Income, consumer preferences, and the future of livestock-derived food demand.

In recent decades there has been a sustained and substantial shift in human diets across the globe towards including more livestock-derived foods. Continuing debates scrutinize how these dietary shifts affect human health, the natural environment, and livelihoods. However, amidst these debates there remain unanswered questions about how demand for livestock-derived foods may evolve over the upcoming decades for a range of scenarios for key drivers of change including human population, income, and consumer preferences. Future trends in human population and income in our scenarios were sourced from three of the shared socioeconomic pathways. We used scenario-based modeling to show that average protein demand for red meat (beef, sheep, goats, and pork), poultry, dairy milk, and eggs across the globe would increase by 14% per person and 38% in total between the year 2020 and the year 2050 if trends in income and population continue along a mid-range trajectory. The fastest per person rates of increase were 49% in South Asia and 55% in sub-Saharan Africa. We show that per person demand for red meat in high-income countries would decline by 2.8% if income elasticities of demand (a partial proxy for consumer preferences, based on the responsiveness of demand to income changes) in high-income countries decline by 100% by 2050 under a mid-range trajectory for per person income growth, compared to their current trajectory. Prices are an important driver of demand, and our results demonstrate that the result of a decline in red meat demand in high-income countries is strongly related to rising red meat prices, as projected by our scenario-based modeling. If the decline in the income elasticity of demand occurred in all countries rather than only in high-income countries, then per person red meat demand in high-income countries would actually increase in 2050 by 8.9% because the income elasticity-driven decline in global demand reduces prices, and the effect of lower prices outweighs the effect of a decline in the income elasticity of demand. Our results demonstrate the importance of interactions between income, prices, and the income elasticity of demand in projecting future demand for livestock-derived foods. We complement the existing literature on food systems and global change by providing quantitative evidence about the possible space for the future demand of livestock-derived foods, which has important implications for human health and the natural environment.

Modeling impacts of faster productivity growth to inform the CGIAR initiative on Crops to End Hunger.

In 2017-2018, a group of international development funding agencies launched the Crops to End Hunger initiative to modernize public plant breeding in lower-income countries. To inform that initiative, USAID asked the International Food Policy Research Institute and the United States Department of Agriculture's Economic Research Service to estimate the impacts of faster productivity growth for 20 food crops on income and other indicators in 106 countries in developing regions in 2030. We first estimated the value of production in 2015 for each crop using data from FAO. We then used the IMPACT and GLOBE economic models to estimate changes in the value of production and changes in economy-wide income under scenarios of faster crop productivity growth, assuming that increased investment will raise annual rates of yield growth by 25% above baseline growth rates over the period 2015-2030. We found that faster productivity growth in rice, wheat and maize increased economy-wide income in the selected countries in 2030 by 59 billion USD, 27 billion USD and 21 billion USD respectively, followed by banana and yams with increases of 9 billion USD each. While these amounts represent small shares of total GDP, they are 2-15 times current public R&D spending on food crops in developing countries. Income increased most in South Asia and Sub-Saharan Africa. Faster productivity growth in rice and wheat reduced the population at risk of hunger by 11 million people and 6 million people respectively, followed by plantain and cassava with reductions of about 2 million people each. Changes in adequacy ratios were relatively large for carbohydrates (already in surplus) and relatively small for micronutrients. In general, we found that impacts of faster productivity growth vary widely across crops, regions and outcome indicators, highlighting the importance of identifying the potentially diverse objectives of different decision makers and recognizing possible tradeoffs between objectives.

Modelling the global economic consequences of a major African swine fever outbreak in China.

African swine fever is a deadly porcine disease that has spread into East Asia where it is having a detrimental effect on pork production. However, the implications of African swine fever on the global pork market are poorly explored. Two linked global economic models are used to explore the consequences of different scales of the epidemic on pork prices and on the prices of other food types and animal feeds. The models project global pork prices increasing by 17-85% and unmet demand driving price increases of other meats. This price rise reduces the quantity of pork demanded but also spurs production in other parts of the world, and imports make up half the Chinese losses. Demand for, and prices of, food types such as beef and poultry rise, while prices for maize and soybean used in feed decline. There is a slight decline in average per capita calorie availability in China, indicating the importance of assuring the dietary needs of low-income populations. Outside China, projections for calorie availability are mixed, reflecting the direct and indirect effects of the African swine fever epidemic on food and feed markets.

Agricultural investments and hunger in Africa modeling potential contributions to SDG2 - Zero Hunger.

We use IFPRI's IMPACT framework of linked biophysical and structural economic models to examine developments in global agricultural production systems, climate change, and food security. Building on related work on how increased investment in agricultural research, resource management, and infrastructure can address the challenges of meeting future food demand, we explore the costs and implications of these investments for reducing hunger in Africa by 2030. This analysis is coupled with a new investment estimation model, based on the perpetual inventory methodology (PIM), which allows for a better assessment of the costs of achieving projected agricultural improvements. We find that climate change will continue to slow projected reductions in hunger in the coming decades-increasing the number of people at risk of hunger in 2030 by 16 million in Africa compared to a scenario without climate change. Investments to increase agricultural productivity can offset the adverse impacts of climate change and help reduce the share of people at risk of hunger in 2030 to five percent or less in Northern, Western, and Southern Africa, but the share is projected to remain at ten percent or more in Eastern and Central Africa. Investments in Africa to achieve these results are estimated to cost about 15 billion USD per year between 2015 and 2030, as part of a larger package of investments costing around 52 billion USD in developing countries.

Climate change effects on agriculture: economic responses to biophysical shocks.

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Managing uncertainty: a review of food system scenario analysis and modelling.

Complex socio-ecological systems like the food system are unpredictable, especially to long-term horizons such as 2050. In order to manage this uncertainty, scenario analysis has been used in conjunction with food system models to explore plausible future outcomes. Food system scenarios use a diversity of scenario types and modelling approaches determined by the purpose of the exercise and by technical, methodological and epistemological constraints. Our case studies do not suggest Malthusian futures for a projected global population of 9 billion in 2050; but international trade will be a crucial determinant of outcomes; and the concept of sustainability across the dimensions of the food system has been inadequately explored so far. The impact of scenario analysis at a global scale could be strengthened with participatory processes involving key actors at other geographical scales. Food system models are valuable in managing existing knowledge on system behaviour and ensuring the credibility of qualitative stories but they are limited by current datasets for global crop production and trade, land use and hydrology. Climate change is likely to challenge the adaptive capacity of agricultural production and there are important knowledge gaps for modelling research to address.