Integrated System for Food and Agriculture Decision Makers

摘要

Need for a coherent and sound data base for policy making and decision taking on food and agriculture related issues was recognized while creating Food and Agriculture Organization of the United Nations (FAO). FAO, recognizing this need, developed food balance sheet in 1949 to bring together data on food supply and production for individual country and subsequently improved in stages. The last such improvement was released in 1996 in a publication entitled "System of Economic Accounts for Food and Agriculture" (SEAFA). SEAFA was designed to meet analytical needs for the formulation of plans and policies relating to food and agriculture by integrating various databases on production, consumption, capital formation relating to crop & animal husbandry, forestry, fishery and food production. SEAFA provided support for analyzing efficiency of production of food and agricultural products and evaluating supply and use of food (in terms of level of nutrition intake). Main objectives of agricultural policies go much beyond this. Policy makers would very much like to look into ways and means to secure improvements in the production and distribution of food and agricultural products which could lead to improve the living condition of rural population. Some of these issues have been discussed at various international forums. However, to make a dynamic decision making it is necessary to integrate all issues in a single envelop. Basic architecture of SEAFA is based on production units viz. institutional units (like agricultural households), agricultural establishments and food & agricultural products. This approach does not allow linking other issues like infrastructural needs, state of environment, etc. to the production system. In the present paper a revised extended system based on area as a unit for compiling the database has been proposed. The new system based on micro-macro linkage using bottom up approach for collection, compilation and analysis of food and agriculture data. The system propagates use of indicators. The approach has in-built advantage of optimum use of partial available data.