Preparation of Supply Utilization Accounts James Geehan Statistician, FAO Rome

Recap: Supply Utilization Accounts (SUAs) Supply Utilization Accounts are time series data dealing with commodity statistics measuring elements of : Domestic Supply (Production, Imports and Opening stocks) AND Domestic Utilization (Exports, Seed, Feed, Waste, Processing, Food, Other utilization, and Closing stocks) Supply and Utilization elements are stored physically together to enable comparison of food availability with food use.

Recap: Supply Utilization Equation These elements can be inter-related in a balancing equation. Where: the sum of the Domestic Supply elements: Production + Imports + Opening stocks equals the sum of the Domestic Utilization elements: Exports + Feed + Seed + Waste + Processing + Food + Other utilization + Closing stocks. Notes: Other Utilization Used to account for food not consumed by the domestic population (e.g., tourists, temporary workers). The SUA will be distorted unless quantities assigned to Other Utilization are not deducted from the food available for consumption.

Recap: Supply Utilization Equation SUAs can most easily be thought of as a single row of data, capturing data for each single commodity, and for each element of the Supply and Utilization equation. Care should be taken to ensure, as far as possible, that the balance of the equation is always maintained. The basic elements (data) of each individual SUA are not related to the other commodities, except the utilization element ‘Processing’, which corresponds to the quantity recorded as ‘Input’ in the account of the derived commodities. Example: SUA for wheat 425,000 Mt

Supply Utilization Accounts – why do we need them? No longer meaningful to deal separately with individual statistical series, such as those of production and trade. Necessary to establish links between data for related commodities and deal with flows and matrices rather than as individual and disparate sets of data. SUAs allow the data for a single commodity to be tracked all the way from production and utilization to final consumption. Storing commodity information in the SUA framework ensures data are internally consistent. Allows comparisons and data quality checks on the statistical data supplied by various national and/or international agencies.

Practical uses of SUAs SUAs are used to prepare a number of statistical measures and outputs, including: Production Index Numbers Self-Sufficiency Ratios Import Dependency Ratios Production Yearbook Trade Yearbook Food Balance Sheets SUAs

Different types of SUA data collected Each year (1961 ->) and for each country (total of about 200) data measuring production, trade, and available food for consumption are collected, or estimated, and compiled for about 1500 food commodity items. Each year (1961 ->) and for each country (total of about 200) data measuring production, trade, and available food for consumption are collected, or estimated, and compiled for about 1500 food commodity items. Supporting or supplementary information (such as harvested area/sown, live animals, milking and slaughtered animals) are also compiled. Supporting or supplementary information (such as harvested area/sown, live animals, milking and slaughtered animals) are also compiled. The data source (official, semi-official or estimates) is also recorded for each figure in order to monitor data quality within the SUA. The data source (official, semi-official or estimates) is also recorded for each figure in order to monitor data quality within the SUA. Additional variables (such as population figures, nutrition factors, and primary commodity conversion factors) are also entered as a pre- requisite for creating Food Balance Sheets. Additional variables (such as population figures, nutrition factors, and primary commodity conversion factors) are also entered as a pre- requisite for creating Food Balance Sheets.

Official Data (Department of Statistics, Ministry of Agriculture) Non-Official Data (Oil World, United States Department of Agriculture) OR Adjusted Official Data Forecasts / Estimates Types of Data in SUA / FBS International or Country-specific Nutritional Factors (Calorie, Fat, Protein Values) Shares & Ratios (Waste, Seeding Rates, Extraction Rates) Building Supply Utilization Accounts

Production Data (Department of Official Statistics or Ministry of Agriculture Official Data) Trade Data (Customs & Excise Department Official Data) Population Data (UN Population Division Official Data) FAO Nutritional coefficients Food (Estimated using Household consumption survey) Stock movements (e.g., estimated from Marketing Authorities) Waste/Seed Feed/Processing (e.g., estimated by Industry experts / Empirical studies) Hard DataSoft Data

Supply Utilization Accounts (SUAs): Preparation steps 1. Data collection: from publications, by questionnaires (annual / virtual / special), by electronic means ( , ftp, web site). 2. Data input: Data control (reconciliation, adjustments and estimates) and data input. 3. Construction of SUA: ensuring the balance of the equations is maintained as far as possible (i.e., supply = utilization). 4. Total calorie/protein/fat supply: for all commodities, applying international or country specific nutritional values to available food quantities. 5. Per caput/per day calorie/protein/fat supply: calculated from the results in step (4.), using total resident population in the country. 6. Standardisation of commodities and creation of the FBS: conversion of all commodities into their primary products equivalent.

Problems related to the preparation of SUAs Incompleteness and inaccuracy of the basic SUA data tend to be the main problems. Even when the data are available, they are not always reliable or consistent with other data sources. Incompleteness Data is usually missing for at least some commodities in the SUA, although data generally tends to be available for the most important food commodities (e.g., wheat, rice, eggs, oils, beef, chicken, etc.) Non-commercial or subsistence production are usually not included or is not available. In some countries this may account for a significant proportion of domestic production. Basic data on the feed, seed and processing of crop and livestock products often does not exist and is reliant industry estimates. Import and export data may be accurate in the majority of countries, but in others, there may be significant amounts of trade across national boundaries, or across open boundaries, that go unrecorded.

Problems related to the preparation of SUAs (continued) Incompleteness (continued) Waste data as a result of storage or transportation may not be available. Other waste information, such as quantities intentionally discarded for the purpose of price control or disease control, may also be unavailable. Information on commercial stocks may be available from official or marketing authorities, factories, wholesalers and retailers, but inventories of catering establishments, institutions and households are unlikely to exist.

Problems related to the preparation of SUAs (continued) Inaccuracy Intra-trade (re-exports) can also distort net trade figures. Capturing accurate data recording mixed crops is also problematic. Also difficulties measuring nomadic and semi-nomadic livestock statistics. Complexity of time reference period / periodicity Often different time-reference periods are used to report production for different commodities. There is no single twelve month period that is fully suitable for recording the supply and utilization of ALL agricultural products. For example, Cassava is not always harvested at the same time

Problems related to the preparation of SUAs (continued) Statistical Discrepancies Statistical Discrepancies are a (positive or negative) imbalance in the Supply Utilization Account. They can be described by three I.’s – 1.Inconsistencies between the basic data (e.g., production, trade). 2.Inevitable – a typical SUA contains 200+ commodities, with over 6,000 data cells for each year. SUA basic data are obtained from different sources, collected in different ways, using different methodologies. Inconsistencies are inevitable. Above all Statistical Discrepancies are… 3.Irritating !

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