- Email: [email protected]
Global food systems: Can foresight learn from hindsight?
Global Food Security 20 (2019) 66–71
Contents lists available at ScienceDirect
Global Food Security journal homepage: www.elsevier.com/locate/gfs
Global food systems: Can foresight learn from hindsight?☆ a,⁎
Karen Brooks , Frank Place a b
T
b
CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI), USA International Food Policy Research Institute, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Foresight Hindsight Food systems Productivity Trade Environmental policy
Construction of plausible scenarios for alternative futures of global food systems requires an understanding of how the past led to the present, and the past's likely relevance to the future. Policy actions affected the past, but are very difficult to foresee. Among those that most shaped global food systems in the last half century are measures that fostered productivity growth, expansion of trade, and the interlinkage of agricultural and environmental policies. Scenarios for global food systems, including those using the quantitative tools of the CGIAR's Global Futures and Strategic Foresight modeling approach, explore alternative assumptions in these three areas, among others. Hindsight can inform foresight by highlighting key elements of the past and forcing transparent examination of whether and how these elements will shape the future.
1. Introduction
2. Change in global food systems: Transformative or incremental?
Foresight analysis looks to the future and is grounded in the past. The nature of change is thus an important conceptual issue, since change links the future with the past. If departures are expected to be massive and rapid (that is, transformative), then the past may have little predictive value for the future. If the future and past, however, are linked through continuous adjustment, then the analyst's task is more straightforward. Relationships observed in the past can be modified to explore alternatives for the future. Among the factors strongly influencing food systems and yet difficult to foresee are policy measures. Policies, particularly in the realm of food and agriculture, are made at the national level in response to national political pressures. In an interlinked world, trading partners will act and react, thereby affecting the outcomes of national policy. The performance of food systems is thus influenced by an iterative process of action and reaction of major players, much of which takes place in the policy realm (Brooks and Place, 2018). Policy change, in turn, can have long-lasting influence on trends, can trigger sharp and unexpected deviations, or can do both simultaneously. An understanding of how policy decisions shaped the past can assist analysts in weighing whether and how relationships observed in prior years should be reflected in plausible scenarios for the future.
Daunting challenges to future food systems – population growth, hunger, obesity, pollution, resource depletion, food waste, climate change, and jobs – are often cited in support of a need for transformative change. For example, the United Nations’ Agricultural Transformation Pathways Initiative states in its introductory text: “Most countries, developed and developing, have to establish clear pathways for making ambitious transformative changes in their agriculture and food systems to ensure that these latest become environmentally, economically and socially more sustainable” (United Nations, 2018, unpaged). In a contrary view, Kates et al. (2012) argue that, rhetoric aside, most change is in fact incremental. In their view, incremental adjustments dominate and give way to transformative change only when scale— assessed by the number of people affected or the magnitude of the shock—overwhelms ongoing processes. The quantitative modeling tools of the CGIAR Global Futures and Strategic Foresight toolkit, like comparable models, are incremental by construction. Such models build in empirical relationships observed in the past and are calibrated on past data. “Business as usual” scenarios can be constructed as projections displaying what the future will look like if the relationships of the past remain steady under predicted drivers of change. The models are not bounded by the past or limited to “business as usual” projections, since they invite thought experiments about alternative futures that can be simulated through perturbations of
☆
This work was undertaken as part of and funded by the CGIAR Research Program on Policies, Institutions, and Markets. PIM is in turn supported by the CGIAR Fund contributors (https://www.cgiar.org/funders/). The work draws on Brooks and Place (2018) with permission of the publisher. ⁎ Corresponding author. E-mail addresses: [email protected] (K. Brooks), [email protected] (F. Place). https://doi.org/10.1016/j.gfs.2018.12.004 Received 24 May 2018; Received in revised form 23 August 2018; Accepted 18 December 2018 2211-9124/ © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
past relationships. The perturbations can be modest and incremental, or transformative and extreme. If the latter, then the structural shifters must be specified exogenously. The Global Futures and Strategic Foresight (GFSF) modeling suite includes several basic building blocks: crop and livestock models that relate the physical performance of technologies to growing conditions; water models that connect precipitation and water availability to demand and alternate uses; a multi-market economic model with assumptions about income and population growth; and models of climate change. Each of these building blocks incorporates relationships and processes broader than the agri-food system; for example, basic plant and animal physiology, macroeconomic and demographic trends, and projections of climate change. Complementing these are assumptions specific to the performance of agri-food systems, many of which have policy-related determinants. Relevant scenarios are constructed through the interplay of the contextual building blocks (that is, physiology, markets, and agroecology) and specific factors that affect the performance of agri-food systems. Of the many factors that have shaped food systems in the recent past, three formative developments are linked closely with policy decisions: productivity growth through technical change; expansion of trade; and interlinkage of agricultural and environmental policy agendas. Since approximately 1970, investment in agricultural research has underpinned a steady march of increased productivity of land and labor. Expansion of trade has interlinked national markets, enhanced competitive pressures, and broadened the scope for specialization. Agricultural and environmental policy agendas at the national and global levels have become intertwined through recognition of agriculture's footprint on the natural resource base and climate. Foresight analysis requires judgment about whether and how the policy decisions behind these forces of the present and past should be reflected in scenarios exploring plausible futures. In 1970 the challenges to food systems appeared daunting, yet the record is one of relative success. Global hunger and poverty have declined markedly, and agriculture has become a more considered user of natural resources. The simplest indicator of the composite effect of rapid technical change, expanded trade, and regulatory intervention for environmental protection can be seen in Fig. 1, which shows the time path of food prices since 1970. The general shape of the curve is well known: a spike in 1972–1974, secular decline and flattening in the 1990s, and an upturn with increased volatility since about 2000. A brief look at the forces generating trends can illustrate factors to be considered in constructing alternative scenarios for the future.
Fig. 1. Trends in deflated food prices, 1970−2016. Source: FAO (2018). "FAO Food Price Index". Real Price index data Accessed on May 9, 2018, http://www.fao.org/worldfoodsituation/foodpricesindex/en/
Fig. 2. Decadal differences in average TFP growth rates by region, 1970−2010. Source: USDA (2016).
in agricultural research (and the innovation systems that take research into use); and (ii) reforms that encourage farmers to use inputs and natural resources more efficiently. Countries and regions can be grouped into four broad categories with regard to their experience with technical change over this period: (i) those that derived growth in TFP largely from investment in agricultural research (for example, Europe and North America); (ii) those that undertook institutional and policy reforms that increased efficiency (for example, the transition from central planning); (iii) those that did both (for example, China and Brazil); and (iv) those that did neither on a scale sufficient to realize significant gains (for example, much of Africa south of the Sahara). High-income countries had a strong foundation of agricultural science and continued to invest in agricultural research, particularly in the early years of this period. Although institutional reforms were modest and incremental, changes in price, credit, and trade policy influenced the returns to research. In Europe and North America changes in farm programs exerted gradual pressures toward increased efficiency at the farm level, and agricultural research provided a steady stream of new technologies and innovations in management. Alston et al. (2015) find a modest slowdown in TFP growth in the United States starting in the mid-1970s and increasingly evident after 1990.1 Productivity is
3. Technical change and productivity growth During the roughly half century since 1970, technical change in agriculture globally accelerated in pace, shifted from a mix of mechanical, biological, and chemical innovations to predominantly biological and computational, and broadened geographically to encompass much of the developing world. According to Fuglie et al. (2012), total factor productivity (TFP) globally roughly doubled in the two decades from 1991 to 2009 compared to the two decades from 1971 to 1990. Growth in output stayed constant at about 2.25% annually, but in the latter part of the period much more of the growth is attributed to increased TFP rather than expansion of area, labor, or purchased inputs. The growth in TFP is unevenly shared across regions (Fig. 2). Differential growth has shifted the global geography of production, with corresponding implications for agricultural trade. Differential growth in productivity has increased competitiveness and net exports of the fast movers. It has also in some cases reduced pressures for area expansion, as for example, through introduction of the Conservation Reserve in the United States (Glauber and Effland, 2018), China's Grain for Green program (Zhong et al., 2018), and the Brazilian measures to reduce deforestation (Ferrira Filho and de Freitas, 2018). Growth in TFP comes from two primary sources: (i) policy decisions encouraging investment
1
The long run trends in productivity built into the GFSF models, as explained below, are derived from past data, with higher weight to recent performance, combined with expert opinion. A higher weight to recent performance will capture a slowdown if it has occurred, but no explicit nonlinear projection of slowing productivity growth is imposed. 67
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
assumed to grow faster in poor countries than in richer ones. Assumptions about future productivity growth are among the key elements of foresight models and are informed by understanding the performance of productivity and what has shaped it. In Western Europe the high support prices and protectionist stance of the Common Agricultural Policy (CAP) in the 1970s and 1980s kept resources, particularly labor, in agriculture. Policies in Western Europe, even after reforms of the CAP, reflect a deliberate tradeoff between growth in productivity and management of the rural landscape through retention of small farms. Growth in TFP accelerated with reforms of the CAP in the mid-1990s, and even more when the CAP's domain expanded with entry of Eastern European countries. The latter embarked on a steep trajectory of efficiency gains from a low base after shedding the strictures of central planning. The shock to agriculture of the early transition from planned to market economies in Eastern Europe and the former USSR caused a precipitous drop in use of fertilizer, chemicals, fuel, and feed. TFP grew as the recovery of output in the 2000s outpaced growth of inputs and incorporated increased efficiency in use of land, labor, and purchased inputs. Very little can be attributed to investment in agricultural research or reforms in the research systems to stimulate agricultural innovation, since neither took place during the crisis years of the transition. Sedik et. al highlight protection, support payments, debt relief, and credit subsidies as key policy measures in Russia accompanying changes in farm structure in pursuit of recovery and increased domestic competitiveness (Sedik et al., 2018). As late as 2013 Russia's research intensity ratio (investment in agricultural research as a share of agricultural value added) was 0.47, considerably below intensity ratios in Western Europe and many developing countries (ASTI, 2018). In contrast, the doubling of TFP in developing countries in the period 1991–2009 relative to 1971–1990 reflects a combination of reforms and investment in agricultural research that propelled productivity upward, especially in Brazil and China. China's institutional and policy reforms associated with introduction of the household responsibility system in 1978 complemented large investments in agricultural science and technology (Zhong et al., 2018). Decollectivization during the period from 1978 to 1984 was followed by gradual market liberalization over the next decade. China's agricultural research capacity was strong even before the reforms, and producers could access an array of new technologies as incentives improved. Investment in agricultural science plateaued for about five years after 1985, and then rose substantially, contributing to China's emergence as a powerhouse of production and agricultural science (Huang et al., 2004). Creation of The Brazilian Agricultural Research Corporation (Embrapa) in 1973 and consistent support for research thereafter brought a remarkable run of 40 years of growth in TFP at an average of 3.5% annually. Ferrira Filho and de Freitas (2018) trace how price and credit policy facilitated a steady expansion of Brazil's agricultural area and the flow of new technologies into production. The pace of technical change was such that when the Brazilian economy liberalized in the mid-1990s and protective walls were lowered, agriculture was competitive and able to benefit from the more open trade regime. In Africa south of the Sahara, policy and institutional reforms in many countries in the 1990s rolled back marketing restrictions that had taxed agriculture. Measured Nominal Rates of Assistance and Producer Support Estimates went from sharply negative to more modestly negative or positive, with significant variability by country, signaling improved but uneven incentives for producers (Anderson and Masters, 2009; FAO, 2018). Reforms in general did not extend to land tenure and investment in agricultural research, key elements in the formula for success in Brazil and China. Growth in investments in agricultural science and technology in Africa south of the Sahara lagged sectoral growth overall, and fell in real terms in a number of countries (Lynam et al., 2016). TFP has grown too slowly to boost local competitiveness relative to growing imports. Most of the substantial growth of the subcontinent in the 2000s came from increased use of land, labor, and
purchased inputs. Intensity of input use is much lower than in other regions, signaling scope for significant increase in the efficient use of purchased inputs. Increased use of purchased inputs without commensurate growth in yields, however, would reduce TFP. This quick survey of a half century of productivity growth in agriculture points to elements that will be important in building future scenarios. How should productivity growth in high-income countries be modeled—growth, slowdown, or steady state? Will investment in agricultural research in the vast territory of the former USSR yield scientific breakthroughs, or will productivity growth stagnate as institutional reforms run their course? Will the world's current remaining productivity reservoir—Africa south of the Sahara—be tapped? The Global Futures and Strategic Foresight modeling approach takes as its baseline an assumption of improvements in yields over time that, to varying degrees, builds on trends observed during the past 50–60 years, extrapolated following the concepts introduced in Evenson and Rosegrant (1995) and Evenson et al. (1999). The nearer future is strongly informed by recent decades, and longer trends are built using multiple sources of information, including expert knowledge of developments in regions and countries. These long-run trends, or intrinsic productivity growth rates, are intended to reflect the expected increases in inputs, improved cultivars, and improvements in management practices. The trends are generally higher for developing than developed countries to reflect the potential for improvement inherent in observed yield gaps. The intrinsic productivity growth rates are exogenous to the model, and changes in them are specified as part of the definition of scenarios. The rates can be modified to explore implications of different rates of area and productivity growth based on changes in investment in research and other factors. For example, extrapolation of relatively favorable recent growth rates for productivity in West Africa suggests that continuation of this modestly optimistic trend will be insufficient to avoid growing food imports and erosion of competitiveness. Increased investment will be needed to accelerate productivity growth and to absorb the rising rural labor force (Wiebe et al., 2017). 4. Trade Over the period since 1960, as discussed in Bouët and Laborde Debucquet (2017), growth in agricultural trade underpinned a major geographic realignment of production and consumption (Fig. 3). Increased competitiveness and productivity growth opened the potential
Fig. 3. Agricultural trade as a share of production, 1961–2013. Source: FAOSTAT; COMTRADE. Note: Calculated as value of agricultural trade for primary and semi-processed agricultural products defined at the HS6 product level (UN COMTRADE) in constant USD (2004–2006) divided by the total value of agricultural production (FAOSTAT) in constant USD (2004–2006). 68
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
thus represent a partially tapped reservoir of potential benefits for food systems in the decades ahead. Trade also entails tradeoffs. Producers in low- and middle-income countries with high costs of production due to poor policy, underinvestment in agricultural research, and weak infrastructure will be increasingly disadvantaged as trade expands, as they have been in the past. Barring productivity growth in low-income countries more rapid than that expressed at present in the intrinsic productivity growth rates of the Global Futures and Strategic Foresight scenarios, gains from trade will continue to flow disproportionately to consumers and to producers in high-income parts of the world. That this may happen even as many low-income, agriculture-dependent countries confront the challenges of employing millions of rural young people illustrates a useful function of foresight analysis; that is, to highlight problematical implications of some likely scenarios. The best policy approach to managing trade-related threats to market share is investment in public goods and services that increase competitiveness. Although the deleterious welfare effects of export bans and import tariffs have been shown clearly, political economy pressures behind them remain strong. The present (2018) political sentiments fueling insularity augur poorly for scenarios embodying rapid expansion of agricultural trade and may imply backtracking on past hard-won gains. The Global Futures and Strategic Foresight modeling approach addresses trade by including price wedges between producing and consuming countries and by allowing products to flow freely once the wedges are incorporated in market transactions. The wedges can represent various combinations of transport costs, policy-induced barriers to trade, and/or inefficiencies along the value chain. Unless trade or trade costs are an explicit feature of the scenario under consideration, the wedges are kept constant over time, with the result that they will not motivate alternate outcomes. This assumption would depart sharply from the historical trends of the past half century, during which shrinkage of the price wedges (that is, falling trade costs) was a major force for change in food systems. Choosing different assumptions about trade and variability in the wedges over time (as is done in some of the work reported in this special issue) offers insights into current trade debates and their implications not only for trade flows, but for productivity growth and job creation in low-income countries. Scenarios could also build in regionally differentiated rates of change in the wedges to show the distributional implications of further trade liberalization or policy reversal.
for farmers to serve markets beyond their local and national boundaries. The Uruguay Round brought negotiated reductions in barriers. Policy reforms at the national level supported liberalization, and trade finance expanded with the growth of financial services. Costs of transport and communications fell. The combination boosted agricultural trade. The growth was not linear, however, as seen in Fig. 3. Moreover, growth in global agricultural trade, while very impressive, lagged the explosive increase in total trade over this period. Agriculture's share of total trade declined. The decline serves as a reminder that agriculture remains protected, but it also reflects the very rapid growth of transnational value chains in the manufacturing sector, with multinational sourcing of parts and assembly. The relative importance of factors boosting agricultural trade is not well researched (Hummels, 2007). Costs of air freight fell (particularly in the two decades between 1955 and 1975), changes in costs of ocean freight were mixed, and products flowed between and among modes of transport. Lighter weight-to-value products took to the air, and liners and tramp shippers adjusted to the advent of containerization and volatile fuel prices. Working with more recent data, von Cramon and Qu (2016) find that trade costs have declined since 1990, with significant regional variation. Costs in Africa were highest in 1990 and fell most in the decades thereafter. Expanded trade increased the influence of the preferences of middle- and upper-income consumers on markets, including in rapidly urbanizing poor and middle-income countries. Concerns about food safety, social and environmental characteristics of production, and nutritional content flowed back to affect production (see summary discussion in IFAD (2016), Part two, chapter 6). These concerns of consumers became increasingly important in determining production patterns and trade flows. Changes in consumer preferences in major markets abroad subjected producers to external regulatory measures mandating traceability, product definition, and status regarding genetic modification. The increased importance of the demand side will likely remain the case in future decades even if the pace of trade liberalization and growth in international trade slow. Domestic urban markets in lowincome countries will continue to grow, and many are likely to show the same characteristics already common in higher-income environments. The content and composition of traded agricultural products changed along with the volume. As shown in Fig. 4, 50 years ago about one-third of traded agricultural products were processed; now almost two-thirds are. This shift in composition has important implications for competitiveness and job creation in agriculture-dependent countries. Countries unable to provide the infrastructure, services, and regulatory framework to support entrepreneurship in food processing are likely to impose competitive disadvantages on 21st century producers even if they invest successfully in technical change in production. Gains from trade in primary and processed agricultural products
5. Environment Agriculture has had first and often undisputed claim to land and water for many centuries. Recognition of competing claims from the environment is a development largely of the latter part of the 20th century. The concept of coordinated management of agriculture and its environmental impacts gained momentum from the 1980s onward. Governments have intervened in markets for environmental goods and services, either to correct market imperfections, of which there are many, or to set new trajectories for growth, for example through carbon taxes or investment incentives. They have done so in a variety of ways, and support for environmental measures within agricultural budgets has risen. For example, attention to the environment has received increasing budgetary allocation in the United States since the Food Security and Rural Investment Act of 2002 and in Europe since the 2003 and 2013 adjustments to the European CAP. A review of available indicators of agriculture's changing environmental footprint in OECD countries shows a glass half full – agriculture continues to affect the environment, often adversely, but the negative impact has been mitigated by productivity growth (allowing more output to be produced with fewer inputs) and by adoption of environmentally friendly practices. Since 1990 the OECD has tracked member countries’ agricultural land area, nutrient use, pesticide use, energy use, soil health, water quantity and quality, contributions to air
Fig. 4. Share of traded agricultural goods that are processed, 1961–2013. 69
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
transportation energy sourced from renewable energy, but in November 2016 issued a proposal that no more than 7% should be produced from food or feed crops (European Parliamentary Research Service, 2018). The foresight models remain well suited to address both climate change and sustainability issues. The agricultural technologies and management practices under development for climate resilience are in general sparing in use of water, efficient in absorption of nutrients, enriching of soil health, and protective of biodiversity. With further development of the models, sustainability indicators can become more prominent. Heightened visibility will facilitate deliberate analysis of tradeoffs where relevant.
quality and greenhouse gas (GHG) emissions, and biodiversity of farm species and wild species on agricultural land. On balance trends in the indicators since 1990 have been favorable. Land area under agriculture fell from the early 1990s to 2010 by nearly 5%. Efficiency of fertilizer use is up, pesticide applications are down, and soil erosion declined or stabilized in most OECD countries (OECD, 2013). Water pollution from agriculture decreased marginally. Agriculture remains, however, a key source of nitrogen and phosphorus detected in surface water in many countries (30–40% contribution). Withdrawal of water for agricultural use increased by 2% during the 1990s but subsequently fell by almost 5% in the first decade of the 2000s (OECD, 2013). Governments have used an array of instruments to address adverse environmental effects associated with agriculture. The instruments include, for example, regulations, payments for natural resource stewardship (often as cross-compliance requirements for receipt of payments for production, area, or income), subsidies to promote environmentally favorable farming systems, improving environmental knowledge through extension, and investing in research on sustainable agriculture. Across all OECD countries, the percentage of income or production support that was tied to satisfaction of environmental conditions increased from 4% in 1986–1988 to 30% in 2006–2008 (OECD, 2010). Developing and middle-income countries have also incorporated elements of environmental management into agricultural policy, but to a lesser degree than in high-income countries. For example, Brazil adopted the new Brazilian Forest Code of 2012 and the Low-Carbon Agriculture Plan of 2010–2011 (Ferreira Filho et al., 2015). China developed one of the largest environmental programs in the world with its Sloping Land Conversion Program or Grain for Green program launched in 1999 (Zhong et al., 2018). Nearly 1 million hectares per year were converted from farmland to forest throughout the first decade of the 2000s. Environmental variables feature in the Global Futures and Strategic Foresight modeling approach through inclusion of land use, water, and GHG emissions, and through the impact of climate change. Additionally, with careful specification of agricultural technologies in the crop and livestock models, environmental consequences such as nutrient runoff, soil depletion, and grassland stress can be included. Treatment of forests and pressures for deforestation are not as well developed in this set of models but can be addressed with others. Sustainability indicators can be explored through construction of scenarios. Rosegrant et al. (2017) use the Global Futures and Strategic Foresight modeling suite to compare alternative scenarios combining packages of investment in agricultural science, water management, and infrastructure and resulting impacts on water use, greenhouse gas emission, and forest cover, along with indicators of agricultural growth and reduction in hunger. The scientific, business, and policy communities have shifted attention to focus on climate change, although the climate-smart and sustainability agendas broadly overlap. Many of the technologies labelled as climate-smart – for example, conservation agriculture and fertilizer micro-dosing – were pioneered to support sustainability. Agriculture contributes about 13% of GHG emissions globally, and 8% in OECD countries (where the transport and energy sectors are large relative to agriculture). During the 1990s, on-farm energy use in OECD countries increased by 5% but then reversed and declined by about 3% in the 2000s (OECD, 2013). Concerns about GHG emissions associated with ruminant livestock are contributing to shifts in consumer preferences, particularly in high-income countries. The pursuit of renewable energy has linked agricultural, environmental, and energy policies through biofuel mandates and markets, and was instrumental in generating the price spike of 2008–2009 shown in Fig. 1. Recent research findings about the impact of biofuel mandates on changes in land use have occasioned reconsideration of the mandates and their role in mitigating climate change (Laborde and Valin, 2012). For example, the European Union set a 2020 target of 10% of
6. Conclusion Over the roughly half century since 1970, a global food system has interlinked markets that in the past were local, national, or regional. Productivity growth, expanded trade, and better management of agriculture's natural resources, among other factors, shaped the global system. The changes since 1970 are on a scale and scope sufficient to qualify for the term “transformational.” Yet they emerged through incremental change punctuated by occasional shocks. A first conclusion of this brief review suggests that even if we choose to apply the term “transformational” to changes foreseen or desired in the coming decades, we need not abandon analytical frameworks constructed on observations and relationships observed in the past. The past remains relevant. It can serve as a springboard to contemplation of plausible futures and provides a backdrop against which assumptions about the future can be considered. Policy decisions, particularly on productivity, trade, and the environment, have shaped the past in ways that a half century ago would have been hard to foresee. Recognizing their importance in the past does not tell us how they will shape the future. Appropriate recognition, however, forces assumptions embedded in foresight to be made explicit. For example, how should we model the geographic distribution of productivity growth in the future? Will the gap between scientifically advanced and lagging agricultural sectors widen or narrow? Should costs of trade be represented to rise, fall, or remain the same, and should trends be differentiated by region? For which resources will depletion worsen and for which will it ease? The models do not tell us the answers to these queries but help us explore the implications of decisions being made now. Hindsight helps clarify assumptions and highlights their importance. It also encourages humility. The best (that is, most realistic) assumptions about future trends will fail to capture shocks that induce deviations. Few foresaw the profound impacts of the economic reforms in China, the collapse of the Soviet Union, or the adoption of biofuel mandates. New developments and unexpected policy shifts are exogenous to foresight modeling and can be included exogenously as part of “what if” scenarios. Some developments likely to produce profound change in food systems can be anticipated on the basis of current knowledge. Among these, the demography of Africa south of the Sahara and South Asia stands out as important. The rapid and clearly foreseeable rise in numbers of rural young people seeking livelihoods creates policy imperatives for investment in productivity growth, management of trade deficits, and readiness for climate change. Foresight modeling informed by hindsight, transparent in presentation of key assumptions, and oriented toward known challenges of the future can be a valuable tool for responsible decision making in the public and private sectors. Declaration of interest The authors have no financial or personal relationships with other people or organizations that could inappropriately influence (bias) their work. 70
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
References
International Fund for Agricultural Development, 2016. Rural Development Report 2016: Fostering Inclusive Rural Transformation. Part 2 Chapter 6 “Agrifood Markets and Value Chains”. IFAD, Rome, Italy. Kates, R.W., William, R. Travis, Wilbanks, Thomas J., 2012. Transformational adaptation when incremental adaptations to climate change are insufficient. Proc. Natl. Acad. Sci. USA 109 (19), 7156–7161. Laborde, D., Valin, H., 2012. Modeling land-use changes in a global CGE: assessing the EU biofuel mandates with the MIRAGE-BioF model (http://www.worldscientific.com/ doi/pdf/). Clim. Change Econ. 3 (3), 39. https://doi.org/10.1142/ S2010007812500170. Lynam, J., Beintema, N., Roseboom, J., Badiane, O., 2016. Agricultural Research in Africa: Investing in Future Harvests. International Food Policy Research Institute (IFPRI), Washington, D.C. OECD, 2013. OECD Compendium of Agri-environmental Indicators. OECD Publishing, Paris, France. https://doi.org/10.1787/9789264181151-en. Organisation for Economic Co-operation and Development (OECD), 2010. Linkages between Agricultural Policies and Environmental Effects: Using the OECD Stylised Agrienvironmental Policy Impact Model. OECD Publishing, Paris, France. https://doi. org/10.1787/9789264095700-4-en. Rosegrant, M., Sulser, T., Mason-D′Croz, D., Cenacchi, N., Nin-Pratt, A., Dunston, S., Zhu, T., Ringler, C., Wiebe, K., Robinson, S., Willenbockel, D., Xie, H., Kwon, H.-.Y., Johnson, T., Thomas, T., Wimmer, F., Schaldach, R., Nelson, G., Willaarts, B., 2017. Quantitative foresight modeling to inform the CGIAR research portfolio. Project Report for USAID. Washington, D.C.: International Food Policy Research Institute (IFPRI). 〈http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/131144〉. Sedik, D., Lerman, Z., Shagaida, N., Uzun, V., Yanbykh, R., 2018. Agricultural and rural policies in Russia. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 17–41. United Nations. Sustainable Development Solutions Network. Agricultural transformation pathways initiative, 2018.〈http://unsdsn.org/what-we-do/solution-initiatives/ national-transformation-pathways-for-sustainable-agriculture-and-food-systems/〉 (Accessed 30 March 2018). United States Department of Agriculture (USDA), 2016. Agricultural total factor productivity data. accessed at 〈https://www.ers.usda.gov/data-products/internationalagricultural-productivity/〉. von Cramon, S., Qu, Y., 2016. Trade costs of heterogeneous agricultural products. Paper presented at the 5th Conference of the Association of Agricultural Economists, Addis Ababa, Ethiopia. Wiebe, Keith D., Stads, Gert-Jan, Beintema, NienkeM., Brooks, Karen, Cenacchi, Nicola, Dunston, Shahnila, Mason-D′Croz, Daniel, Sulser, TimothyB., Thomas, TimothyS., 2017. West African Agriculture for Jobs, Nutrition, Growth, and Climate Resilience. IFPRI Discussion Paper 1680. International Food Policy Research Institute (IFPRI), Washington, D.C. Zhong, F., Chen, K., Zhu, J., 2018. Agricultural and rural policies in China. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 393–413.
Agricultural Science and Technology Indicators (ASTI), 2018. 〈https://www.ifpri.org/ program/agricultural-science-and-technology-indicators-asti〉. International Food Policy Research Institute, Washington, D.C. (Accessed 10 August 2018). Calculated Russian research intensity ratio based on OECD and Eurostat data. Alston, J., Anderson, M.A., Pardey, P., 2015. The Rise and Fall of U.S. Farm Productivity Growth, 1910–2007 (Staff Paper P15-02). Department of Applied Economics, University of Minnesota, St. Paul, Minnesota. Anderson, K., Masters, W., 2009. Distortions to Agricultural Incentives in Africa. World Bank, Washington D.C. Bouët, Antoine, Laborde Debucquet, David (Eds.), 2017. Agriculture, Development, and the Global Trading System: 2000–2015. International Food Policy Research Institute (IFPRI), Washington, D.C. https://doi.org/10.2499/9780896292499. Brooks, K., Place, F., 2018. Global interlinkage of national agricultural and rural policies: technical change, trade, and the environment. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 483–502. European Parliamentary Research Service. 2018. “Promoting renewable energy sources in the EU after 2020.” Briefing, EU Legislation in Progress, 16 March 2018 〈http:// www.europarl.europa.eu/RegData/etudes/BRIE/2017/599278/EPRS_BRI(2017) 599278_EN.pdf〉. Evenson, R.E., Rosegrant, M.W., 1995. Productivity projections for commodity marketing modeling. Paper presented at the Final Workshop of the International Cooperative Research Project on Projections and Policy Implications of Medium and Long-term Rice Supply and Demand, Beijing, April 23–26. Evenson, R.E., Pray, C., Rosegrant, M.W., 1999. Agricultural Research and Productivity Growth in India. IFPRI Research Report No. 109. Washington, DC: International Food Policy Research Institute (IFPRI). Food and Agriculture Organization of the United Nations (,FAO, ), 2018. Agricultural Policy Incentives in Sub-Saharan Africa in the Last Decade (2005–2016). FAO, Rome. FAOSTAT. 〈http://www.fao.org/faostat/en/#data〉. Ferreira Filho, J.B.S., Horridge, M., Diniz, T.B., 2015. The New Forest Code and Greenhouse Gas Emissions in Brazil. GTAP Conference paper #4644. Melbourne, Australia. Ferrira Filho, J.B.S., de Freitas Vian, C.E., 2018. Agricultural and rural policies in Brazil. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. . World Scientific, pp. 223–245. Fuglie, K., Wang, S.L., Ball, V.E., 2012. Productivity Growth In Agriculture: An International Perspective. CABI, Cambridge, Massachusetts. Glauber, J., Effland, A., 2018. US agricultural policy. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 17–41. Huang, J., Hu, R., Rozelle, S., 2004. China's agricultural research system and reforms: challenges and implications to the developing countries. Asian J. Agric. Dev. 1 (1), 1–17. Hummels, D., 2007. Transportation costs and international trade in the second era of globalization. J. Econ. Perspect. 21 (3), 131–154.
71
Contents lists available at ScienceDirect
Global Food Security journal homepage: www.elsevier.com/locate/gfs
Global food systems: Can foresight learn from hindsight?☆ a,⁎
Karen Brooks , Frank Place a b
T
b
CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI), USA International Food Policy Research Institute, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Foresight Hindsight Food systems Productivity Trade Environmental policy
Construction of plausible scenarios for alternative futures of global food systems requires an understanding of how the past led to the present, and the past's likely relevance to the future. Policy actions affected the past, but are very difficult to foresee. Among those that most shaped global food systems in the last half century are measures that fostered productivity growth, expansion of trade, and the interlinkage of agricultural and environmental policies. Scenarios for global food systems, including those using the quantitative tools of the CGIAR's Global Futures and Strategic Foresight modeling approach, explore alternative assumptions in these three areas, among others. Hindsight can inform foresight by highlighting key elements of the past and forcing transparent examination of whether and how these elements will shape the future.
1. Introduction
2. Change in global food systems: Transformative or incremental?
Foresight analysis looks to the future and is grounded in the past. The nature of change is thus an important conceptual issue, since change links the future with the past. If departures are expected to be massive and rapid (that is, transformative), then the past may have little predictive value for the future. If the future and past, however, are linked through continuous adjustment, then the analyst's task is more straightforward. Relationships observed in the past can be modified to explore alternatives for the future. Among the factors strongly influencing food systems and yet difficult to foresee are policy measures. Policies, particularly in the realm of food and agriculture, are made at the national level in response to national political pressures. In an interlinked world, trading partners will act and react, thereby affecting the outcomes of national policy. The performance of food systems is thus influenced by an iterative process of action and reaction of major players, much of which takes place in the policy realm (Brooks and Place, 2018). Policy change, in turn, can have long-lasting influence on trends, can trigger sharp and unexpected deviations, or can do both simultaneously. An understanding of how policy decisions shaped the past can assist analysts in weighing whether and how relationships observed in prior years should be reflected in plausible scenarios for the future.
Daunting challenges to future food systems – population growth, hunger, obesity, pollution, resource depletion, food waste, climate change, and jobs – are often cited in support of a need for transformative change. For example, the United Nations’ Agricultural Transformation Pathways Initiative states in its introductory text: “Most countries, developed and developing, have to establish clear pathways for making ambitious transformative changes in their agriculture and food systems to ensure that these latest become environmentally, economically and socially more sustainable” (United Nations, 2018, unpaged). In a contrary view, Kates et al. (2012) argue that, rhetoric aside, most change is in fact incremental. In their view, incremental adjustments dominate and give way to transformative change only when scale— assessed by the number of people affected or the magnitude of the shock—overwhelms ongoing processes. The quantitative modeling tools of the CGIAR Global Futures and Strategic Foresight toolkit, like comparable models, are incremental by construction. Such models build in empirical relationships observed in the past and are calibrated on past data. “Business as usual” scenarios can be constructed as projections displaying what the future will look like if the relationships of the past remain steady under predicted drivers of change. The models are not bounded by the past or limited to “business as usual” projections, since they invite thought experiments about alternative futures that can be simulated through perturbations of
☆
This work was undertaken as part of and funded by the CGIAR Research Program on Policies, Institutions, and Markets. PIM is in turn supported by the CGIAR Fund contributors (https://www.cgiar.org/funders/). The work draws on Brooks and Place (2018) with permission of the publisher. ⁎ Corresponding author. E-mail addresses: [email protected] (K. Brooks), [email protected] (F. Place). https://doi.org/10.1016/j.gfs.2018.12.004 Received 24 May 2018; Received in revised form 23 August 2018; Accepted 18 December 2018 2211-9124/ © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
past relationships. The perturbations can be modest and incremental, or transformative and extreme. If the latter, then the structural shifters must be specified exogenously. The Global Futures and Strategic Foresight (GFSF) modeling suite includes several basic building blocks: crop and livestock models that relate the physical performance of technologies to growing conditions; water models that connect precipitation and water availability to demand and alternate uses; a multi-market economic model with assumptions about income and population growth; and models of climate change. Each of these building blocks incorporates relationships and processes broader than the agri-food system; for example, basic plant and animal physiology, macroeconomic and demographic trends, and projections of climate change. Complementing these are assumptions specific to the performance of agri-food systems, many of which have policy-related determinants. Relevant scenarios are constructed through the interplay of the contextual building blocks (that is, physiology, markets, and agroecology) and specific factors that affect the performance of agri-food systems. Of the many factors that have shaped food systems in the recent past, three formative developments are linked closely with policy decisions: productivity growth through technical change; expansion of trade; and interlinkage of agricultural and environmental policy agendas. Since approximately 1970, investment in agricultural research has underpinned a steady march of increased productivity of land and labor. Expansion of trade has interlinked national markets, enhanced competitive pressures, and broadened the scope for specialization. Agricultural and environmental policy agendas at the national and global levels have become intertwined through recognition of agriculture's footprint on the natural resource base and climate. Foresight analysis requires judgment about whether and how the policy decisions behind these forces of the present and past should be reflected in scenarios exploring plausible futures. In 1970 the challenges to food systems appeared daunting, yet the record is one of relative success. Global hunger and poverty have declined markedly, and agriculture has become a more considered user of natural resources. The simplest indicator of the composite effect of rapid technical change, expanded trade, and regulatory intervention for environmental protection can be seen in Fig. 1, which shows the time path of food prices since 1970. The general shape of the curve is well known: a spike in 1972–1974, secular decline and flattening in the 1990s, and an upturn with increased volatility since about 2000. A brief look at the forces generating trends can illustrate factors to be considered in constructing alternative scenarios for the future.
Fig. 1. Trends in deflated food prices, 1970−2016. Source: FAO (2018). "FAO Food Price Index". Real Price index data Accessed on May 9, 2018, http://www.fao.org/worldfoodsituation/foodpricesindex/en/
Fig. 2. Decadal differences in average TFP growth rates by region, 1970−2010. Source: USDA (2016).
in agricultural research (and the innovation systems that take research into use); and (ii) reforms that encourage farmers to use inputs and natural resources more efficiently. Countries and regions can be grouped into four broad categories with regard to their experience with technical change over this period: (i) those that derived growth in TFP largely from investment in agricultural research (for example, Europe and North America); (ii) those that undertook institutional and policy reforms that increased efficiency (for example, the transition from central planning); (iii) those that did both (for example, China and Brazil); and (iv) those that did neither on a scale sufficient to realize significant gains (for example, much of Africa south of the Sahara). High-income countries had a strong foundation of agricultural science and continued to invest in agricultural research, particularly in the early years of this period. Although institutional reforms were modest and incremental, changes in price, credit, and trade policy influenced the returns to research. In Europe and North America changes in farm programs exerted gradual pressures toward increased efficiency at the farm level, and agricultural research provided a steady stream of new technologies and innovations in management. Alston et al. (2015) find a modest slowdown in TFP growth in the United States starting in the mid-1970s and increasingly evident after 1990.1 Productivity is
3. Technical change and productivity growth During the roughly half century since 1970, technical change in agriculture globally accelerated in pace, shifted from a mix of mechanical, biological, and chemical innovations to predominantly biological and computational, and broadened geographically to encompass much of the developing world. According to Fuglie et al. (2012), total factor productivity (TFP) globally roughly doubled in the two decades from 1991 to 2009 compared to the two decades from 1971 to 1990. Growth in output stayed constant at about 2.25% annually, but in the latter part of the period much more of the growth is attributed to increased TFP rather than expansion of area, labor, or purchased inputs. The growth in TFP is unevenly shared across regions (Fig. 2). Differential growth has shifted the global geography of production, with corresponding implications for agricultural trade. Differential growth in productivity has increased competitiveness and net exports of the fast movers. It has also in some cases reduced pressures for area expansion, as for example, through introduction of the Conservation Reserve in the United States (Glauber and Effland, 2018), China's Grain for Green program (Zhong et al., 2018), and the Brazilian measures to reduce deforestation (Ferrira Filho and de Freitas, 2018). Growth in TFP comes from two primary sources: (i) policy decisions encouraging investment
1
The long run trends in productivity built into the GFSF models, as explained below, are derived from past data, with higher weight to recent performance, combined with expert opinion. A higher weight to recent performance will capture a slowdown if it has occurred, but no explicit nonlinear projection of slowing productivity growth is imposed. 67
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
assumed to grow faster in poor countries than in richer ones. Assumptions about future productivity growth are among the key elements of foresight models and are informed by understanding the performance of productivity and what has shaped it. In Western Europe the high support prices and protectionist stance of the Common Agricultural Policy (CAP) in the 1970s and 1980s kept resources, particularly labor, in agriculture. Policies in Western Europe, even after reforms of the CAP, reflect a deliberate tradeoff between growth in productivity and management of the rural landscape through retention of small farms. Growth in TFP accelerated with reforms of the CAP in the mid-1990s, and even more when the CAP's domain expanded with entry of Eastern European countries. The latter embarked on a steep trajectory of efficiency gains from a low base after shedding the strictures of central planning. The shock to agriculture of the early transition from planned to market economies in Eastern Europe and the former USSR caused a precipitous drop in use of fertilizer, chemicals, fuel, and feed. TFP grew as the recovery of output in the 2000s outpaced growth of inputs and incorporated increased efficiency in use of land, labor, and purchased inputs. Very little can be attributed to investment in agricultural research or reforms in the research systems to stimulate agricultural innovation, since neither took place during the crisis years of the transition. Sedik et. al highlight protection, support payments, debt relief, and credit subsidies as key policy measures in Russia accompanying changes in farm structure in pursuit of recovery and increased domestic competitiveness (Sedik et al., 2018). As late as 2013 Russia's research intensity ratio (investment in agricultural research as a share of agricultural value added) was 0.47, considerably below intensity ratios in Western Europe and many developing countries (ASTI, 2018). In contrast, the doubling of TFP in developing countries in the period 1991–2009 relative to 1971–1990 reflects a combination of reforms and investment in agricultural research that propelled productivity upward, especially in Brazil and China. China's institutional and policy reforms associated with introduction of the household responsibility system in 1978 complemented large investments in agricultural science and technology (Zhong et al., 2018). Decollectivization during the period from 1978 to 1984 was followed by gradual market liberalization over the next decade. China's agricultural research capacity was strong even before the reforms, and producers could access an array of new technologies as incentives improved. Investment in agricultural science plateaued for about five years after 1985, and then rose substantially, contributing to China's emergence as a powerhouse of production and agricultural science (Huang et al., 2004). Creation of The Brazilian Agricultural Research Corporation (Embrapa) in 1973 and consistent support for research thereafter brought a remarkable run of 40 years of growth in TFP at an average of 3.5% annually. Ferrira Filho and de Freitas (2018) trace how price and credit policy facilitated a steady expansion of Brazil's agricultural area and the flow of new technologies into production. The pace of technical change was such that when the Brazilian economy liberalized in the mid-1990s and protective walls were lowered, agriculture was competitive and able to benefit from the more open trade regime. In Africa south of the Sahara, policy and institutional reforms in many countries in the 1990s rolled back marketing restrictions that had taxed agriculture. Measured Nominal Rates of Assistance and Producer Support Estimates went from sharply negative to more modestly negative or positive, with significant variability by country, signaling improved but uneven incentives for producers (Anderson and Masters, 2009; FAO, 2018). Reforms in general did not extend to land tenure and investment in agricultural research, key elements in the formula for success in Brazil and China. Growth in investments in agricultural science and technology in Africa south of the Sahara lagged sectoral growth overall, and fell in real terms in a number of countries (Lynam et al., 2016). TFP has grown too slowly to boost local competitiveness relative to growing imports. Most of the substantial growth of the subcontinent in the 2000s came from increased use of land, labor, and
purchased inputs. Intensity of input use is much lower than in other regions, signaling scope for significant increase in the efficient use of purchased inputs. Increased use of purchased inputs without commensurate growth in yields, however, would reduce TFP. This quick survey of a half century of productivity growth in agriculture points to elements that will be important in building future scenarios. How should productivity growth in high-income countries be modeled—growth, slowdown, or steady state? Will investment in agricultural research in the vast territory of the former USSR yield scientific breakthroughs, or will productivity growth stagnate as institutional reforms run their course? Will the world's current remaining productivity reservoir—Africa south of the Sahara—be tapped? The Global Futures and Strategic Foresight modeling approach takes as its baseline an assumption of improvements in yields over time that, to varying degrees, builds on trends observed during the past 50–60 years, extrapolated following the concepts introduced in Evenson and Rosegrant (1995) and Evenson et al. (1999). The nearer future is strongly informed by recent decades, and longer trends are built using multiple sources of information, including expert knowledge of developments in regions and countries. These long-run trends, or intrinsic productivity growth rates, are intended to reflect the expected increases in inputs, improved cultivars, and improvements in management practices. The trends are generally higher for developing than developed countries to reflect the potential for improvement inherent in observed yield gaps. The intrinsic productivity growth rates are exogenous to the model, and changes in them are specified as part of the definition of scenarios. The rates can be modified to explore implications of different rates of area and productivity growth based on changes in investment in research and other factors. For example, extrapolation of relatively favorable recent growth rates for productivity in West Africa suggests that continuation of this modestly optimistic trend will be insufficient to avoid growing food imports and erosion of competitiveness. Increased investment will be needed to accelerate productivity growth and to absorb the rising rural labor force (Wiebe et al., 2017). 4. Trade Over the period since 1960, as discussed in Bouët and Laborde Debucquet (2017), growth in agricultural trade underpinned a major geographic realignment of production and consumption (Fig. 3). Increased competitiveness and productivity growth opened the potential
Fig. 3. Agricultural trade as a share of production, 1961–2013. Source: FAOSTAT; COMTRADE. Note: Calculated as value of agricultural trade for primary and semi-processed agricultural products defined at the HS6 product level (UN COMTRADE) in constant USD (2004–2006) divided by the total value of agricultural production (FAOSTAT) in constant USD (2004–2006). 68
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
thus represent a partially tapped reservoir of potential benefits for food systems in the decades ahead. Trade also entails tradeoffs. Producers in low- and middle-income countries with high costs of production due to poor policy, underinvestment in agricultural research, and weak infrastructure will be increasingly disadvantaged as trade expands, as they have been in the past. Barring productivity growth in low-income countries more rapid than that expressed at present in the intrinsic productivity growth rates of the Global Futures and Strategic Foresight scenarios, gains from trade will continue to flow disproportionately to consumers and to producers in high-income parts of the world. That this may happen even as many low-income, agriculture-dependent countries confront the challenges of employing millions of rural young people illustrates a useful function of foresight analysis; that is, to highlight problematical implications of some likely scenarios. The best policy approach to managing trade-related threats to market share is investment in public goods and services that increase competitiveness. Although the deleterious welfare effects of export bans and import tariffs have been shown clearly, political economy pressures behind them remain strong. The present (2018) political sentiments fueling insularity augur poorly for scenarios embodying rapid expansion of agricultural trade and may imply backtracking on past hard-won gains. The Global Futures and Strategic Foresight modeling approach addresses trade by including price wedges between producing and consuming countries and by allowing products to flow freely once the wedges are incorporated in market transactions. The wedges can represent various combinations of transport costs, policy-induced barriers to trade, and/or inefficiencies along the value chain. Unless trade or trade costs are an explicit feature of the scenario under consideration, the wedges are kept constant over time, with the result that they will not motivate alternate outcomes. This assumption would depart sharply from the historical trends of the past half century, during which shrinkage of the price wedges (that is, falling trade costs) was a major force for change in food systems. Choosing different assumptions about trade and variability in the wedges over time (as is done in some of the work reported in this special issue) offers insights into current trade debates and their implications not only for trade flows, but for productivity growth and job creation in low-income countries. Scenarios could also build in regionally differentiated rates of change in the wedges to show the distributional implications of further trade liberalization or policy reversal.
for farmers to serve markets beyond their local and national boundaries. The Uruguay Round brought negotiated reductions in barriers. Policy reforms at the national level supported liberalization, and trade finance expanded with the growth of financial services. Costs of transport and communications fell. The combination boosted agricultural trade. The growth was not linear, however, as seen in Fig. 3. Moreover, growth in global agricultural trade, while very impressive, lagged the explosive increase in total trade over this period. Agriculture's share of total trade declined. The decline serves as a reminder that agriculture remains protected, but it also reflects the very rapid growth of transnational value chains in the manufacturing sector, with multinational sourcing of parts and assembly. The relative importance of factors boosting agricultural trade is not well researched (Hummels, 2007). Costs of air freight fell (particularly in the two decades between 1955 and 1975), changes in costs of ocean freight were mixed, and products flowed between and among modes of transport. Lighter weight-to-value products took to the air, and liners and tramp shippers adjusted to the advent of containerization and volatile fuel prices. Working with more recent data, von Cramon and Qu (2016) find that trade costs have declined since 1990, with significant regional variation. Costs in Africa were highest in 1990 and fell most in the decades thereafter. Expanded trade increased the influence of the preferences of middle- and upper-income consumers on markets, including in rapidly urbanizing poor and middle-income countries. Concerns about food safety, social and environmental characteristics of production, and nutritional content flowed back to affect production (see summary discussion in IFAD (2016), Part two, chapter 6). These concerns of consumers became increasingly important in determining production patterns and trade flows. Changes in consumer preferences in major markets abroad subjected producers to external regulatory measures mandating traceability, product definition, and status regarding genetic modification. The increased importance of the demand side will likely remain the case in future decades even if the pace of trade liberalization and growth in international trade slow. Domestic urban markets in lowincome countries will continue to grow, and many are likely to show the same characteristics already common in higher-income environments. The content and composition of traded agricultural products changed along with the volume. As shown in Fig. 4, 50 years ago about one-third of traded agricultural products were processed; now almost two-thirds are. This shift in composition has important implications for competitiveness and job creation in agriculture-dependent countries. Countries unable to provide the infrastructure, services, and regulatory framework to support entrepreneurship in food processing are likely to impose competitive disadvantages on 21st century producers even if they invest successfully in technical change in production. Gains from trade in primary and processed agricultural products
5. Environment Agriculture has had first and often undisputed claim to land and water for many centuries. Recognition of competing claims from the environment is a development largely of the latter part of the 20th century. The concept of coordinated management of agriculture and its environmental impacts gained momentum from the 1980s onward. Governments have intervened in markets for environmental goods and services, either to correct market imperfections, of which there are many, or to set new trajectories for growth, for example through carbon taxes or investment incentives. They have done so in a variety of ways, and support for environmental measures within agricultural budgets has risen. For example, attention to the environment has received increasing budgetary allocation in the United States since the Food Security and Rural Investment Act of 2002 and in Europe since the 2003 and 2013 adjustments to the European CAP. A review of available indicators of agriculture's changing environmental footprint in OECD countries shows a glass half full – agriculture continues to affect the environment, often adversely, but the negative impact has been mitigated by productivity growth (allowing more output to be produced with fewer inputs) and by adoption of environmentally friendly practices. Since 1990 the OECD has tracked member countries’ agricultural land area, nutrient use, pesticide use, energy use, soil health, water quantity and quality, contributions to air
Fig. 4. Share of traded agricultural goods that are processed, 1961–2013. 69
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
transportation energy sourced from renewable energy, but in November 2016 issued a proposal that no more than 7% should be produced from food or feed crops (European Parliamentary Research Service, 2018). The foresight models remain well suited to address both climate change and sustainability issues. The agricultural technologies and management practices under development for climate resilience are in general sparing in use of water, efficient in absorption of nutrients, enriching of soil health, and protective of biodiversity. With further development of the models, sustainability indicators can become more prominent. Heightened visibility will facilitate deliberate analysis of tradeoffs where relevant.
quality and greenhouse gas (GHG) emissions, and biodiversity of farm species and wild species on agricultural land. On balance trends in the indicators since 1990 have been favorable. Land area under agriculture fell from the early 1990s to 2010 by nearly 5%. Efficiency of fertilizer use is up, pesticide applications are down, and soil erosion declined or stabilized in most OECD countries (OECD, 2013). Water pollution from agriculture decreased marginally. Agriculture remains, however, a key source of nitrogen and phosphorus detected in surface water in many countries (30–40% contribution). Withdrawal of water for agricultural use increased by 2% during the 1990s but subsequently fell by almost 5% in the first decade of the 2000s (OECD, 2013). Governments have used an array of instruments to address adverse environmental effects associated with agriculture. The instruments include, for example, regulations, payments for natural resource stewardship (often as cross-compliance requirements for receipt of payments for production, area, or income), subsidies to promote environmentally favorable farming systems, improving environmental knowledge through extension, and investing in research on sustainable agriculture. Across all OECD countries, the percentage of income or production support that was tied to satisfaction of environmental conditions increased from 4% in 1986–1988 to 30% in 2006–2008 (OECD, 2010). Developing and middle-income countries have also incorporated elements of environmental management into agricultural policy, but to a lesser degree than in high-income countries. For example, Brazil adopted the new Brazilian Forest Code of 2012 and the Low-Carbon Agriculture Plan of 2010–2011 (Ferreira Filho et al., 2015). China developed one of the largest environmental programs in the world with its Sloping Land Conversion Program or Grain for Green program launched in 1999 (Zhong et al., 2018). Nearly 1 million hectares per year were converted from farmland to forest throughout the first decade of the 2000s. Environmental variables feature in the Global Futures and Strategic Foresight modeling approach through inclusion of land use, water, and GHG emissions, and through the impact of climate change. Additionally, with careful specification of agricultural technologies in the crop and livestock models, environmental consequences such as nutrient runoff, soil depletion, and grassland stress can be included. Treatment of forests and pressures for deforestation are not as well developed in this set of models but can be addressed with others. Sustainability indicators can be explored through construction of scenarios. Rosegrant et al. (2017) use the Global Futures and Strategic Foresight modeling suite to compare alternative scenarios combining packages of investment in agricultural science, water management, and infrastructure and resulting impacts on water use, greenhouse gas emission, and forest cover, along with indicators of agricultural growth and reduction in hunger. The scientific, business, and policy communities have shifted attention to focus on climate change, although the climate-smart and sustainability agendas broadly overlap. Many of the technologies labelled as climate-smart – for example, conservation agriculture and fertilizer micro-dosing – were pioneered to support sustainability. Agriculture contributes about 13% of GHG emissions globally, and 8% in OECD countries (where the transport and energy sectors are large relative to agriculture). During the 1990s, on-farm energy use in OECD countries increased by 5% but then reversed and declined by about 3% in the 2000s (OECD, 2013). Concerns about GHG emissions associated with ruminant livestock are contributing to shifts in consumer preferences, particularly in high-income countries. The pursuit of renewable energy has linked agricultural, environmental, and energy policies through biofuel mandates and markets, and was instrumental in generating the price spike of 2008–2009 shown in Fig. 1. Recent research findings about the impact of biofuel mandates on changes in land use have occasioned reconsideration of the mandates and their role in mitigating climate change (Laborde and Valin, 2012). For example, the European Union set a 2020 target of 10% of
6. Conclusion Over the roughly half century since 1970, a global food system has interlinked markets that in the past were local, national, or regional. Productivity growth, expanded trade, and better management of agriculture's natural resources, among other factors, shaped the global system. The changes since 1970 are on a scale and scope sufficient to qualify for the term “transformational.” Yet they emerged through incremental change punctuated by occasional shocks. A first conclusion of this brief review suggests that even if we choose to apply the term “transformational” to changes foreseen or desired in the coming decades, we need not abandon analytical frameworks constructed on observations and relationships observed in the past. The past remains relevant. It can serve as a springboard to contemplation of plausible futures and provides a backdrop against which assumptions about the future can be considered. Policy decisions, particularly on productivity, trade, and the environment, have shaped the past in ways that a half century ago would have been hard to foresee. Recognizing their importance in the past does not tell us how they will shape the future. Appropriate recognition, however, forces assumptions embedded in foresight to be made explicit. For example, how should we model the geographic distribution of productivity growth in the future? Will the gap between scientifically advanced and lagging agricultural sectors widen or narrow? Should costs of trade be represented to rise, fall, or remain the same, and should trends be differentiated by region? For which resources will depletion worsen and for which will it ease? The models do not tell us the answers to these queries but help us explore the implications of decisions being made now. Hindsight helps clarify assumptions and highlights their importance. It also encourages humility. The best (that is, most realistic) assumptions about future trends will fail to capture shocks that induce deviations. Few foresaw the profound impacts of the economic reforms in China, the collapse of the Soviet Union, or the adoption of biofuel mandates. New developments and unexpected policy shifts are exogenous to foresight modeling and can be included exogenously as part of “what if” scenarios. Some developments likely to produce profound change in food systems can be anticipated on the basis of current knowledge. Among these, the demography of Africa south of the Sahara and South Asia stands out as important. The rapid and clearly foreseeable rise in numbers of rural young people seeking livelihoods creates policy imperatives for investment in productivity growth, management of trade deficits, and readiness for climate change. Foresight modeling informed by hindsight, transparent in presentation of key assumptions, and oriented toward known challenges of the future can be a valuable tool for responsible decision making in the public and private sectors. Declaration of interest The authors have no financial or personal relationships with other people or organizations that could inappropriately influence (bias) their work. 70
Global Food Security 20 (2019) 66–71
K. Brooks, F. Place
References
International Fund for Agricultural Development, 2016. Rural Development Report 2016: Fostering Inclusive Rural Transformation. Part 2 Chapter 6 “Agrifood Markets and Value Chains”. IFAD, Rome, Italy. Kates, R.W., William, R. Travis, Wilbanks, Thomas J., 2012. Transformational adaptation when incremental adaptations to climate change are insufficient. Proc. Natl. Acad. Sci. USA 109 (19), 7156–7161. Laborde, D., Valin, H., 2012. Modeling land-use changes in a global CGE: assessing the EU biofuel mandates with the MIRAGE-BioF model (http://www.worldscientific.com/ doi/pdf/). Clim. Change Econ. 3 (3), 39. https://doi.org/10.1142/ S2010007812500170. Lynam, J., Beintema, N., Roseboom, J., Badiane, O., 2016. Agricultural Research in Africa: Investing in Future Harvests. International Food Policy Research Institute (IFPRI), Washington, D.C. OECD, 2013. OECD Compendium of Agri-environmental Indicators. OECD Publishing, Paris, France. https://doi.org/10.1787/9789264181151-en. Organisation for Economic Co-operation and Development (OECD), 2010. Linkages between Agricultural Policies and Environmental Effects: Using the OECD Stylised Agrienvironmental Policy Impact Model. OECD Publishing, Paris, France. https://doi. org/10.1787/9789264095700-4-en. Rosegrant, M., Sulser, T., Mason-D′Croz, D., Cenacchi, N., Nin-Pratt, A., Dunston, S., Zhu, T., Ringler, C., Wiebe, K., Robinson, S., Willenbockel, D., Xie, H., Kwon, H.-.Y., Johnson, T., Thomas, T., Wimmer, F., Schaldach, R., Nelson, G., Willaarts, B., 2017. Quantitative foresight modeling to inform the CGIAR research portfolio. Project Report for USAID. Washington, D.C.: International Food Policy Research Institute (IFPRI). 〈http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/131144〉. Sedik, D., Lerman, Z., Shagaida, N., Uzun, V., Yanbykh, R., 2018. Agricultural and rural policies in Russia. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 17–41. United Nations. Sustainable Development Solutions Network. Agricultural transformation pathways initiative, 2018.〈http://unsdsn.org/what-we-do/solution-initiatives/ national-transformation-pathways-for-sustainable-agriculture-and-food-systems/〉 (Accessed 30 March 2018). United States Department of Agriculture (USDA), 2016. Agricultural total factor productivity data. accessed at 〈https://www.ers.usda.gov/data-products/internationalagricultural-productivity/〉. von Cramon, S., Qu, Y., 2016. Trade costs of heterogeneous agricultural products. Paper presented at the 5th Conference of the Association of Agricultural Economists, Addis Ababa, Ethiopia. Wiebe, Keith D., Stads, Gert-Jan, Beintema, NienkeM., Brooks, Karen, Cenacchi, Nicola, Dunston, Shahnila, Mason-D′Croz, Daniel, Sulser, TimothyB., Thomas, TimothyS., 2017. West African Agriculture for Jobs, Nutrition, Growth, and Climate Resilience. IFPRI Discussion Paper 1680. International Food Policy Research Institute (IFPRI), Washington, D.C. Zhong, F., Chen, K., Zhu, J., 2018. Agricultural and rural policies in China. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 393–413.
Agricultural Science and Technology Indicators (ASTI), 2018. 〈https://www.ifpri.org/ program/agricultural-science-and-technology-indicators-asti〉. International Food Policy Research Institute, Washington, D.C. (Accessed 10 August 2018). Calculated Russian research intensity ratio based on OECD and Eurostat data. Alston, J., Anderson, M.A., Pardey, P., 2015. The Rise and Fall of U.S. Farm Productivity Growth, 1910–2007 (Staff Paper P15-02). Department of Applied Economics, University of Minnesota, St. Paul, Minnesota. Anderson, K., Masters, W., 2009. Distortions to Agricultural Incentives in Africa. World Bank, Washington D.C. Bouët, Antoine, Laborde Debucquet, David (Eds.), 2017. Agriculture, Development, and the Global Trading System: 2000–2015. International Food Policy Research Institute (IFPRI), Washington, D.C. https://doi.org/10.2499/9780896292499. Brooks, K., Place, F., 2018. Global interlinkage of national agricultural and rural policies: technical change, trade, and the environment. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 483–502. European Parliamentary Research Service. 2018. “Promoting renewable energy sources in the EU after 2020.” Briefing, EU Legislation in Progress, 16 March 2018 〈http:// www.europarl.europa.eu/RegData/etudes/BRIE/2017/599278/EPRS_BRI(2017) 599278_EN.pdf〉. Evenson, R.E., Rosegrant, M.W., 1995. Productivity projections for commodity marketing modeling. Paper presented at the Final Workshop of the International Cooperative Research Project on Projections and Policy Implications of Medium and Long-term Rice Supply and Demand, Beijing, April 23–26. Evenson, R.E., Pray, C., Rosegrant, M.W., 1999. Agricultural Research and Productivity Growth in India. IFPRI Research Report No. 109. Washington, DC: International Food Policy Research Institute (IFPRI). Food and Agriculture Organization of the United Nations (,FAO, ), 2018. Agricultural Policy Incentives in Sub-Saharan Africa in the Last Decade (2005–2016). FAO, Rome. FAOSTAT. 〈http://www.fao.org/faostat/en/#data〉. Ferreira Filho, J.B.S., Horridge, M., Diniz, T.B., 2015. The New Forest Code and Greenhouse Gas Emissions in Brazil. GTAP Conference paper #4644. Melbourne, Australia. Ferrira Filho, J.B.S., de Freitas Vian, C.E., 2018. Agricultural and rural policies in Brazil. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. . World Scientific, pp. 223–245. Fuglie, K., Wang, S.L., Ball, V.E., 2012. Productivity Growth In Agriculture: An International Perspective. CABI, Cambridge, Massachusetts. Glauber, J., Effland, A., 2018. US agricultural policy. In: Meyers, W., Johnson, T. (Eds.), Handbook of International Food and Agricultural Policies: Volume 1 Policies for Agricultural Markets and Rural Economic Activity. World Scientific, pp. 17–41. Huang, J., Hu, R., Rozelle, S., 2004. China's agricultural research system and reforms: challenges and implications to the developing countries. Asian J. Agric. Dev. 1 (1), 1–17. Hummels, D., 2007. Transportation costs and international trade in the second era of globalization. J. Econ. Perspect. 21 (3), 131–154.
71