessity, this research would focus less on global food provision and more on local strategies for climate-resilient agriculture.Future Research eds n Synthesizing and scaling up successful local efforts to promote increased food production through climate-resilient sustainable methods. Far too little research draws on the vast wealth of field experience, much of which has been led by farmers working closely with researchers and extension agents. Scaling up successful strategies can close yield gaps efficiently, improving food security for the most vulnerable in a sustainable way. n Bringing stakeholders to the table: There must be a process to bring researchers into direct dialogue with farmers and developing country governments, so that research priorities and methodologies can be informed by the communities that will (ideally) benefit from the results. 23
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  25. 25. 21 van Dijk, Michiel (2012). A review of global scenario exercises for food security analysis: Assumptions and results. Food Secure Working paper. Hague, Food Secure. 22 Conforti, Piero (2011). Looking Ahead in World Food and Agriculture: Perspectives to 2050. Rome, Italy, Food and Agriculture Organization. 23 Reilly, Michael and Dirk Willenbockel (2010). “Managing uncertainty: a review of food system scenario analysis and modelling.” Philosophical Transactions of the Royal Society 365: 3049-3063. (p 2053) 24 Reilly, Michael and Dirk Willenbockel (2010). “Managing uncertainty: a review of food system scenario analysis and modelling.” Philosophical Transactions of the Royal Society 365: 3049-3063. 25 Carpenter, S.R., P.L. Pingali, E.M. Bennett and M.B. Zurek (2005). Ecosystems and human well-being: findings of the Scenarios Working Group of the Millennium Ecosystem Assessment. Millennium Ecosystem Assessment Series. Washington, DC, Island Press. 26 Paillard, Sandrine, Bruno Dorin, Tristan Le Cotty, Tevecia Ronzon and Sebastien Treyer (2011). Food Security by 2050: Insights from the Agrimonde Project. European Foresight Series EFP Brief, CIRAD, INRA, Paillard, Sandrine, Sebastien Treyer and Bruno Dorin, Eds. (2011). Agrimonde: Scenarios and Challenges for Feeding the World in 2050. Versailles Cedex, Editions Quae. 27 Reilly, Michael and Dirk Willenbockel (2010). “Managing uncertainty: a review of food system scenario analysis and modelling.” Philosophical Transactions of the Royal Society 365: 3060. 28 Fischer, G. (2011). How can climate change and the development of bioenergy alter the long-term outlook for food and agriculture? Looking Ahead in World Food and Agriculture: Perspectives to 2050. P. Conforti. Rome, Italy, Food and Agriculture Organization: 95-158. 29 Willenbockel, Dirk (2011). Exploring Food Price Scenarios Towards 2030 with a Global Multi-Region Model. Oxfam Research Reports. Oxford, Oxfam International: 26. 30 Willenbockel, Dirk (2011). Exploring Food Price Scenarios Towards 2030 with a Global Multi-Region Model. Oxfam Research Reports. Oxford, Oxfam International. 31 Ibid. 32 Fischer, G. (2011). How can climate change and the development of bioenergy alter the long-term outlook for food and agriculture? Looking Ahead in World Food and Agriculture: Perspectives to 2050. P. Conforti. Rome, Italy, Food and Agriculture Organization: 95-158. 33 Nelson, Gerald C., Mark W. Rosegrant, Amanda Palazzo, Ian Gray, Christina Ingersoll, Richard Robertson, Simla Tokgoz, Tingju Zhu, Timothy B. Sulser, Claudia Ringler, Siwa Msangi and Liangzhi You (2010). Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options. Washington, DC, International Food Policy Research Institute. 34 Fuss, Sabine, Petr Havlík, Jana Szolgayová, Erwin Schmid and Michael Obersteiner (2011). Large-Scale Modelling of Global Food Security and Adaptation under Yield Uncertainty. EAAE 2011 Congress. Zurich, European Association of Agricultural Economists (EAAE). 35 Nelson, Gerald D., Dominique van der Mensbrugghe, Tomoko Hasegawa, Kiyoshi Takahashi, Ronald D. Sands, Page Kyle and Katherine Calvin (2013). “Agriculture and Climate Change in Global Scenarios: Why Don’t the Models Agree?” Agricultural Economics forthcoming. 36 Ackerman, Frank and Elizabeth A. Stanton (2013). Climate Impacts on Agriculture: A Challenge to Complacency? Working Paper. Medford, MA, Global Development and Environment Institute (GDAE). 37 Fischer, G. (2011). How can climate change and the development of bioenergy alter the long-term outlook for food and agriculture? Looking Ahead in World Food and Agriculture: Perspectives to 2050. P. Conforti. Rome, Italy, Food and Agriculture Organization: 95-158. 38 von Lampe, Martin, Dirk Willenbockel, Elodie Blanc, Yongxia Cai, Katherine Calvin, Shinichiro Fujimori, Tomoko Hasegawa, Petr Havlik, Page Kyle, Hermann Lotze-Campen, Daniel Mason d/Croz, Gerald D. Nelson, Ronald D. Sands, Christoph Schmitz, Andrzej Tabeau, Hugo Valin, Dominique van der Mensbrugghe and Hans van Meijl (2013). “Why Do Global Long-term Scenarios for Agriculture Differ? An overview of the AgMIP Global Economic Model Intercomparison.” Agricultural Economics forthcoming. 25 END NOTES
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  27. 27. ACKNOWLEDGMENTS This report was written by Timothy A. Wise and Kristin Sundell, with support from Marie Brill. It is adapted with permission from the recent Tufts University working paper, “Can We Feed the World in 2050? A Scoping Paper to Assess the Evidence,” by Timothy A. Wise: http://www.ase.tufts.edu/gdae/policy_research/FeedWorld2050.html. Timothy A. Wise is Director of the Research and Policy Program at Tufts University’s Global Development and Environment Institute. He is grateful to Elise Garvey for invaluable research assistance and to unnamed reviewers, whose comments and suggestions vastly improved the content and presentation of this paper. ActionAid works with people living in poverty and those acting in solidarity to end poverty and injustice. We fight hunger, hold companies and governments accountable, seek justice and education for women, and help people to cope with emergencies in over 40 countries around the world.
  28. 28. ActionAid USA 1420 K Street, NW, Suite 900 Washington, DC 20005 www.actionaidusa.org (202) 835-1240 info@actionaid.org

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