Research by Wageningen University & Research (WUR) on global food and nutrition security focuses on the question how to achieve transitions to a food system that will be adequately equipped to nourish the growing world population. One of the challenges of this transition is to evolve to a food system that will be sustainable (resource-efficient and with minimal impact on climate change and global warming), yielding affordable, trustworthy (safe), high-quality food products. This particular report is part of a study on the redesign of food value chains from linear value chains into circular adaptive value chain networks for nutrition and food security (Redesign or Adaptive Value Chain Networks for food and nutrition security (AdVaNs)). In view of the global trends of world population growth, urbanization, the efficient use of natural resources, mitigation of the impact of food production on climate change and global warming, this research addresses global food and nutrition security by developing a forecast model for the content and composition of local food baskets. Enablers of changes in these future food baskets are the growing economic welfare, advancing information technologies and sustainability issues that affect regional and global value chains. Knowledge about these trends in this future demand on food is searched for by policy makers and governments that are in need of accurate and reliable quantitative information for strategic decision-making. By developing forecasting models that are dedicated to human nutritional needs and consumption patterns, historic quantitative data can be transferred into future trends and predictions regarding food demand in specific regions. A methodology, using autonomous time based linear regression, was developed by the authors to predict a future food basket in terms of energy, composition and products for the near future in 2030 based on available historical data. The methodology was used for 4 regions in Mexico (Mexico City, North-, South- and Central Mexico). Also the amount of micro-nutrients, including vitamins and minerals, in the food was estimated. The forecasted results were also categorised by two demographic characteristics: income class (low income vs. high income) and the residential environment (urban vs. rural environment). The forecasting is based on FAO data in combination with national data for the prediction of the specific regional food baskets in Mexico. The results show that the urban region obtains more energy and vegetables, fruit and meat, having also the more wealthy class of the population. Also in Mexico most proteins and carbohydrates are consumed as part of staple foods. In this research validation of the methodology was carried out by using data from the past to predict the situation in 2011 of the composition of the food basket. This comparison of the present data with the forecasted data shows that this linear regression method can be used to forecast the food basket in 2030 for a majority of product groups, but to a smaller extent for milk and pulses in particular
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