The Agricultural Outlook 2021-2030 is a collaborative effort of the Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture Organization (FAO) of the United Nations. It brings together the commodity, policy and country expertise of both organisations as well as input from collaborating member countries to provide an annual assessment of the prospects for the coming decade of national, regional and global agricultural commodity markets. The publication consists of 11 Chapters; Chapter 1 covers agricultural and food markets; Chapter 2 provides regional outlooks and the remaining chapters are dedicated to individual commodities.

Includes entries for maps and atlases.

This comprehensive overview of local food systems explores alternative definitions of local food, estimates market size and reach, describes the characteristics of local consumers and producers, and examines early indications of the economic and health impacts of local food systems. Defining ¿local¿ based on marketing arrangements, such as farmers selling directly to consumers at regional farmers¿ markets or to schools, is well recognized. Statistics suggest that local food markets account for a small, but growing, share of U.S. agricultural production. For smaller farms, direct marketing to consumers accounts for a higher percentage of their sales than for larger farms. Charts and tables.

This study has developed a methodology that builds on the approach to measuring agricultural research returns suggested by Alston et al (1995). We have integrated a herd model to measure the potential size of impact of a new technology, GIS, to predict where this impacts is likely to be felt, and the economic surplus model to estimate some of the costs of trypanosomosis, the potential benefits of controlling it, and potential returns to vaccine research. The advantage of this approach is that it uses field data and GIS analysis to determine where and how much impact research will have on livestock productivity, rather than 'guesstimates' by researchers, as has often been done in previous studies of returns to agricultural research. It is an approach, however, that requires much data and the type of information that is still scarce in many developing countries. This includes evidence of the productivity impacts of a given livestock technology at the herd, rather than individual animal level, and access to GIS data at the lowest adminstrative level possible (e.g. district). Ideally, household level survey data are used to complement the GIS data and verify the recommendation domain. Thus this approach will be enhanced in future analyses by the availability of a wider range of data collected at the household level from different livestock production systems to examine more closely the question 'Impact on whom?'