PurposeChina's remarkable income growth has changed the food landscape in recent years. Chinese consumers are demanding greater food quantity and quality and changing the nutrient content of their diets. Most food demand studies are based on data from earlier time periods before these structural changes had taken hold. The purpose of this paper is to show how the rapid change in food markets and surprisingly slow growth of food imports warrants a new assessment of food demand in China.Design/methodology/approachEngel equations measuring elasticities of food quantity and quality purchases with respect to household income are estimated. These estimates are then converted to nutrient elasticities to show how the availability of nutrients varies with income based on the Engel demand relationship.FindingsThe income elasticities diminish as income rises. Households in the top tier of the income distribution appear to have reached a saturation point in the consumption of most food items. As income rises, most additional spending is on foods with higher unit values that may reflect better cuts of meat or branded items. The pattern of food purchases for households at different income levels suggests that protein, saturated fat, and cholesterol intake rises with increased income. The change in diets prompted by rising income is most pronounced for low‐income households.Originality/valueThis paper applies a unique approach to measure income, quality, and nutrient elasticities within the same framework of Engel relationship. The finding has important implications for opening new market opportunities of imported foods and understanding dietary change in China.
Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China’s chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for ¼ of the sample data which was not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China’s first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives.
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