The FAP Data Bank. Part 1: Organization, Contents and Management

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NOT FOR QUOTATION WITHOUT PERMISSION OF THE AUTHOR

THE FAP

DATA

BANK

PART 1: ORGANIZATION, CONTENTS AND MANAGEahENT

U. Sichra

Working Papers a r e interim r e p o r t s on work of t h e International Institute for Applied Systems Analysis a n d have received only limited review. Views o r opinions expressed herein do n o t necessarily represent those of t h e Institute o r of i t s National

ember

Organizations.

INTERNAT1 ONAL IYSTIWTE FOR APPLIED SYSTEMS ANALYSIS 2361 Laxenburg, Austria

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Understanding the n a t u r e and dimensions of the world food problem and the policies available to alleviate it has been the focal point of the IIASA Food and Agriculture Program (FAP) since i t began in 1977.

National food systems a r e highly interdependent, and yet the major policy options exist a t t h e national level. Therefore, to explore these options, it is necessary both to develop policy models For national economies and to link them together by trade and capital transfers. Over the years FXP has, with the help of a network of collaborating institutions, developed and linked national policy models of twenty countries, which together account for nearly 80 percent of important agricultural attributes such as area, production. population, exports, imports and so on. The remaining countries a r e represented by 14 somewhat simpler models of groups of countries.

To support the work. a d a t a bank was organized a t t h e very beginning of FAP. The FAP data bank has grown in size and complexity and now coqtains large volumes of data obtained from different sources.

Ulrike Sichra has described t h e organization, contents and management of the data bank in this paper. Methods and practice for updating and aggregation are described in an accompanying paper.

Kirit

S.

Parikh Program Leader Food and Agriculture Program

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PREFACE

The FAP Data Bank is a large collection of data from different sources and constitutes a basic element in t h e modelling activities of t h e Food a n d Agricul- t u r e Program. This d a t a bank was c r e a t e d at the very beginning of the Food and Agriculture Program a n d h a s grown ever since, in size and complexity. In order to b e t t e r describe t h e

F U

Data Bank a n d to document i t s contents, t h e vast amount of information h a s been split into two parts:

"Part 1:" Organisation, Contents and Management

"Part 2:" 'Updating a n d Aggregating

-

Methods and Practice

P a r t 2 is designed for those who will take care of updating of t h e FAP Data Bank. That volume n o t only assumes t h a t the reader is familiar with P a r t 1, b u t also t h a t she or h e is an experienced computer user, preferably a t IIASA

P a r t 1, this document, is t h e introductory paper on which P a r t 2 is based.

I t addresses a general audience, interested in data for agricultural modelling, serving a t the same time a s a document for the FAP modelling activities. The t e r m "aggregation" will frequently be used in this paper. To understand it in its whole complexity t h e reader is referred to:

"The Aggregation of t h e Agricultural Supply Lrtilisation Accounts", WP-83- 42, IIASA.

In t h a t paper the methodology and details of aggregations a r e described a t length.

I t is hoped t h a t the two p a r t s describing the FP9 Data Bank, of which this is t h e first, will satisfy a long felt need for documentation and clarification.

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The n a t u r e of this paper makes it impossible to list all t h e persons and organisations t h a t helped towards i t s coming into existence. The main contri- butions to t h e wealth of data come from t h e following institutions;

*

The Food and Agriculture Organisation of the United Nations (FAO) Rome, Italy,

*

The International Labour Organisation of t h e United Nations (ILO), Geneva.

Switzerland; and

+ The World Bank, Washington DC. USA

To these organisations the FAP is deeply indebted, recognizing t h a t without t h e i r active support the FAP Data Bank would hardly have come i n t o existence.

Most of t h e past a n d present staff of t h e F M has been helpful in one way o r o t h e r to creating the FAP Data Bank. and t h u s originating this paper. Many suggestions from both leaders of t h e program, Ference Rabar a n d Kirit S-Parikh have contributed to the usefulness of t h e data b a n k Numerous persons in t h e FAP Collaborating Network have made available new data for t h e i r country, o r have updated the existing data for it. Our deep gratitude is addressed t o them.

Without t h e dedication of Guenther Fischer t h e Data Bank a n d i t s managing routines would not have evolved. Bozena Lopuch and Stefanie Hoffmann worked with big dedication on t h e CME3 a n d fertilizer data. The formatting efforts of Lilo Roggenland a n d Bonnie Riley c a n be directly seen. Without t h e careful reviewing done by Gerhard Kroemer a n d Laslo Zeold many p a r t s would have remained unclear.

And l a s t but not least we wish to thank all the users of t h e FAP d a t a bank who by using the data, and with t h e i r questions, and correction of e r r o r s and have helped t h e FAP Data Bank t o become a useful instrument in t h e modelling activities of the FAP.

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vii

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...

?

6.2. I need the following data

...

^!

6.2.1. Hard Copy 6.2.2. Binary Data 6.2.3. Magnetic Tape

6.3. Correct t h e Following Data 6.4. Include New Series

6.5. Aggregations

- 6.6. Compare Different Time Series 7. Data and Their Contents

7.1. Countries 7.2. Commodities 7.3. Files

8. Exceptions a n d Corrections

8.1. Exceptions for Feed Programs 8.2. Exceptions For Kenya

8.3. Exceptions for Australia (and New Zealand) 8.4. Exceptions and Corrections for New Zeaiand 8.5. Corrections for Other Countries

9. Interactions with Other Institutions

9.1. Food and Agriculture Organization of the United Nations.

FAO, Rome

9.2. Center For World Food Studies, Amsterdam 9.3. F r e e University of Brussels

References

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Appendices Appendix 1:

Appendix la:

Appendix 2:

Appendix 2a:

Appendix 2b:

Appendix 3:

Appendix 3a:

Appendix 4:

Appendix 5:

Appendix 6:

Appendix 7:

Appendix 8:

Appendix 9:

Appendix 10:

Appendix 11:

FA0 Countries FAP Countries

FA0

+

FAP Commodities Macro Commodities FAP Commodities FA0 Elements, Group1 FAP Elements, Group2

Explanation of t h e Output from "suputa"

Type of Data, Files. Coverage

Fertilizer. Method, Sources, Coverage Output from " s b

Output from "pprd"

Output from "suputa"

Output from "cvt"

Sample Control File for Plotting Tables

Table 1: Factors for Capital Stock Table 2: Exceptions for New Zealand

1: Chain of SUA's Figure 2: Aggregations Figure 3: SUA Flow

Figure 4: Aggregation Chain of 260 Commodities Figure 5: Aggregation of 27 Commodities

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THE FAP DATA BANK

PART I: ORGANIZATION. CONTENTS

1. Introduction

The modelling activities of the Food a n d Agriculture Project a t IIASA rely, among others, on an extensive s e t of data. Broadly speaking this data can be divided into time series a n d single items.

In this paper emphasis is given to t h e time series d a t a a n d only in one instance factors a r e discussed. which do not change i n time (nutritional values).

The purpose of t h e following pages is t o present an overview of t h e data, mostly referring to agriculture, which is available in computerized form and c a n be accessed a t I I M A with t h e help of staff members of t h e

FXP.

The s t r u c t u r e of t h e data files, its origin and contents is presented in t h e first sections. In some instances the methodology for arriving a t t h e time series is presented in great detail. The n e i t sections deal with the logistics of handling t h e data, like looking a t data, extracting data. updating time series, plotting data. etc. As this publication is not only meant for readers outside FAP, but also for the staff who actually handle t h e data bank, some sections a r e included which should support them in t h e i r daily work.

This paper concludes with a n overview of the d a t a available a t

FAP

i n computerized form, its deviations from t h e original state, and the Institutions with whom FAP interacts for d a t a gathering purposes. More details on some of t h e data origins a n d computations can be found in f u r t h e r working papers. listed in t h e references section.

In order to help t h e reader of this paper, a n d the user of the data.

numerous appendices have been included. which tabulate countries. commodities, etc. or display sample outputs of the d a t a b a n k

A word of caution for the computer expert; the term data bank is used here, not for a sophisticated d a t a base, relational or network like. obeying an even more sophisticated data base management system. In this document data bank is a s e t of sequentially organized time series, in machine readable form which obey an internal logic a n d c a n be manipulated by, for example. Fortran programs. The

FM

chose this mode of d a t a handling due to the lack of space on t h e inhouse computer for storing large amounts of data, and in order t o gain maximum flexibility with respect to exchanging data with other collaborating institutions.

2. Organization

The FAP data bank consists of an arbitrary number of time series stored in an arbitrary number of files which can be located on disk and/or magnetic tape.

Independent of the physical location, t h e files a r e organised in the same way:

every record consists of 7 integers anti 16 pairs of real and character81 vari- ables, which are stored sequentially, in binary (unformatted) rnocie in the file.

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Therefore, t h e s t a t e m e n t

will always be u s e d for reading, and

is always t h e write s t a t e m e n t ; prior t o any o t h e r s t a t e m e n t t h e following declaration s t a t e m e n t h a s to be made:

One c a n imagine t h e d a t a being stored on tape or disk in t h e following way:

Codes and d a t a have t h e following meaning:

system code, 2 digits, value.mostly 11, not used

c o u n t r y code. ¹t o 3 digits. e.g. 9,11,231 (see Appendix 1)

commodity code, 1 t o 4 digits, e.g. 1,15,882,1532 (see Appendix 2, 2a) element code. 1 to 2 digits, e.g. 3.15 ( s e e Appendix 3, 3a)

dimension code, 1 digit; 1,2.3 or 4 (see Appendix 3. 3a) first y e a r indicator, 2 digits, e.g. 61,65,66

.

creation d a t e , 1 t o 4 digits. not used. often set.

d a t a of y e a r "first y e a r indicator"

d a t a of y e a r "first y e a r indicator",

+

1 d a t a of y e a r "first y e a r indicator", + 2

x(16) d a t a of y e a r "first year indicator",

+

15

s(1) s t a t u s indicator for y e a r of "first year indicator"

(2) s t a t u s indicator for y e a r of "first year indicator", + 1

s(16) s t a t u s i n d i c a t o r for y e a r of "first year indicator", +15

2.1. System Code icd(1)

The system code icd(1) is used a t FA0 for file keeping purposes, but has not been taken i n t o a c c o u n t a t IIASA. The Location however is reserved, and t h e code from FA0 is generglly taken over, b u t no program takes i t as a parameter.

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2.2. Country Code icd(2)

The country code icd(2) is taken directly from FA0 with the exception of 3 codes:

Country code 0 is used at FA0 only for international factors (e.g.

nutrients). In the FAP data bank it also stands for aggregates, e.g. all FAP countries t o "one" country, for t h e country code in the world market prices, or in t h e flle with the averages over all countries.

Code 888 is used for t h e EEC aggregate, and 777 for the CMEA aggregate.

In Appendix 1 a list of all countries and their codes is given.

This list covers all possible FA0 country codes. which does not mean t h a t the FAP Data Bankcontains information for each of these countries. Only a subset of the FA0 countries in Appendix 1 is dealt with a t FAP. The selection was done on the basis of major economic indicators like production, imports and exports of agricultural products. and population and area. The modeling activities a t FAP also influenced t h e choice. The aim was to choose a minimum set of countries which jointly cover a t least 80111 of the world's total of any given indicator. Together with t h e constraints of availability of data and the range of FAP's collaborating institutions, t h e countries listed in Appendix la. called the FAP4 countries, were chosen. For the countries with an '*' there a r e Supply Utilisation Accounts (SL:.As) available a t all stages of aggregation for all time spans

.

For the countries without the marker only some aggregations are covered in the F M Data Bank

The data dictionary for countries is stored in the file nfao.2. This is the file used when producing data listings. Any new country codes which will be printed in full text have to appear in the file nfao.2. If no e n t r y is there the data record is stored. but the deciphered listing will have

"******"

entries instead of the country's name. This same comment applies for commodities icd(3), elements icd(4) and dimensions icd(5).

2.3. Commodity Code icd(3)

The commodity codes icd(3) .are partly taken from FA0 (main commodities and derived products) and partly designed a t FAP (aggregations to 27 and 16 commodities, macro data, etc). In Appendix 2 one finds all possible commodity codes and their corresponding text.

The tirst few lines of this appendix are:

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"group"

0 1 14 14 0 3 17 17 16 - 02 0 3

t e x t

population

macroeconomics 1 macroeconomics 2 t o t a l t r a d e

l a n d u s e irrigation l a n d u s e wheat flour

The first 4 digits in e a c h l i n e above a r e t h e commodity code, i.e.

0001=population, 0003=total t r a d e , e t c ; t h e l a s t 2 digits a r e t h e "group" a commodity belongs to. This information i s only s t o r e d in t h e dictionary file nlao.3.1 ( o r nfao.3.22) ( s e e also Appendix 2 a n d Appendix 2a) to be u s e d i n t h e listing program. when t h e telrt for t h e e l e m e n t s is selected, a n d is n o t i n c l u d e d in t h e d a t a r e c o r d itself.

The "group" codes give f u r t h e r information a b o u t t h e commodity: main c r o p commodities belong t o g r o u p 02, derived c r o p p r o d u c t s t o g r o u p 03, etc. In the e l e m e n t list (Appendix 3 a n d 3a), e l e m e n t 4 (yield, e x t r a c t i o n r a t e ) h a s t h e same code, whether in g r o u p 0 2 o r 03, b u t t h e t e x t t h a t goes with i t is diflerent, for convenience of t h e r e a d e r .

P r o g r a m s which write t e x t l o r t h e d a t a a n d their codes t a k e t h e commodity t e x t from t h e file nfao.3.1 a n d nlao.3.22. The second file is a s u b s e t of t h e first.

a n d heips t o s p e e d u p p r o c e s s i n g y h e n very aggregated d a t a h a s t o be printed.

a s t h e commodity choice i s m u c h smaller t h e n .

2.4. Element Code icd(4)

The meaning of t h e e l e m e n t codes icd(4) is listed in Appendix 3 a n d 3a. The first 2 digits a r e t h e commodity g r o u p t h e s e elements belong to, t h e l a s t 2 digits a r e t h e a c t u a l element codes.

As a n example t a k e a m a i n c r o p product, a n d a main animal product. The elements c a n be 1, 2,

...

u n t i l 17, t h e corresponding t e x t is:

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e l e m e n t t e x t

c r o p animal

opening s t o c k s a r e a so-

a r e a h a r v e s t e d yield

production imports from s t o c k s t o s t o c k s e x p o r t s feed s e e d waste processing food

non-food closing s t o c k s seeding r a t e

opening n u m b e r

potential n u m b e r of females of reproducing age a c t u a l n u m b e r of females reproducing

b i r t h r a t e b i r t h s live imports from s t o c k t o stocks live e x p o r t s

---

n a t u r a l d e a t h s n u m b e r s l a u g h t e r e d

---

o t h e r utilisation closing s t o c k s take-off r a t e

The d a t a dictionary design is s u c h t h a t t h e r e may not be more t h a n 17 ele- m e n t s in e a c h group. This h a s historical reasons a n d is r e l a t e d t o the FA0 d a t a files design.

2.5. Dimension Code icd(5)

The fifth code icd(5) in a d a t a r e c o r d is called dimension. It c a r r i e s information on t h e u n i t of m e a s u r e m e n t of t h e d a t a which follows. There can be u p t o 4 dimensions, a n d i n g e n e r a l t h e following convention i s active:

icd(5) t e x t

1 q u a n t i t y measure 2 value measure 3 u n i t price 4 u n i t price

There a r e some e x c e p t i o n s however i n t h e aggregations for

FAP,

which will be discussed l a t e r .

In Appendix 3 a n d 3 a t h e t e x t for t h e 3 dimensions i s also given (in columns 3 t o 5). There is no t e x t for icd(5)=4 due to programming reasons. In the same way a s t h e e l e m e n t s t h e dimensions also have different text. depending on t h e g r o u p a commodity belongs to. The d a t a dictionary for t h e dimensions is t h e s a m e a s for t h e elements, i.e. t h e file bin.1 (bin.22). which a r e random a c c e s s files in binary form.at.

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2.8. F k s t Year Indicator icd(6)

The first y e a r indicator, s t o r e d i n t h e 6th position of the code field (icd(6)), is u s e d for t h e mapping between t h e d a t a which follows a n d t h e y e a r s of t h e calendar. It h a s n o t e x t a s s o c i a t e d with it.

2.7. Creation Date icd(7)

The l a s t code icd(7) is n o t meaningful for

FAP

purposes.

2.8. Status Indicator

Each y e a r of d a t a has. immediately following it, a n i n d i c a t o r for t h e s t a t u s of t h e data. These a r e s ( l ) , s(2),

...

s(16). This o n e c h a r a c t e r c a n be;

s(i> t e x t

0 o r blank official figure

I unofficial figure

F FA0 e s t i m a t e C c a l c u l a t e d

AIter going t h r o u g h some of t h e aggregation programs o t h e r s t a t u s indica- t o r s may be found, b u t similar t o icd(1) a n d icd(7) t h i s information is n o t r e l e v a n t when processing t h e d a t a in F.W.

I t h a s already been pointed o u t t h a t t h e r e c o r d s a r e written sequentially i n t o a file, a n d t h a t a n y n u m b e r of r e c o r d s c a n be organized i n t o a file. The o r d e r of t h e r e c o r d s m u s t .be by increasing code n u m b e r s icd(2); icd(3),

...,

icd(5), with icd(5) (dimension) changing first. This is a m u s t b e c a u s e most pro- g r a m s rely on t h e f a c t t h a t t h e d a t a is s o r t e d i n t h i s way, a n d would otherwise r e p o r t on missing data. o r do wrong things. From t h e d a t a point of view however i t i s irrelevant in which s e q u e n c e d a t a is stored.

As a consequence of t h i s ordering s c h e m e t h e time s e r i e s on a specific file a r e ordered by increasing c o u n t r y code icd(2). within a c o u n t r y by increasing commodity code icd(3), withing a commodity by increasing e l e m e n t code icd(3) a n d within a n e l e m e n t by i n c r e a s i n g dimension code icd(5). An example of some t i m e s e r i e s could be:

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icd(1) icd(2) icd(3) icd(4) icd(5) icd(6) icd(7) data

The data records a r e designed such t h a t they can only contain exactly 16 years of data. If for any number of years between the "first y e a r indicator" year a n d "fist year i n d i c a t o r U + l 5 data does not exist, zeros a r e filled in. Therefore.

zero can mean t h a t e i t h e r d a t a is not available, t h a t i t h a s not been inputted. or t h a t it is really zero. In general it is clear form t h e type of series what a zero entry could mean. In t h e case of element 8 ( t o stocks), a zero e n t r y can frequently be found. Production (element 5) of a commodity might be zero as of a certain year, or up t o a certain year, if t h a t product h a s been newly introduced or its production given up. Time series with only zeros a s data a r e generally not t o be found in t h e d a t a bank.

All existing d a t a management programs see t o it t h a t no 2 records with the same code a r e c r e a t e d If there should be such 2 records however, search programs would only pick up the Arst.

From t h e logical a n d data organisational point of view it does not make any difference whether t h e r e a r e 16 years in each time series. o r less. or more. or if t h e number of years is variable-But t h e computer programs t h a t handle the data are designed s u c h t h a t they require exactly 16 years, a n d most programs even rely on t h e fact t h a t the Arst year indicator is the same for all series in one file. The logic of t h e search programs also suggests this.

In the future. zvith more data coming in. it would be useful to adapt some of t h e programs (printing, reading, merging) to allow for variable number of years.

For this purpose t h e first entry in the code field (icd(1)) or t h e last entry (icd(7)) could be t h e number of years in t h e time series.

The read and write statements would then look somewhat like this:

Aggregation and price producing programs should probably be left with t h e b e d number of years p e r time series (16 currently), a n d series starting in different years should not be put into the same file a s t h e programs do not check for each read d a t a record the first year indicator.

Currently t h e r e a r e time series available which s t a r t in 1961, 1965 and 1966. The series starting in 65 have t h e average 1961-1965 data as an e n t r y for 1965; the o t h e r series always have yearly data. A s a consequence of its Data Management System. FA0 only reports on integer time series (no digits after the decimal point). For this reason t h e 4th element icd(4)=4 (extraction rate, yield, exchange rate. e t c ) a r e expressed in o t h e r units than expected; they have to be divided by 10**4 in order to arrive at t h e right order of magnitude.

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This exception applies t o e l e m e n t icd(4)=4 in g r o u p s 1 t o 17 a n d g r o u p 24.

A f u r t h e r exception is e l e m e n t icd(4)=13 in groups 23 a n d 29 ( e x c h a n g e rates).

As a n example. production i s r e p o r t e d i n metric tons, a r e a h a r v e s t e d in h e c t a r e , yield in 100gr/ha.

The d a t a files a t FAP (IIASA) c a n be found on disk a n d o n tape. Tapes c a n be

"mt-tapes", which is g e n e r a l l y t h e c a s e for small d a t a files. which easily fit on disk, a n d which c a n b e quickly r e s t o r e d from tape. There a r e some t a p e s which have only o n e d a t a file on them. These have b e e n p u t o n t o t a p e b y using t h e UNIX c o m m a n d dd. without a n y blocking o r converting. This is g e n e r a l l y done with large d a t a files (1000. blocks o r more), which c a n t h e n be processed directly f r o m t a p e a n d do n o t have t o be w r i t t e n t o disk Arst (for extraction, aggregation, e t c ) .

3. Types of Data FUes

I t c a n easily be s e e n t h a t t h e r e a r e a n u m b e r of classification m e t h o d s for t h e d i d e r e n t files of t h e FAP Data Bank. They c a n be s o r t e d depending on t h e i r origin, t h e i r c o n t e n t s , t h e i r time span. etc.

3.1. Data Files by Origin

Taking t h e origin of t h e d a t a files a s a classification c r i t e r i o n , o n e c a n divide t h e FAP Data i n t o t h r e e main groups:

1. Original FA0 files;

e.g. P r o d u c t i o n a n d Trade Yearbooks, original Supply Utilization Accounts.

FA0 population d a t a , p r o d u c e r prices, n u t r i t i o n a l values.

2. Aggregated data. c r e a t e d a t TIAS&

e.g. ag, ag27, ag9, vavo27, vavo9.

3. Other Origin:

e.g. l a n d data. labour force data. ILO data. m a c r o d a t a

This grouping i s useful from t h e u s e r ' s point of view, a s t h e d a t a c a n t h u s be u n d e r s t o o d by origin, a n d t h e s e a r c h for mistakes (e.g. wrong code o r data) c a n be made m o r e efficient (is t h e s o u r c e FAO. ILO, a c o m p u t e r program a t IIASA.

t h e aggregation logic. e t c ??). On t h e o t h e r h a n d some programs n e e d t o h o w t h e origin of t h e d a t a in o r d e r t o produce c o r r e c t results.

In t h e d a t a files of g r o u p 1 (original from F.40) t h e dimensions ( s t o r e d in icd(5)) h a v e a diflerent meaning t h a n in t h e d a t a of g r o u p 2 (results from aggregations) c r e a t e d a t IJASA.

In g r o u p 1 (original FA0 d a t a ) t h e dimensions a n d t h e i r meanings a r e dimension t e x t

(icd(5))

1 q u a n t i t y in mt

2 value in 1000 c u r r e n t US 3 price i n NC/mt

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In group 2 (aggregations made a t IIASA) t h e corresponding table reads:

dimension t e x t (icd(5))

1 q u a n t i t y in 1000 U S

2 q u a n t i t y i n m t "equivalent"

3 p r i c e ( u n i t s e e l a t e r ) 4 p r i c e ( u n i t s e e later)

The value dimension of all commodities a r e left o u t in t h e files of t h i s group. The Arst level of aggregation, although c r e a t e d a t IIASA. follows t h e dimension conventions of g r o u p 1. I t i s important t o be aware of this. PlLin d a t a Ales (without t e x t ) might be i n t e r p r e t e d wrongly without t h e information.

The program which adds t e x t t o t h e raw d a t a ( s u p u t a f ) n e e d s t h e p a r a m e t e r 1"

f o r Ales i n g r o u p 1 a n d "22" for files in g r o u p 2. The files i n g r o u p 3 c a n be t r e a t e d a s if t h e y belong t o g r o u p 1.

3.2. Data N e s by Content

-

The UN Food a n d Agriculture Organization (FAO) i n Rome,

-

The International Labour Organization (ILO) in Geneva.

-

The World Bank in Washington DC,

-

various reports, s t a t i s t i c a l yearbooks, calculations, etc.

From t h e FAP's modeling point of view t h e most i n t e r e s t i n g block of d a t a was the one From FAO. This i s c o n s t i t u t e d by t h e Production a n d Trade Year- books (on magnetic tape) first used by FAP to clearly identify t h e modeling work. i t s coverage, scope, e t c ; a n d t h e Supply Utilization Account (SUA), which h a v e since c o n s t i t u t e d t h e basis of t h e F M models. Consequently t h e g r e a t e s t eflorts were invested in t h e s e p a r t s of t h e FAP Data B a n k Time s e r i e s on p r i c e s a n d nutritional values also belong t o this group.

The ILO d a t a on population. labour force a n d Labour participation r a t e s a r e t h e basis for the population data. FA0 also provided some i n p u t t o t h i s section, on which will be r e p o r t e d below.

The World Bank d a t a for macroeconomic indicators is t h e basis for t h e FAP time s e r i e s on GXP, expenditures, e t c in c u r r e n t a n d c o n s t a n t values.

(17)

And Anally a n u m b e r of reports. s t a t i s t i c a l yearbooks, e t c were used t o fill gaps in years, definitions. commodities, etc.

3.2.1. Production and Trade Yearbooks

A n u m b e r of time s e r i e s between 1961 a n d 1976, from which t h e Production a n d Trade Yearbooks a r e printed a t FAO, a r e available i n computerized form a t IIASA These t i m e s e r i e s s e r v e d a s a basis .for F M ' s modelling work. In t h e meantime more t h a n o n e u p d a t e of Supply Utilization Accounts have a r r i v e d a t IIASA a n d t h e original Production a n d Trade Yearbook time s e r i e s from FA0 have become l e s s important.

All t r a d e a n d production of a g r i c u l t u r a l p r o d u c t s c a n be found i n t h e SUA time series, a n d in much finer detail. There are. however, f u r t h e r t i m e s e r i e s in i n p u t s t o a g r i c u l t u r e in t h e P r o d u c t i o n Yearbook files which c a n n o t be found in t h e SUA files.

Most of t h e FA0 c o u n t r i e s l i s t e d i n Appendix 1 a n d t h e commodities shown in Appendix 2 a r e included i n t h e Production a n d Trade Yearbook s e r i e s . The Production Yearbook Ale only gives n u m b e r s on production (in mt. e.g. wheat, o r n u m b e r e.g. t r a c t o r s , cattle). The Trade Yearbook file r e p o r t s on imports and exports in q u a n t i t y a n d value. The original files did n o t have t h e sophisticated s t r u c t u r e of t h e FAP d a t a b a n k but were subsequently adapted in o r d e r t o have a uniform s t r u c t u r e . These two t i m e s e r i e s a r e n o t actively used by t h e FAP any more.

3.2.2. Supply Utilization Accounts

The Supply Utilisation Accounts a r e a n extremely important s o u r c e of information for t h e FAP modeling work because with t h e i r d a t a it is possible to t r a c e in detail t h e supply a n d demand of agricultural goods. not only for n a t u r a l products s u c h a s maize, apples, c a t t l e , b u t also for processed or derived pro- d u c t s s u c h a s s t a r c h , c a n n e d fruits o r sausages. In Figure.1 o n e c a n s e e how t h e chain of supply and d e m a n d (utilisation) i s built, always keeping i n mind t h a t t h e balance between supply a n d demand h a s t o be met.

I t is evident t h a t t h e a m o u n t of information i n t h e SUA is v e r y l a r g e and n o t easily s t o r a b l e in one file. The a g r i c u l t u r a l models developed a t IIXA do not have a s detailed a commodity classification as FAO. Therefore i t was n e c e s s a r y t o arrive a t a much s m a l l e r commodity classification which could be u s e d in the national models. A n u m b e r of c o m p u t e r programs w e r e developed to r e d u c e the a m o u n t of information available t o a manageable n u m b e r (Figure.2).

A s h o r t example h e r e should make t h e m e t h o d of t h e aggregation, a s applied t o t h e SUA's. clear. In Figure.3 t h e so called "wheat t r e e " i s shown.

Each box r e p r e s e n t s a commodity (wheat. flour, bran, cake. e t c ) , t h e c o n n e c t i n g flows show t h e dependencies. Flour a n d b r a n r e s u l t simultaneously from wheat.

Cakes, pastry a n d macaroni a r e made e a c h from a s e p a r a t e a m o u n t of flour(*).

The subdivision of e a c h box sh'ows, in scale, t h e a m o u n t s of t h e various supply (production, import, from stock) a n d demand e l e m e n t s ( t o stock. exports, feed.

seed. waste, processing. food, o t h e r utilisation)(+). The width of t h e s t r e a m s corresponds t o t.he e x t r a c t i o n r a t e of t h e various products (e.g flour=0.25, bran=0.75). The SUA's a r e c a l c u l a t e d s u c h t h a t demand and supply a r e equal, (*) The other products contained in a cake, e.g. eggs, miik, etc. are not reported in the SUA's.

(+) The scaling corresponds t o Argentina, 1970 valaes.

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Figure 1:

S U A

supply Utilization supply Utilization

Production

Imports PRODUCT

Waste

Food

I I

from Stocks

Processed ³

\

⁼

^Input ^\

(19)

Rgure 2:

AGGREGATIONS 600 comm.

WHEAT Flour Bran

macaroni

Pastry

. .

APPLES juice canned a.

cider

WHEAT

APPLES PORK CATTLE

Meat POULTRY

Fat

frozen m.

8 8

27 comm.

16 comm.

WHEAT 3

. ^'WHEAT

. . . .

FRUITS + OTHER FOOD

NUTS

^{0 '}

. . .

. OTHER MEAT I

(20)

(21)

t h e discrepancy is g e n e r a l l y a t t r i b u t e d t o waste.

The aim of t h e aggregation is t o express all demand a n d supply, of wheat a n d i t s p r o d u c t s i n t h i s c a s e , in wheat t e r m s only. This means t h a t with t h e help of e x t r a c t i o n r a t e s t h e d e m a n d of derived products c a n be "converted b a c k t o t h e main product. In Figure.4 t h i s is shown graphically, also i n s c a l e , for t h e s a m e products a s in Figure.3. The production a m o u n t of wheat m u s t not c h a n g e a f t e r aggregation. b u t all o t h e r e l e m e n t s may. if somewhere i n t h e c h a i n of derived p r o d u c t s s u c h a n e l e m e n t occurs.

The e l e m e n t "processing" disappears completely from t h e a g g r e g a t e d product, a s all is e x p r e s s e d in t e r m s of wheat, a n d n o processing is n e c e s s a r y .

In t h e a g g r e g a t e d a c c o u n t s i t is n o longer possible t o identify t h e origin of.

for example. imports. They c a n stem from imported p u r e wheat. o r from pastry, being imported. Similarly i t i s not possible to s e e in t h e original (disaggregated) a c c o u n t s which flour is t a k e n for cake production. t h e nationally produced o r t h e imported one. The "wheat tree" is a r a t h e r c l e a r and.easy flow of quantities.

If o n e looks a t o t h e r commodities, like milk, oil seeds. etc., t h e flow becomes more complicated b u t t h e s a m e philosophy is applied for t h e i r aggregation.

The n e x t aggregation steps. from 260 main commodities t o 27 commodities in t h e d e t a i l e d FA.P4 list, a n d 16 commodities in t h e small F U 4 list, a r e very similar a s c a n - b e s e e n in Figure.5. The differences a r e t h a t h e r e t h e production of t h e a g g r e g a t e i s composed of t h e production of all participating commodities, a n d t h a t i n s t e a d of e x t r a c t i o n r a t e s appropriate weights a r e used t o express t h e participating e l e m e n t (e.g. pork in mt) in t e r m s of t h e aggregate ( o t h e r meat in m t protein). illso in t h i s last Agure t h e boxes for o t h e r m e a t , poultry a n d eggs.

pork a n d flsh a r e drawn i n scale for Argentina in 1970.

The aggregations were c a r r i e d o u t for e a c h c o u n t r y which p a r t i c i p a t e s i n t h e FAP modeling effort. All d e t a i l s for it c a n be found in [ I ] a n d 121.

FA0 h a s a c c o u n t s for all i t s member c o u n t r i e s , s t a r t i n g with 1961. The aggregations however have only been c a r r i e d o u t for a s e l e c t e d number of coun- t r i e s ( t h e FAP4 c o u n t r i e s l i s t e d i n Appendix l a ) . The reason for t h i s i s t h a t e a c h c o u n t r y might have i t s own commodity t r e e s a n d would n e e d s e p a r a t e checking, for which t h e r e is n e i t h e r time nor manpower available a t FAP, if i t was t o be done for all

FAP

c o u n t r i e s .

The p r i c e d a t a for t h e various commodities of the.SUAs, a t all levels of aggregation, is also p a r t of t h e SUA files. The details of t h e i r origin a n d calculation methods a r e d i s c u s s e d a t length in [2].

T h e r e a r e t h e following types of prices:

t y p e e l e m e n t code dimension code i c d(4) icd(5) p r o d u c e r p r i c e s 5 3

import p r i c e s 6 3

export p r i c e s 9 3

world prices 9 3

f e e d p r i c e s 10 3

food p r i c e s 14 3

o t h e r util. p r i c e s 15 3

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WHEAT AGGREGATW SUDD~V Demand.

ARGENTINA 1970

AGGREGATION OF S U h TO MAIN COMMODITIES

Demand BREAD

0

/ PASTRY Production

,waste 'Exports

(23)

Figure 5:

ARGENTINA 1970 AGGREGATION TO SMALL LIST

Demand

(24)

For the commodities of t h e 2nd Level of aggregation (ag27) t h e r e exist also prices with dimension code

=

4. This is due t o the fact that for some commodities the elements a r e measured in 2 dimensions:

I...

=

1000 U S 70 a n d 2...= mt

The computer programs which deal with these data know about these peculiari- ties.

3.2.3. Population Data

The d a t a on population a n d its derived quantities like labour force and rural population c a n be found in several files. There is a file called pop.fap4 which contains only these time series, and t h e same population data records a r e also in t h e file which contains the macro data (all.fap4)

The "commodity" population (icd(3)

=

1) is stored in one dimension (icd(5)

=

1) in the FAP d a t a bank a n d consists of the follo~ving 4 elements:

element text icd(4)

1 Total population

14 Total labour force

16 Agricultural Labour force 1 7 Nonagricultural labour force In all cases t h e unit of,measurement is 1000 persons.

3.2.3.1. Sources

The largest quantity of homogeneous population d a t a is frcm ILO. Origi- nally the format of t h e s e time series was different from t h e r e & of the d a t a in t h e FAP d a t a bank. After going through a transformation this data is now acces- sible in the sarnz way a s t h e SUAs.

The population d a t a from ILO covers the following aspects:

*

population, total. agriculture, non-agriculture, by age group, sex;

*

activity r a t e s by age group, sex and sector;

*

labour force, total. by age group, sex and sectors.

All this is given in 5 year steps from 1955 to 2000 (with some exceptions).

The time series a r e n o t from a census. but a r e estimates a n d projections. The methodology is described in [4]. Most countries of the world a r e covered by ILO.

Currently t h e country code in these time series is t h e same as t h e one from FAO, due to conversion done a t FAP. Further one can find population data in the SUAs (total population all original from FAO). This is yearly data, and is expected to be consistent with t h e r e s t of FiAO's statistics.

For the time period covering 1966 to 1981. (the latest release of the SUAs) besides population d a t a on total, the following elements a r e also found on the original SUA tapes:

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commodity element dimension text icd(3) icd(4) icd(5)

1 14 1 ag.pop/ tot. pop

1 15 1 rur.pop/tot.pop

1 16 1 tot.lab/tot.pop

1 17 1 ag.lab/tot.lab

3.23.2. Method

The Arst time series (total population) can be copied from t h e

SUA

tape without further processing. The three other elements require some calculations and recoding before they can be incorporated into the d a t a bank. The ILO time series a r e t h e basis for calculating participation rates a n d rural population. The following assumptions were made:

The labour force splits between agriculture and non agriculture in the same way as population splits between rural and urban.

The development of l a t ~ u r force (total, agriculture and non-agriculture) follows a linear trend between t h e years reported by ILO (5-year steps).

The reason for these assumptions is that data is available on rural and urban population for a number. of years, whereas t h e labour force d a t a for t h e different sectors can only be retrieved for a few years from t h e ILO data. On the basis of these assumptions and on the available information from FA0 and ILO t h e following steps were performed:

1. Take t h e time series for total population (1961

-

1976) for e a c h country from t h e original SUA data me.

2. Take t h e time series (1950-2000 in 5 year steps) for total labour force for each country from t h e ILO data file.

countries.

3.2.4. Macro Data

In the FAP Data Bank the term Macro Data is used for macroeconomic data, i.e. GDP, expenditures, etc.. but also for population. fertilizer and exchange rates.

Unfortunately there is no .comprehensive publication available, which would contain all macroeconomic data required For all years and all countries.

I t was therefore necessary to rely on a number of sources For the data collection.

3.2.4.1. Sources

These were the main sources for the times series:

Labour force estimates and projections from the International Labour Organisation (ILO) in Geneva and Supply Utilisation Accounts (SUA) from FAO, Rome (see previous section):

(b) World Tables from the World Bank. Washington DC.

(c) National Account Statistics, from t h e United Nations.

(d) National Accounts for the OECD countries.

(e) FA0 Trade Yearbooks.

(f) Fertilizer Yearbooks from FAO.

(g) Experts from the countries being modeled.

Non-Agriculture)

-

Agriculture Capital Stock

-

Non-Agriculture Capital Stock

-

Agricultural Investments

The C a p i t a l s t o c k s (Total, Ag a n d Non-ag) were c a l c u l a t e d using a c o m p u t e r program which. depending o n the availability of d a t a , u s e s different methods.

Method 1 Known:

DT(t): absolute depreciation a t c o n s t a n t prices lor t h e whole economy I T ) A t ) : Gross i n v e s t m e n t s total a n d i n t o agriculture, a t c o n s t a n t p r i c e s Assumptions:

dl": depreciation r a t e for t h e whole economy

B

^: proportion of total capital s t o c k being used i n a g r i c u l t u r e

(KA = B *

KT)

E: t h e relation of depreciation r a t e of t h e whole economy t o t h a t of a g r i c u l t u r e (dA

=

E

*

dT)

For t h e base y e a r (1970):

a n d for all o t h e r years:

if DT(t) is n o t given t h e n :

D T ( ~ )

=

K T ( ~ ) * ~ T

Method 2:

Known:

DT(t) Assumptions:

8:

proportion of total capital s t o c k being in a g r i c u l t u r e KA(t)

=

,f3

*

KT(t)

dT: t h e depreciation r a t e of t h e whole economy

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Calculate for all years:

The minimum data required for both methods is: DT(70). d,

8

and IT(t). and GDP(t) a t c u r r e n t and constant prices in order to arrive a t the necessary deflators for the depreciation. In Table 1 below t h e factors used for t h e different countries a r e shown. The time series on Fertilizer and Pesticides a r e explained in the next section.

3.2.5. Fertilizer Data

The data on fertilizer is included in the file with macroeconomic data.

There are a number of remarks to be made about these time series. It would be very useful to have information on fertilizer consumption for the different kinds of crops in terms of quantity and money, as well as some information on t h e subsidization of this means of production. This need is sometimes satisfied in the detai1ed.country models, which operate with data provided by t h e home institutions of the corresponding modellers. In this case, however, t h e aim i s t o provide consistent time series for a number of countries which are more o r less comparable.

The experienced collector and user of actual data in agriculture might be aware of the diflculties one runs into by.the above mentioned aim. In order to ease t h e work efforts have been concentrated on two of four types of time series, and even these two types cannot be computed or collected for all FAPB countries. For the Basic Linked System information on quantity and value of fertilizer consumed in a country, for all types of land (agricultural and pasture) is needed. There are many different kinds of fertilizer, which can be grouped according to their main components into nitrogenous. phosphate and potash fertilizer. In most countries the nitrogenous fertilizer plays the most important role, although there are some exceptions. Therefore information on nitrogen consumption in the countries to be modelled has been collected, on a yearly basis, measured in metric tons. Similarly it has been tried to arrive a t t h e yearly total expenditure of all three kinds of fertilizer by the farmers. The ratio of total expenditure divided by consumption of nitrogen was then computed a s

"fertilizer price".

The aim in the fertilizer section of the F'AP Data Bank was to arrive a t four types of series (covering the years between 1961 and 1976):

1. Total fertilizer consumption measured in 1000 units of national currency 2. Nitrogen consumption measured in metric tons

3. Fertilizer price in units of national currency per metric ton (as explained above)

4. Intermediate consumption of nonagricultural goods in agriculture in 1000 units of national currency (e.g. water. electricity. machinery, fertilizer, etc.)

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Table 1. Factors for Calculating Capital Stocks

code Country d b e t a Argentina

Australia Austria (*) Bel-Lux Banglad Brazil Bulgaria Canada China C SSR Denmark Egypt France GDR FRG Greece Hungary India Indonesia Ireland Italy Japan Kenya Mexico Netherlands New Zealand Nigeria Pakistan Poland Portugal Romania Spain Sweden (*) Thailand USSR UK USA

(*) time s e r i e s on capital stock provided by c o u n t r y experts

3.2.5.1. Sources

The s e a r c h For d a t a h a s been limited t o a small n u m b e r of publications from FAO. s o t h a t t h e time s e r i e s remain somehow comparable. The most r e c e n t publications were taken when available, otherwise older issues were also u s e d Sometimes t h i s method c a u s e d some conflicts. a s t h e d a t a differed drastically from one publication y e a r t o t h e next. This problem was e n c o u n t e r e d in t h e 4th time series (intermediate consumption of nonagriculture t o agriculture) a n d sometimes also i n the Arst (total consumption of fertilizer).

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Series 1 and 4 were taken From the Economic Accounts for Agriculture. FAO, Issue 1 (1961 to 1971) and Issue 2 (1965 to 1977). Although these issues claim to cover all years of interest, this is not the case for all countries. Only seldom data for 1976. t h e last year of the time series, could be found.

Series 1 was sometimes computed by other methods, if it could not be Pound in the above mentioned sources, or it was left out altogether, since it does not play a crucial role i n t h e modelling work.

Series 2. (consumption of nitrogen fertilizer measured in metric tons) was taken from t h e Fertilizer Yearbooks of FAO, issues 1980. 1979 or 1978 (depending on t h e year needed), and earlier issues, called Annual Fertilizer Review, also by FAO, for t h e years 1977 back to 1960.

Series 3 (fertilizer

rice)

was computed a t FAP, and the sources used were numerous. All the publications mentioned above were consulted. a s well a s Pro- duction Yearbooks a n d Trade Yearbooks of F.40 (issues between 1963 and 1979).

The World Tables, of t h e World Bank, were consulted for appropriate exchange rates. Participants of t h e FAP collaboration network calculated t h e time series needed for some countries. adapting them to t h e specific characteristics of these countries.

3.2.5.2. Methods

In the ideal case one would have preferred to use only one method for each of the four series. Then the data would also be comparable across countries.

Unfortunately t h i s was not possible due to the lack of information found in the sources consulted. For each time series appropriate methods were chosen and used accordingly, a s d a t a were available. This procedure was applied to each country independently. In Appendix 6 (Country table of sources and references) one can And t h e details for each country.

Series 1:

Total f i r t i l i z e r Consumption in 1000 u n i t s of :Vation& C u n z n c y

Not much etlorts were invested in this series, a s i t is not being directly used in t h e modelling efforts. Besides, in the ideal case, the product of Series 2 (consumption of nitrogen in mt) and Series 3 (fertilizer price) leads to Series 1.

If some years a r e missing it stems from the fact t h a t t h e mentioned source does not report on those years, o r that the time series in different issues a r e too different Prom each other.

Series 2:

C o r n n a p t i o n of Nitrogen in Met* Tons

This was t h e easiest series of all to assemble. The sources mentioned before have r a t h e r detailed and complete information on this item.

Series 3: .

Fertiiizer PTice in Nationnl C w e n c y per Metric Ton

The biggest effort has been invested in this series, as homogeneous data for all countries could not be found, and even within a count.ry all t h e years neede.d could not be covered. Depending on the availability of data one (or more) of the following methods was used. giving preference to t h e first, then t h e second.

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t h i r d , e t c .

Method I: Calculate t h e "fertilizer price" ( t o t a l fertilizer consumption in u n i t s of n a t i o n a l c u r r e n c y by t o t a l n i t r o g e n u s e i n rnt) for o n e y e a r ( t ) a n d for all t h e o t h e r y e a r s multiply t h i s p r i c e by t h e corresponding fertilizer p r i c e index ( r e p o r t e d i n t h e Fertilizer o r P r o d u c t i o n Yearbooks).

~ ( t )

=

s e r i e s 1 (t)

/

s e r i e s 2 ( t )

p(t+n)

=

p(t)

*

i n d e x ( t + n ) n =

...,

-3,-2,-1,1,2,3

,...

This i s t h e "cleanest" method. b u t i t could only be applied t o t h e most developed c o u n t r i e s , a n d n o t e v e n h e r e t o all ( s e e Appendix 6).

Method 2: Not only for o n e year. a s i n Method 1, b u t for all y e a r s , c a l c u l a t e t h e p r i c e a s r a t i o of total consumption of fertilizer i n national c u r r e n c y by t o t a l u s e of n i t r o g e n i n m e t r i c tons.

p(t)

=

s e r i e s 1 ( t )

/

s e r i e s 2 ( t ) t=1,2,3

...,

16

In s o m e c a s e s this m e t h o d was u s e d for all y e a r s available, a n d t h e missing y e a r s were calculated with m e t h o d 1. It also proved useful t o apply t h i s m e t h o d for checking purposes.

Method 3: This procedure involves a fair amount of calculation a n d assumes t h a t information needed for t h e first 2 methods is n o t available, o r t h a t i t i s n o t very reliable o r gives "strange" results. In t h e Fertilizer a n d P r o d u c t i o n Yearbooks from FA0 o n e c a n sometimes find p r i c e s paid by farmers for different kinds of fertilizers, a s well a s t h e consumption figures of these kinds. The p r i c e s a r e sometimes r e p o r t e d in national c u r r e n c y . sometimes in U S ( t h e r e f o r e t h e n e e d of e x c h a n g e r a t e s ) .

where.

C ⁼

s u m over all i

i

consumption of kind k of potash fertilizer,

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Also this method was used for checking purposes when other methods gave rise to doubts, or all y e a r s could not be completed and t h e r e was too big a difference between methods. This is also a suitable method to arrive a t Series 1 (total fertilizer consumption in national currency) when needed.

One should not forget t h a t "price paid by farmers" sometimes includes subsidies, sometimes not. A s t h e r e is n o consistent information for all countries on subsidies this problem h a s been neglected. The "policy module" is expected to tackle i t when necessary.

Method 4: For some countries, especially developing countries, neither information on price index nor prices paid by farmers could be found. Further most of t h e s e countries a r e mainly importers of fertilizers. From the Trade Yearbooks information on total imports of fertilizers in value terms could be compiled. and in t h e Fertilizer Yearbooks information on total imports in quantity terms was available. On t h e assumption that the import price would be charged to the farmer one could then calculate t h e "FAP fertilizer price".

where

ImvaC

=

import value of crude fertilizer

ImvaM

=

import value of manufactured fertilizer ImquN

=

import quantity of nitrogen fertilizer ImquP

=

import quantity of phosphate fertilizer ImquK

=

import quantity of potash fertilizer

ConsN

=

consumption quantity of nitrogen fertilizer ConsP

=

consumption quantity of phosphate fertilizer ConsK

=

consumption quantity of -potash fertilizer

I t is conceivable t h a t this method might introduce a large error in the "fertilizer price". A t the same time this is the last resource of information one has a n d t h u s t h e last chance. When t h e price was calculated in this way, every effort was made to arrive a t t h e complete time series (1961 to 1976). In case of missing years other methods were used and cross-checked with several other years t o be s u r e t h a t t h e e r r o r was not too great.

Series ^4:

M e m e d i a t e C ~ n s u m p t i o n ^ofN o n a g r i c u l t u r e in A g r i c u l t u r e

This time series was taken over From the Economic Accounts for Agricul- ture. when available, otherwise t h e series was left out for the country and/or years which were not reported on. The term "year" generally refers to the crop year from July 1 to June 30, a n d is counted lor the year into which the starting month falls. In the reference books used one can sometimes find data for 1961/62 for example. In such cases the datum was assigned to the first year (1961). For more details on subsidies, reference period. etc., consult the notes in t h e sources of the data.

(34)

3.2.5.3. Organization of the Time Series

The time series on fertilizer a r e organized in the same way a s the other time series in the FAP Dgta Bank. For each country t h e r e a r e u p to 3 records of d a t a ( o n e for each series).

The fertilizer (and related series. i.e. intermediate i n p u t of nonagriculture) all have t h e same commodity code: 3110.

The diflerent element a n d dimension codes are:

element dimension

- -

1 2 total consumption of fertilizer (1000 national currency) 2 1 consumption of nitrogen fertilizer (mt)

2 3 consumer price of fertilizer (nc per mt)

6 2 intermediate consumption of nonag in ag (1000 nc) The creation date is only sometimes set. and of n o importance to us here.

The status indicator has no meaning here. ?Vhen a datum h a s a zero entry i t can mean t h a t either no data a r e available, or too small a n amount. Usually it means the former. In Appendix 6 one can identify for each country and type of time series from which source i t stems and/or which method was used for calculating it. The missing years (between 1961 and 1976) a r e also identified. The time series for the EC (icd(2)=888) has been calculated by adding up all time series of t h e corresponding member countries. Each national c u r r e n c y h a s been converted into EUROs, which is the "EC currency".

3.2.6. Data on Area

Currently the FAF' Data Bank has only one file with data on area. This file s t a r t s with 1961 a n d covers 16 years.

There. is only one commodity in the area file:

i c d(3) text

12 land use

and i t has 4 elements:

ekement text i c d(4)

1 total a r e a

(including land and area under inland water bodies) 6 arable land and under permanent crops (7

+

12) 7 arable land

(temporary crops counted once. temporary meadows and pastures, market and kitchen gardens.

temporarily fallow or lying idle) 12 under permanent crops

(crops need not be replanted every year, excludes t r e e s for wood or timber)

The FAP Data Bank. Part 1: Organization, Contents and Management

DATA

ember

S.

F U

-

*

*

-

-

CONTENTS

...

...

+

THE FAP DATA BANK

PART I: ORGANIZATION. CONTENTS

FXP.

FAP

FM

.

+

+

.

"******"

...

---

---

FAP,

FAP

...

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-

-

-

-

-

-

-

-

-

-

-

S U A

supply Utilization supply Utilization

Production

Imports PRODUCT

Food

from Stocks

\

Input \

AGGREGATIONS 600 comm.

WHEAT Flour Bran

macaroni

. .

APPLES juice canned a.

cider

WHEAT

APPLES PORK CATTLE

Meat POULTRY

Fat

frozen m.

27 comm.

16 comm.

WHEAT 3

. 'WHEAT

. . . .

FRUITS + OTHER FOOD

NUTS

. . .

. OTHER MEAT I

FAP

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