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This study evaluates the differences between the Exact Affine Stone Index (EASI) demand model estimates obtained using Consumer Expenditure Survey (CEX) data and Nielsen Homescan data. Results indicated that elasticities obtained from CEX and Homescan data–based demand models differ not only statistically but also economically. Own-price elasticities obtained from the CEX data–based demand model were more inelastic than those obtained from the demand model estimated using Homescan data. Further, differences between expenditure elasticities did not follow a specific pattern. We found evidence suggesting that the main source of differences is the price index used for the estimation.

The prevailing discourse on the future of agriculture is dominated by an imbalanced narrative that calls for food production to increase dramatically—potentially doubling by 2050—without specifying commensurate environmental goals. We aim to rebalance this narrative by laying out quantitative and compelling midcentury targets for both production and the environment. Our analysis shows that an increase of approximately 25%–70% above current production levels may be sufficient to meet 2050 crop demand. At the same time, nutrient losses and greenhouse gas emissions from agriculture must drop dramatically to restore and maintain ecosystem functioning. Specifying quantitative targets will clarify the scope of the challenges that agriculture must face in the coming decades, focus research and policy on achieving specific outcomes, and ensure that sustainable intensification efforts lead to measurable environmental improvements. We propose new directions for research and policy to help meet both sustainability and production goals.

The impact of changes in income distribution on food demand in the urban Jiangsu province of China is estimated in this study. Findings suggest that changes in income distribution have a considerable impact on the demand for individual food commodity groups. Therefore, given that a significant change in income distribution has occurred in urban China, food demand projections should account for expected changes in future income distribution.

Clearer understanding is needed of the premises underlying SI and how it relates to food-system priorities. Food security is high on the global policy agenda. Demand for food is increasing as populations grow and gain wealth to purchase more varied and resource-intensive diets. There is increased competition for land, water, energy, and other inputs into food production. Climate change poses challenges to agriculture, particularly in developing countries ( 1 ), and many current farming practices damage the environment and are a major source of greenhouse gases (GHG). In an increasingly globalized world, food insecurity in one region can have widespread political and economic ramifications ( 2 ).

Estimating future demand for food is a critical aspect of global food security analyses. The process linking dietary changes to wealth is known as the nutrition transition and presents well-identified features that help to predict consumption changes in poor countries. This study proposes to represent the nutrition transition with a nonhomothetic, flexible-in-income demand system. The resulting model is estimated statistically based on cross-sectional information from FAOSTAT. The model captures the main features of the nutrition transition: rise in demand for calories associated with income growth; diversification of diets away from starchy staples; and a large increase in caloric demand for animal-based products, fats, and sweeteners. The estimated model is used to project food demand between 2010 and 2050 based on a set of plausible futures (trend projections and Shared Socioeconomic Pathways scenarios). The main results of these projections are: (a) global food demand will increase by 47%, less than half the growth in the previous four decades; (b) this growth will be attributable mainly to lower-middleincome and low-income countries; (c) the structure of global food demand will change over the period, with a doubling of demand for animal-based calories and a much smaller 19% increase in demand for starchy staples; and (d) the analysis of a range of population and income projections reveals important uncertainties—depending on the scenario, the projected increases in demand for animal-based and vegetal-based calories range from 74% to 114%, and from 20% to 42%, respectively.

Global food demand is increasing rapidly, as are the environmental impacts of agricultural expansion. Here, we project global demand for crop production in 2050 and evaluate the environmental impacts of alternative ways that this demand might be met. We find that per capita demand for crops, when measured as caloric or protein content of all crops combined, has been a similarly increasing function of per capita real income since 1960. This relationship forecasts a 100–110% increase in global crop demand from 2005 to 2050. Quantitative assessments show that the environmental impacts of meeting this demand depend on how global agriculture expands. If current trends of greater agricultural intensification in richer nations and greater land clearing (extensification) in poorer nations were to continue, ∼1 billion ha of land would be cleared globally by 2050, with CO2-C equivalent greenhouse gas emissions reaching ∼3 Gt y–1 and N use ∼250 Mt y–1 by then. In contrast, if 2050 crop demand was met by moderate intensification focused on existing croplands of underyielding nations, adaptation and transfer of high-yielding technologies to these croplands, and global technological improvements, our analyses forecast land clearing of only ∼0.2 billion ha, greenhouse gas emissions of ∼1 Gt y–1, and global N use of ∼225 Mt y–1. Efficient management practices could substantially lower nitrogen use. Attainment of high yields on existing croplands of underyielding nations is of great importance if global crop demand is to be met with minimal environmental impacts.

A censored Exact Affine Stone Index incomplete demand system is estimated for 23 packaged foods and beverages and a numéraire good. Instrumental variables are used to control for endogenous prices. A half-cent per ounce increase in sugar-sweetened beverage prices is predicted to reduce total calories from the 23 foods and beverages but increase sodium and fat intakes as a result of product substitution. The predicted decline in calories is larger for low-income households than for high-income house-holds, although welfare loss is also higher for low-income households. Neglecting price endogeneity or estimating a conditional demand model significantly overestimates the calorie reduction.

Objective To quantify the relation between food prices and the demand for food with specific reference to national and household income levels.Design Systematic review with meta-regression.Data sources Online databases of peer reviewed and grey literature (ISI Web of Science, EconLit, PubMed, Medline, AgEcon, Agricola, Google, Google Scholar, IdeasREPEC, Eldis, USAID, United Nations Food and Agriculture Organization, World Bank, International Food Policy Research Institute), hand searched reference lists, and contact with authors.Study selection We included cross sectional, cohort, experimental, and quasi-experimental studies with English abstracts. Eligible studies used nationally representative data from 1990 onwards derived from national aggregate data sources, household surveys, or supermarket and home scanners.Data analysis The primary outcome extracted from relevant papers was the quantification of the demand for foods in response to changes in food price (own price food elasticities). Descriptive and study design variables were extracted for use as covariates in analysis. We conducted meta-regressions to assess the effect of income levels between and within countries on the strength of the relation between food price and demand, and predicted price elasticities adjusted for differences across studies.Results 136 studies reporting 3495 own price food elasticities from 162 different countries were identified. Our models predict that increases in the price of all foods result in greater reductions in food consumption in poor countries: in low and high income countries, respectively, a 1% increase in the price of cereals results in reductions in consumption of 0.61% (95% confidence interval 0.56% to 0.66%) and 0.43% (0.36% to 0.48%), and a 1% increase in the price of meat results in reductions in consumption of 0.78% (0.73% to 0.83%) and 0.60% (0.54% to 0.66%). Within all countries, our models predict that poorer households will be the most adversely affected by increases in food prices.Conclusions Changes in global food prices will have a greater effect on food consumption in lower income countries and in poorer households within countries. This has important implications for national responses to increases in food prices and for the definition of policies designed to reduce the global burden of undernutrition.

This paper discusses the influences on food and farming of an increasingly urbanized world and a declining ratio of food producers to food consumers. Urbanization has been underpinned by the rapid growth in the world economy and in the proportion of gross world product and of workers in industrial and service enterprises. Globally, agriculture has met the demands from this rapidly growing urban population, including food that is more energy-, land-, water- and greenhouse gas emission-intensive. But hundreds of millions of urban dwellers suffer under-nutrition. So the key issues with regard to agriculture and urbanization are whether the growing and changing demands for agricultural products from growing urban populations can be sustained while at the same time underpinning agricultural prosperity and reducing rural and urban poverty. To this are added the need to reduce greenhouse gas emissions and to build resilience in agriculture and urban development to climate change impacts. The paper gives particular attention to low- and middle-income nations since these have more than three-quarters of the world's urban population and most of its largest cities and these include nations where issues of food security are most pressing.

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.