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A m 1’08 QUAITI‘I'AHVI MICRO-LIVE. mm Ill
DEVELOPING COUNTRIES USING DATA BASE MANAGED”? SYS'I'IIB
By

Ali Kamel Mohamed Kamel

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements

for the degree of
IABTIR.OF SCIENCE
Department of Agricultural Economics

1986

W”- "-3658!

ABSTRACT

A FRAIEIORK FOR.QDANTITATIVE MICRO-LEVEL RESEARCH IN
DEVEDOPING COUNTRIES USING DATA BASE-MANAGEMENT SYSTEMS

By

Ali Kamel Mohamed Kamel

Agriculture plays a vital role in the economy of
developing countries. These countries face two fundamental
problems; a rapid increase in population growth and a severe
shortage in cultivable land. Also, there is a void of
reliable micro and macro economic data. Insufficient
economic data is being collected, analysed, and fed into the
decision-making process. These factors led to the failure of
the current systems to generate the economic data and
analysis needed for the development of sound plans and
rational agricultural policies in developing countries.
Therefore, it is essential to have reliable and adequate
data set to establish a basis for farm management
information system to support the operation, management, and
decision making functions in developing countries.

The purpose of this study is to design a framework for
quantitative micro-level data collection and analysis using

database management systems such as FARMAP and dBASE III

Ali K. M. Kamel

PLUS and establish a basis for farm management information
system with its four components: descriptive, diagnostic,
prescriptive, and predictive information. A set of
comparison criteria was identified to study the usefulness
of both programs to carry out micro-level research in
developing countries.

Due to limited funds and time constraints, secondary
data was chosen from another farm survey project of a
developing country ( Indonesia, West Java). A modest sample
size of ten farms was used to accomplish the objectives of
the study.

Both FARMAP and dBASE III PLUS are very powerful and
excellent tools for carrying out micro level research in
developing countries. They provide a unified system for data
collection and analysis and a basis for farm management
information system which is highly needed in developing
countries to correctly evaluate policy alternatives. They
also provide means of interfacing data with other
statistical and economic planning tools needed for further

processing.

Dedicated to my wife, Eman.

ii

ACKNONLEDGMENTS

I wish to extend my deepest and sincere thanks to many
individuals and institutions who facilitated my graduate
study.

I first want to express my deepest appreciation to
Professor Stephen B. Harsh, my major professor and Chairman
of my Guidance Committee for his guidance, advice, humanity,
understanding, and flexibility. My thanks goes to the other
members of my thesis committee, Professor Rick Bernsten, for
his positive role, invaluable help, and encouragement, and
Professor Elias Dinopoulos for his constructive criticism
and valuable suggestions. I also want to extend my gratitude
to all the professors and staff of the Department of
Agricultural Economics at Michigan State University,
particularly, Professor L. V. Manderscheid , and Professor
Glenn Johnson.

My appreciation also goes to the staff of the
Agricultural Economics Computer Lab., Mr. Chris Wolf, Wendy
Peters, and Jeff Anderson, for their help during my computer
work. I want to express my sincere appreciation to the staff
at the Office of International Students and Scholars,
particularly, Dr. Ken Ebert, and Mrs. Elda Keaton for their

help and support.

iii

Special thanks also goes to all the people at USAID-
Egypt, USDA/OICD/IT, MOA/Foreign Agricultural Relation
Department, and Agricultural Mechanisation Project,
particularly, Mr. M. M. Dessouki, Mr. M. Helmi, Mr. Ali
Bashad, Dr. Ahmed F. El-Sahrigi, Mrs. Musu Clemens, Miss
Tanya Hennant, and Mr. Jeff Lee, for their financial and
administrative support.

I especially want to thank and acknowledge my loving
wife, Eman, for typing my thesis and for all the sacrifices,
encouragement, patience, and love she gave me. Special
thanks goes also to Marwa, my loving daughter.

Loving thanks to my father’s soul, my mother, my sister
and brothers. And above all, my praise and thanks be to God

, Most Gracious, Most Merciful, and Most Compassionate.

iv

TABLE OF CONTENTS

Page

LIST OF TABLES ............................................ ix

LIST OF FIGURES .......................................... xii
CHAPTER

1 INTRODUCTION .......................................... 1

1.1 Agriculture and the Economy of Egypt .............. 1

1.2 Statement of the Problem ......................... 5

1.3 Purpose of the Study ............................. 11

1.4 Objectives of the Study .......................... 12

1.5 Organization of the Study ........................ 12

2 BACKGROUND TO THE STUDY .............................. 14

2.1 The Agricultural Sector in Egypt ................. 14

2.1.1 The Country: Basic Information ............ 14

1. Location ................................. 14

2. Climate .................................. 16

3. Population ............................... 16

4. Soil ..................................... 18

. Water Resources .......................... 19
General Features of Egyptian Agriculture..20

. Cropping Seasons ......................... 20

5
2
1
2. Cropping Patterns and Cropping Rotations.20
3. Land Tenure........; ..................... 22
4

. Agricultural Inputs ...................... 24

CHAPTER Page

5. Field Crop Production .................... 27
6. Animal Production ........................ 29
2.1.3 The Policy Environment .................... 33
1. Price Policy ........................... 33
2. Extension and Research .................. 34
3. Agricultural Marketing .................. 34

2.3 Review of Major Studies on Database Management

Systems and Data Types and Structures ........... 35

3 METHODOLOGY AND ANALYTICAL PROCEDURES

3.1 Data Requirements and Selection of Data Sources..43
3.2 Data Preparation, Transformation and

Modification ..................................... 45
3.3 Methodology ...................................... 46

4 THE MODEL

4.1 The Structure of FARMAP .......................... 51
4.1.1 Introduction .................................. 51
4.1.2 Computer Requirements ......................... 53

4.1.3 FARMAP Data Structure and Record Type Groups..54

4.1.3.1 FARMAP Data Structure ...................... 54
4.1.3.2 Record Type Groups ......................... 63
4.1.4 Data Processing Stages ........................ 73
4.1.4.1 Data Storage Stage (STOl) .................. 73
4.1.4.2 Validation Stage ........................... 75

vi

CHAPTER Page

4.1.4.3 Tabulation Stage ........................... 79
4.1.4.4 Further Processing Stage ................... 81
4.1.5 Analytical Procedures ......................... 81
1. Data Storage ................................. 81

2. Data Validation .............................. 86

3. Data Tabulation .............................. 90

4. Advanced Processing .......................... 94

4.2 The Structure of dBASE-III PLUS .................. 98
4.2.1 Introduction .................................. 98
4.2.2 Computer Requirements ......................... 99
4.2.3 dBASE Data Structure ......................... 100
4.2.3.1 Technical Specifications .................. 100
4.2.3.2 Database Files and Structure .............. 101
4.2.3.3 dBASE III PLUS Commands and Functions ..... 104
4.2.4 Analytical Procedures ........................ 105
1. Introduction ................................ 105

2. Creation of Database Structures ............. 107

3. Editing Process ............................. 121

4 The Reporting Process ....................... 122

5 THE RESULTS.

5.1 The Results of FARMAP ........................... 130
5.1.1 Introduction ................................. 130
5.1.2 Results of Standard Tabulation ............... 131

5.1.2.1 Farm Mode Tables.....; .................... 131
5.1.2.2 Activity Mode Tables ...................... 145

vii

CHAPTER Page

5.2 The Results of dBASE III PLUS ................... 155
5.3 Results of Advanced Processing .................. 161
5.3.1 Statistical Analysis ......................... 162
5.3.2 Forward Planning (Predictive Analysis) ....... 166
5.3.3 Whole-Farm Planning .......................... 171
5.3.4 Whole-Farm Production Function Estimate ...... 175

5.3.5 Multi-Year Production Variability Estimate...177

5.4 Features and Limitations ........................ 180
5.4.1 FARMAP Features and Limitations ........... 180
5.4.2 Features and Limitations of dBASE III ..... 183

5.5 Conclusion ...................................... 189

6 SUMMARY, POLICY IMPLICATIONS AND SUGGESTIONS FOR
FUTURE RESEARCH.
6.1 Summary ......................................... 193
6.2 Policy Implications ............................. 200
6.3 Limitations of the Study and Suggestions for

Future Research ................................. 202
APPENDIX A— FARMAP Group Means Results ................... 204
BIBLIOGRAPHY ............................................. 208

viii

see->9

LIST OF TABLES

Table Page
1.1 VALUE OF AGRICULTURAL PRODUCTION FOR EGYPT,

Years 1978-81 ......................................... 4
2 BALANCE OF PAYMENTS IN GOODS AND SERVICES OF EGYPT,

Selected Years 1952-80, Egypt ......................... 6
1 CLIMATOLOGICAL NORMALS OF EGYPT ...................... 17
2 URBAN AND RURAL DISTRIBUTION OF EGYPT ................ 17
3 DISTRIBUTION OF LAND OWNERSHIP IN EGYPT,

Years 1952-1961 ...................................... 23
4 PRODUCTION BY COMMODITY 0F EGYPT, 1971/81 ............ 28
5 COMPARISON OF EGYPTIAN AND WORLD YIELDS OF 13 FIELD

CROPS ON THE BASIS OF 3-Year (1978-80) AVERAGES ...... 30
6 COMPARISON OF AVERAGE U.S. AND EGYPTIAN CROP YIELDS

UNDER IRRIGATED CONDITIONS ........................... 31
7 ESTIMATED LIVESTOCK NUMBERS IN EGYPT,

Selected years ....................................... 32
1 FARMAP STANDARD RECORD TYPE CONTENT AND DATA SOURCE..56
2 CONTENT OF COMMON DATA FIELDS BY DATA TYPES FOR

FARMAP ............................................... 58
3 INPUT-OUTPUT AND OPERATION CODES SUMMARY FOR

FARMAP .................................... , ........... 62
4 GENERAL DATA RECORD TYPE FOR FARMAP .................. 64
5 FARMAP FREE FORMAT RECORD TYPE ‘20’ FORMAT ........... 66
6 FARMAP RESOURCE DESCRIPTION RECORD TYPE GROUPS ....... 67
7 FARMAP HOUSEHOLD RECORD TYPE ‘110’ FORMAT ............ 69
8 FARMAP RESOURCE UTILIZATION RECORD TYPE ‘700’

FORMAT ............................................... 72
9 EXAMPLES OF FARMAP CODING SHEETS ..................... 82

ix

Table
4.10

5.

OIUIGGU

1a

.1b
.10
.1d

.1e

.lf

.1g

.10
.11
.12
.13
.14

Page
dBASE III PLUS FILES’ TYPES ........................ 102
FARMAP STANDARD TABLE-FARM MODE- HOUSEHOLD
COMPOSITION ........................................ 134
FARMAP STANDARD TABLE-FARM MODE- LAND USE .......... 134
FARMAP STANDARD TABLE-FARM MODE- CROPS GROWN ....... 136
FARMAP STANDARD TABLE-FARM MODE- NET WORTH
STATEMENT .......................................... 136
FARMAP STANDARD TABLE—FARM MODE- ECONOMICS ......... 139
FARMAP STANDARD TABLE-FARM MODE- CASH AND KIND
FLOW SUBTABLE ...................................... 142
FARMAP STANDARD TABLE-FARM MODE— POWER USE ......... 143
FARMAP STANDARD TABLE-FARM MODE- POWER TYPES ....... 144
FARMAP STANDARD TABLE-ACTIVITY MODE- RICE
ACTIVITY ........................................... 147
FARMAP STANDARD TABLE-ACTIVITY MODE- TWO-WHEEL
TRACTOR RENTAL ACTIVITY ............................ 150

FARMAP STANDARD TABLE-ACTIVITY MODE- OFF-FARM JOB..152
FARMAP STANDARD TABLE-ACTIVITY MODE- RENTED-OUT

LAND ACTIVITY ...................................... 153
FARMAP STANDARD TABLE-ACTIVITY MODE- GENERAL FARM,
TAXES .............................................. 154
dBASE III RESULTS- HOUSEHOLD COMPOSITION OUTPUT

TABLE .............................................. 157
dBASE III RESULTS- LAND CHARACTERISTICS OUTPUT

TABLE .............................................. 158
dBASE III RESULTS- FARM ECONOMICS OUTPUT TABLE ..... 159
RESULTS OF PROGRAM EXTRAC .......................... 184
RESULTS OF STATISTICAL ANALYSIS .................... 165
INVENTORY OF FARM RESOURCES WORKSHEET .............. 188
AN EXAMPLE OF RICE ACTIVITY BUDGET ................. 170

Table Page

5.15 EXAMPLE OF INTERFACING FARMAP AND dBASE III PLUS

WITH LINEAR PROGRAMMING DATA REQUIREMENTS .......... 174
5.16 ACTIVITY GROSS MARGINS PER ACRE FOR EXAMPLE

PROBLEM ............................................ 178
5.17 EXAMPLE OF INTERFACING FARMAP AND dBASE III PLUS

WITH MOTAD DATA REQUIREMENTS ....................... 179
5.18 FARMAP AND dBASE III PLUS COMPARISON ............... 188
A.1 FARMAP GROUP MEANS TABLES ........................... 204

xi

N

hbghbgfihfbfo

LIST OF FIGURES

Figure Page
1.1 THE CULTIVATED AREA IN EGYPT AS A PERCENT OF

TOTAL AREA ............................................ 2
2 PRODUCTION AND UTILIZATION OF MAJOR FOOD STABLES

OF EGYPT, Years 1960-1980 ............................ 10
1 GEOGRAPHICAL MAP OF EGYPT ............................ 15
2 COMPARISON OF EGYPTIAN AND WORLD YIELD OF 5 SELECTED

FIELD CROPS .......................................... 30
3 COMPARISON OF AVERAGE U.S. AND EGYPTIAN CROP YIELDS..31
1 FLOWCHART OF FARMAP STORAGE STEP 1 (STOl) ............ 74
2 STEPS IN FARMAP DATA VALIDATION STAGE ................ 78
3 STEPS IN FARMAP DATA TABULATION STAGE ................ 80
4 FARMAP SECONDARY INPUT FILE STOI.DAS CONTENT ......... 84
5 FARMAP COMMAND FILE ENTST.CMF ........................ 87
6 FARMAP COMMAND FILE TAB42.CMF ........................ 95
7 FARMAP COMMAND FILE EXTRAC1.CMF ...................... 96
8 PROGRAM ALI.PRG TO TRANSFER FARMAP DATA FILES INTO

dBASE III PLUS DATA FILES ........................... 108

dBASE III PLUS MASTER FILE FARMAP.DBF SCREEN FORM...112
.10 dBASE III PLUS FILE RT110.DBF SCREEN FORM .......... 113
.11 dBASE III PLUS FILE RT200.DBF SCREEN FORM .......... 114
.12 dBASE III PLUS FILE RT210.DBF SCREEN FORM .......... 115
.13 dBASE III PLUS FILE RT230.DBF SCREEN FORM .......... 116
.14 dBASE III PLUS FILE RT310.DBF SCREEN FORM .......... 117
.15 dBASE III PLUS FILE RT410.DBF SCREEN FORM .......... 118

hbbbhufih

xii

Figure

4.18 dBASE
4.17 dBASE
4.18 dBASE
4.19 dBASE
4.20 dBASE
4.21 dBASE

Page
III PLUS FILE RT510.DBF SCREEN FORM .......... 119
III PLUS FILE RT700.DBF SCREEN FORM .......... 120
III PROGRAM FIN1.PRG ......................... 123
III PROGRAM FIN2.PRG ......................... 124
III PROGRAM FIN3.PRG ......................... 125
III PROGRAM FIN4.PRG ......................... 126

xiii

CHAPTER 1
INTRODUCTION

1.1 Agriculture and the Economy of Egypt

Agriculture plays a vital role in the economy of Egypt.
It is the basic and most important sector, and the back-bone
of the economy of the country. Egypt has one of the richest
endowments of agricultural resources on the African
continent, which include unusually favorable land, water,
and climatic resources. The total area of Egypt is about one
million square kilometers, or nearly 238 million feddans1
of which only 6 million feddans are cultivated. This
represents only 3 percent of the total area and deserts
cover the remaining 97 percent (Figure 1.1). This nation’s
farmland, the Nile Valley, is a strip of 600 miles long and
up to 10 miles wide before broadening out into the fertile
Delta region lying between Cairo ( the nation’s capital) and
the coast. The total area under cultivation in Lower Egypt
is 3.67 million feddans, 1.2 million feddans in Middle
Egypt, and 1.06 million feddans in Upper Egypt. However, the
total area cultivated is about 10 million feddans with the
use of multiple cropping practices. This accomplished with
the assistance of perennial irrigation after the completion
of the first phase of the High Dam in 1965.

The population of Egypt was 45.2 million in (1983)2,

1 one feddan = 1.038 acres = 4200 square meters
2

World Bank. WM. 1985.

 

 

a
O
r
a
1
a
A
t
f.
0
an
3
t
m
l
8
m
e
8
O
r
a
r

 

THE CULTIVATED AREA IN EGYPT AS A PERCENT OF

1.1
TOTAL AREA

Figure

and thus the man/land ratio equals to about 0.14 cultivated
feddans per capita. The average annual population growth
rate is about 2.5 percent which adds over one million
newborns each year. Egypt's total GDP equals US $27.92
billion of which 20 percent is produced by the agricultural

sector. The GDP average annual growth rate for the
agricultural sector was approximately 2.5 percent for 1973-

83. The value added in agriculture was 84.7 billion in 1983.
The GNP per capita amounted to 3700 per year with an average
annual growth rate of 4.2 percent during 1973-83. About 50
percent of the total labor force (i.e. the population
between the age of 15-64) is engaged in agriculture.

The agricultural sector consists mainly of two major
sub-sectors ( Table 1.1):

1. The Plant Production Sub-Sector: includes crops,

vegetables, and horticulture. This sub-sector

represents more than 70 percent of the total value of

the agricultural production.

2. The Animal Production Sub-Sector : includes meat,

milk and dairy products, wool, eggs, poultry, and honey

and wax. This sub—sector represents 30 percent of the

total value of agricultural production.

Table 1.1 VALUE OF AGRICULTURAL PRODUCTION FOR EGYPT.
Years 1976-61.
Item 1978 1979 1960 1981

(Current prices in millions LEI)

Crop production

 

Field crops 1600 1799 2201 2534
Vegetables 411 518 547 622
Fruits 172 224 255 294
Aromatic and
medicinal .__22 __12 __15 __15
Subtotal 2405 2560 3021 3466
Animal production
Meat 365 409 549 565
Poultry 112 120 166 214
Milk and dairy
products 304 346 408 517
Eggs 66 76 96 137
Wool 3 4 4 4
Honey and wax __5 __§ ._fi __fi
Subtotal 652 962 1229 1441
Total 3251 3522 AZQQ $291
mum... I633 ’55-? {632 {133
Grand total 2291 253i fllflfi flfihl
1One LE Egyptian Pound = as a 1.19 in 1963

US 8 0.74 in 1986 after the government
devalued local currency by more than 50 X.

Source: Ministry of Agriculture, 1963.

1.2 State-Ont of the Problem

Egypt’s agriculture has been limited to a narrow strip
of land along the banks of the Nile River in Upper Egypt and
the fan-shaped Delta in Lower Egypt. The country faces two
fundamental problems; a rapid growth in population and a
severe shortage of cultivable land. While the cultivated
area remains almost unchanged, the population has more than
quadrupled from 11 million in 1907 to 45.2 million in 1983
with an average annual growth rate of 2.5 percent.

As late as 1960 Egypt was essentially self-sufficient
in food production. However, in the last two decades
Egyptian food production has failed to keep pace with
utilisation (i. e. human and animal consumption, industrial
use, and waste), and the country has become increasingly
dependent upon food imports to meet its needs. In 1961 Egypt
imported US 84 billion of agricultural commodities while
exporting only US $700 million of agricultural commoditiesl.
Until the early 1970‘s, Egypt‘s agricultural sector was a
major contributor to the national economy and a major source
of foreign exchange earnings. Since the October War of 1973,
however, the economic situation of Egypt has changed
dramatically. Petroleum exports, remittances from Egyptians
working abroad, tolls from the Suez Canal, and tourism have
become the major sources of foreign exchange (Table 1.2).

1International Agricultural Development Services,
Increasing Egyptian Agricultural Production Through
Strengthened Research and Extension Programs, 1984.

Table 1.2 BALANCE OF PAYMENTS IN GOODS AND SERVICES OF EGYPT
Selected Years 1952-80, Egypt.

 

Exports (f.o.b.)
Cotton
Petroleum

Other

Total

Services
Sues Canal
Remittances
Other net
Total (net)

Imports (0. i. f.)
Food

Other

Total

Net balance on
goods and services
Debt payment

Foreign exchange
deficit

Supply of funds
Change in reserves

+345
+96
+443
+66

-26
+62

-52
-508
-558

-73

-61
+11
-70

+362

0
+152
+514

+164
0
-56
+106

~201
-703
-904

-202
-58

-256
+203
-53

(US 6 million)

+666
+113
+323
+1,124

0
+66
+79

+165

-208
-l,458
-1,884

-854
-407

-1: 061
+945
-116

-1,355
—8,851

-1,273
-899

-2,172
+2,113
-59

+650
+2.545
+1.480
+4.675

-2,590
-7,457
-10,047

-606
‘13 313

-1,921
+1.397
-524

Sources: The figures for 1952-73 are from K. Ikram, Egypt:

1981.

The later years are from Egypt, Central Bank
of Egypt. Economic Bulletin. various issues.

Compared with the other sectors, the agriculture sector has
shown the slowest growth, about 2.5 percent per year between
1975 and 1960.

Several factors have contributed to the agricultural
sector’s current situation of a rapidly widening gap between
the production and consumption of agricultural commodities.
These factors include:

1. A relatively low level of investment in agricultural
research and extension programs to develOp and make
available improved technology to the farmer.

2. Production of food and agricultural products has
grown slowly .

3. A dramatic lack of reliable micro and macro economic
data to carry out microlevel research. The absence of
quantitative analyses, microcomputer applications, database
management systems, and modern analytical techniques led to
a very poor and unreliable data on which to base policy
decisions.

With regard to the data situation, Robert Mabro (1974),
argues that,

"There is a wealth of statistical material on the

Egyptian economy, but little is usable without much

processing and elaboration".

Difficulties with validity, and consistency of Egyptian
data have confronted most researchers. Scobie (1961)

expressed typical frustration as illustrated by the

statement:

”One cannot help express grave concern about the
permanent damage done to the import data as officially
reported by Egyptian authorities. A researcher who may
be interested in this type of economic activity will
have to check the official data against the world‘s
commodity exports to Egypt.“

The current system fails to generate data and analyses
needed to develop sound plans and rational policies.
Decision-makers do not have the information necessary for
sound decision-making. Data currently available do not
provide the basis for economic and financial policy
analyses. Therefore, decision makers are tempted to
institute policies which are politically popular because
they do not comprehend the economic costs.

The data collection and analysis problem has four
inter-related aspects: (1) insufficient economic data is
being collected, analysed and fed into the decision-making
process; (2) the capacity to utilise whatever data and
analyses available is not sufficiently developed; (3) links
which integrate the research and analysis process into the
decision-making process are weak or missing; and (4) the
data has limited value because delays and errors related to
the manual checking and tabulation of the collected data.

4. The demand for food has been rising sharply as a
result of rapid population growth, and substantial increases
in per capita food consumption due to increasing incomes and
substantial government subsidies for food.

5. The agricultural land resources are scarce. They

represent only about 3% of the total area. Moreover, a

complete soil survey of the present cultivated land has
revealed that the productivity of fully one-half of its area
has deteriorated to the extent that it is now classified as
medium or poor soils, as a result of perennial irrigation
without adequate accommodation for proper drainage (El-
Tobgy, 1976).

6. The influence of the government policies, such as
setting of prices for inputs and outputs, subsidies,
planting quotas, and resource allocations, on agricultural
production and cropping patterns. These policies, especially
price polices and subsidies, encouraged consumption and
discouraged production.

The effect of the previously discussed factors has
caused a rapidly widening gap between the levels of
production and food consumption (Figure 1.2). This gap will
continue to widen unless something substantial is done to
expand production and reduce the rate of growth in domestic
consumption.

Even with the utilization of high crop yielding
varieties, based on the world standards or the U.S. average
yields for comparable field crops Egypt has enormous
potentials to further increase its agricultural output on
existing arable lands (Egypt: Strategies for Accelerating
Agricultural Development, MOA/USAID in cooperation with
IADS/USDA, 1962). The failure to develop and use improved
production technology is one of the most important factors

preventing the realisation of the nation‘s agricultural

10

 

 

 

N
O

LEGEND

oo
\

__ Production

_- Utilization

d d ae—fi
N P 03
\
\
\\
\

o
‘\

 

Mojor Food Commodities (millions meteric tons)

 

 

 

1960 1966 1972 1978
1963 1969 1975 1980

 

 

Figure 1.2 PRODUCTION AND UTILIZATION OF MAJOR FOOD STABLES
0F NYPT, Years 1960-1960.

Source: adapted from Wally, Y. ,
. 1962.

11

development potentials. Priority should be given to
investigating economic aspects of the Egyptian agricultural
systems because of the enormous influence of government
policies.

There are numerous constraints facing the full
development of agriculture in Egypt and problems lying ahead
which are far greater than the problems that have been
solved. It is true that a concerted effort has been made
during the last two decades and that the results have been
gratifying, but there still remain several serious problems
which are awaiting future solutions and actions (El-Tobgy,
1974).

1.3 Purpose of the Study

This study has as its focus subsistence and semi-
subsistence smallholder agriculture with strong linkages
between farm production and household consumption. The
agricultural sector of Egypt will be descirbed as an example
of the subsistence smallholder agriculture in developing
countries. The study addresses potential of the usefulness
of using some database management systems such as FARMAP and
DBASE III as economic research tools. The purpose of the
study is to design a framework for quantitative data
collection and analysis, using FARMAP and dBASE III and thus
create a unified system of rural data collection and
analysis. A system of this nature will strengthen planning

and policy decision because better information will be

12

available on resource use and production technologies

employed.

1.4 Objectives of the Study

The objectives of the study are as follows:

1. To describe the Egyptian agricultural sector and the
constraints which limit agricultural development.

2. To develop an analytical model which can help economic
analysts better utilise Database Management Systems
(DBMS’s) through evaluating and describing FARMAP and dBABI
III PLUS structures and techniques.

3. To study the usefulness of using DBMS’s in the
quantitative analysis and to develop a database structure
appropriate for farm management and production research in a
farming systems context. Also, to evaluate the impact of
using microcomputers in data processing and analyses.

4. To review how DBMS’s can be interfaced with economic
analysis techniques such as statistical methods and planning
techniques.

5. To develop data management policy recommendations for the

Ministry of Agriculture in Egypt.

1.5 Organisation of the Study

Chapter 2 describes the Egyptian agricultural sector,
its development, and its policy problems. It also reviews
major studies on database management systems (DBMS’s),

and data types and structures.

13

Chapter 3 contains the methodology, describes data
sources and steps employed in designing data structures. In
Chapter 4, the model is presented with a description of the
structure of FARMAP and dBASE III PLUS. Chapter 5 describes
the results of the application of FARMAP and dBASE-III PLUS,

their limitations and operational difficulties, and a brief
discussion of other complementary techniques which can be

interfaced with both programs to conduct economic analysis.
A summary, policy implications, limitations of the
study, and suggestions for future research are presented in

Chapter 8.

CHAPTIB 2
BGCKBHDUID 10 THE STUDY

2.1 The Agricultural Sector in Egypt

In this section, the agricultural sector of Egypt is
described as an example of the subsistence smallholder
agriculture in developing countries. The emphasis is given
to describe the constraints which limit agricultural

development.
2.1.1 The Country : Basic Information
Lunatics

Egypt occupies the north eastern corner of Africa. It
is located between latitudes 22 and 32 north and longitudes
24 and 37 east, and bordered by the Mediterranean Sea to the
north, Sudan to the south, the Red Sea to the east, and
Libya to the west Figure 2.1. The total area of the country
is 1,001,450 square kilometers and the country is divided in
to three geographic regions:

a. The Nile Valley and Delta
b. The Eastern Desert and Sinai
c. The Western Desert

Only about three percent of the total area is
cultivated which represents the narrow strip of land along
the banks of the Nile in Upper Egypt and the Nile Delta.

14

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

’tM‘ NE DWERRANEAN ' , EGYPT
\ Helium) -.-.-Mcmwonal modules
. '0 ‘V‘ .........eouoeares -_ cl
’1 ‘ Mun " . . Cun'uated are)
’. . v,“ {.‘-‘\._ ' ‘. +e-e-eOISC!
o’fi‘; 0' ".~
3 7.1%“ ,. E‘
2 . {3’4},- f \_
~ 1039*" '
.I 11:: 24b .vs::- J
3 55" 0. ‘8‘.“-..,-ou/
I
‘ i ' Sahariya Oasis
23 i ‘n :Av‘eo s A U 0 I
)- l ' ‘ ~
3 ¢ 1! h R 8 .I t
' 9' A "v ".1
“g "Q ' i "e . t
i ‘ - .‘ ,3" an basis A
-i “am 4
.35 is" .. v " "
: . .e.‘ 0
am “a o 4_
. O '_ e v... A
g. ‘Q {10...}. e1. 6’ .
i e Datum Oess'uk'fl.‘ 5‘ , J0
! m awn-3 1
4 ! s ' '
g a
a! a! 9: \
245
f .i . - .—
! o . .
I 2 '
g: .- -
a.‘
J 5 .3 {c .- .. Lake Nasser
; ._.-O-. I’ o
's I "e ‘ L". . . .
:2 . -LJ- _._ J . . .
' a n "" -----‘ v’~--- -.-.- -.-._.m-.-.-.-.- .-.- Q- - -.- -.-._.-.< «9-.-.-----.¢-'-' -‘,

 

Figure 2. 1 GEOGRAPHICAL MAP OF EGYPT.

15

2.91m

Egypt has a moderate climate the year around. Table
2.1 gives the seasonal and annual averages of temperature,
relative humidity and rainfall for selected locations. The
climate can basically be divided into two climatic zones.
The first comprises the Delta and the Mediterranean coast
which is characterised by a mild and somewhat rainy winter
and warm rainless summer. The second covers the rest of
Egypt south of Cairo and has a mild, almost rainless winter
and a dry hot summer.

In general rainfall is negligible and Egypt totally
depends on irrigation for its agriculture except for a very
small area in the northern desert coast, where rainfed
barley, castor bean, figs, olives, and almonds are grown.
This limits the cultivated area to only three percent of the
total area irrigated from the Nile River.

3.£onulati.cn

The population of Egypt is 45.2 million. Its average
annual growth rate for the 1963/73 period was 2.3 percent.
It increased to 2.5 percent for the period from 1973 to
1983. Several population projections estimate that Egypt‘s
population will reach 52 millions in 1990 and 63 million in
year 2000 (World Bank,1985). The majority of population is
rural, but the ratio of rural / urban population has been

declining during the last three decades (Table 2.2).

16

Table 2.1 CLIMATOLOGICAL NORMALS OF EGYPT

Locality January Ave. July Ave. Annual Ave.
Temp. Humid. Temp. Humid. Temp. Humid. Rain
max. min. X max. min. % max. min. % m m
Mediterranean

Coast: 18.1

9. 70 29.7 22.3 73 24.6 16.2 70 134.2
Delta 19.6 6.

8

6

79 34 5 20 2 71 27.9 14 0 74 46.7

6

9 . . .

Cairo 19.4 .6 59 35.4 21.5 52 28.1 15.4 55 23.8
0

Upper

Egypt 21.4 61 37.8 21.8 43 30.8 14.9 49 6.4

1) Temperature in Celsius

Source: adapted from EL-TobEY. H. A.," Contemporary
Egyptian Agriculture." 1976.

Table 2.2 URBAN AND RURAL DISTRIBUTION OF EGYPT

Year Urban Rural Total
Thousands

1937 3,981 11,940 15,921
1947 5,880 13,087 18,967
1960 9,834 16.251 26.085
1966 12,385 17,691 30,076
1976 16,098 20,589 36,687
1983 20,340 24,860 45,200

Sources: data for the period from 1937 to 1976 is adapted
from CAPMAS, " Population Census of Egypt", 1983,
and for the year 1983 from , World Bank "World
Development Report", 1985. .

17

About 50 percent of the total labor force, which is 57
percent of the total population, is engaged in agriculture.
With the limited amount of cultivated land available, the
population density per square kilometer of inhabited land
exceeds 1290. Therefore, unless progress is made, food
demand will substantially and continuously exceed food

supply.
4.3211

The soil of Egypt may be classified in the following
groups :
1. Alluvial: level, deep, black, heavy to medium in texture,
constituting about 75 percent of the cultivated area in the
Nile Valley and the Delta.
2. Marine alluvial: recent, level, heavy in texture, black
in color, and mostly saline. It is located around northern
lakes.
3. Residual calcareous (Brown Calcy): light to medium in
texture and undulated. It extends along the Mediterranean
Coast.
4. Sandy soils and sand dunes: located mostly in Sinai and
the Oases. .
5. Gravely sandy soils: undulating, mostly in the eastern
and western deserts.

Before the construction of the High Dam, the amount of
suspended matter carried by the Nile river from its sources

in Ethiopian and Victorian plateaus used to reach its

18

maximum during the flood season. This suspended matter
contained 55 to 64 percent clay, 25 to 30 percent silt, 6 to
17 percent fine sand, and negligible amount of coarse sand.
However, these amounts have been decreased substantially
after the construction of the High Dam.

In general, the Egyptian cultivated area can be graded
mostly as excellent, with respect to soil quality.

Despite the large land area (238 million feddans), land
suitable for development and cultivation is a great
constraint. Only about six million feddans are under
cultivation with a cropping intensity of about 1.9

crop/season.

5mm

Egyptian agriculture is confined to the Nile Valley and
Delta and is totally dependent on irrigation from the Nile
River, as the country is practically rainless. Two
exceptions are the groundwater irrigated land in many
depressions in the Western Desert and the small rainfed area
along the Mediterranean Coast and Sinai. After the
construction of the High Dam in 1965, the perennial
irrigation system was introduced, it has been possible to
obtain two or three crops a year from the cultivable land,
However, imperfect water management is a serious constraint.
Conveyance losses in cn-farm channels are high because of
poor design. Poor land-leveling, and low flow-rates cause

uneven field distribution and total water applications that

19

greatly exceed actual crop needs. Inadequate drainage
results from poor maintenance of field drains and/or main
drains. These imperfections results in reduced yields, and
restrict crop intensification. In addition, waterlogging and

secondary salinisation are by-products of heavy irrigation.

2.1.2 General Features of Egyptian Agriculture
LitrmimLSaasons

The agricultural year is divided into three seasons. It
starts with the winter season on the first of September. The
main winter craps include wheat, barley, clover (Egyptian
berseem), broad beans, lentils, fenugreek, chickpeas,
lupine, onion, flax, and winter vegetables. Planting starts
in October and harvesting starts in April. The second season
is the summer season. Summer crops include: cotton, rice,
maize, sorghum, summer onion, groundnuts, sugar cane,
sesame, millet, and summer vegetables. These are usually
planted in March to June and harvested between August and
November. The last season is the nili season, named after
the Nile flood season. The nili crops include maize,
sorghum, rice, and vegetables. These are planted in July and
August and harvested in October and November.

2W

Due to the availability of water all year round,
continuous cropping is the general feature of Egyptian

agriculture. Crop rotations relate to the sequence of crops

20

during the cropping season of the year and/or successive
years, depending on the area cultivated by the main crop in
the rotation. Crop cultivation usually follows a two or
three year rotation. Crop rotations also vary according to
the crops involved and the soil fertility. The most common
one is a 3-year cotton rotation, in which the area is
divided into approximately three equal plots. The first is
planted with a temporary cover (catch) crop of clover in
winter, followed by cotton which is planted in March as the
summer crop. The second plot is planted with clover or
winter legumes and the third plot is planted with wheat in
winter. Both the second and third plots are followed by
either maize, rice, or sorghum as summer crops. The sequence
is rotated among the three blocks during the second and
third year, and so on.

The second common rotation is a 2-year cotton rotation,
where the area is divided into two equal plots. The first is
sown by temporary clover in winter followed by cotton as the
summer crop. The second plot is divided into two sub-plots,
one for clover or legumes and the other for wheat or barley,
both are followed by maize, rice, or sorghum. The sequence
is alternated in the second year between the two plots. In
addition, there are 3 to 8-year sugar cane rotations with
the inclusion of other crops and vegetables in different
rotations . Inter-cropping, where a secondary crop is grown
simultaneously with the main crop, _is recently adapted and

is becoming more commonly used.

21

These special features of the Egyptian cropping system
are related to the severe pressure on the limited cultivated
land. In addition, pricing policies, required planting area
regulations, delivery quotas and other sorts of government
interventions, affect the cropping patterns and cropping

rotations dramatically as will be explained in sub-

consequence sections.

:3.an

One of the problems which has hampered the application
of modern technology in Egyptian agriculture is the severe
fragmentation of land holdings occurring throughout Egypt.
Many farms have become so small that they are no longer able
to support the farm families living on the farm. This
fragmentation is mainly a result of Egyptian Government’s
policies. These policies are related identified desirable
sociopolitical results. In addition, it is also a result of
the traditional Muslim pattern of inheritance (i.e. when a
farmer dies, holdings are distributed among all the farmer’s
children).

Three major Agrarian Reform Acts have been issued. The
1952 Act lowered the maximum holding to 200 feddans (1
feddan = 1.038 acres = 4200 square meters). After the 1961
and 1969 Acts, the limit of maximum holdings was lowered to
50 feddans per individual owner and 100 feddans per family
(i.e. family includes husband, wife, and minor children)
(see Table 2.3).

22

Table 2.3 DISTRIBUTION OF LAND OWNERSHIP IN EGYPT.
Years 1952-1961.

 

Holding Size Landowners Area Percent of Percent of

(feddans) (’000) owned landlords area owned
(1000fd.)

Less than 5 5,842 2,122 94 3 35.4
5-10 79 528 2.8 8.8
10-20 47 838 1.7 10.7
20-50 22 654 0.8 10.9
50-100 6 430 0 3 14.3
More than 200 2 1,177 0 1 19.9

Total 2,801 5,984 100.0 100.0

(2) After 1952 Land Reform Law

Less than 5 2,841 2,781 94.4 46.5
5-10 79 526 3.6 8.8
10—20 47 638 1.6 10.7
20-50 30 818 1.0 13.7
50-100 8 430 0.2 7.2
100-200 3 437 0.1 7.2
More than 200 2 354 0 1 5.9

Total 3,008 5,984 100.0 100.0

(3) After 1961 Land Reform Law

Less than 5 2,919 3,173 94.1 52.1
1-10 80 526 2.8 8.6
10-20 65 938 2.1 10.5
20-50 26 818 0.8 13.5
50—100 8 430 0.2 7.1
100 5 500 0.2 8.2
More than 100 0 0 0 0

Total 3,101 6,385 100.0 100.0

Source: adapted from Ill-Tobey. H. A” W
Agriculture. 1982.

23

As the farm size decreases, the livelihood it provides
also decreases unless farmed with greater intensity. This
situation has led to a questionable economic viability of
these small farms with a rapid increase in their rental
values (i.e. the gross and net margin are very small).

A project was started in 1981 for consolidating small
holdings by regrouping them into larger blocks at the
village level without affecting private ownership rights.
The consolidated plots range from 20 to 300 feddans. The
consolidation program has enabled the country to plan and
control areas to be planted by crops every year. However,
there exist many limitations facing the full application of

this program.

4.Agr.icultural_lnnuts

This section reviews and assesses the status of the
major agricultural inputs affecting crop production. The
inputs considered include improved seed production, hired
agricultural labor, equipment and machinery resources,
agricultural chemicals, and agricultural credit. Irrigation

water supplies was considered earlier.

a. Improved Seed Production

Availability of quality certified seed has been
identified consistently as a major bottleneck for achieving
higher yields. Certified seed growers are paid a premium

over the current price of the commercial crop. This premium

24

is used to defray part of the cost of seed cleaning,
bagging, transportation, certification, etc.

Because of the high seeding rates used by Egyptian
farmers (which are 3 to 5 times those of the international
level) and because of poor seedbed preparation and hand
sowing operation there is never enough certified seeds to
meet the farmers’ need. Therefore, the farmer uses his own
seeds to supplement to good seeds. Also, the introduction of
better machinery for seedbed preparation and fungicidal seed
dressing would reduce the quantity of certified seeds

required.

b. Hired Agricultural Labor

The issues of labor availability are important because
they are basic considerations in the selection of
appropriate mechanization strategies, cropping patterns, and
improvements in the general rural welfare. Recent increases
in wage rates and occasional shortages of hired labor are
due principally to supply constraints due to migration of
agricultural laborers. Other reasons for occasional
shortages include military service, increase enrollment in
school, and increased farm fragmentation ( Goueli, 1981).
Yet, no precise measurement of supply/demand variables
affecting agricultural labor markets exists. Currently,
there is a need for inducing selective mechanization and the
adoption of technologies without large labor-displacing
effects (Taylor, 1983).

25

c. Agricultural Mechanization

Mechanization of agriculture in Egypt is limited in
scope. Most of the farm -operations are still done by
manpower and draft animals. The data on the amounts,
capacity and condition of various types of farm equipment
are subject to controversy. However, a substantial
proportion of plowing, water pumping, spraying, and
threshing are now partly mechanized.

The need to expand research and development regarding
machinery and equipment utilization for Egyptian farming
conditions is crucial.

d. Agricultural Chemicals

Farmers are supplied with subsidized fertilizers and
pesticides through Agricultural Cooperative and Credit
Banks. Allocations and availability of fertilizers are
currently a major problem. However, serious problems and
losses exist in handling, storage and delivery of fertilizer
supplies. In addition, fertilizer is wasted due to over-
irrigation, and broadcasting by hand which leads to
volatilization of ammonia nitrogen. More research and
development are needed with respect to appropriate

fertilizing levels and application methods.

e. Agricultural Credit

The availability of agricultural credit, both in cash

and in kind, to farmers plays an important part in the

26

Egyptian Agriculture. The Principal Bank For Development and
Agricultural Credit (PBDAC) is the only source of
institutional agricultural credit. It provides subsidized,
in-kind and cash loans, and also subsidized basic crop
inputs such as fertilizer, seed, and pesticides. Production
inputs are subsidized both in terms of input prices and
interest rates. However, the bank conservative policies
(i.e. loans for farm equipment require 5 feddans security
for tractor and 3 feddans for water pumps) limit the ability
of small farmers and tenants to obtain medium and long term
credit.

5-Wustim

Egypt’s total crop production has increased from 21.317
million metric ton in 1971 to 25.524 million metric ton in
1981. This represents a total increase of 19.7 percent or an
average annual increase of 1.9 percent. During this same
period the rate of population increase has risen by 2.9
percent annually. (Table 2.4)

In comparison to world agriculture, Egypt ranks among
the leaders in average production per unit area (see Table
2.5). Considering the unique nature of Egyptian agriculture
-- the completely irrigation-based agricultural system with
high quality water, the deep alluvial rich soils of the Nile
River and Delta, and the optimum climatic conditions for
agriculture,-- there is great potential for increasing

productivity in all crops by a minimum of 50 percent as

27

Table 2.4 PRODUCTION BY COMMODITY OF EGYPT, 1971/81.

 

Wheat 1,732 1,884 1,933 1,796 1,938
Rice, paddy 2,507 2,242 2,351 2,384 2,236
Corn 2,342 2,641 3,117 3,230 3,307
Barley 76 89 132 107 103
Sorghum 854 824 858 643 653
Broad beans 256 234 231 213 208
Lentils 50 51 16 7 5
Potatoes 451 709 772 1,214 1,210
Sweet potatoes 84 75 63 86 88
Onions 571 550 599 588 854
Sugarcane 7,498. 7,918 8,296 6,818 8,818
Cotton 510 441 438 520 508
Cotton seed 886 819 736 844 800
Flaxseed 10 23 31 34 27
Soybeans 1 2 79 72 130
Peanuts 33 25 33 32 33
Sesame seed 21 14 9 18 17
Sunflower seed 33 11 7 9 8
Cabbage 275 299 348 353 374
Tomatoes 1,637 1,729 2,198 2,571 2,453
Oranges 707 819 843 921 895
Tangerines 101 90 85 70 73
Lemons 70 51 58 72 60
Apples 35 35 32 27 35
Pears 15 20 30 52 55
Bananas 95 110 113 133 137
Grapes 121 221 274 299 298
Olives 8 6 5 4 5
Dates 340 396 377 446 391
Meats 330 360 436 472 493
Milk 1,651 1,759 1,801 1,865 1,902
Wool, greasy 3 3 4 4 4
Total (million

US 3) 1,868 1,891 2,070 2,205 2,280
Source: adapted from MOA/USAID/IADS/USDA Egypt:

WW. 1982.

28

illustrated in Tables 2.5 and 2.6, and Figures 2.2 and 2.3.
Farmers are constrained from producing at optimum levels
because of current government policies and practices (e. g.
lack of facilities and/or resources for scientists to carry
out yield increasing research and low prices for farmers’
product). In addition, lack of effective credit, extension
services, input delivery, and other needs hampered the level

of production.

6. Animal Production

Domestic animals in Egypt supply meat, milk, eggs,
wool, hides and manure and are used as draft animals. In
Table 4.7, estimated livestock numbers are illustrated.
Egypt has one of the most dense livestock populations in the
world--about one animal unit (cow equivalent) per feddan
for the agricultural production area. Current policies
favor the expansion in the production of meat and animal
products at the expense of crops needed for human
consumption or export crops. Additionally, price policies
made it more profitable for farmers to shift from growing
stable food crops to the production of livestock feed. Egypt
imports substantial quantities of poultry, red meat and
dairy products. Many factors limit achieving optimum
production including unstable feed supply, lack of reliable
information on livestock systems, low productivity of work
cattle, and fragmentation of farms. Less information exists

regarding potential improvement in animal production.

29

Table 2.5 COMPARISON OF EGYPTIAN AND WORLD YIELDS OF 13

FIELD CROPS ON THE BASIS OF 3-Year (1978-80)
AVERAGES.

 

Crop World Yield Wm Yield _ Egyptian v1.14 as x—

tons/ha tons/ha of World Yield
Maize 3.126 .664 124
Barley 1.977 .662 136
Onion 12.431 2 .517 213
Sugarcane 56.533 6 .661 146
Wheat 1.906 .241 170

Broadbean 1.001 .190 219

QGOHHOF‘HNGNONM

Flax(Fibre) 0.417 240

(seed) 0.466 .219 260
Rice 2.723 .643 207
Groundnut 0.964 .663 175
Lentil 0.611 .046 171
Sesame 0.294 .696 305
Potatoes 12.294 1 .295 107
Tomatoes 20.955 1 .262 62

 

Source: Pew. 1960. and IDA.
WW. various issues.

 

 

6 LEGEND
2%? EGYPT
5 ._ - WORLD

5.

\\\\\\‘

 

field (tons/ho)
u

\\

 

 

 

/

Z /

% %

/ %
w % /
o- % é

   

 

Maize Barley Wheot Rice Sesame

Figure 2.2 COMPARISON OF EGYPTIAN AND WORLD YIELD OF
5 SELECTED FIELD CROPS.

Source: adopted from Table 2.5 dotc.

 

 

 

30

Table 2.6 MARISON 01" AVERAGE U.S. AND EGYPTIAN cm?
YIELDS UNDER IRRIGATED WNDITIONS.

 

 

 

 

Crop Average Yields (MT/ha)
Egypt U.S
Maize 4.0 9 6
Sorghum 3.6 6 5
Rice 5.6 5 5
Barley 2.7 . 5 7
Soybeans 1.1 3 4
Sesame 0.6 2 0
Groundnuts 2.4 3 4
Source: IDA/USAID/IADS/USDA,_Egm
. 1962
1° LEGEND
9 J» 7//% EGYPT
8 p, I U. s.
7 l.

 

Yield (MT/ho)
an

 

 

 

 

 

'2
3‘» g;
/
2 d» g
Z
1 v y
2
O f 2
Maize Rice Soybeans Groundnuts
Sorghum Barley Sesame
Figure 2.3 COMPARISON OF AVERAGE U. 8. AND EGYPTIAN
CROP YIELDS.

 

Source: adapted from Table 2.5 data.

 

31

Table 2.7 ESTIMATED LIVESTOCK NUMBERS IN EGYPT.
years .

selected

Cattle Buffalo Sheep Goats Camels

 

----------------- (’000) -----------------
1960 1,867 1,761 2.220 1.588 154
1970 2,115 2.009 2,066 1,155 127
1976 2,589 2,512 2,554 1,440 43

average annual rate of growth (percent)

1960-70 1.3 1.2 -.7 -3.1 -3.6
1970-78 2.5 3.0 2.7 2.8 -3.8

Source: Fitch, J. and I. Soliman. “ Livestock Economy in

Egypt“. Economics Working Paper 29,
University of California, 1981.

32

ARE/NOA.

2.1.3 The Policy Environment
LErisajnlisx

Since Egypt’s Revolution in 1952 the government has
continuously imposed a high degree of control on
agricultural prices. Government-set low producer prices act
as disincentives to increase domestic agricultural
production, and government-subsidized low consumer prices
act as incentives to encourage consumption. Accordingly,
inefficient allocation of resources and inequitable sectoral
income distribution are the results of those intervention.
The government’s policy of procuring major crops (such as
cotton, rice, and wheat) at a price below world prices makes
it relatively unprofitable for farmers to produce these
crops. As a result, farmers transfer resources to the
production of those commodities which are relatively more
profitable (i.e. those with free market prices such as
fruits, vegetables and berseem). Because of those shifts in
agricultural production, a system of forced delivery or
quotas, allotments were imposed and subsidized inputs
offered. Input subsidies on fertilizers, I seeds and
chemicals--intended to partially offset low produce prices--
, also contributed to the misallocations of resources.

Allowing prices to reflect opportunity costs will
produce incentives for farmers to reallocate resources and
will also provide incentives to adopt yield-increasing
technologies. In addition, a better understanding of all the

33

interconnections-—via sets of more reliable data and better
analysis--is called for in order to determine the actual
effect of any proposed policy changes and also to assist

policy-makers in setting their decisions.

KW

The extension service is reported to suffer from a
variety of problems generally encountered in developing
countries: inadequate funding, lack of equipment, very weak
linkage between research and extension, and low salaries.

Research tends to be fragmented and compartmentalized
with relatively little collaboration among researchers in
different sections working on related problems. Other
problems include: lack of laboratories, equipment and
supplies, and absence of interdisciplinary research teams.
There exists a relatively low level of public investment in
agricultural research and extension programs to develop and
make available improved technology to farmers.

The vital roles that research and extension must play
in the development of the agricultural sector and the
importance of agriculture to the national economy point to
the need for careful, and comprehensive planning of the

national research and extension effort.

3W

Marketing of agricultural products in Egypt represents
a bleak picture. Handling, storing, transporting,

34

processing, and marketing agricultural output are recognized
as potentially serious constraints to agricultural
development. Additionally, there exist serious lack of
marketing data available to researchers and policy makers.
Large quantities of food and agricultural products are never
consumed because of spoilage and other losses. On the other
hand, prices set by the government on most food products
generally take the form of price ceilings and are applied
without consideration of quality or condition of the
product. High import tariffs charged on such items as
refrigeration equipment (100%), small trucks (200%), and
household refrigerators (250%) constrain improvements in
marketing system.

Conducting marketing and economic research regarding
alternative marketing methods, the functioning of marketing

channels, and efficiency are badly needed.

2.3 Review of Major Studies of Database Management
Systems and Data Types and Structures

In this section, a review of related studies on data
base management systems (DBMS), and data types and
structures will be presented.

Dill and Spahr (1984) defined a data base management
system (DBMS) as an application tool used to develop an
information system-—a system that accepts raw data as
inputs, performs certain processes, and produces meaningful

information as an output from the system. The principle

35

purpose of a DBMS is to provide controlled, integrated
storage of data in order to reduce, if not eliminate, data
redundancy. They described redundant data as the same data
field being found in two or more places within the data base
which results in poor utilization of storage space and
degrades the performance of the system. Other reasons for
having integrated storage of data include: avoiding
inconsistency, sharing of data, enforcing standards,
applying security standards, maintaining integrity, and
balancing conflicting requirements. A

There are three common storage structures used by
DBMS’s--hierarchical, network, and relational. A
hierarchical data base refers to a tree structure
relationship between the records, i.e., a family tree is a
good example of this system. A network data base can also
refer to a tree structure relationship, however, a “child"
in a network structure is not limited to one immediate
"parent“ as in the hierarchical structure but rather can
have many parents. The third type, the relational structure,
is the most popular and is the easiest to utilize for most
problems. With this type of storage structure, the data are
stored as two dimensional tables each table is stored in a
separate file. Each column represents a particular data
field and each row, comprised of several fields, is a
record. The relationships between the data are made at the
file level rather than at the record level as in the

hierarchical and network structures.

36

Gotlieb and Gotlieb (1978) reviewed DBMS concepts. A
data base contains two types of information: descriptions of
entities and representations of relationships. An entity is
an object that has independent existence, in the context of
the application for which the data base is intended and is
described by a set of characteristics, or attributes. A
relationship is a named association among sets of entities.
Relationship kinds include: hierarchical or 1:n (which are
one to many relationships), mzn, or many to many , and 1:1
binary relationships. A data-base management system
maintains a data structure representation of entities and
relationships. The logical structure, or data model, with
its operation set, constitutes the interface through which
the data base is accessed. Data-base management systems
(DBMS’s) employ the basic techniques for mapping information
into storage, and for providing retrieval to it. Entities
are represented by grouping attribute values together to
form records and relationships are expressed through
proximity, or records are related in the same block,
position, or records related in different files, and pointer
mechanisms (e.g. pointer values and hash tables). The data
model characterizes DBMS.

There are two main approaches to data modeling, network
and relational. In the network model, entities and
relationships appear explicitly. Entities are represented by
records, and relationships by links which are mzn mappings

connecting sets of records together. A hierarchical model is

37

a special case of network model which occurs when all links
are 1:n and directed away from a root type record. In the
relational model, there is no distinction between entities
and relationships. The relation appears as a table in which
the rows represent entities, and the columns, attributes.

Dill and Spahr also stated that the data should be
normalized to be properly stored in a relational data base.
A data set is said to be in a particular normal form if it
satisfies certain constraints. These constraints are first
to fifth normal forms, where a key (field or group of
fields), is used to identify a record. For example, in
second normal form every non-key field must be fully
dependent on the primary key field. In third normal form
every non-key field must be fully dependent on the primary
key and that all non-key fields be mutually independent.

In addition, prior to using a DBMS, every input, all
processes and all outputs must be completely defined through
structured system analysis. This technique uses a procedure
called "top down analysis". The application is first defined
at its highest level of abstraction (level 0). The inputs,
outputs, and processes are all defined in very general
terms. The next step is to analyze the process defined at
level 0, i.e. identify the source and destination entities,
the function of the process, and the inputs and outputs.
Finally validate the model. Once the data base is fully
designed and the information system is completely analyzed,

the system is ready for implementation using a data base

38

management system.

The major points to consider when selecting a DBMS
include whether its features are suitable for the specific
information system being developed, hardware constraints,
capacity, and efficiency, the user friendliness and degree
of programming expertise required.

Barnnstrom (1983) defined a data base as a collection
of interrelated data organized for ease of update and
retrieval. Electronic data bases can be manipulated and
updated with much less difficulty than with traditional
methods. Choosing a DBMS for the microcomputer depends on
the skills and aptitude of the user, the planned use of the
data base, and the budget constraint.

Most DBMS software uses the concepts of fields and
records to reference specific data in a data base. Each
field must have a declared maximum length and a declared
data type. The data in a data base can be conceptually
visualized as a large table with field headings across the
top of the columns and record numbers or names at the left
edge of the rows. The most common tasks of data base
management software include:

1. Accessing specific information at random.

2. Generating reports on a routine basis.

3. Sorting and indexing the information on a variety of
fields and conditions.

4. Creating electronic forms for ease of data entry and

verification.

39

5. Updating and modifying selected records in a data base
easily.

6. Creating new data bases from existing files and appending
data into the data base from other sources.

There are two common types of microcomputer data base
managers: 1) file manager, and 2) relational managers. With
the file manager all data are contained in a single file and
special routines are used to update and manipulate the
files. With relational data base managers (RDBMS) the data
are stored in the forms of tables with rows and columns and
new tables can be created consisting of fields of two or
more existing tables. Not all relationships need be
anticipated before the data base is created which is one of
the most powerful features of the relational scheme.

Harsh, et al., (1981) described the role of information
in decision making and referred to Davis statements:

In general, the value of information is the value of
the change in decision behavior caused by the
information less the cost of the information. In other
words, given a set of possible decisions, a decision
maker will select one on the basis of the information
at hand. If new information causes a different decision
to be made, the value of the new information is the
difference in value between the outcome of the old
decision and that of the new decision, less the cost of
obtaining the information.

They distinguished between raw data and usable
information and explained how the transformation of raw data
into usable information took place. They stated, since the

major purpose of a management information system is to

assist the manager in performing his functions, therefore

40

the components of a management information system should
have a close relationship to these functions of a problem
definition, observation, analysis, planning, and decision
making. They also discussed the four major components of a
farm management information system: descriptive, diagnostic,
predictive, and prescriptive. Descriptive information
portrays the "what is" condition and describes the state of
the farm, or some physical, biological, sociological, or
economic aspect of the farm at a specified point in time. It
includes financial (e.g. net worth, income statements, tax
reports, and enterprise accounts), physical, biological, and
engineering descriptive information (e.g. dairy production,
records, soil tests, and so forth).

The second component, diagnostic information, describes
"what is wrong” condition and has two major uses: to define
problems that develop in the farm business, and to exploit
any opportunities that should arise in the farm business,
through the use of "management by exception” i.e. a
management tool whereby a farm’s performance levels are
compared with those of other farms with similar resources
and technology.

Predictive information concerns with "what if...?" and
is generated from an analysis of possible future events. It
is valuable to the farm manager in weighing his expectations
of future outcomes in order to define or avoid problems in
advance. It is needed by farm managers to reduce risk and

uncertainty. With the increasing use of computers, linear

41

programming, and more sophisticated techniques are being
used. The final component, prescriptive information,
portrays the "what should be done" question. It requires the
utilization of the predictive information together with the
assumptions and conditions a farm manager wishes to impose
upon a decision. Thus, the basis for making a decision is
provided by an evaluation of the predicted outcomes together

with the goals and values of the manager.

42

CHAPTER 3

DATA SOURCES AND NETHODODOGY

3.1 Data Requirements and Selection of Data Sources

Due to lack of reliable data along with limited funds

and time constraints, secondary data were used to carry out
the research. In addition, no reliable micro-level economic
data of the Egyptian agricultural sector were available to
satisfy the data requirements to carry out the research.
Therefore, an effort was made to select data which represent
the same features of the agricultural sector of Egypt and,
more generally, the subsistence or semi—subsistence
agricultural sectors in developing countries. Additional
factors taken into consideration include: 1) data must be
sufficient enough to establish a basis for management
information system, with its four components: descriptive,
diagnostic, predictive, and prescriptive information, to
support the operations and decision making functions, 2)
data must be reliable, adequate, and applicable for
processing and use by FARMAP and dBASE III PLUS to
accomplish the research objectives. Thus, enough and good
data were essential to cover different aspects such as
resource availability, the level of assets and liabilities,
and cost and return information. For example, farm data must
provide most of the descriptive information needed by farm

managers including financial (net worth, income, and cash

43

44

flow statements, tax records, depreciation schedules, and
enterprise accounts), resource (soil tests, machinery, and
labor records, and farm map), production (crops yield, and
meat production), and other technical data. Production data
including crop population and animal conditions were also
required. These data requirements are essential for a
thorough farming systems research analysis.

Accordingly, data were chosen from a farm survey
project, Consequences of Small Rice Farm Mechanization
Project (IRRI/USAID Contract No. tac 1468). This project was
a cross-country study begun in early 1978. The data
gathering component of the study consisted of two parts: 1)
a series of cross—sectional surveys, and 2) a complementary
daily recordkeeping system on selected farms. The survey
assembled all basic information on farm operations. The
study was conducted in three Asian countries, the
Philippines, Thailand, and Indonesia.

A sample size of 10 households was chosen from the
larger survey project to carry out the research. This modest
size sample was considered large enough to fulfill the
objectives of the research project. Also, because of the
manual task of codes interpretation and transfer to FARMAP
and dBASE III PLUS, time available did not permit more farms
to be included in the study. The sample was randomly
selected from the data of West Java site (Indonesia).

Data stored on magnetic tapes were retrieved with the

assistance of Agricultural Economics Computer Laboratory

45

Staff at Michigan State University and a printout (i.e.
paper copy) was obtained of the related sample size for
the wet season. These data were originally stored in the

magnetic tapes using the appended FAD/FMDCAS (Food and

Agriculture Organisation/Farm Management and Data Collection
Analysis System, which is an electronic data processing,
storage and retrieval system). They were stored on 80 column
records, each column or group of columns represent specific
data fields. Each data field comprised 1 to 8 characters
width. Data for each farm consisted of about 50-70 records.
Also, data for the wet season were transferred from the
magnetic tapes into floppy disks for microcomputer

processing.

3.2 Data Preparation, Transformation and Modification

Pro-coded data were stored in appended FAO/FMDCAS
forms. These forms included: resource utilization, disposal
of -products, marketing of the product, household and farm
labor, farm land, crops grown, animals, buildings, farm
implements and tools, other assets, financial liabilities,
farm machinery, inventory change, home consumer durables,
extension services, actual and ideal dates of crucial farm
activities, cropping pattern history, and demographic
information forms.

Data interpretation was done manually using the FAO
coding system and Consequences of Small Rice Farm

Mechanization questionnaire. The codes were numeric and

46

ranged from one to three digits. A check was done to ensure
the validity of data for each farm used. Errors were
eliminated by two ways. The first method was accomplished by
checking the consistency of the data codes with the standard
FAO coding system and the codes list appeared on the
original questionnaire. The second used range checking
procedures. This was done manually within and among records
of different households. In addition, data transformation
was made twice and a comparison was made between the two
results.

Whenever errors occurred, corrections of codes were
made based on the best information available. For example,
some data codes were not found in the FAO coding list due to
data entry errors (e. g. power input code 145 was not found
in the coding list, however, from the related plant
operation code, i.e. sprayer, it was estimated that this
power input code might represent sprayer, and so on). After
performing manual interpretation, data were coded using
FAD/FARMAP (Food and Agriculture Organization of the United

Nation/Farm Analysis Package) coding system.

3.3 Methodology

It was hoped that the study could:

1. develop appropriate pro-coded data forms required for
farming system research analysis;

2. develop appropriate procedures for processing these

forms; and

47

3. demonstrate the usefulness of using FARMAP and dBASE
III PLUS and compare the advantages and disadvantages
of each of them relative to the comparison criteria
which will be discussed later.

To meet the above mentioned objectives of the study,
the same coding system was used in processing FARMAP and
dBASE III PLUS programs. Data forms and structures were
intentionally designed in similar fashion in both cases.
However, when using dBASE III PLUS some simple modifications
were done on FARMAP records type formats to create new
database forms which simplified the data entry process for
the users. These newly designed dBASE forms and structures
can be used in other farming systems surveys and studies.
However, some modification might be needed to better meet
the requirements and objectives of these projects.

The data forms were grouped into the following major
components:

1- General Survey Information Forms:

There are several general survey forms including survey
title, data sources, sampling procedures, sample size,
conversion rates of local units, and national and regional
information. The latter includes the role of agricultural
sector, agricultural policies, government policies, and
demographic and sociological information. Other local
information such as location, soil catena, climatic
characteristics, seasons, production hazards, and user-

defined codes definition was included on separate forms.

48

2. Resource Descriptive and Quantitative Data Forms:

Household members, permanent labor, temporary labor,
off-farm work, education, health status, and demographic
data were covered under this section. Also farm data
including land characteristics, crop characteristics,

animals, physical and financial assets, and credit and
liabilities were coded and recorded separately. These data

will be used in the socio-economic analysis context.

3. Resource Flow Data Forms:

These forms include all resource flow data required for
farming system analysis including net income, cash and kind
flow, human power use, machine power use, and animal power
use. Additionally, farm operations, input use, yields and
other forms of output, household consumption, and other
fixed and variable expenses, are all included in this
context.

To study the usefulness of using database management
systems (DBMS’s), to carry out micro-level research in
developing countries, two packages were used. These are
FARMAP (Food and Agriculture Organization/Farm Analysis
Package) and dBASE III PLUS (a commercial data base manger).
FARMAP was chosen because of its applicability and its
widespread usage in developing countries. While dBASE III
PLUS (by Ashton Tate) was selected because it represents one
of the most sophisticated and integrated relational data
base managers among the commercial software packages, and

one of best selling particularly in developing countries.

49

The same data will be employed for both FARMAP and
dBASE III. The structures of both databases will be shown in
the next_ chapter. FARMAP will be used for data storage,
validation, and tabulation and each usage will be discussed
in detail in the next chapter. Unique data structures will
be created for processing the data by dBASE III. The results
of using dBASE III and FARMAP will be presented in chapter
5.

Two types of microcomputers were used in data storage,
processing and retrieval. The first was an ITT XTRA without
a hard (fixed) disk. The second was a Zenith model Z-200
(IBM AT compatible) with a hard disk for faster processing
and manipulation of data. The reason for that was to
demonstrate two different versions of FARMAP. One version is
non-menu-driven and does not require a microcomputer with
hard disk for data processing. The other version, the menu-
driven, requires a hard disk.

FARMAP coding system was employed in processing FARMAP
as well as dBASE III PLUS. Some modifications in coding and
recording data were applied to dBASE III data structures.
However, the procedures for processing with both programs
were made as consistent as possible to make the comparison
of the two systems more meaningful (e.g. the advantages and
disadvantages of each program could be noted).

Most data base management systems are able to perform
a common set of tasks. Among those tasks are: access

specific information at random, generate reports, sort and

50

index information, create forms for ease of data entry and
validation, update and modify selected records and/or
fields, create new data bases from existing files, append
data from other sources, and export data to statistical and
planning tools for further analysis. Therefore, the
comparison criteria between FARMAP and dBASE III will be
mainly based on the above mentioned factors. In addition to
those mentioned above, other factors which will also be used
include: software /cost, hardware requirements, user-
friendliness, level of support by other software packages
and by the developer, maximum number of records allowed,

online help, and level of user control. The results will be

discussed in detail in the next chapters.

CHAPTER 4
THE MODEL

4.1 The Structure of FARMAP
44.1mm

FARMAP is the Farm Analysis Package, developed by the
Farm Management and Production Economics Service (AGSP) of
the Food and Agriculture Organization (FAO) of the United
Nations. It was developed, following its predecessor FMDCAS
(Farm Management Data Collection Analysis System), in
response to the need for computerized tools for the rapid
and flexible processing of rural household survey dates
FARMAP can also be used for the storage of survey data in a
standard form for future retrieval and multipurpose
analyses. It offers a basis for a unified system of rural
data collection, analysis, storage, and retrieval.

FARMAP is written in FORTRAN IV (FORmula TRANslator)
computer language. The package can be installed on micro,
mini or mainframe computers. These versions are consistent
in terms of the command files, coding system, programs, and
technical manuals; however, they vary in processing speed,
memory and disk space, file’s names conventions, and
installation procedures. The following procedures utilize
programs from the package

* data transfer to computer

* data validation and correction

* processing of group of farms, parcels, and activities

51

52

data

* comparative analysis of selected observations

* generation of cross tabulation tables

FARMAP focuses on the farm-household. The interviews of
household members are the major source of information. Also,
it was designed for the application of farming system
approach to smallholder of subsistence and semi-subsistence
agricultural production in developing countries. These
sectors are mainly characterized by:1 1) humans or animals
are the main sources of power, 2) low capitalization of the
farms, 3) absence of records of production, income and
expenditures, 4) large quantities of production consumed on—
farm or given in kind, and 5) on-farm production of many
inputs.

Therefore, emphasis is given to labor analysis, input
and output coefficients, farm economics, income, and
relative proportions of cash and kind. However, small scale
cash farming and data from large scale mechanized farming
can also be analyzed using FARMAP.

The level of detail in data sets can vary considerably.
For example, FARMAP can handle a few or several .thousand
items of data information per household. It also can be
employed in single interview surveys or in multiple visit
surveys. Likewise, topics covered can be restricted to one

aspect of production or can encompass the whole production

adapted from FAO Farm Analysis Package, vol. 1, FAO, Rome
1983.

53

and consumption system.

The standard output tables generated by the package
include information on household composition, labor, land,
assets, liabilities, net worth, economics, power use, and so
forth. Furthermore, additional tables can be designed by the

user according to any particular needs of the survey.

4.1.2 WW:

FARMAP can be used on a wide range of computers, e.g.
micro, mini, or mainframe. The programs are written in
FORTRAN IV and for microcomputers which are the focus of the
study, both MS-DOS and CP/M operating systems can be used.

The minimum hardware requirements differ depending on
the size of data sets, single or multiple visits, the level
of output required. For instance, for only data storage and
validation a 64 K bytes of RAM (Random Access Memory) and
one or two floppy drives will be sufficient. However, for a
full package utilization the amount of RAM must be larger
(e.g. 512 K bytes) and at least two floppy drives should be
available. For the menu-driven version of FARMAP a hard disk
of 10 mega bytes, and at least one floppy drive, is
essential. Other software complementary packages are also
required, for example, statistical, and linear programming
packages, since FARMAP does not provide statistical and
planning tool capabilities needed for a thorough farm
management analysis and a complete micro level research.

In addition to the above mentioned hardware and

54

software requirements, system analysts or programmers are
required for initial installation and maintenance of FARMAP
(as recommended in the technical manuals, especially for
installing the package on mini and mainframe computers).
These requirements vary depending on the level of processing
and tabulation. For example, advanced validation as well as
advanced tabulation require programming experiences to
design the command files required to generate user-defined
tables. However, basic validation and tabulation require
minimum knowledge of computer programming especially when

using the menu-driven version of FARMAP.

4.1.3WMWS

4.1.3.1 FARMAP Data Structure
LWARMALData

Data are stored in FARMAP using the related FARMAP
coding system (i.e., all item names have been assigned code
numbers). The code numbers, as they are compact and easily
handled by programs, are replaced by labels or abbreviated
names for each item in the processing of reports.

Data stored may appear in two forms

* 80 column records (one line)
* standard FARMAP records of 27 data fields all of

which are numeric (decimal numbers) on six lines.

55
2. W

The standard formats allow up to 980 record types of
which 900 record types remain free for the user to specify
the format for recording special purpose information. All
the 980 possible record types have a record type code
indicating the information contained on that record. FARMAP
categorizes information according to certain levels,

including national, regional and farm (Table 4.1).

56

Table 4.1 FARMAP STANDARD RECORD TYPE CONTENT AND DATA
SOURCE

 

Record Data source Content
type

 

010--099 national, regional general survey description

local
100—109 farm, activity, interviews
plot
110-199 farm, activity, household
plot
200-299 farm, activity, land-crops
plot
300-399 farm, activity livestock
plot
400-499 farm, activity, fixed and liquid assets
plot
500—599 farm, activity, liabilities
plot
600-699 farm, activity, general farms and stocks
plot ,
700-799 farm, activity, resource flow
plot ’

 

Source: adapted from FAO, FAO Farm Analysis Package, volume 1,
Rome 1983.

57

The order of the records of each farm data determines
the sequence of subtables in a farm table during data
processing. Due to the sorting capability of FARMAP

programs, questionnaires may contain record types in any

order.

Additional user-defined record types can be used to
store information not included in standard records and
required for specific analysis. They have codes in the range
100-980, excluding standard record codes and certain
prohibited codes (see FARMAP user’s manual for more

details).

2W

Each standard record contains 27 data fields. The
number of essential data fields, i.e., which must be filled
out, depends on the survey. However, there are minimum
number of data fields required for basic FARMAP processing.

FARMAP standard records are designed with a common
structure (i.e., many data fields have the same use in some
or all record types). Table 4.2 below lists data fields
which have common usage for the three different group of
records, which are:

1) general survey description data (record types 20-99)

2) resource description data (record types 100-699), and

3) resource flow data (record types 700-799).

58

Table 4.2 CONTENT OF COMMON DATA FIELDS BY DATA TYPES FOR

FARMAP.

 

27 field record

 

Data 80 column -------------------------------
Field General Resource Resource
Number Survey Description Flow
1 not used farm number farm number
code code
2 record type record type record type
020-099 100-699 700-799
3 not used (activity)* (activity)
4 not used component for component for
mixed activity mixed activity
5 (sequence compound code: compound code:
number) (parcel-plot- parcel-plot-
season) season
6 user-defined user-defined user-defined
code code
12 variable quantity quantity
13 variable conversion conversion
code code
15 variable current value value
24 variable cash or kind code ---
25 variable inventory(change) ---
26 variable date years ago
27 variable consumer groups consumer groups

code

code

 

*the bracketed items are optional.

Source:

adapted from FAO Farm Analysis Package,
FAO, Rome 1983.

volume 1,

59

Data field 1 contains the farm code for resource
description data and resource flow data. Data field 2
contains the record type code on every record. The activity
and component codes appear in data field 3 and 4
respectively, on both resource utilization and resource
description records. Data fields 3 and 4 are not employed in
general survey description data. In general survey
description, data field 5 contains the sequence number when
more than one record are used with the same type of
information. While, in resource utilization and resource
description data records it is used to store the reference
code identifying a parcel, plot and season. Data field 6 is
left free for user-defined codes during processing. Data
fields 12, 13, 15, and 27 contain quantity, quantity
conversion code, value and consumer group respectively on
all resource description and resource utilization records.
Data fields 12-27 are used for defining variables on general
survey records. Data fields 24,25 and 26 store cash-kind
information, type of inventory or inventory change and dates
respectively, in all farm description data. Finally, data

field 26 contains dates on resource flow records.
‘1.th

Each data field stores either a code (qualitative
information) or quantitative information. The codes cover a
wide variety of qualitative information (e.g. activity, type

of input/output, and dates). Quantitative information

60

include area, value, quantity of input, yield and so forth.

The coding used in FARMAP is numeric not alphabetic,
since, the package is language independent designed for
worldwide application. Numeric codes are also used to speed
up recording, transfer information to computer readable
form and structure information for easier calculation.

The coding system comprise the following several
principles (see Table 4.3) '

1) FARMAP programs work on code ranges rather than

individual codes,

2) all codes are numeric, from 1 to 5 digits in size,

3) a particular code has a unique meaning on any one

record type and in any one data field,

4) on diverse record types or in different data fields,

the same code can have various meanings,

5) codes have been chosen in meaningful ranges to

facilitate easy combination of similar categories of

data,

6) codes have been organized logically in groups of 10,

100 or 1000 codes (decimal grouping) where analysis is

likely to proceed at different levels of aggregation,

7) the figure 9 is used to mean ‘undetermined’, e.g. 9,

90-99 or 900-999, which is convenient when missing data

occur,

8) codes numbers have been selected so that, after

arrangement in ascending order,. the information is in

the correct sequence for the output tables,

61

9) there are free codes in any given range for
definition by the user, also the final digit of the
four digit FARMAP codes is left free for user
specification,

10) activity, output, material inputs and operation
codes have unique ranges for plant, animal and special
activities, and

11) all power input (human, animal or machine),
implements, tools, share-rent and other fixed costs
have the gene codes for plant, animal and special

activities.

62

Table 4.3 INPUT-OUTPUT AND OPERATION CODES SUMMARY FOR

 

 

 

FARMAP
Activities
Plant Animal Special

Activity 10-3999 5000-5999 1000-8999
Operation 10—1999 2000-3999 4000-4999
Outputs(income) 10-299 300-599 600-899
Variable Costs:

material inputs 1000-1999 2000-2999 3000-3999

human power 4000-4999 4000-4999 4000-4999

animal power 5000-5999 5000-5999 5000-5999

machine power 6000-6999 8000-6999 6000-6999

implements-tools 7000-8999 7000-8999 7000-6999
Fixed Costs:

share-rent 9000-9199 9000-9199 9000-9199

other fixed

costs 9200-9799 9200-9799 9200-9799

taxes 9800-9899 9800-9899 9800-9899

 

Source: adapted from FAO Farm Analysis Package, volume 1,
FAQ, Rome 1983.

63

4.1.3.2 Wanna

As mentioned earlier, FARMAP record groups can be

divided into three main categories. These are:

LW

The general survey description data contain information
which documents the background and relevant environmental
data of the survey, conversion factors to standardize
weights and measures, sample design information and farming
system utilized. These data records (see table 4.4) are
separately stored from the farm information.

All general data are stored in a separate file,
therefore, there exist two data files per survey

1) general data file which contains general data and
stores numeric and alphabetic information for Record types
‘10-100’, and

2) farm data file which stores farm data and numeric
general data and contains only quantitative information

(Record type ‘10’, ‘51’, ‘52’, ‘88’, ‘100-989’).

64

K.

Table 4.4 GENERAL DATA RECORD TYPE FOR FARMAP*

 

 

Record Type Content
010-019 Data identification and data status.
020-029 Survey information.
020 Location (country, region, administrative
areas, longitude, latitude, . . . etc. )
021 Survey objectives, organizations...etc.
022 author, date, duration, presurvey design.
023 Survey design, sample design,...and so on.
024 techniques used for assessing weights and
measures
025-029 free for user definition
030-039 National-regional information
030 agricultural sector role
031 land tenure policy
032 price, marketing and credit policy
033 settlement patterns, demography, education
034 ethnic groups, languages, religions...
035 leadership patterns
036-039 free for user definition
040-049 Local information
040 location, altitude
041 demography, soil catena, depth, drainage,..
042 soil erosion
043 ecology, vegetation, seasons.
044 government and other services available
045 farming system, technology
046 prices of major inputs and products
047-049 free for user definition
050-059 climate and production hazards
050 general description of microclimate, trends.
051 precipitation,. temperature, humidity, and
others
052 production hazards (e.g. frost, drought,
pest, disease, animal attack, predation)
053-059 free for user definition
060-069 New code definitions, plus free records

 

65

Table 4.4 (Cont’d)

 

 

Record Type Content
070-079 Community economics, structure
070 village size, district size, spatial
distribution of settlements, connections and
relationship of settlements,...etc.
071 emigration and immigration, causes thereof
072 dominant economy of the community, source of
income
073 leadership and decision making,distribution
of political power, role of women
074-079 free for user definition
080-089 Conversions
081 conversions of weights and measures to user
designated non standard metric units
082 conversion of weight and measures to standard
metric system and currency to US dollars
083 uniform code conversions
084 uniform amount, price and value modification
or substitution
088 seasons

080,85-87,089
090-099

free for user definition

All of them are left free for definition by
the user

 

* See FARMAP User’s Manual, vol. 3, for more details.

Source: adapted from FAO FARMAP USER’s Manual, vol. 1-3, FAO,

Rome

1985.

66

Most of the general survey description data record
types are used for storing text describing different aspects
of the survey background. An example of the free format for

general data record types appears below in Table 4.5.

Table 4.5 FARMAP FREE FORMAT RECORD TYPE ‘20’ FORMAT

 

 

Starting Width Data Content Status*
column of field
number field number
1 1 -- batch (for mini R
and mainframe
computers)
2 4 1 not used
6 3 2 record type
-- -- 3-4 not used
9 3 5 sequence number
-- -— 8 not used
12 68 7-23 description
80 1 24-27 not used

 

* Status codes used as follows:
R: highly recommended
E: essential for basic processing, use only as specified

Source: adapted from FAO Farm Analysis Package, vol. 1, FAO,
Rome 1983.

Data fields 7-23 are used to record information on the
design and conduct of the survey, and any other local and
regional information. Each record is repeated as necessary
to record all the desired information. The sequence number
(in data field 5) ensures that the order of records is

maintained.

6?
KW

Farm level data are divided into resource description
and resource flow or utilization data. Resource description

are grouped as shown in Table 4.6.

Table 4.6 FARMAP RESOURCE DESCRIPTION RECORD TYPE GROUPS*

 

 

Group Record Content
Type
Interview 100 interview
Household 110 household
(110-199) 120 permanent labor
130 off-farm work
140 anthropometry
150 health situation
160 education-literacy
170 agricultural knowledge
Land-crops 200 farm
(200-299) 210 land characteristics
230 crop characteristics
Animals 310 livestock
(300-399)
Fixed and liquid assets 410 fixed assets
(400-499) 420 structure characteristics
430 financial assets
Liabilities 510 credits and debts
(500-599)
Stocks 610 stocks
(800-899)

 

* See FARMAP User’s Manuals for more details.
Source: adapted from FAO Farm Analysis Package, volume 1,
FAQ, Rome 1983.

68

In all of the above mentioned record types, data fields
1 and 2 are used to record farm code and record type
respectively. Data fields 3, 4 and 5 can be used to store
activity, component and parcel-plot-season, respectively,
when survey objectives require that.

The interview record ‘100’ is the first record filled
in during interviews. It includes sample number, zone-
stratum, length of interview, the estimated data quality and
identity of the interview, the estimated data quality and
identity of the interviewer, interview location, and so on.
The record is completed once per interview in single visit
or several visit survey. However, it should be filled in as
necessary in multiple visit surveys.

The household records group is the second record type
in the resource description information group. This group
includes all relevant information about household members,
permanent laborers, off-farm work, anthropometric
measurements, health status, literacy and education, and
agricultural knowledge.

Table 4.7 illustrates an example of the household
record types group (i.e., record type ‘110’ format). It is
used to record descriptive information concerning individual
members or groups of person in the household. All family
members, permanent laborers, servants and relatives in the

same residence are recorded in this record type.

69

Table 4.7 FARMAP HOUSEHOLD RECORD TYPE ‘110’ FORMAT

 

 

 

Starting Width Data Content of Status*
column of field data
number field number field
1 1 -- batch (for mini and
computers) E
2 3 1 farm number E
5 1 27 consumer group
6 3 2 record type E
- - 3-5 not used R
- - 6 not used
9 4 7 serial number
13 2 8 relationship to read of
household R
15 3 9 Age-sex category
18 2 10 civil status
20 4 (2+2) 11 age,year-months
24 2 12 number of persons E
26 2 13 years in district A
28 2 14 years farming A
30 2 15 education
32 2 16 mother tongue A
34 3 17 time in residence, percent E
37 3 18 time available for work,
percent E
40 6 19 type of off-farm work
46 6+ 20 height
52 6 21 weight
58 6 22 number of other languages
spoken A
64 8 23 distance of birth place,
KM A
70 6 24 user defined
76 2 25 type of inventory(change) E
76 3 (2+1) 26 month-week of inventory
(change) R
*-
Status code used as follows:
E essential for basic processing, use only as specified
R highly recommended
A suggested
+decimal assumed between the 3rd and 4th digits
Source: adapted from FAO Farm Analysis Package, volume 1,

FAQ, Home 1983.

70

Land resources record group, record numbers 200-299,
covers information related to the quantity, quality and
utilization of the land. It includes general farm
information, land characteristics and crop characteristics.
The farm record ‘200’ contains a summary of farm value and
areas devoted to crop, pasture and forest. Whereas, land
characteristics record ‘210’ is normally used for individual
parcels and contains information on the location, area,
value, and physical characteristics of each parcel. Crop
characteristics record ‘230’ is used to record plant
arrangements and all other information relevant to cropping
patterns.

Animal record ‘310’ contains general information
concerning livestock. For example, management practice,
values, age, cost, condition score, weight and purpose for
keeping are stored in this record type.

Fixed and liquid assets, other than land, crops and
livestock, are described in assets records group ‘410-499’.
Standard FARMAP record types are ‘410’ (fixed assets), ‘420’
(structural characteristics) and ‘430’ (financial assets).
The rest of the record types are free for user definition.

Physical assets record ‘410’ describes information
about permanent structures, land improvements, machinery,
implements, tools and consumer durables. The structure
characteristics record ‘420’ provide descriptive information
concerning physical assets as a measure of standard of

living. Information about financial assets, the present

71

status of cash holdings and credit granted by the farmer are
recorded on record type ‘430’. Liabilities are all recorded

on credit record ‘510’ and stocks on record ‘610’.

2W

Resource flow records contain two main record types.
They are:

* resource utilization record type ‘700’

* household consumption-expenditure record type ‘750’

Resource utilization records (type ‘700’) are the
largest number of records in most surveys. They contain all
resource flow quantitative information used in net income
and power use analyses. Details of all resources used on'
parcels and animals which form part of the farm are recorded
in these records. All farm activities and income are also
described.

An example of resource utilization record type ‘700’
format is shown in Table 4.8. It contains farm number,
component (i. e. for whenever a mixed activity comprised
more than one component, one record should be filled for
each component), combination code for parcel-plot-season,
kind of input-output, farm operations, date, quantities,

price or value, and so forth.

72

Table 4.8 FARMAP RESOURCE UTILIZATION RECORD TYPE ‘700’

 

 

FORMAT
-—*

Starting Width Data Content of Status
column of field data
number field number field

1 1 -- batch(for mini and main-

frame computers) E

2 3 1 farm number E
5 1 27 consumer group

6 3 2 record type E
9 4 3 activity

13 4 4 component R
17 5 (2+2+1) 5 parcel-plot-season E
- - 8 not used
22 4 7 input-output E
26 4 8 operation E
30 1 9 frequency
31 2 10 month E
33 1+ 11 week
34 9 12 quantity E
43 2 13 quantity conversion code
45 2+ 14 source-destination E
47 10 15 price or value E
57 1 16++ cash-kind E
58 1 17 price-value
59 4 18 receiving activity A
63 4 19 component A
67 5 (2+2+1) 20 parcel-plot-season A
72 4 21 input-output A
76 2 22 serial number A
-- - 23-24 not used
78 1 25 user defined
79 2 26 years ago A

 

*Status code used as follows:

E essential for basic processing, use only as specified
R highly recommended

A suggested

+decimal assumed between the 3rd and 4th digits

++data fields 17-21 are only used when recording transfers
between activities or plots (i. e. when an output of an
activity is also an input to a second activity, data fields
17-21 show information about that second activity, for
example maize activity as an input to dairy cattle activity)

Source: adapted from FAO Farm Analysis Package, volume 1,
FAO, Rome 1983.

73

Consumption and expenditure information are recorded on
household consumption-expenditure record ‘750‘. Record ‘750‘
contains information like the general consumption item, form
of consumption, frequency, quantity, price and value, and
other user defined fields.

In general, the user can define additional record types
other than those mentioned above which are standard FARMAP
record types. Also, within standard FARMAP record types
there exist many data fields which are left free for
definition by the user. However, there are some prohibited
record types and codes that the user cannot use ( see FARMAP

User’s Manual, vol. 3).

4.1.4 Data Processing Stages

4.1.4.1 WW1).

In this stage, data are transferred from the coding
sheets (precoded questionnaire) into the computer. For
microcomputers, there exist two ways for data entry. The
first one is done using MS-DOS line editor (EDLIN). This
method was not appropriate for alphanumeric data entry.
Therefore, an advanced word processor was used.

The second method of data entry is through the use of
program ENTERD which allows only numeric data entry.
Individual farm data for resource description information
and resource flow data records are.entered interactively.

Record type, farm number and all other relevant data fields

74

must be included in a command file which will be activated
when running program ENTERD.

Figure 4.1 shows a flowchart for FARMAP storage step.
The keyboard data entry is the input with the command file
ENTST.CMF. The outputs are two file: the primary output file
(STOl. BIN) and the massage file (STOl. MES)

I command file I
I ENTST. CMF I

 

 

 

 

 

Keyboard entry
(input) ----------------
e I
: :
I I
: :
I program ENTERD STORAGE STAGE I
I I : I
E E
I I
I output I I message I
I file I I file I
I I I I
I I I I
I ST01.BIN I I ST01.MES I
: : : :

 

 

Figure 4.1 FLOWCHART OF FARMAP STORAGE STEP 1 (ST01).
Source: adapted from FARMAP, Primer for MS-DOS

microcomputers, FAO, provisional edition.

75

The output file might be formatted or unformatted.
The unformatted binary files occupies less disk space and
are faster in processing. The formatted output file contains
the standard FARMAP records of 27 data fields on six lines.
The unformatted file is a binary file which is only readable
using FARMAP programs DISPLAYB or CORREC. The latter is used

to correct errors in data fields and/or records.

4.1.4-ZYaIidatianjtm

Checking and modification of data occur during the
validation process. There are three steps of the data
validation process. These steps are:

(1) Validation Step 1 (VALl): program MODCON and command
file VAL1.CMF are used to check on information on a single
record. The following tasks are performed in this step:

* adding missing component codes for mixed activities

(e.g. maize in the maize-beans mixture);

* calculating the values for unpriced power inputs such

as family labor;

* calculating total quantities or areas according to

the specified conversion factors;

* calculating total quantities and values of

consumption; and

* duplicating of record type ‘700’ for inter-plot or

inter-activity.

* performing within-record validation to check that

codes and quantities lie within accepted ranges.

(2)

files

76

Validation Step 2 (VAL2): various programs and command

perform the following six tasks to perform range

checks on the magnitude of inputs and outputs:

(3)

i. program SORT and command file VAL21.CMF are used to
sort data records in appropriate order.

ii. program EXTRAC and command file VAL22.CMF are used
to prepare livestock inventory data.

iii. program MODCON and command file VAL23.CMF are used
to select from the database existing data items and
generate new data items such as making monthly totals
from daily data.

iv. program SORT and command file VAL24.CMF are used to
sort data records in appropriate order.

v. program UNIT and command file VAL25.CMF are used to
aggregate material input quantities and values, to
divide by area or number of animals, and to store the
results in artificial records (type ‘1700’).

vi. program MODCON and command file VAL26.CMF are used
to perform range checking.

Validation Step 3 (VAL3): four programs and their

associated command files are used to perform multi-record

consistency checks which comprise the following four tasks:

i. program MODCON and command file VAL31.CMF are used
to select records for which multi-record consistency
checks are desired.

ii. program SORT and command file VAL32.CMF are used to

sort data records in appropriate order.

77

iii. program EXTRAC and command file VAL33.CMF are used

to create artificial records containing summary of

selected records.

iv. program MODCON and command file VAL34.CMF are used

to perform consistency checks on artificial records

according to specified range values.

The required input, command, message, and primary and
secondary output files are shown in Figure 4.2. The command
and the message files appear with the extensions ‘.CMF’ and

‘.MES’ respectively.

78

Storage stage
command file
VAL1.CMF

STO .BIN

-- Huh-.-

I
I
I program MODCON
VALIDATION STEP 1 I
(VALl) I

I

I

 

I I
output file message file

 

 

 

 

 

VAL1.BIN VAL1.MES
I command files
I VAL21.CMF
I VAL22.CMF
I VAL23.CMF
I VAL24.CMF
I VAL25.CMF
I VAL26.CMF
': I
I I programs SORT-
I VALIDATION STEP 2 I EXTRAC
I (VALZ) I MODCON
I I SORT
output files message files UNIT
VAL21.BIN VAL21.MES MODCON
VAL22.BIN VAL22.MES
VAL23.BIN VAL23.MES
VAL24.BIN VAL25.MES
VAL25.BIN VAL26.MES
(VAL2.BIN)
I command files
I VAL31.CMF
I VAL32.CMF
I VAL33.CMF
I VAL34.CMF
I I
I I
I I programs MODCON
I VALIDATION STEP 3 (VAL3) I SORT
I I EXTRAC
I I MODCON
output file message files
VAL3.BIN VAL31.MES
VAL33.MES
VAL34.MES

Figure 4.2 STEPS IN FARMAP DATA VALIDATION STAGE

Source: adapted from FARMAP User’s Manual, volume 2, FAQ,
Rome, 1985.

79

4.1.4-3W
FARMAP tables can be produced in three different modes

which correspond to levels in the hierarchy of data. These

are. * farm mode, one table for an entire farm
* activity mode, one table for each activity on a farm
* plot mode, one table for each plot of land.
Standard subtables cover the topics of household
composition, land resources, cropping pattern, animal

resources, net worth, economics, cash-kind flow, and power
use and types (human, animal, and machine).

FARMAP tabulation process comprises the following four
processing steps:

1- Tabulation Step 1 (TABl): checking, modification.

2- Tabulation Step 2 (TABZ): aggregation of input and

output into subtotals.

3- Tabulation Step 3 (TAB3): individual tabulation.

4- Tabulation Step 4 (TAB4): group means tabulation.

Figure 4.3 shows different input, command, message, and
output files utilized during tabulation process. The command
and message files appear with the extensions ‘.CMF’ and
‘.MES’ respectively. Extensions ‘.BIN’ and ‘.DAT’ represent
the binary unformatted and the ASCII formatted files,
respectively. The "M" appearing on output files indicates
the three modes mentioned above (i.e., F for farm, A for
activity, and P for plot). In addition to the the above four

tabulation steps, step TAB5 is used to generate advanced

user-designed tables.

 

80

Validation stage

I
input file
VAL1.BIN command file
TABl.CMF
I I

I
TABULATION STEP 1 (TABl) I
I
I

 

program MODCON

 

 

I
output file message file
TABl.BIN TAB1.MES
I command files
I TABZl.CMF
I TA322.CMF
I I
I I
I I programs SORT
I TABULATION STEP 2 (TAB2) I AGGREG
I I
I I

 

, message file
output files TAB22.MES

TA821.BIN
. --------- (TABZ.BIN) command files
I TAB31.CMF
I TAB32.CMF
TAB33.CMF

............................ programs SORT

 

 

 

I
I I TABULATION STEP 3 (TAB3) I TABLES
I I I LABEL
I I I
I I I
I output files message files
I TAB31M.DAT TAB32M.MES
I TAB32M.DAT TAB33M.MES
I TAB3M.DAT
I command files
I TAB41.CMF
I TAB42.CMF
I TAB33.CMF
I I
I I
I I I programs SORT
I I TABULATION STEP 4 (TAB4) I MEANS
I I TABLES
I I LABEL
output file message file
TAB4M.DAT TAB4M.MES

Figure 4.3 STEPS IN FARMAP TABULATION STAGE

* M stands for the three different modes, 1. e. F for farm,
A for activity, and P for plot modes.
Source: adapted from FARMAP User’s Manual, FAQ, Rome 1983.

81

4.1.4.4W

Further processing is usually desirable following
tabulation stage. This stage allows the transfer of FARMAP
data files to other packages for further processing. FARMAP
contains program EXTRAC to transfer the required data fields
and records into an ASCII file format. Since, FARMAP does
not support further statistical and planning models, other

packages must be used for these types of analysis.

4.1.5 Analytical Procedures

As mentioned earlier, two versions of FARMAP were used,
i.e., the menu-and the non-menu driven. Any differences
between the two versions, or any special procedures
performed when running each of them will be discussed.

The different stages of processing and the detailed

analytical procedures are discussed below.

PM

General survey data was transferred from the coding
sheets into the computer storage media (floppy disks) using
commercial word processor, since, it was inconvenient to use
the line editor (program EDIT of FARMAP, or program EDLIN of
MS-DOS). Examples of the coding sheets used for recording
data identification record type ‘010’, and general survey

information record type ‘020’ are shown on Table 4.9. These

82

Table 4.9 EXAMPLES OF FARMAP CODING SHEETS

I Record I Survey I

I type I code I

Data field number I 2 I 7 I
Columns number I 6-8 I 12-17 I
EGY263

I FRAMEWORK FOR MICRO-LEVEL
I RESEARCH IN DEVELOPING

I COUNTRIES USING DATABASE
I MANAGEMENT SYSTEMS

I Record I Sequence I
I type I number I
Data field number I 2 I 5 I
Columns number I 6-8 I 9-11 I
I 020 I 1 I
I 020 I 2 I
I I I
I I I
I I I
I 020 I 3 I
I I I
I I I
I I I
I 020 I 4 I
: 020 : 5 :
I I I
I I I
I 020 I 6 I
I
I

Source: adapted from FAO Farm Analysis
FAO, Rome 1985.

COUNTRY: EGYPT

MAIN OBJECTIVE: STUDY
THE USEFULNESS OF USING
DATA .

BASE MANAGEMENT SYSTEMS
(DBMS) IN MICRO-LEVEL
RESEARCH

IN DEVELOPING COUNTRIES
RESEARCH DESIGNER: ALI
KAMEL M. KAMEL

DATE: JUNE-SEP. 1986.

Package, Users Manuals,

83

forms can be used to design questionnaires for rural survey
data. A separate secondary input file was obtained ST01.DAS,
which is shown on Figure 4.4. This file contains all
descriptive information in alphanumeric fields such as
survey title; research objectives; location, regional, and
national information and so forth. Also, numeric fields
concerning local conversion factors and user-defined data
fields are included in that file.

However, resource description information and resource
flow/utilization data were entered into the computer using
program ENTERD of FARMAP. Minor modifications were done to
the command file to alter the data entry forms in order to
eliminate redundancy. The command file ENTST.CMF is the way
of telling the main program ENTERD of which record types and
data fields will be selected for processing. Therefore, this
command file was modified to remove undesired data fields
and record types or to add user-defined data fields.

As shown in Figure 4.5, only record types ‘110’, ‘200’,
‘210’, '230’, ‘410’, ‘510’ and ‘700’ were employed. They
represent household, farm, land characteristics, crop
characteristics, physical assets, credit, and ~resource
utilization data records, respectively. The user can add or
eliminate any records types or data fields required
according to type and objectives of the survey. Also, it
should be noticed that not all the 27 data fields of the
standard FARMAP record must be filled in.

The primary output file ST01.BIN was generated.

84

*s********** GENERAL SURVEY INFORMATION DATA *xxxxxxxxsxxxzx
**********#*** survey title & code *************************
010 EGY263FRAMEWORK FOR MICRO-LEVEL RESEARCH IN DEVELOPING
010 COUNTRIES USING DATABASE MANAGEMENT SYSTEMS
*************** survey information *************************
020 COUNTRY: EGYPT

020 MAIN OBJECTIVE: STUDYING THE USEFULNESS OF USING DATA
020 BASE MANAGEMENT SYSTEMS (DBMS) IN MICRO-LEVEL RESEARCH
020 IN DEVELOPING COUNTRIES.

020 RESEARCH DESIGNER: ALI KAMEL M. KAMEL

020 DATE: JUNE-SEP. 1986.

023 DATA SOURCES: CONSEQUENCES OF SMALL RICE FARM

023 MECHANIZATION PROJECT, WEST JAVA, INDONESIA.

023 INTERNATIONAL RICE RESEARCH INSTITUTE.

023 SAMPLE SIZE: 10 FARMS OF ABOUT 600 FARMAP RECORDS.
*************** local information ******X*******************
040 CROP: RICE

040 VARIETIES: MODERN HIGH YIELDING

040 LOCATION: WEST JAVA, INDONESIA.

O40 SEASON: WET SEASON 1979

040 FARMING SYSTEM: LOW AND DECLINING PRODUCTIVITY
040 CHARACTERIZED BY MOSTLY TRADITIONAL TECHNOLOGIES

040 SOIL TEXTURE: CLAY LOAM

O40 SOIL COLOR: BLACK

**************** climate and production hazards ************
050 1 RAIN: NOVEMBER-FEBRUARY

050 2 ANNUAL RAINFALL: 1055 mm

050 3 AVERAGE SUMMER TEMPERATURE: 28 DEGREES (C).
**************** new code definitions **********************
060 121013 1Ha HECTARE HECTARES * area conversion
060 2230 7 89HYVRHGHYLDVAHIGH YIELDING VARIETIES

060 3700 36019TRACTRACTORITRACTOR RENTAL INCOME

060 4410 78999TOOLTOOLS ASSORTED TOOLS

********** conversion factors xx****************************
060 570013 1Ha HECTARE HECTARES

060 670013 2Hrs HOURS HOURS

060 770013 3Ltr LITRES LITERS

060 870013 4K6 KILOGRM KILOGRAMS

060 970013 5No. NUMBERS NO. OF ITEMS

********** user-defined codes ******************************
060 11700 74399HLABHLABOR HIRED LABOR

060 12700 71639INSCINSCTCD INSECTCIDES

060 13700 76019TWT TWTRACT TWO WHEEL TRACTOR

060 14700 77149ROTRROTARY ROTARY

060 15700 77339SPRYSPRAYER SPRAYER

060 16700 710295DLGSDLINGS SEEDLINGS

060 17700 77039PLG PLOUGH PLOUGH

060 18700 77049HRW HARROW HARROW

060 19700 74499CHLACHLABOR CONTRACT HIRED LABOR

AUNHQkaNl-fi

mflmUkWNI-i

Figure 4.4 FARMAP SECONDARY INPUT FILE STOl.DAS CONTENT.

060
060
060
060
060
060
060
060
060
060
060
060
060
060
060
060
060
060

20700
21700
22700
23700
24700
25700
26700
27700
28700
29700
30700
31700
32700
33700
34700
35700
36700
37700

85

71329TSP TSPHOST TRI-SUPER PHOSPHATE

71209UREAUREA UREA

74159FHH FHHMEMR FEMALE HOUSEHOLD MEMBER<15YRS
99SBD SDBDPREPSEEDBED PREPARATION
139HRWGHARROWG HARROWING

159BFRTBASFERT BASIC FERTILIZER APPLICATIONS

699HTW H,T& W HARVESTING, THRES. & WINNOWING
709DRG DRYING DRYING

459TFRTTOPFRT TOP DRESSING FERTILIZERS
ZOSSDLGPULSDLG PULLING OF SEEDLINGS

229TRSGTRNSPLG TRANSPLANTING

39399GFRMGENFRMA GENERAL FARM ACTIVITY

79899TXS TAXES TAXES

38399RNT RENTOUT RENT OUT

7 859RINCRENTINC RENT-OUT INCOME

74699HLA HLABOR HIRED LABOR

55999TFM TOTFARM TOTAL FARM AREA

380990JB OFF/JOB OFF-FARM JOB

mmmmmmmm

Figure 4.4 (Cont’d)

86

This file containing 621 records with an average of 62 data
records per farm and is the primary input file for the next
step, (i.e., data validation). This file was stored in a
binary form which is not easily read by human beings. Binary
files are used because they occupy less disk space and can
be processed faster.

To check data entered and correct any errors, two
FARMAP programs were used. The first one (program DISPLAYB)
displays the binary records in a readable form. The second
one, CORREC, corrects erroneous data fields and/or deletes
undesired records. Both programs were employed to make

necessary correct 1 OTIS .

2-D I 1! 1.1 !'

After execution of storage stage, data were checked and
modified in validation stage. Automatic correction and
modification are not done by FARMAP. The user has to check
or modify data fields or records. There was a difference
between executing the validation stage using the menu- and
the non-menu driven versions of FARMAP. The processing time
using the menu driven version was faster and it was easier
than using non-menu driven version.

Checking that data fields fall within acceptable limits
was done in step VALl using program MODCON of FARMAP. The
command file VAL1.CMF was executed and corrections were done
using programs DISPLAYB and CORREC (mentioned above). This

step, VALl, performs within-record validation, i.e., checks

87

FILE ENTST.CMF

************************************************************
* THIS COMMAND FILE WAS ALTERED TO INCLUDE ONLY THE DESIRED¥
* RECORD TYPES AND/OR DATA FIELDS. IT CONTAINS RECORD TYPE *
* CODE, DATA FIELD CONTENTS, DATA FIELD NUMBERS, AND THE *
* THE ACCEPTABLE LOWER AND UPPER LIMITS RESPECTIVELY. THE X
* Z’s ALLOW FOR ZERO ENTRIES WHENEVER MISSING OR UNDETERM- *

* INED OBSERVATIONS OCCUR. *
************************************************************
700 ACTIVITY 03 10.00 9899.00
700 COMPONENT 04 10.00 9898.00 2
700 PARCEL/PLOT/SEASON 05 1000.00 99899.00
700 INPUT-OUTPUT 07 10 00 9899.00
700 OPERATION 08 10.00 8899.00 2
700 FREQUENCY 09 0.00 3.00
700 MONTH 10 1.00 12.00 2
700 WEEK 11 1.00 4.00 z
700 QUANTITY 12

700 QUANTITY CONV.CODE 13

700 SOURCE-DESTINATION 14 1.00 99.00 2
700 PRICE OR VALUE 15

700 CASH/KIND 16 0.00 8.00
700 ENTER '1’ IF PRICE 17 1.00 1.00 z
230 ACTIVITY 03 10.00 4499.00
230 COMPONENT 04 10 00 3998.00 2
230 PARCEL/PLOT/SEASON 05 1000 00 99989.00
230 VARIETY 07 1.00 99.00
230 AREA 12 0.01 999899.00
230 AREA CONV.CODE 13

230 VALUE 15 0.01 999999.00 z
230 CASH-KIND OF CHANGE 24 1.00 9.00 z
230 TYPE INVENT (CHANGE) 25 1.00 89.00
230 DATE INVENT.(CHANGE) 26 1.00 124.00 2
110 RELAT. TO HEAD HHOLD 09 1.00 99.00
110 AGE—SEX CATEGORY 09 10 00 898.00
110 AGE, YRS-MONTHS 11 0.00 9900.00
110 N0.0F PERSONS 12 1.00 99 00
110 YRS IN DISTRICT 13

110 YRS FARMING 14

110 YRS EDUCATION 15

110 TIME IN RESIDENCE % 17 0.00 100.00
110 AVAIL.FOR WORKX 18 0.00 100.00
110 TYPE OFF-FARM WORK 19 0.00 999.00
110 TYPE INVENT (CHANGE) 25 1.00 89 00
110 DATE INVENT.(CHANGE) 26 1.00 124.00 z
210 PARCEL (1:1000) 05 1000.00 99000.00
210 TENURE 07 1.00 99.00 2

Figure 4.5 FARMAP COMMAND FILE ENTST.CMF.

Source: adapted from FAO Farm Analysis Package, vol. 3, FAQ,
Rome 1985.

210
210
210
210
210
210
210
410
410
410
410
410
410
410
410
410
410
410
410
410
410
410
510
510
510
510
510
510
510
510
510
510
510
510
510
510
200
200
200
200
200
200
200
200
750
750
750
750
750

LAND USE TYPE

AREA

AREA CONV.CODE
IMPROVED LAND VALUE
CASH-KIND OF CHANGE.
TYPE INVEN T (CHANGE)
DATE INVENT.(CHANGE)
(ACTIVITY)
(COMPONENT)
(PARCEL/PLOT/SEASON)
ASSET ITEM

LOCATION

CAPACITY, SIZE

AGE, YEARS-MONTHS
REMAINING LIFE (YRS)
NUMBER OF ITEMS
TOTAL PRESENT VALUE
SALVAGE VALUE
OWNERSHIP

CASH/KIND OF CHANGE
INVENT.TYPE (CHANGE)
DATE INVENT.(CHANGE)
(ACTIVITY)
(COMPONENT)
(PARCEL/PLOT/SEASON)
ITEM BOUGHT W/LOAN
TYPE OF CREDIT
SOURCE

YRS-MTHS OBTAINED
DURATION (YRS-MTHS)
ANNUAL INTEREST %
ORIGINAL PRINCIPAL
OUTSTAND(AMT REPAID)
CASH/KIND OF CHANGE
INVENT . TYPE (CHANGE)
DATE INVENT.(CHANGE)
FARM AREA

AREA CONVERSION
FARM VALUE

CROP AREA

PASTURE AREA

FOREST, WASTELAND
INVENT. TYPE ( CHANGE)
DATE INVENT.(CHANGE)
CONSUMP/EXPEND ITEM
OUTPUT CODE FOR FOOD
FREQUENCY

MONTH

WEEK

Figure 4.5 (Cont’d)

HHHH 0H

10:
1000.
100.

OHHHO

HH
HHOOOHH

.01
.00
:00
.00
.00
.00
.00
.00

.00
.00

.01
.00
.00
.00
.01

.00
.00
.00
.00
.00
.00
.00

99.
999999.

999999.
9.

89.
124.
9899.
9899.
99999.
999.
99.

999999

999999

999999

89.
124.
9899.
799.

12:

.00
.00

.00
.00
.00
.00
.00
.00

:00

.00
9.
89.
124.
.00

00
00

N NNNN NN

N NNNNN N

NNNN

750
750
750
750
750
750
430
430
430
430
430
430
430
430
430
430
430
430

QUANTITY

QUANTITY CONVERSION
SOURCE

VALUE (OR PRICE)
CASH/KIND

ENTER ’1’ IF PRICE
(ACTIVITY)
(COMPONENT)

TYPE OF LOAN/ASSET
DESTINATION/LOCATION
YRS-MTHS GRANTED
DURATION (YRS-MTHS)
ANNUAL INTEREST %
ORIGINAL PRINCIPAL
OUTSTAND(AMT REPAID)
CASH/KIND OF CHANGE
INVENT.TYPE (CHANGE)
DATE INVENT.(CHANGE)

Figure

89

24
25
26

1.00

1.00
1.00
10.00
10.00
1.
1.00

0.01
1.00
1.00
1.00

(Cont’d)

99.

9899:
9899.

99:

999999.
9.
89.
124.

N NNN

90

on information on a single record. However, range checks on
the magnitude of inputs (step VALZ) and multirecord
consistency checks (step VALS) can also be done. These
later steps, VAL2 and VAL3, are advanced and optional
validation steps and do not affect the course of processing.
In step VALl the following modifications were done
1. Missing component codes, in record types ‘230-700’, were
set equal to the corresponding activity codes.
2. Valuation of unpriced power inputs (e.g., family labor)
in record type ‘700’. Because of lack of enough data these
unpriced power inputs were set equal to zero matching with
the standard FARMAP command file VALIST.CMF.
3. Conversion of unit prices into total values for record
types ‘700’ and ‘750’.
4. Conversion of quantities/areas according to the user-
defined conversion factors. This step is appropriate for
local units conversions
5. Calculation of total quantities and values according to
the frequency codes.
6. Standard checks were included to check for illegal record
types, activities, components, and so forth. Also to check

for quantities, monetary values and missing codes.

3-DataJahulatiQn

There exists two levels of tabulation: standard
tabulation and advanced or user-defined tabulation. 0n

standard tabulation a set of standard FARMAP command files

91

was used with minor modifications. A number of standard
subtables, and also some user-defined subtables were
produced as will be discussed in detail in the next chapter.

Three different modes correspond to different levels
in the hierarchy of data can be produced, i.e., farm,
activity, and plot modes. Only farm and activity modes were
processed, since, plot mode was not applicable.

.The standard FARMAP subtables cover the following

topics:

* household composition

* land resources

* cropping pattern

* animal resources

* net worth

* economics

* cash-kind flow

* power use and type (human, machine and animal)

Different FARMAP programs were used including:
1) program MODCON for checking and modification of data
was used in tabulation step 1 (TABl), discussed earlier,
2) programs FMSORT and AGGREG for reorganization and
partial aggregation in tabulation step 2 (TABZ), discussed
earlier.
3) programs FMSORT, TABLES, and LABEL for production of
individual tables in tabulation step 3 (TAB3), discussed
earlier, and

4) in tabulation step 4 (TAB4), programs FMSORT, MEANS,

92

TABLES, and LABEL were employed to produce mean tables
of the 10 farms as discussed previously.

The standard FARMAP command files were used including
TABl, TAB22, TAB32M (where M stands for mode. F for farm, A
for activity, P for plot, and Y for power types), TAB33, and
TAB42 with ‘CMF‘ extension.

During step TABl the following data modifications were
processed:

1. Final checks for illegal codes.

2. Recoding of months of survey year so that the first

month (November) is shown in the first columns of

FARMAP subtables, or to any required order.

3. Creation of ‘702’ records for power use subtables.

4. Creation of ‘671’ records for net worth subtables.

5. Creation of ‘700’ records for calculating cash-kind

flow, and depreciation or appreciation.

6. Assigning general farm activity code when no

activity is specified.

7. Duplication of ‘310’ records for age-sex changes.

8. Creation of ‘232’ records for crop list subtable.

The level of aggregation was set to the ‘disaggregated
level’, and number of farm was set to 10 in step TABZ which
depends on the choice of the user.

Tabulation step TAB3 were processed according to the
standard FARMAP command files. However, some modifications
were done on the command files to alter the new labels for

user-defined field codes;

93

As mentioned above, there exist three different modes:

farm,

activity and plot modes. In the activity mode the

following subtables were requested for each activity (per

unit area):

1.

QQQQOIQ-CON

Crops grown.

Economics.

Cash-kind flow.

Human power type.

Human power use.

Animal power type (not applicable).
Animal power use (not applicable).
Machine power type.

Machine power use.

Two runs, using different sorting, were performed to

produce the farm mode subtables. The first run processed the

following subtables:

1.

HH
HO

CDQQQU'bOJN

Household composition.

Land use.

Crops grown.

Seasonal animal resources (not applicable).
Animal resources. (not applicable).

Net worth statement.

Economics.

Cash kind flow.

Human power use.

Animal power use (not applicable).

Machine power use.

94

Human, animal, and machine power type subtables were
produced in a separate run, using a different sorting.

In order to produce means subtables of the 10 farms
under consideration, step TAB4 was employed. The command
file ‘TAB42.CMF’ (see Figure 4.6) was modified to alter the
number of selected farms to be included in the mean. This
number was used as a divisor in order to get the means
subtables.

It should be noted that, all the animals subtables were
excluded from the analysis due to lack of enough
information. The user must refer to FAQ farm Analysis
Package (FARMAP), User’s Manuals, Volumes 1-3, for more
details.

4—Ad1ansediracassing

In order to interface FARMAP with other statistical and
linear programming packages, program EXTRAC of FARMAP was
used. The command file associated with that program should
be designed by the user.

The command file ‘EXTACI.CMF’ is shown on Figure 4.7.
It produced ASCII file which was exported to statistical
package, ABSTAT, for further processing. Any data fields
and/or record type can be processed according to the user’s
choice and the objectives of the study. Data required for
linear programming analysis, estimations of whole farm
production functions, and obtaining information regarding

multi-year variability for risk analysis can also be

95

FILE TAB42.CMF

#*************************x*********************************
The user must specify whether all or selected farms(which*
ones) are to be included in the mean. If ’ALL’ is *
indicated, the total number of farms in the data file *
must be supplied. This number is used by MEANS as a *
divisor. In this example all the 10 farms are included. *
The FUNCTIONS commands allow for transfer of operations *
transfer and are followed by record types and codes for *
transfer operations for data fields 2-27 in groups of 2 *
or 3. The BREAK parameter enables the user to control the*
grouping or aggregation of output records. The EACH para-*
meter indicates that one output record will be produced *
for every input record.(See FARMAP USER’s MANUAL, vol. 2 X
for more details). *
***********************x************************************

SELECT ALL 10 *10 farms are included in the data file

*************

FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS

FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
FUNCTIONS
BREAK 5

1101
1102
1103
1104
1105
1106
1107
1109
2100
2300

3201
3202
3203
3204
3206
3207
3208
3209
3210
3211
3212
6710

333
333
333
333
333
333
333
333
333
555

333
333
333
333
333
333
333
333
333
333
333
333

112
112
112
112
112
112
112
112
333
115

133
133
133
133
133
113
113
113
113
113
113
352

222
222
222
222
222
222
222
212
332
252

311
311
311
311
311
111
111
111
111
111
111
222

222
222
222
222
222
222
222
222
121
221

132
132
132
132
132
112
112
112
112
112
112
222

222
222
222
222
222
222
222
222
223
222

222
222
222
222
222
222
222
222
222
222
222
222

222
222
222
222
222
222
222
222
323
252

222
222
222
222
222
222
222
222
222
222
222
222

222
222
222
222
222
222
222
222
333
111

222
222
222
222
222
221
221
221
221
221
221
222

222
222
222
222
222
222
222
222
333
155

222
222
222
222
222
111
111
111
111
111
111
225

33

EACH

55 BREAK 7
2310 333 333 333 323 333 333 333 333 33 BREA 3
BREA 4 BREA 5 BREA 7

BREAK
BREAK
BREAK
BREAK
BREAK
BREAK
BREAK
BREAK
BREAK
BREAK
BREAK
EACH

mmmmmmmmmmm

6800 333 322 222 222 222 222 222 223 22 BREAK 25

FUNCTIONS 7091 333 322 222 222 222 222 222 222 32 BREAK 26
FUNCTIONS 7092 333 322 222 222 222 222 222 222 32 BREAK 26
FUNCTIONS 7093 333 322 222 222 222 222 222 222 32 BREAK 26

************************************************************

Figure 4.6 FARMAP COMMAND FILE TAB42.CMF

Source: adapted from FAQ, FAQ Farm Analysis Package, Vol.

Rome, 1985.

2.

96

FILE EXTRAC1.CMF COMMAND FILE
*x******x*x*x**x***********xxx*xxxxxx***********************

* THIS FILE TRANSFER SPECIFIC DATA FIELDS AND RECORDS *

* REQUIRED FOR OTHER COMPLEMENTARY SOFTWARE, e.g. *
* STATISTICAL AND/0R LINEAR PROGRAMMING PACKAGES. *
* ADAPTED FROM FARMAP USER’S MANUAL, JULY 1986. *

****************************xxx*****************************

FORMAT USERSPECIFIED 1 8 * request 8 variables

(2X, F4.0, 1X, ’:’, 5(1X,F5.0, ’:’), F9.2, ’:’,F9.2)

MODE FARM * process the farm mode

TITLE 5 1 * title of five lines and output record of 1 line
*8)": HOUSEHOLD *8! *3“! FARM *2”!!!

I l
FARM :NO. 3 YRS. : YRS. : YRS. 3 N0. : VAR. 3 GROSS
NO. : OF : OLD- : HEAD : FAR- : PAR_ : COSTS : INCOME
: MEM- : EST : : MING : CELS : :
:BERs: : : ' ' '

I I
SELECT 110 * select record type 110 (household record)
TRANSFER MAXIMUM 11 3 MAXIMUM 1 1 ACCUMULATE 12 2
SELECT 110 8 1.0 1.0 * select record type, data field, and
* upper and lower limit of the data.
TRANSFER MAXIMUM 11 4 MAXIMUM 11 5
SELECT 210
TRANSFER FREQUENCY 2 6 MAXIMUM 1 1
SELECT 700 3 10.0 5999.0 7 1000.0 8999.0
TRANSFER ACCUMULATE 15 7 MAXIMUM 1 1 * to add related data
* fields together.
SELECT 700 3 10.0 9999.0 7 10.0 999.0
TRANSFER ACCUMULATE 15 8 MAXIMUM 1 1

Figure 4.7 FARMAP COMMAND FILE EXTRAC1.CMF.

97

extracted by using program EXTRAC of FARMAP. This procedures
will be discussed in the next chapter.
The advanced processing stage was employed only on the

rice activity, which is the main activity of the 10 farms.

Results of FARMAP analysis, limitations and operational
errors, and advanced processing, will be discussed in

Chapter 5.

98

4.2 Structure of dBASE III PLUS

42.1mm

dBASE III PLUS is a software package marketed by Ashton
Tate, Inc., and is a powerful development tool for
microcomputers. It is a relational database management
system. The data is arranged in the form of a matrix, with
the rows of the matrix forming each individual record in the
database, and the columns of the matrix forming the
individual fields of information across all records.

dBASE III PLUS can be used in the conceptualization and
creation of databases for numerous types of applications.
The basic features of it include:

* Creation of unique database structures to fit

specific problems.

* Displaying, editing, modifying, and documenting the

structure of the database files.

* Physical sorting and logical indexing of the

databases.

* Creation of reports and labels with totals and

averages.

* Writing menu-driven systems for inexperienced users.

* Generating screen formats for easier data entry.

* Multiple files manipulation (e.g. combining several

files, editing files simultaneously, using and

processing up to 15 files simultaneously).

* A programming language which has a variety of uses

99

including mathematical, relational, logical, and string
operations to create relational database structures. It
is a high level procedural langauge which contains an
application encoder and editor. Also it has many
commands and functions including mathematical, basic
statistical, and string manipulation functions. In
addition there exist many classes of commands which can
be used for creation of files, addition, editing,
modifying, manipulating, and displaying of data.

* A full screen assistant and help menu.

* An applications’ generator for novice users which can

be used to generate wide variety of applications

without knowing dBASE III PLUS programming and
features.

There are two ways to use dBASE III PLUS. The first is
through the assistant; which is, a collection of menus. The
second is using "dot commands" to perform directly any
operation.

dBASE III PLUS can be used either as a stand-alone
system for a single-user, or it can be networked in a
multiuser local-area network (LAN) environment. This study

only used single-user system, and the LAN environment will

not be addressed.

42.2w

dBASE III PLUS can be executed on a variety of

microcomputers, under any one of the popular operating

100

systems (e.g. MSDOS, PC—DOS, CP/M, UNIX, XENIX). However,
mainframe and minicomputer versions are not available.

A microcomputer with a two floppy drives and a minimum
of 256 K bytes of RAM (Random Access Memory) is recommended.
However, for a full utilization of the package, 640 K bytes
of RAM is essential.

In addition, hard disk operation is highly recommended
for faster and smoother processing.

dBASE III PLUS provides an ‘assistant‘ menu-driven
feature to provide the novice with the ability to create
data files and other supporting files (e.g. screen format
information, index and sort files). Also, it provides an
‘application generator‘ utility programs to assist the user

in building and creating a wide variety of application.

4.2-Sim
4.2.3.11schnigaLSmifinatims

dBASE III PLUS is a programming language developed to
handle relational database files. Up to one billion records
or 2 billion bytes can be processed depending on the
computer memory available. The maximum record size is 4000
bytes in a database file and 512 K bytes in database memo
files with, up to 128 data fields.

The field width varies according to the field types.
For example, a maximum of 254 bytes for character, 8 bytes
for date, 1 byte for logical, 5000 bytes for memo, and 19

bytes for numeric fields. Fifteen open files of all types

101

(of which 10 open database files, seven open index file, and
one open format file per active data base file can be
processed in the same time.

The largest and smallest positive numbers which can be
handled by dBASE are 1 x 10+99 and 1 X 10-307, respectively.
The numeric accuracy is fifteen digits with a maximum of
nine digits being to the right of the decimal point.
However, the decimal point and the sign each use one digit
place. Hence, the accuracy is 13 when comparing non-zero
numbers with decimal points.

Finally, the command line may contain up to 254

characters as maximum length .

4.2.3.2WW

There are 13 specialized formats to save information on
files. Each serves a specific processing need. They are
described in Table 4.10

The structure of a database file is established by
defining each of the fields’ name, type, and width in the
database. Field names may be up to 10 characters in length
but must begin with a letter. They may contain letters,
numbers, and underscores, but, embedded blank spaces are not
permitted.

There are five types of fields used which have
variable field width. These are:

1. Character fields: they are used to store any

printable ASCII characters including letters, numbers,

102

Table 4.10 dBASE III PLUS FILES’ TYPES.

 

Description

 

contains sets of related database files
and their associated operational files
(such as .fmt,.frm, and .lbl files).

stores data in records and fields, and
rows and columns respectively. Each record
contains a set of unique information.

store the information of memo fields.

provides the means to access a database in
logical (alphabetical, chronological, or

numerical) order rather than the physical

order (the order in which the records

were entered).

contains instructions that have been stored
as programs.

create screen forms for use in data entry
and printed output.

contains the information needed to print
labels.

saves the contents of memory variables for
later use.

restricts the records (through filter condi-
tions) that are displayed in commands that
require a database file to be in use.

contains information that is used to
generate and modify format files.

contains the names of database files, their
associated indexes, format files, a list of
selected field names, filter conditions
and relationships between these files.

contains the information needed to prepare
reports.

interfaces dBASE III with other software
packages. These are ASCII files.

File Files
name extention
Catalog .cat
Database .dbf
Memo .dbt
Index .ndx
Command 8c . prg
Procedures
Format .fmt
Label .lbl
form

Memory .mem
Query .qry
Screen .scr
View .vue
Report frm
Text txt
Source:

exerted from Learning and Using dBASE III PLUS,
User’s Manual, Ashton Tate, 1985.

103

special symbols, and blank spaces. The maximum field
size is 254 characters.

2. Date fields: they are used to store dates. The
maximum field size is 8 bytes.

3. Numeric fields: these are of two types: integer and
decimal. The field width is the number of digits the field
can hold. The decimal point and sign each count as one
digit. The maximum field width is 19 bytes. 4. Logical
fields: they only accept single 'characters (width)
representing True/False values. For example, T, t, Y, y for
true and F, f, N, n for false.

5. Memo fields: these accommodate large blocks of
textual information and are stored in auxiliary file to
database file. The field size is variable from 0 up to
a maximum of 5000 bytes depending on the size of the
memo related to the record being entered.

Additionally, there exist a special type of variables
that store data outside the database file structures. These
are the memory variables which provide a convenient way of
temporary storage of data. These variables can be used for
further calculations in programming applications. For
example, the contents of any data fields might be summed or
averaged and the result stored as a memory variable. Memory
variable names can have up to 10 characters long. There are
four kinds of memory variables: character, date, numeric,
and logical. The normal dBASE III allows for up to 256

memory variables.- However, by modifying the dBASE

104

configuration file the number can be increased.

4.2.3.3WJWW

There are also a set of dBASE commands and functions
which are very useful. There are various classes of
commands which perform the following major tasks:

1. Creation of files: the followings are selected dBASE III

commands used to create files:

COPY: copies database in use to a new database.
CREATE: creates a new database file.
CREATE REPORT: creates a report form file.
CREATE SCREEN: creates a screen file, a format file,
and optionally a database file.
SORT: creates a sorted version of the active database
file.

2. Addition of data: the following commands add new records
to databases:
APPEND: adds data at the end of a database file.

BROWSE: adds data at the end of a database file.
INSERT: inserts data into a database file.

3. Editing of data: the following dBASE commands edit the
data within a database:

EDIT: alters data fields in a database.

DELETE: marks records for deletion.

PACK: removes records marked for deletion.

REPLACE: replaces data fields with specified values.

4. User assistance: the following commands give on-line
information:
ASSIST: menu-driven; aids execution of dBASE commands.
DIR: displays files on designated disk drive or
directory.
HELP: menu-driven; explains commands and other
information.

5. Data display: the following commands display selected

105

data from a database:

0... SAY: displays user—formatted data on the screen or
printer.
LIST: lists records and fields.
REPORT: displays a report of data.
SUM: computes and displays the sum of an expression for
a database records specified.

In addition, there also exist classes of commands which
perform other tasks including positioning record pointer,
manipulating databases, using memory variables, programming,
parameter control, and debugging commands.

dBASE III functions are also very useful. The
following examples illustrate some of these functions:

ABS() returns the absolute value of a number.
BOF() locates the beginning of file
INT() provides the intger value of an expression.
ISLOWER() evaluates for lower case input.
ISUPPER() evaluates for upper case input.
LTRIM() removes leading blanks from a character
expression.
MAX() determines the greater of two values.
MIN() determines the lesser of two values.
OS() returns the name and version of the operating
system.
SUBSTR() checks for the occurrence of a required string
of characters within a character field.
STR() views a numeric field as a character field.
TYPE() finds out the type definition of any field
(character, numeric, logical, date, or memo).
VAL() derives numeric data from a character field.

These commands and functions with their related syntax

are explained in details in dBASE III PLUS User’s Manuals.
4.2.4Ana11tigaLErggedunes
114W

The same data used with FARMAP were also processed

using dBASE III PLUS. The reason for using the same data

106

with both programs is to make the comparison more
meaningful.

First, FARMAP binary files were transferred into an
ASCII files by using program DISPLAYB of FARMAP along with
MS—DOS utilities and a special editor called ‘P- Edit’.
Then, program ALI.prg was designed using dBASE programming
techniques to transfer FARMAP data into dBASE file
structure. The program is shown in Figure 4.8.

Two files are listed in this program: STRUCTUR.dbf and
DATA.dbf. The first file contained FARMAP ASCII data on
single-lines’ records. Each standard FARMAP records (i. e.
of 27 data fields on six lines) comprised six single-line
records. The structure of this file was first created based
on 80-columns single field’s records. Then, database
structure of the second file was created based on multiple
lines/fields’ records to transfer each six single-line
records to one multi-line record. Using dBASE ‘DO’ command,
to run program ‘ALI prg’, data were transferred to
‘DATA.dbf’ file. ‘SELECT’ command was employed to use
multiple databases simultaneously,

The ‘APPEND BLANK’ command of dBASE III was executed to
add blank records to the structure of ‘DATA’ database file.
Whereas, the ‘REPLACE’ command was used to replace and
exchange data fields between the two files. The ‘SELECT’
commands was used to manipulate multiple files. Other

commands and functions are explained earlier.

107

1W2:

After transferring FARMAP data file into dBASE file
structures, various database structures were created. To
fully utilized dBASE III PLUS, the two ways of using dBASE
(the menu-driven assistant and the dot commands) were both
performed throughout the analysis.

New database files were created for each correspondent
FARMAP record type. In addition, a master file was created
to control the filter conditions upon which specific data
fields and/or records can be processed. The followings are
database files which were created:

* RT110.dbf, which corresponds to FARMAP Household record
type ‘110’.

* RT200.dbf, which corresponds to FARMAP Farm record type
‘200’.

* RT210.dbf, which corresponds to record type ‘210’; land
characteristics record.

* RT230.dbf, which corresponds to FARMAP record type ‘230’;
Crop characteristics record.

* RT310.dbf, which corresponds to FARMAP Animal record type
‘310’.

«)i-

RT410.dbf, which corresponds to Physical assets record
type ‘410’.

* RT700.dbf, which corresponds to FARMAP Resource flow
record type ‘700’ -

In addition to the above mentioned database files, a

108

************************************************************

* THIS IS A PROGRAM TO CONVERT FARMAP MULTI-LINES RECORDS
* INTO dBASE III PLUS SINGLE-LINE RECORDS(each 6 lines of

* program structur.dbf are combined into
* in program data.dbf)"ALI.PRG JULY,

1986)

one single line

*
*
X
*

************************************************************

select 1

use structur

select 2

use data

do while .not.
append blan
select 1

eof()
k

replace data->a1 with val(substr(text ,1 ,4))

replace data->a2 with val(substr(text ,11 ,5))

skip
select 2
a3

replace
replace
replace
replace

a4
a5
a6

replace a7
select 1
skip

select 2
replace
replace
replace
replace
replace
select 1
skip

select 2
replace
replace
replace
replace
replace
select 1
skip

select 2
replace
replace
replace
replace
replace

a8

all
a12

al3
a14
a15
a16
a17

a18
a19
a20
a21
a22

Figure

with
with
with
with
with

with val(substr(structur->text ,1
a9 with val(substr(structur->text ,15 ,4))
a10 with val(substr(structur->text ,25

val(substr(structur->text
val(substr(structur->text
val(substr(structur—>text
val(substr(structur—>text

val(substr(structur->text

.1 .7))
.12 .8))
.24 .7))
.40 ,5))

,51

.11))

,7))

.7))

with val(substr(structur->text ,39 ,4))
with val(substr(structur->text ,47 ,12))

with
with
with
with
with

with
with
with
with
with

va1(substr(structur->text
val(substr(structur->text
val(substr(structur->text
val(substr(structur->text
val(substr(structur->text

val(substr(structur->text
val(substr(structur->text
val(substr(structur->text
val(substr(structur->text
val(substr(structur->text

INTO dBASE III PLUS DATA FILES.

,1
,12

,4))
.8))

.22.12))

,40
,53

,1

,13
,27
,39
,52

.6))
,4))

,4))
,4))
,4))
,4))

,4))

4.8 PROGRAM ALI.PRG TO TRANSFER FARMAP DATA FILES

109

select 1
skip
select 2
replace a23 with val(substr(structur->text ,1 ,4))
replace a24 with val(substr(structur->text ,13 ,4))
replace a25 with val(substr(structur->text ,27 ,4))
replace a26 with val(substr(structur4>text ,39 ,4))
replace a2? with val(substr(structur->text ,52 ,4))
select 1
skip
select 2

close all *to close all files

enddo '

Figure 4.8 (Cont’d)

110

master file; FARMAP.dbf; was created to control data
hierarchy and levels of aggregation.

To achieve the objectives of the study, screen forms
were designed for each database file to make data entry
process much more easier. This was mainly done to overcome
the difficulty of FARMAP data entry process. Also, these
screen forms contain codes definitions for easier references
and to facilitate data entry process and to eliminate
errors.

The structures of database files and their associated
screen forms are listed in Figure 4.9-1?. The two ways of
using dBASE were employed ( the menu-driven assistant and
advanced programming). The X’s appearing on the figures
represent data fields’ width. For instance 2 X’s represent 2
characters data field width, and so on. Each figure has two
main parts: I) feild definition for screen, and 2) the
screen format. The first part contains data fields’ names,
types, width, database file’ name, and decimal locations for
numeric data fields. The second part which is the screen
format show data fields arranged for easier data entry, and
explain codes definition to eliminate errors.

It should be mentioned that checking for data entry
errors was not done because the same FARMAP data were
transfer ~d after performing the checking and validation
procedu. s mentioned earlier. However, dBASE III PLUS
features allow for error traping and performing checking and

validation of data. This can be done by using the ‘RANGE’

111

option of. the MODIFY command of dBASE to .specify the
accepted lower and upper ranges of each data field. For
example, any data lie outside these specified limits will be
rejected by the program. Also, dBASE provides a built-in
editor to check for data fields types according to the
specified database structure. In addition, dBASE III PLUS
provides many commands for editing and checking the data
within a database. For example, the command EDIT alters data
fields in a database, and the command CHANGE edits specified
fields and records in a database. 1

Database files were checked and validated using the
DISPLAY command of dBASE III PLUS. Through the use of this
command the following operations were displayed on the
screen or on paper:

1) display all the data from a database

2) display a single or a group of records from the

database

3) display a specified field from selected records

4) display record(s) that fulfill a simple or a complex

condition

5) display records or fields from multiple databases

6) display any combinations of the above

112

Field definitions for screen : C:FARMAPF.scr

Page Row Col Data Base Field Type Width Dec
1 9 40 FARMAP VILLAGE_NO Numeric 2 0
1 11 25 FARMAP FARM_NUMBR Numeric 2 0
1 11 57 FARMAP SEASON Character 3
1 13 38 FARMAP DATE Date 8

Content of page : 1

RECORD DESCRIPTION: MASTER FILE "FARMAP.DBF"
RECORD NUMBER: FARMAP.DBF

VILLAGE_NO XX

FARM NUMBER XX SEASON XXX
DATE XXXXXXXX

Figure 4.9 dBASE III PLUS MASTER FILE FARMAP.DBF
SCREEN FORM.

Field definitions for Screen :

113

C:RT110F.scr

Page Row Col Data Base Field Type Width Dec
1 5 48 RT110 RECORDTYPE Character 3
1 8 56 RT110 SERIALNUM Numeric 2 O
1 9 32 RT110 RELATOHHOU Character 2
1 14 36 RT110 AGE Numeric 2 0
1 14 62 RT110 SEX Logical 1
1 15 20 RT110 YRSFRMING Numeric 2 0
1 15 50 RT110 YRSEDUCATE Numeric 2 0
1 16 27 RT110 TIMERESID Numeric 3 O
1 16 59 RT110 TIMEWORK Numeric 3 O
1 17 26 RT110 OFFRMWORK Character 6
1 8 22 RT110 NAME Character 5
1 12 22 RT110 AGESEXCATG Character 3
1 14 21 RT110 NUOFPERSON Numeric 2 0
1 14 62 RT110 SEX Logical 1
1 9 56 RT110 CIVILSTATS Character 2
l 17 60 RT110 TYPINVTORY Character 2
1 5 25 RT110 FARM_NUMBR Numeric 2 0
1 4 35 RT110 VILAGE_NO Numeric 2 0

Content of page : 1
xx********************************************

* RECORD DESCRIPTION: HOUSEHOLD RECORD *
* RECORD NUMBER : RT110.DBF *
x -------------------------------------------- x
* VILLAGE No. XX *

* FARM NUMBER XX RECORD TYPE XXX *
*****************xxxxxxx**x*******************

3 NAME (last,first) XXXXXXXXXXXXXXX SERIAL NUMBER XX
3 RELATION TO HEAD-HOUSEHOLD XX CIVIL STATUS XX
3 (1 head, 2 spouse, 3 child, (19 single, 49
: 7 parent, 99 per. labor) married,59 widowed
: , 99 undet.)
: AGE-SEX CATEGORY XXX (69 head, 129 spouse/daughter,
3 149 child son, 269 per. labor, 159 son,
3 109 child daughter)

3 NO. PERSONS XX AGE (Years) XX SEX(MALE(M)/FEMALE(F) X
3 YEARS FARMING XX YEARS OF EDUCATION XX

3 TIME IN RESIDENCE (%) XXX TIME AVAIL. FOR WORK (%) XXX
3 TYPE OF OFF-FARM WORK XXXXXX TYPE INVENTORY CHANGE XX
3 (9 closing,11 born,51 died,.
3 88 left,85 sale,40 purch.):

Figure 4.10 dBASE III PLUS FILE RT110.DBF SCREEN FORM.

114

Field definitions for Screen : C:RT200F.scr

Page Row Col Data Base Field Type Width Dec
1 4 38 RT200 VILLAGE_N0 Character 2
1 5 22 RT200 FARM_NUMBR Character 2
1 5 55 RT200 RECORDTYPE Character 3
1 9 19 RT200 NUOFPACELS Numeric 2 0
1 9 67 RT200 DISTANCETO Numeric 2 O
1 10 22 RT200 REFR_POINT Character 10
1 11 18 RT200 DIRCTNTORP Character 3
1 12 13 RT200 FARMAREA Numeric 8 2
1 12 55 RT200 AREACONVER Character 1
1 13 21 RT200 ELEVATION Numeric 4 0
1 13 59 RT200 FARM_VALUE Numeric 10 O
1 15 15 RT200 AREACROPED Numeric 6 2
1 15 39 RT200 AREAPASTUR Numeric 6 2
1 15 63 RT200 AREAFOREST Numeric 6 2
1 17 17 RT200 NOCATTLE Numeric 4 O
1 17 4O RT200 NOSHEEP Numeric 4 0
1 17 65 RT200 NOGOATS Numeric 4 O
1 19 17 RT200 NODONKEYS Numeric 4 0
1 19 34 RT200 OTHERS Numeric 4 0
1 19 66 RT200 TYPINVTORY Character 2

Content of page :

l

>3!#03!****:&***************3k*3!30303030.!*************************

* RECORD DESCRIPTION: FARM RECORD *
* RECORD NUMBER: RT200.DBF *
x ------------------------------------------------------ x
* VILLAGE_NO xx *

* FARM NUMBER XX RECORD TYPE XXX *
*xx*xxxxx***********xx*xxxxx****************************

NUMBER OF PARCELS XX DISTANCE T0 REFERENCE POINT (km) XX
REFERENCE POINT (RP) XXXXXXXXXX (e.g. market, home,...etc.)
DIRECTION T0 RP XXX (Nznorth, stouth, NW:north west,..etc.)
FARM AREA XXXXXXXX AREA CONVERSION CODE X (1 hectare )

ELEVATION (meters) XXXX FARM VALUE XXXXXXXXXX

AREA CROPPED XXXXXX AREA PASTURE XXXXXX AREA FOREST XXXXXX

No. 0F CATTLE XXXX No. OF SHEEP XXXX No. OF GOATS XXXX

No. OF DONKEYS XXXX OTHERS XXXX TYPE OF INVENTORY CHANGE XX

Figure 4.11 dBASE III PLUS FILE RT200.DBF SCREEN

FORM.

Field definitions for Screen :

115

C:RT210F.scr

Page Row Col Data Base Field Type Width Dec
1 42 RT210 VILLAGE_NO Numeric 2 0
1 5 24 RT210 FARM_NUMBR Numeric 2 O
1 5 62 RT210 RECORDTYPE Numeric 3 0
1 8 21 RT210 PRCPLTSESN Character 5
1 10 9 RT210 TENURE Character 2
1 12 15 RT210 LANDUSETYP Character 2
1 13 12 RT210 FARMAREA Numeric 8 2
1 13 45 RT210 AREACONVER Character 1
1 15 13 RT210 IRRIGATION Character 3
1 16 11 RT210 DRAINAGE Character 2
1 18 18 RT210 WATERDEPTH Numeric 3 O
l 18 36 RT210 TOPOGRAPHY Numeric 2 0
1 19 15 RT210 SOILTEXTUR Numeric 2 0
1 20 13 RT210 SOILCOLOR Numeric 2 0
1 21 33 RT210 SOILDEPTH Numeric 4 2
1 21 69 RT210 TYPINVTORY Character 2
1 14 40 RT210 LANDVALUE Numeric 8 0

Content of page

1

*x***********************x**************x**********xx*xxxx
* RECORD DESCRIPTION: LAND CHARACTERISTICS

1K

*

RECORD NUMBER: RT210.DBF

XL"

at

x ———————————————————————————————————————————————————————— x

* VILLAGE_NO XX *

* FARM NUMBER XX RECORD TYPE XXX *
*xxxx*****************x***********************************

PARCEL~PLOT-SEASON XXXXX (1000 owned,31000 rented in, 50000
rented out, 80000 communal)
TENURE XX (29 owned and managed, 49 rented in and managed,
59 rented out and managed, 89 communal)
LAND USE TYPE XX (19 rainfed, 29 irrigated, 89 mixture)
FARM AREA XXXXXXXX AREA CONVERSION CODE X (1 hectare)
FARM LAND VALUE XXXXXXXX
IRRIGATION XXX (199 flood,299 river,319 well,499 bore,
599 tank,799 other)
DRAINAGE XX (19 very poor,29 poor,39 occasional waterlogging,
49 good, 59 excellent)
WATER DEPTH (m.) XXX TOPOGRAPHY XX (9 level, 19 hilly)
SOIL TEXTURE XX (13 sandy clay, 21 clay loam, 22 sandy
clay loam)
SOIL COLOR XX (29 red, 59 black)
SOIL DEPTH (m.) XXXX TYPE OF INVENTORY CHANGE XX

Figure 4.12 dBASE III PLUS FILE RT210.DBF SCREEN FORM.

Field definitions

Page Row Col Data Base Field Type Width
1 4 42 RT230 VILLAGE_NO Numeric 2
1 5 25 RT230 FARM_NUMBR Numeric 2
l 5 60 RT230 RECORDTYPE Character 3
1 9 11 RT230 ACTIVITY Character 4
1 9 69 RT230 COMPONENT Character 4
l 10 21 RT230 PRCPLTSESN Character 5
1 11 9 RT230 VARIETY Character 2
1 12 20 RT230 PLANTARRNG Character 2
1 14 20 RT230 PUPOSGRWNG Character 2
1 15 8 RT230 AREA Numeric 8
1 15 46 RT230 AREACONVER Character 1
1 16 18 RT230 PLTCONDTON Character 1
1 17 26 RT230 PLTPOPULTN Numeric 3
1 18 13 RT230 CASH_KIND Character 1
1 19 26 RT230 TYPINVTORY Character 2

Content of page : l

for Screen :

C:RT230F.scr

********************************************************

* RECORD DESCRIPTION: CROP CHARACTERISTICS RECORD *
* RECORD NUMBER: RT230.DBF *
x —————————————————————————————————————————————————————— x
* VILLAGE_NO xx x

* FARM NUMBER XX RECORD TYPE XXX *
k******x**********************************************x*

ACTIVITY XXXX (29 rice, 19 wheat, 919 cotton) COMPONENT XXXX
PARCEL-PLOT-SEASON XXXXX (cc,tt,s)

VARIETY XX (89 modern high yielding, 49 local, ..... etc.)
PLANT ARRANGEMENT XX (19 broadcast, 29 scattered, 59
regular, 69 ridges)

PURPOSE OF GROWING XX (29 consumption, 39 mixed food/cash,
49 cash)
AREA XXXXXXXX AREA CONVERSION CODE X
PLANT CONDITION X (1 excellent,
5 very poor)
PLANT POPULATION (’000)
CASH_KIND
5 all cash)

TYPE OF INVENTORY CHANGE XX ( 9 closing inventory)

(1 hectare)
2 good, 3 average, 4 poor,

XXX (e.g. # of plants/ha)
X ( 1 all kind, 2 25%cash, 3 50%cash,4 75%cash,

Figure 4.13 dBASE III PLUS FILE RT230.DBF SCREEN FORM.

117

Field definitions for Screen C:RT310 scr
Page Row Col Data Base Field Type Width Dec
1 4 38 RT310 VILLAGE_N0 Numeric 2 0
1 5 24 RT310 FARM_NUMBR Numeric 2 0
1 5 54 RT310 RECORDTYPE Numeric 3 0
1 8 10 RT310 ACTIVITY Numeric 4 0
1 10 12 RT310 COMPONENT Numeric 4 0
1 10 51 RT310 PRCPLTSESN Character 5
1 11 18 RT310 AGESEXCATG Character 3
1 12 20 RT310 MGMTPRCTIC Character 2
1 13 8 RT310 BREED Character 3
1 14 19 RT310 AGE Numeric 4 2
l 14 50 RT310 REMANGLIFE Numeric 2 0
1 15 19 RT310 NO_ANIMALS Numeric 3 0
1 15 44 RT310 SERIAL_NO Numeric 3 0
1 16 23 RT310 PURCH_COST Numeric 8 0
1 16 60 RT310 PREST_VALU Numeric 8 0
1 17 23 RT310 SLVAG_VALU Numeric 8 0
1 18 23 RT310 PURPOSE Character 2
1 19 12 RT310 CONDITION Character 1
1 20 18 RT310 0WNER_STAT Character 2
1 21 16 RT310 AVE_WEIGHT Numeric 4 0
1 21 52 RT310 CASH_KIND Numeric 1 0
1 22 26 RT310 TYPINVTORY Character 2
*xxxxxx**x**xxx******xx*****************************
* RECORD DESCRIPTION: ANIMAL RECORD *
* RECORD NUMBER: RT310.DBF *
x —————————————————————————————————————————————————— x
* VILLAGE_NO XX *
* FARM NUMBER XX RECORD TYPE XXX *
*xxt*****************x*******x**************xxx****x
ACTIVITY XXXX (5099 cattle,5199 buffaloes,5299 sheep, 5399

goats,5499 horses,5599 pigs,5699 camels,5799 poultry)
COMPONENT XXXX PARCEL-PLOT-SEASON XXXXX
AGE-SEX CATEGORY XXX (199 female,299 male, 999 undet.)
MANAGEMENT PRACTICE XX (19 extensive grazing,49 herded,99

undetermined)

BREED XXX (199 local,299 crossbred,499 exotic,999 undet.)
AGE (yrs.,months) XXXX REMAINING LIFE (years) XX
NUMBER OF ANIMALS XXX SERIAL NUMBER XXX
PURCHASE COST(total) XXXXXXXX PRESENT VALUE (total) XXXXXXXX
SALVAGE VALUE (total) XXXXXXXX
PURPOSE FOR KEEPING XX (29 consumption,49 cash,89 mixed)
CONDITION X (1 excellent,2 good,4 poor,5 very Poor,9 undet)
OWNERSHIP STATUS XX (29 owned,49 taken in,59 given out)
AVERAGE WEIGHT XXXX CASH/KIND OF CHANGE X
TYPE OF INVENTORY CHANGE XX

Figure 4.14 dBASE III PLUS FILE RT310.DBF SCREEN FORM.

118

Field definitions for Screen : C:RT410F.scr

Page Row Col Data Base Field Type Width Dec
1 4 38 RT410 VILLAGE_N0 Numeric 2 0
1 5 24 RT410 FARM_NUMBR Numeric 2 0
1 5 53 RT410 RECORDTYPE Character 3
1 9 14 RT410 ACTIVITY Character 4
1 10 14 RT410 COMPONENT Character 4
1 10 41 RT410 PRCPLTSESN Character 5
1 11 14 RT410 ITEM_CODE Character 4
1 12 14 RT410 LOCATION Character 2
1 13 17 RT410 CPACTYSIZE Numeric 6 O
1 14 14 RT410 AGE Numeric 4 2
1 14 47 RT410 REMANGLIFE Numeric 4 2
1 15 16 RT410 N0_ITEMS Numeric 2 0
1 15 39 RT410 ORIG_COST Numeric 9 0
1 16 17 RT410 PRESTVALUE Numeric 9 0
1 16 45 RT410 SALVGVALUE Numeric 9 0
1 17 13 RT410 CONDITION Character 1
1 18 20 RT410 OWNERSTAT Character 2 3
1 19 13 RT410 CASH_KIND Character 1
1 19 44 RT410 TYPINVTOR Character 2

***************************************************

* RECORD DESCRIPTION: PHYSICAL ASSETS RECORD *
* RECORD NUMBER: RT410.DBF *
x ————————————————————————————————————————————————— x
* VILLAGE_N0 XX *
* FARM NUMBER XX RECORD TYPE XXX *

***************************************************

3 ACTIVITY XXXX (29 rice, 9399 general farm activity) 3
3 COMPONENT XXXX PARCEL-PLOT—SEASON XXXXX (99999 not appl.3
3 ITEM_CODE XXXX (6019 small tractor, 8999 tools) 3
3 LOCATION XX (11 farm, 12 home, 13 market, 19 warehouse) 3
3 CAPACITY/SIZE XXXXXX (e.g. horsepower, cubic meters,etc.)3
3 AGE (years) XXXX REMAINING LIFE (years) XXXX 3
3 NO. OF ITEMS XX ORIGINAL COST XXXXXXXXX 3
3 PRESENT VALUE XXXXXXXXX SALVAGE VALUE XXXXXXXXX 3
3 CONDITION X (1 excellent, 3 good, 4 poor, 5 very poor) 3
3 OWNERSHIP STATUS XX (29 owned,49 rented in,59 rented out):
3 CASH_KIND X TYPE OF INVENTORY CHANGE XX (9 closing) 3

Figure 4.15 dBASE III PLUS FILE RT410.DBF SCREEN FORM.

Field definitions for Screen : C:RT510.scr
Page Row Col Data Base Field Type Width Dec
1 4 41 RT510 VILLAGE_N0 Numeric 2 0
1 5 27 RT510 FARM_NUMBR Numeric 2 O
1 5 56 RT510 RECORDTYPE Character 3
1 8 14 RT510 ACTIVITY Character 4
1 9 14 RT510 COMPONENT Character 4
1 9 4O RT510 PRCPLTSESN Character 5
1 10 17 RT510 ITEM_PURCH Character 4
1 11 15 RT510 CREDITTYPE Character 2
1 12 19 RT510 SOURCLOCTN Character 2
1 13 17 RT510 WHNOBTAIND Date 8
1 13 52 RT510 DURATION Character 4
1 14 12 RT510 APR Numeric 4 2
1 14 47 RT510 OTHRCHARGS Numeric 2 0
1 15 23 RT510 PRINCIPAL Numeric 8 0
1 15 58 RT510 OUTSTANDNG Numeric 8 0
1 16 26 RT510 PROP_CASH Numeric 2 0
1 17 22 RT510 REPMT_SCHD Numeric 2 0
1 18 12 RT510 PURPOSE Character 2
1 19 12 RT510 SECURITY Character 2
1 20 24 RT510 DIFFICULTY Character 1
1 21 14 RT510 TYPINVTORY Character 2
************x**********x***x*************************
* RECORD DESCRIPTION: CREDIT RECORD *
* RECORD NUMBER: RT510.DBF *
x ——————————————————————————————————————————————————— *
* VILLAGE_NO XX *

* FARM NUMBER XX RECORD TYPE XXX *
*****x*xx*************************x*xx**x*******xxxxx

ACTIVITY XXXX (29 rice, 9399 general farm activity)
COMPONENT XXXX PARCEL-PLOT-SEASON XXXXX (99999 not

applicable)
ITEM PURCHASED XXXX

I I
3 (6529 pump, 6019 tractor,1000 3
3 material,1999 undet.) 3
3 CREDIT TYPE XX (39 given, 59 received) 3
3 SOURCE,LOCATION XX (19 bank, 39 cooperative, 79 trader) 3
3 WHEN OBTAINED XXXXXXXX DURATION(years,months) XXXX 3
3 APR (X) XXXX OTHER CHARGES (%) XX 3
3 ORIGINAL PRINCIPAL XXXXXXXX OUTSTANDING XXXXXXXX 3
3 PROPORTION OF CASH (%) XX 3
3 REPAYMENT SCHEDULE XX (13 annual, 39 lump sum) 3
3 PURPOSE XX (39 farm inputs, 49 farm capital items) 3
3 SECURITY XX (19 land, 49 livestock, 89 mixed, 99 undet.) 3
3 DIFFICULTY OBTAIN. X (l easy,3 moderate,5 very difficult)3
3 TYPE INVENTORY XX ( 9 closing inventory) 3

_--—--------------—_-—--------------------—-—_-—--_—----~_-—
---——---—-—-------—’_o--_----_----—-_---—----------——--—-—_——

Figure 4.16 dBASE III PLUS FILE RT510.DBF SCREEN FORM.

Field definitions for Screen :

120

C:RT700F.scr

Page Row Col Data Base Field Type Width Dec
1 4 29 RT700 FARM_NUMBR Numeric 2 0
1 4 48 RT700 RECORDTYPE Numeric 3 0
l 7 12 RT700 ACTIVITY Numeric 4 0
1 9 12 RT700 COMPONENT Numeric 4 0
1 9 42 RT700 PCLPLTSESN Numeric 5 0
1 10 14 RT700 INPT_OUTPT Numeric 4 0
1 13 12 RT700 OPERATION Numeric 4 0
1 17 12 RT700 FREQUENCY Numeric l 0
1 17 57 RT700 MONTH Numeric 2 0
1 18 12 RT700 QUANTITY Numeric 8 2
1 18 43 RT700 QTYCONVERS Numeric 1 0
1 19 20 RT700 SOURCEDSTN Numeric 2 0
1 21 12 RT700 PRICE_VALU Numeric 8 2
1 21 32 RT700 CASH_KIND Numeric 1 0
1 22 13 RT700 PRICEVALUE Numeric l 0
1 22 66 RT700 TYPINVTORY Numeric 1 —0

*********X************************************

* RECORD DESCRIPTION: RESOURCE UTILIZATION *
* RECORD NUMBER: RT700.DBF *
x ———————————————————————————————————————————— *

* FARM NUMBER XX RECORD TYPE XXX *
***xxxxxxxxxxxxxxxxx*********xxxxxxxxxxxxxxxxx

ACTIVITY XXXX (29 rice,6019 tractor rental,8099 off-farm job
,9399 tax paid, 8399 income from land rent)

COMPONENT XXXX PARCEL-PLOT—SEASON XXXXX (pcpc,ptpt,sn)

INPUT/OUTPUT XXXX (10-999 produce,1000-1999 plant material
inputs, 3000-3999 special activ.material inputs,4000-

6999 power inputs, 7000-8999 equipment, 9000-9199
rent,9899 taxes)

OPERATION XXXX (99 seedbed prep.,109 ploughing,139 harro~
wing,159 appl. basic fert.,199 land prep.,209
seedling,229 transplanting,369 insecticide applic.,429
weeding,459 top dress. fert.,619 harvest ,699 harv.;
thres.&winnowing,709 drying)

FREQUENCY X (1 daily,2 weekly,3 monthly)

QUANTITY XXXXXXXX QTY CONVER.

MONTH XX
CODE X (1 ha,2 hrs,3

ltrs,4 kg,5 items)
SOURCE DESTINATION XX (39 on-farm transfer,49 friends/labor,

59 shop, 69 market, 9 off—farm transfer,4 tax payment transfer)

PRICE/VALUE XXXXXXXX CASH/KIND X (1 kind,2 75%kind,3
50%kind,4 75%cash,5 cash)
PRICE/VALUE X (0 value,1 unit price) TYPE OF INVENTORY

CHANGE X (9 closing)

Figure 4.17 dBASE III PLUS FILE RT700.DBF SCREEN FORM.

lmhmas

The DISPLAY, BROWSE, EDIT and LIST commands of dBASE
III PLUS were used to edit data fields and records. These
commands display, alter, edit, and list specified data
fields or records within a database ( see section 4.2.3.3
for more details). Since, the same FARMAP data were used,
only minor modifications were employed. For instance,
alphanumeric data fields were added since it was not
possible to process them with FARMAP. Also, the whole digits
of numeric fields were entered manually. This was done on
land characteristics record type, since only seven digits of
the numeric fields were entered using FARMAP. However, the
monetary value exceeded that limits. Therefore, no scaling
factor was used with dBASE.

In addition to the above mentioned commands, other
dBASE III commands were used including the APPEND, INSERT,
DELETE, PACK, USE, and ZAP commands. The two ways of using
dBASE III were performed: the menu-driven assistant, and the
dot commands (programming).

Using the REPLACE command data fields were modified to
replace numeric codes with labels (This is similar to
program LABEL of FARMAP).

To prepare the data for generating reports, all
database files were sorted and/or indexed. Sorting involved
the physical resequensing of the records in a database.
Another databases, identical in structure and size to the

original files, but with records physically rearranged in

122

the required sequence were created. Sorting were done on

multiple fields (namely, village number, farm number,
activity, and input/output) in ascending/ descending
sequence.

However, logical indexing were also done without

changing the original files. The index files were created
based on multiple data fields only in ascending order. Those
index files played a role in subsequent commands, in that
the records were processed by the commands in the logical

order of the index.
4W

To produce reports, some modifications were made to the
structure of databases. The programs designed to modify
database structures are listed in Figures 4.18-21. Each
program was designed for one or multiple purpose. For
example programs FINl, FIN2, FIN3, and FIN4.prg were
designed to create labels by replacing numeric fields with
appropriate alphanumeric character fields. Also, they were
designed to get the sum for each input/output quantities,
prices or values of each farm.

However, the four different programs mentioned were
merged together into one command file, i.e., FINTOT.PRG to
facilitate processing.

After preparing the database files to produce reports’
new database files were created with additional character

fields (labels). The report format files were created using

123

********************x**x**************x*****************x***

* THIS COMMAND FILE WAS DESIGNED TO SUM THE QUANTITIES AND *

* VALUES FOR EACH FARM TRACTOR RENTAL ACTIVITY. *
************************************************************

select 1

use xrt700

select 2

use rt700all

m_farm_num = 1

do while m_farm_num <= 10
append blank
select 1
goto top
sum quantity to q1 for inpt_outpt < 4000 .and. inpt_outpt
> 1000 .and. farm_numbr = m_farm_num .and. activity = 6019
sum price_valu to p1 for inpt_outpt < 4000 .and. inpt_outpt
> 1000 .and. farm_numbr = m_farm_num .and. activity = 6019
sum quantity to qO for inpt_outpt (2 1000 .and. farm_numbr
= m_farm_num .and. activity = 6019
sum price_valu to p0 for inpt_outpt <= 1000 .andt
farm_numbr = m_farm_num .and. activity = 6019
sum quantity to q2 for inpt_outpt > 2000 .and.
inpt_outpt < 5000 .and. farm_numbr = m_farm_num .and.
activity = 6019
sum pricefivalu to p2 for inpt_outpt > 2000 .and.
inpt_outpt < 5000 .and. farm_numbr = m_farm_num .and.
activity = 6019
select 2
replace qtyl with ql
replace farm_numbr with m_farm_num
replace pricel with pl
replace qtyO with go
replace priceO with p0
replace qty2 with q2
replace price2 with p2
replace activity with 6019
m_farm_num = m_farm_num + 1

enddo

close all

use rt700all

Figure 4.18 dBASE III PROGRAM FIN1.PRG.

124

************************X*****************¥****************

* THIS FILE WAS CREATED TO SUM THE QUANTITIES AND VALUES *
* OF EACH FARM FOR RENTED-OUT LAND ACTIVITY. *
******************************************x****************
select 1
use xrt700
select 2
use rt700all
m_farm_num = 1
do while m_farm_num (2 10

append blank

select 1

goto top

sum quantity to go for inpt_outpt <= 1000 .and.
farm_numbr = m_farm_num .and. activity = 8399

sum price_valu to p0 for inpt_outpt <= 1000 .and.
farm_numbr = m_farm_num .and. activity = 8399
select 2

replace farm_numbr with m_farm_num
replace qtyO with qO
replace priceO with p0
replace activity with 8399
m_farm_num = m_farm_num + 1

enddo

close all

use rt700all

Figure 4.19 dBASE III PROGRAM FIN2.PRG.

125

************************************************************

* THIS PROGRAM WAS CREATED TO SUM QUANTITIES AND VALUES 0F *

* EACH FARM FOR INCOME FROM OFF-FARM JOB ACTIVITY. *
****************************xxxxxxx**********************x**

select 1

use xrt700

select 2

use rt700all

m_farm_num = 1

do while m_farm_num <= 10
append blank
select 1
goto top
sum quantity to qO for inpt_outpt (2 1000 .and.
farm_numbr = m_farm_num .and. activity = 8099
sum price_valu to p0 for inpt_outpt <= 1000 .and.
farm_numbr = m_farm_num .and. activity = 8099
select 2
replace farm_numbr with m_farm_num
replace qtyO with q0
replace priceO with p0
replace activity with 8099
m_farm_num = m_farm_num + 1

enddo

close all

use rt700all

Figure 4.20 dBASE III PROGRAM FIN3.PRG.

126

*******************x**x*xxx*******************x*************
* THIS PROGRAM WAS DESIGNED TO SUM QUANTITIES AND VALUES OF*
* EACH FARM FOR THE RICE ACTIVITY. *
************************xx*xxxxxxx***x***************x*xx***
select 1
use xrt700
select 2
use rt700all
m-farm_num = 1 '
do while m_farm_num <= 10
append blank
select 1
goto top
sum quantity to ql for inpt_outpt < 2000 .and.
inpt_outpt > 1000 .and. farm_numbr = m_farm_num .and.
activity = 29
sum price_valu to p1 for inpt_outpt < 2000 .and.
inpt_outpt > 1000 .and. farm_numbr = m_farm_num .and.
activity = 29

sum quantity to qO for inpt_outpt <= 1000 .and.
farm_numbr = m_farm_num .and. activity 2 29
sum price_valu to p0 for inpt_outpt <2 1000 .and.
farm_numbr = m_farm_num .and. activity = 29
sum quantity to q2 for inpt_outpt > 2000 .and.

inpt_outpt < 5000 .and. farm_numbr = m_farm_num .and.
activity 2 29
sum price_valu to p2 for inpt_outpt > 2000 .and.

inpt_outpt < 5000 .and. farm_numbr = m_farm_num .and.
activity 2 29
sum quantity to q3 for inpt_outpt > 5000 .and.

inpt_outpt < 8000 .and. farm_numbr = m_farm_num .and.
activity = 29

sum price_valu to p3 for inpt_outpt > 5000 .and.
inpt_outpt < 8000 .and. activity 2 29 .and. farm_numbr =
m_farm_num

sum quantity to q4 for inpt_outpt : 9000 .and.
farm-numbr = m_farm_num

sum price_valu to p4 for inpt_outpt = 9000 .and.
farm.numbr = m_farm_num

sum quantity to q5 for inpt_outpt = 9899 .and.
farm_numbr = m_farm_num .and. activity = 9399

sum price_valu to p5 for inpt_outpt = 9899 .and.
farm_numbr = m_farm_num .and. activity 2 9399
select 2

replace qtyl with ql

replace farm-numbr with m_farm_num
replace pricel with p1

replace qtyO with q0

replace priceO with p0

replace qty2 with q2

Figure 4.21 dBASE III PLUS PROGRAM FIN4.PRG.

replace
replace
replace
replace
replace
replace
replace
replace

127

price2 with p2
qty3 with q3
price3 with p3
qty4 with q4
price4 with p4
qty5 with q5
price5 with p5
activity with 29

m_farm_num = m_farm_num + 1

enddo
close all

use_rt700all

Figure 4.21 (Cont’d)

128

the built-in reporting facility of dBASE.
The steps involved to create format files include:

* Creating the general layout of each report: the
general layout of the report determines page title,
page width and margins, number of lines per page, page
spacing, and printing forms.

* Creating groups and sub—groups: in this step, totals,
subtotals and sub-subtotals were obtained for the desired
data fields of each database files.

* Filling out the content of the report: the content of
each column of each report was determined from data fields
or memory variables. Arithmetic operations (i.e., multipli-
cation, addition, subtraction and so forth), were performed
to get the required results. In addition, new data fields
were defined, e.g., by adding two or more fields together.

Multiple databases were also used simultaneously to
create farm reports. This was done by selecting two or more
database files (including the Master file), based on key
fields.

While the built-in reporting feature of dBASE provides
great flexibility in creating report formats, there also
exists a set of restrictions (e. g. the user has to be
content with the format as presented by dBASE). Therefore,
the same reports were created by designing special dBASE
programs (without the built-in reporting facility of dBASE).
The same results were obtained with more user control on the

required output and the report formats.

129

The results obtained will be discussed in Chapter 5.
The limitations of dBASE III PLUS are also discussed in the

next chapter.

CHAPTER 5

THE RESULTS

5.1 The Results of FARMAP

54.1w

FARMAP output tables were produced in two modes: farm
and activity mode. A summary table for the entire farm was
produced in the farm mode for each of the 10 farms. A
second run was required to produce the power types’
subtables in the farm mode. In the activity mode, one table
was obtained for each activity on each farm. Means of the
ten farms were produced in three separate runs for the farm
and activity modes.

Using the standard tabulation procedures the following
tables were obtained:

1. Table 5.1, Farm Mode: it includes different subtables
covering the topics of household composition, land
resources, crops list, net worth, economics, cash-kind flow,
and human and machine power use.

2. Table 5.2, Farm Mode (Power Types): it contains human and
machine power types.

3. Tables 5.3-7 , Activity Mode: these tables cover the same
topics as the farm mode except that 'the household
composition subtable and net worth statement subtable are
not applicable. Also, one table was produced for each

activity of each of the ten farms.

130

131

4. Means Tables: means for the ten farms are shown in
Appendix A for the two different tables of the farm mode,
and the activity mode’ tables.

Because of the similarity of the output for the ten
farms, only one farm’ tables (i.e., farm number 9) are
presented here. The output of farm number 9 was selected
because all different activities are covered under that
farm. Also, the output tables for all the ten farms would be
very long and would add little in terms of understanding.

It should be noted that the monetary unit of the
Indonesian currency (Rupiah) is small when compared to th;
United States dollar (1 US 3 = 625 rupiahs in the survey
year, 1970-80). Therefore, all values and unit prices are
stated in 100 units. The unit of area is hectare.

Due to the lack of statistical capabilities of FARMAP,
the advanced tabulation procedure was used. The output of
this procedure and the results of the statistical analysis
will be discussed in detail in a subsequent section.

The following discussion illustrates different tables

and their associated subtables.

51.2an
5.1.2.1Earm_MO_da_T_ahl_es

As mentioned earlier, two separate runs with separate
sorting requirements were required .to produce the standard
farm mode tables. The power types’ tables of the farm mode

required different sorting procedures than that needed for

132

the other farm mode subtables. The following subtables were

produced after the first run (Table 5.1).

Lilausflioldjomsitionjumm

The HOUSEHOLD COMPOSITION subtable was calculated from
information contained in the household records type ‘110’.
It contains information about household members living in
the same place including permanent laborers. This summary
(Table. 5.1a) provides basic demographic information,
consumer units indicating the demand for food, and labor
supply.

For farm 9, the first row of the HOUSEHOLD COMPOSITION
subtable shows the total number of persons, 6, in the
household, the number of family members by age category
(aged, adult, youth, and children), the number of permanent
laborers (PERMLAB), and the number of other person (OTHER)
in the household. The same information are shown on the
second and third row divided into sex categories (males and
females).

The consumer units were calculated from the age—sex
group, percent of time in residence, and consumer unit
equivalent. Information on age-sex group and percent of time
in residence were taken directly from household records type
‘110’. The default value of the consumer unit equivalent
derived from FAO experience were employed. For example, 1.00
was used for adult male, 0.80 for adult female and youth

male, 0.75 for youth female, and 0.50 for children. As shown

133

on Table 5.1a, the consumer units of the adults are 1.8
which are one male and one female. Whereas, they are 1.0 for
two children.

Similarly, the labor supply information is based on the
age-sex grouping, percent of time available for work, and
adult-equivalent. The default values for the adult-
equivalent were 0.5 for aged and youth, 1.0 for adult and
laborer, and zero for children. For instance, the labor
supply is totaled to 2.5 units which is composed of 2 adults
and 1 youth. The second youth was not calculated based on
the information about his non-availability for work.

Finally, information on the head of household are shown
on the last line of the household composition subtable. For
example, he is 38 year old, male, has attended 5 years of
schooling, and resided 12 months on the farm during the year

under observation.

KW

The LAND RESOURCES subtable provides information about
the land area under various types of tenure and irrigation
practices. The data were derived from the land
characteristics records type ‘210’. The information provided
helps evaluate the economic productivity of the farm and are
very useful for making interfarm comparative analyses.

The subtable (Table 5.1b) shows that 7.1 hectares is
the total farm area, of which 6.4 is owned and managed and

0.7 is rented out. All of the owned and managed land area is

134

Table 5.1a FARMAP STANDARD TABLE - FARM MODE - COMPLETELY
DISAGGREGATED - HOUSEHOLD COMPOSITION.

*************************************************************

TOTAL FAMILY PERMLAB OTHERS
AGED ADULT YOUTH CHILDREN

PERSONS 6.0 .0 2.0 2.0 2.0 .0 0
FEMALE 1.0 .0 1.0 .0 .0 .0 0
MALE 5.0 .0 1.0 2.0 2.0 .0 0
CONSUMER UNITS 4.4 .0 1.8 1.6 1.0 .0 .0
LABOUR SUPPLY 2.5 .0 2.0 .5 .0 .0 .0

HEAD: SEX M AGE 38 YRS SCHOOLING 5.0 MTHS RESIDENCE 12.0

*************************************************************

Table 5.1b FARMAP STANDARD TABLE - FARM MODE - COMPLETELY
DISAGGREGATED - LAND USE.

**************xxxxx*********************xxxxxxxxxxxxxxxxxxxxx
TOTAL ANN.RF. ANN.IRR PERM.RF PERM.IR PAS. OTHER

OWNED AND

MANAGED 6.4
RENTED IN

AND MANAGED .0
RENTED OUT

AND MANAGED 7 .0
TOTAL 7.1 .0 6.4 .0 .0 .0 .7

*************************************************************

135

irrigated (ANN. IRR), while the rented out land is
undetermined (OTHER). No land fell under the other
categories, i.e., annual rainfed (ANN.RF.), permanent
rainfed (PERM.RF), permanent irrigated (PERM.IRR), or

pasture.

RM

The CROPS LIST subtable (see Table 5.1c) contains
information on crops grown, variety, and plot area for every
crop. This information was derived from the crop
characteristics records type ‘230’.

As shown, modern rice variety is grown on parcel 1 in
the wet season (code 1001). The area allocated for growing
rice is 6.43 hectares which is owned and managed. The rented

out land area is not included.

4W

The NET WORTH STATEMENT subtable provides information
on value of farm assets, debts, the farm net worth, and any
changes in each of these categories during the survey
period. This subtable was calculated from record types ‘210’
(land characteristics), ‘230’ (crop characteristics), ‘410’
(physical assets), and ‘510’ (debts).

As shown in Table 5.1d, the first row of the NET WORTH
STATEMENT subtable shows the closing inventory value of the
farm assets in 1000’s rupiahs (e.g., land = 26000, and

physical assets = 830), its debts, and the total net worth

136

Table 5.10 FARMAP STANDARD TABLE - FARM MODE - COMPLETELY
DISAGGREGATED - CROPS GROWN.

*************************************************x*********xx
** CROPS GROWN **
CROP COMPONENT PARCEL-PLOT VARIETY AREA

RICE RICE 1001. MODERN 6.43

**********************************#**************************

Table 5.1d FARMAP STANDARD TABLE - FARM MODE - COMPLETELY
DISAGGREGATED - NET WORTH STATEMENT

*************************************************************

LAND PH.ASSET PRCROP ANMALS OTHRS DEBTS TOTAL

CLOSING

INVENTORY 26000.0 830.0 .0 .0 .O -80.0 26750.0
INCOMING AND

APPRECIATION .0 .0 .0 .0 .0 .0 .0
OUTGOING AND

DEPRECIATION .0 .0 .0 .0 .0 .0 .0

*************************************************************

137

(=26750) which is assets minus debts. The assets are broken
down into five categories: land, physical assets (including
buildings, tools, machinery, and consumer durables),
permanent crops, animals, and others. Appreciation and
depreciation are also shown on the second and third lines of

the net worth subtable, respectively.

5W

The ECONOMICS subtable of Table 5.1e provides the major
part of information on economic performance of the farm. The
data were obtained from the resource flow record type ‘700’.

The ECONOMICS subtable gives the resource economics of
the entire farm. It is structured vertically and
horizontally. The vertical structure is divided into two
main sections, resource economics and farmer economics, to
differentiate between resource productivity and tenant
farmer economics. To illustrate, the resource economics
columns show all resource flows, regardless of the origin of
inputs or the destination of outputs. While, the farmer
economics columns show all resource flow originating from or
ending with the farmer. Therefore, the difference between
them is the treatment of transfer payments and inputs
acquired from sources other than the farmer. For example,
rent, share payments, taxes, and off-farm income are
excluded from the resource economics section of the
ECONOMICS subtable.

The columns titles show the returns, costs and

138

quantities under the resource economics section, and
quantity, price, value, and percent cash (%CASH) under the
farmer’s economics section. Percent cash indicates the
percent of value that was actually in cash.

In the farm mode, quantities and prices are suppressed,
except for labor inputs. This is because these non—labor
inputs are often stated in different units (i. e. kilograms,
tons, or liters) and thus the totals would be meaningless.

The horizontal divisions group together the same inputs
and outputs in rows. These rows include income, variable
costs (material, human power, and machine power inputs), and
fixed costs (rent, insurance, interest, depreciation, and
taxes). These pre-defined groupings are denoted by the row
titles GROSS INCOME, GROSS MARGIN, USER-SUPPLIED TITLE, NET
RETURN BEFORE TAXES, and NET RETURN AFTER TAXES. The
difference between gross income and variable costs gives the
gross margin. The net return before taxes is calculated by
subtracting selected fixed costs (depreciation, interest,
and insurance) from the gross margin. And by subtracting
taxes from net return before taxes, the net return after
taxes is obtained. The user-supplied title can be used for
user definition title when the land and family labor are
valued (which is not the case here), or to indicate rent and
share costs.

The gross income of four activities is shown in Table
5.1e. These activities are rice (RICE), two—wheel tractor

rental (TWTRCR), off-farm job (OFFFRMJB), and rented—out

139

Table 5.1e FARMAP STANDARD TABLE - FARM MODE - COMPLETELY
DISAGGREGATED — ECONOMICS SUBTABLE.

*xxxxxxx***xx****x*x*****************************************
** ECONOMICS **
( IN UNITS OF )

AREA 7.14 QUANTITY 1.000 PRICE 1.000 VALUE 10.000

ACT COMP RESOURCE ECONOMICS FARMER ECONOMICS
QUANTITY VALUE QUANTITY PRICE VALUEXCASH
RICE RICE 2516 2516 82
TWTRCTR TWTRCTR 147 100
OFFFRMJB OFFFRMJB 110 75
RNTINCOM RNTINCOM 80
GROSS INCOME 2516. 2853. 80
MATERIALS
RICE RICE 86 86 93
TWTRCTR TWTRCTR 26 100
LABOUR
RICE RICE 3806 736 3806 1.94 736 17
TWTRCTR TWTRCTR 336 .73 25 100
DRAFT
POWER
RICE RICE
VARIABLE COSTS 822. 872. 29
GROSS MARGIN 1694. 1981.
USER-SUPPLIED TITLE 1694. 1981.
NET RETURN BEFORE TAXES 1694. 1981.
GENFARM GENFARM 80 100
NET RETURN AFTER TAXES 1694. 1901.

**************************************************************

140

land (RNTINCOM). Their gross incomes in 1000’s rupiah are
2516, 147, 110, and 80 respectively. The components (COMP)
have the same names as the activities, since there were not
any mixed activities ( e.g. maize-beans, dairy cattle—corn,
and so on) in the analysis.

Material inputs are shown for the rice and two-wheel
tractor rental activities. They are 86 and 26 (1000’s
rupiah), respectively. The net return after taxes for the
whole farm amounts to Rp. 1,901,000 for the farmer
economics, and Rp. 1,694,000 for the resource economics
sections. This difference is due to the exclusion of
transfer payments (taxes, rental,.etc.) and off-farm income

from the resource economics section.

SW

The CASH-KIND FLOW subtable displays the flows of cash
and kind for the whole farm based on the information
provided from record types ‘700’ (resource flow records) and
‘230’ (crop characteristics records). It contains important
information needed to make judgments concerning debt
servicing or repayment capacity.

The CASH-KIND FLOW subtable of farm number 9 is shown
in Table 5.1f. Area 7.14 indicates the total area of the
farm in hectares. In units of 10.000 indicates the scaling
factor for the units displayed (it should be recalled that
data were manually scaled and entered in 100 units before

any scaling by the program, therefore, the scaling factor is

141

in 1000 units, 10 by FARMAP and 100 manually).

The header of the CASH-KIND FLOW subtable contains the
total flows and abbreviations for the 12 months. The sum of
the cash flow (the first line) and the kind flow (the second
line) appears on the totals (the third line) for the whole
year and for each month. For example, the cash flow for the
whole year is Rp. 1,950,000, and the kind flow is Rp. -
49,000. The latter is negative due to home consumption
and/or payments paid in kind for labor or material inputs.
The total flow is the sum of the cash and kind flow, which
is Rp. 1,901,000.

WW

The monthly distributions by activity and component are
shown on the POWER USE (human, and machine) subtables. The
total area of the farm and scaling factor (which is done
automatically by the program) are listed in Table 5.1g, at
the top of the POWER USE subtables. Two activities, rice and
tractor rental, are displayed on the HUMAN POWER USE
subtable which sum to 4142 hours. And, only the rice
activity appears on the MACHINE POWER USE subtable with a
total of 120 hours of machine power use. The monthly
distributions are shown and the rest of POWER USE subtables
are self-explanatory. The user can define different time
intervals such as daily, weekly, or bi-monthly power tables.
This can be done by changing the output tables’ format in

the command files.

142

Table 5.1f FARMAP STANDARD TABLE - FARM MODE - COMPLETELY
DISAGGREGATED - CASH AND KIND FLOW SUBTABLE.

**************************************************************

** CASH KIND FLOW **
AREA 7.14 IN UNITS OF 10.00000
TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
CASHFLOW 1950. -115 74 97 1903 0 0 0 0 0 0 0 -9
KINDFLOW -49. -9 -50 0 18 0 0 0 0 0 0 0 -8
TOTALS 1901. -123 24 97 1920 0 0 0 0 0 0 0 ~17

***************************************************************

Table 5.1g STANDARD TABLE - FARM MODE - COMPLETELY
DISAGGREGATED - POWER USE SUBTABLE

x***x****************************************************xxxx
** HUMAN POWER USE **
MONTHLY DISTRIBUTION BY ACTIVITY AND COMPONENT

AREA 7.14 IN UNITS OF 1.00000
ACT COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
RICE RICE 3806. 654 1298 1770 84
TRAC TRAC 336. 336

TOTALS 4142. 654 1298 336 1770 0 0 0 0 0 0 0 84
**************************************xx************************
** MACHINE POWER USE **
MONTHLY DISTRIBUTION BY ACTIVITY AND COMPONENT

AREA 7.14 IN UNITS OF 1.00000
ACT COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
RICE RICE 120. 32 48 40
TOTALS 120. 32 48 0 0 0 0 0 0 0 0 0 40

***************************************************************

143

As mentioned earlier, two runs with different sorting
requirements were required to produce the farm mode
subtables. The first run produced the above mentioned
subtables in the farm mode. The second run produced the
POWER TYPE subtables (shown in Table 5.2) which are
discussed below.

The POWER TYPE subtables show the origins of power
(human, and machine). There was not enough data in the
sample to generate a animal power subtable. The area of the
farm which is 7.14 hectares and the scaling factor in units
of 1.00 are shown at the top on the POWER TYPE subtables
(Table 5.2).

There are three categories on the HUMAN POWER subtable:
the farmer (FARMER), temporary labor (TEMPLAB), and the
hired labor (HRLABOR). Their total is 4142 hours for the
entire farm activities. The farmer exerted only 16 hours,
since he has off-farm job (services and tractor rental) in
addition to the farm work. The temporary labor (seasonal
labor) represents the largest part of the power origin ( e.
g., 3040 hours). The hired labor constitutes almost 25%, or
1086 hours, of the total human power.

The MACHINE POWER TYPE subtable contains three
categories: tractor, plough, and sprayer. They sum up to 120

hours use.

144

Table 5.2 FARMAP STANDARD TABLE - FARM MODE - FULLY
AGGREGATED- POWER TYPES.

*************************************************************

FARM NUMBER 9.0

*************************************************************

** HUMAN POWER TYPE **
MONTHLY DISTRIBUTION BY HUMAN POWER CATEGORY

AREA 7.14 IN UNITS OF 1.00000
I/O COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP 0
FARMER 16. 16

TEMPLAB 3040. 204 698 336 1754

HRLABOR 1086. 450 600

TOTALS 4142. 654 1298 336 1770 0 0 0 0 0 0 0
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

xx MACHINE POWER TYPE xx
MONTHLY DISTRIBUTION BY MACHINE POWER CATEGORY

AREA 7.14 IN UNITS OF 1.00000
I/O COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
TRACTOR 32. 16 16
PLOUGH 32. 16 16
SPRAYER 56. 48 8

TOTALS 120. 32 48 0 0 0 0 0 0 0 0 0 40

**************************************************************

CT

48
36
84

145

54.2.2ActixitLM9dflahles

In the activity mode, one table was produced for each
activity of each farm. The HOUSEHOLD COMPOSITION and the NET
WORTH subtables are not applicable in the activity mode.

As shown in Tables 5.3—7, the same subtables as those
produced in the farm mode are listed. However, some
differences between the two modes are presented. Unlike the
farm mode, one table is produced for each activity of each
farm in the activity mode. For example, different tables
with their associated subtables are listed for each of the
five activities of farm number 9. They are: rice, 2-wheel
tractor rental, off—farm job income, and rented-out land
income activities. In addition, taxes are assumed to be as a
general farm activity which appears on a separate table
(Table 5.7). This is handled in this manner because taxes
are paid for the whole farmland area, not for a specific
activity.

Only the rice growing activity is discussed here,
because of the similarity of the outputs for the other
activities little would be gained from outputting similar
tables. Also, the crops list, land resources, cash-kind
flow, power use and type subtables have exactly the same
interpretation as in the farm mode except that they are
calculated separately for each activity on a unit of area
basis. Therefore, they are excluded from the following

discussion.

146

The economics subtables have the same vertical and
horizontal structure as in the farm mode. However, the
quantities are not suppressed for the same units of inputs
and outputs. For example, material inputs in kilograms or
liters are grouped separately based on the quantity
conversion factors provided for each activity. Also, in the
activity mode, outputs and variable costs are given in a per
unit of area or per head/animal basis, not as a total for
the entire farm. For instance, the average productivity of
rice for farm number 9 is 4852 kilograms/hectare.

The scaling factors appear at the top of the economics
subtable (i. 9., price and value in units of 100 which were
done manually). The area is one hectare units, based on a
total of 6.43 hectares, which is only the area allocated to
rice (not the total area of the farm which is 7.14
hectares). Other than those mentioned differences, the rest
of the economics subtable is exactly the same as in the farm

mode (discussed earlier on section 5.1.2.1).

147

Table 5.3 FARMAP STANDARD TABLE - ACTIVITY MODE

AGGREGATED- RICE ACTIVITY.

- PARTLY

**************¥***********************************************

FARM NUMBER 9.0

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
** CROPS GROWN **
PARCEL-PLOT

1001.

CROP
RICE

COMPONENT
RICE

VARIETY
MODERN

AREA
6.43

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED

*** ACT RICE

**************************************************************

** ECONOMICS xx

( IN UNITS OF )
AREA 1.000 QUANTITY 1.000 PRICE 1.000
(FROM TOTAL AREA 6.43)

VALUE 1.000

I/O COMP RESOURCE ECONOMICS F A R M E R E C O N O M I C S

QUANTITY VALUE QUANTITY PRICE VALUE %CASH
GRAIN RICE 4852 3913 4852 .81 3913 82
GROSS INCOME 3913. 3913. 82
MATERIALS
PLANTMAT RICE 9 9
FERT—N RICE 78 78 100
MATERIAL RICE 34 34 100
CHEMICAL RICE 6 6 100
OTHER RICE 7 7 100
LABOUR
FARMER RICE 2 2
TEMPLAB RICE 421 967 421 2.30 967_ 16
HRLABOR RICE 169 177 169 1.05 >177 26
DRAFT
POWER
TRACTOR RICE
PLOUGH RICE
SPRAYER RICE
VARIABLE COSTS 1278. 1278. 25
GROSS MARGIN 2635. 2635.
USER-SUPPLIED TITLE 2635. 2635.
NET RET. BEFORE TAXES 2635. 2635.
NET RETURN AFTER TAXES 2635. 2635.

148

Table 5.3 (Cont’d)

**************************************************************

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED

***************************************************************

** CASH KIND FLOW **
AREA 1.00 (FROM TOTAL AREA 6.43) IN UNITS OF 1.00000
TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
CASHFLOW 2878. —178 -13 0 3083 0 0 0 0 0 0 0 -13
KINDFLOW -243. -14 -244 0 27 0 O 0 0 0 0 0 -13
TOTALS 2635. -192 -258 0 3110 0 0 0 0 0 0 0 -26

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED
***************************************************************
xx HUMAN POWER TYPE xx
MONTHLY DISTRIBUTION BY HUMAN POWER CATEGORY AND COMPONENT
AREA 1.00(FROM TOTAL AREA 6.43) IN UNITS OF 1.00000

I/O COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
FRMR RICE 2 2

TLAB RICE 421: 32 109 273 7
HLBR RICE 169. 70 93 6
TOTALS 592. 102 202 o 275 o o o o 0 0 0 13

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xx HUMAN POWER USE **
MONTHLY DISTRIBUTION BY OPERATION AND COMPONENT

AREA 1.00(FROM TOTAL AREA 6.43) IN UNITS OF 1.00000
OP COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT

SBED RICE 6. 6
SOIL RICE 25. 17 7
PLNT RICE 42. 42

PROT RICE 7. 7

CARE RICE 51. 43 8

HARV RICE 449. 187 263

DRYG RICE 12. 12

TOTALS 592. 102 202 0 275 0 0 0 0 0 0 0 13

***************************************************************

149

Table 5.3 (Cont’d)

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xx MACHINE POWER TYPE xx
MONTHLY DISTRIBUTION BY MACHINE POWER CATEGORY AND COMPONENT

AREA 1.00 (FROM TOTAL AREA 6.43) IN UNITS OF 1.00000
I/O COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
TRTR RICE 5. 2 2
PLGH RICE 5. 2 2
SPRY RICE 9. 7 1
TOTALS 19 . 5 7 O 0 0 0 0 0 0 0 0 6

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED
***********************************************************¥***
xx MACHINE POWER USE xx
MONTHLY DISTRIBUTION BY OPERATION AND COMPONENT

AREA 1.00(FROM TOTAL AREA 6.43) IN UNITS OF 1.00000
OP COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
SBED RICE 1. 1
SOIL RICE 10. 5 5
PROT RICE 7. 7
TOTALS 19. 5 7 0 0 0 0 0 0 0 0 0 6

***************************************************************

150

Table 5.4 FARMAP STANDARD TABLE - ACTIVITY MODE - PARTLY
AGGREGATED— TWO—WHEEL TRACTOR RENTAL ACTIVITY.

xxx ACT TWTRCTR
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

** ECONOMICS **
( IN UNITS OF )
AREA NOT APPL QUANTITY 1.000 PRICE 1.000 VALUE 1.000

I/O COMP RESOURCE ECONOMICS F A R M E R E C O N O M I CS

QUANTITY VALUE QUANTITY PRICE VALUE %CASH
POWER TWTRCTR 7 210.00 1470 100
GROSS INCOME 1470. 100
MATERIALS
FUEL-LUB TWTRCTR 164 100
SERVICES TWTRCTR 93 100
LABOUR
TEMPLAB TWTRCTR 336 .73 245 100
DRAFT
POWER
VARIABLE COSTS 502. 100
GROSS MARGIN 968.
USER—SUPPLIED TITLE 968.
NET RETURN BEFORE TAXES 968.
NET RETURN AFTER TAXES 968.

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED

***************************************************************

** CASH KIND FLOW **
AREA NOT APPL IN UNITS OF 1.00000
TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
CASHFLOW 968. 0 0 968 0 0 0 0 0 0 0 0 0
TOTALS 968. 0 0 968 0 0 0 0 0 0 0 0 0

STANDARD TABLE — ACTIVITY MODE - PARTLY AGGREGATED
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
** HUMAN POWER TYPE **

MONTHLY DISTRIBUTION BY HUMAN POWER CATEGORY AND COMPONENT

AREA NOT APPL IN UNITS OF 1. 00000
I/O COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
TLAB TRAC 336.336
TOTALS 336.0 0 336 0 O O O 0 O 0 0 0

*************X*************************************************

151

Table 5.4 (Cont’d)

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED
***************************************************************
** HUMAN POWER USE **
MONTHLY DISTRIBUTION BY OPERATION AND COMPONENT

AREA NOT APPL IN UNITS OF 1.00000
OP COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
SBED TRAC 336. 336
TOTALS 336. 0 0 336 0 0 0 0 0 0 0 0 O

***************************************************************

152

Table 5.5 FARMAP STANDARD TABLE — ACTIVITY MODE - PARTLY
AGGREGATED

xxx ACT OFFFRMJB (OFF-FARM JOB)

***************************************************************

** ECONOMICS **
( IN UNITS OF )

AREA NOT APPL QUANTITY 1.000 PRICE 1.000 VALUE 1.000
I/O COMP RESOURCE ECONOMICS F A R M E R E C O N 0 M I CS
QUANTITY VALUE QUANTITY PRICE VALUE %CASH
SERVES OFFFRMJB 100 11.00 1100 75
GROSS INCOME 1100 75
MATERIALS
VARIABLE COSTS
GROSS MARGIN 1100
USER-SUPPLIED TITLE 1100
NET RETURN BEFORE TAXES 1100
NET RETURN AFTER TAXES 1100

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED

***************************************************************

** CASH KIND FLOW **
AREA NOT APPL IN UNITS OF 1.00000
TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
CASHFLOW 825. 0 825 0 0 0 0 0 0 0 0 0 0
KINDFLOW 275. 0 275 0 0 0 0 0 0 0 0 0 0
TOTALS 1100. 0 1100 0 O 0 0 0 0 0 0 0 0

**************************************************************

153

Table 5.6 FARMAP STANDARD TABLE - ACTIVITY MODE - PARTLY
AGGREGATED

xxx ACT RNTINCOM (RENTED—OUT LAND)

***************************************************************

** ECONOMICS **
( IN UNITS OF )
AREA NOT APPL QUANTITY 1.000 PRICE 1.000 VALUE 1.000

I/O COMP RESOURCE ECONOMICS FARMER ECONOMICS
QUANTITY VALUE QUANTITY PRICE VALUE XCASH
RNTINCOM RNTINCOM .71 1126.76 800
GROSS INCOME 800
MATERIALS
VARIABLE COSTS
' GROSS MARGIN 800
USER-SUPPLIED TITLE 800
NET RETURN BEFORE TAXES 800
NET RETURN AFTER TAXES 800

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED

***************************************************************

** CASH KIND FLOW **
AREA NOT APPL IN UNITS OF 1.00000
TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
KINDFLOW 800. 0 800 0 0 0 0 0 0 0 0 0 0
TOTALS 800. 0 800 0 0 0 0 0 0 0 0 0 0

**************************************************************

154

Table 5.7 FARMAP STANDARD TABLE - ACTIVITY MODE - PARTLY
AGGREGATED

xxx ACT GENFARM (GENERAL FARM, TAXES)

***************************************************************

** ECONOMICS **
( IN UNITS OF )
AREA NOT APPL QUANTITY 1.000 PRICE 1.000 VALUE 1.000

I/O COMP RESOURCE ECONOMICS F A R M E R E C 0 N 0 M I C S
QUANTITY VALUE QUANTITY PRICE VALUE XCASH

GROSS INCOME

MATERIALS

LABOUR

DRAFT

POWER

VARIABLE COSTS

GROSS MARGIN
USER-SUPPLIED TITLE

NET RETURN BEFORE TAXES

TAXES GENFARM 800 100
NET RETURN AFTER TAXES -800

STANDARD TABLE - ACTIVITY MODE - PARTLY AGGREGATED

***************************************************************

** CASH KIND FLOW **
AREA NOT APPL IN UNITS OF 1.00000
TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
CASHFLOW -800. 0 0 0 -800 0 0 0 0 0 0 0 0
TOTALS -800. 0 0 0 -800 0 0 0 0 0 0 0 0

***************************************************************

155

5.2 The Results of dBASE III PLUS

The same data used with FARMAP, were also processed
using dBASE III PLUS for comparison purposes. The results
are shown in Tables 5.8-12 for the 10 farms. The household
composition, land characteristics, and farm economics tables
were processed based on the procedures described in Chapter
4. The household composition (Table 5.8) shows more
information than those of FARMAP on all members of the
household. This information includes number of persons, age,
sex, percent of time available for work, age/sex category,
labor and consumer units. The same FARMAP codes and consumer
and labor unit equivalents were used to calculate the
consumer and labor units (see discussion in section
5.1.2.1). The age/sex categories are denoted by HEAD,
SPOUSE, SON, YNGSON, DAUGTR, YDAGTR, PLABOR for the head of
the household, spouse, son (10-20 years), young son (less
than 10 years), daughter (10-20 years), young daughter (less
than 10 years), and permanent laborer, respectively. The sex
is M for male and F for female. The subtotal for each farm
and the total for the 10 farms are provided. For example,
for farm number 1 the head of household is male, 39 year
old, and is 100 percent available for work. He is equivalent
to one consumer and one adult labor unit.

The land characteristics output, Table 5.9, includes
additional information needed for farm management systems

analysis which is not contained in FARMAP outputs. These

156

information include farm area, tenure, land value,
topography, soil texture, and soil color. The capability of
dBASE III to process character data fields (in addition to
numeric, date, logical, and memo fields), was utilized to
produce this information. Farm area and land value subtotals
for each farm and the totals for the 10 farms, are
displayed. The soil texture and color are clay loam and
black, respectively. They are denoted by CLYLOAM and BLCK.
The remaining part of the table is self-explanatory.

Farm economics results (shown on Table 5.10), are
similar to those processed by FARMAP. However, more
aggregations were done to obtain more usable data and to
improve understanding by producing less lengthy results. For
example, the farm and activity modes were merged together
when different activities were sub-grouped under each farm
data.

For each of the 10 farms, gross income, variable costs
(material, human power, machine power), fixed costs (rent
and taxes), the subtotal for each activity, and the total
for each farm are provided. The scaling factors for values
and unit prices are in units of 100 rupiah. For example, the
gross income of the rice activity for farm number 1 is Rp.
1,807,000, and materials input is Rp. —32,900 (which appears
in negative sign for calculating the net income of the

farm).

157

Table 5.8 dBASE III RESULTS- HOUSEHOLD COMPOSITION
OUTPUT TABLE.

No. OF AGE SEX TIME AGE/SEX LABOR CONSUMER

PERSONS WORK CATEGORY UNITS UNITS
xx FARM NUMBER 1
1 39 M 100 HEAD 1.00 1.00
1 30 F 100 SPOUSE 1.00 0.80
1 15 M 100 SON 0.50 0.80
1 14 M 100 SON 0.50 0.80
1 12 M 100 SON 0.50 0.80
1 8 M 0 YNGSON 0.00 0.50
** Subtotal **
6 118 3.50 4.70
** FARM NUMBER 2
1 40 M 100 HEAD 1.00 1.00
1 36 F 100 SPOUSE 1.00 0.80
1 16 M 100 SON 0.50 0.80
1 14 F 100 DAUGTR 0.50 0.80
** Subtotal **
4 106 3.00 3.40
x* FARM NUMBER 3
xx FARM NUMBER 9
1 38 M 100 HEAD 1.00 1.00
1 31 F 100 SPOUSE 1.00 0.80
1 14 M 100 SON 0.50 0.80
1 12 M 100 SON 0.00 0.80
1 7 M 0 YNGSON 0.00 0.50
1 4 M 0 YNGSON 0.00 0.50
** Subtotal **
6 106 2.50 4.40
** FARM NUMBER 10
1 38 M 100 HEAD 1.00 1.00
1 7 M 0 YNGSON 0.00 0.50
** Subtotal **
3 70 1.80 2.30

xxx Total xxx
56 962 28.30 41.20

Table 5.9 dBASE III RESULTS- LAND CHARACTERISTICS

158

OUTPUT TABLE.

LAND

VALUE(000)

TEXT.

**

**

*Jk

**

*Jk

**

**

**

#33!

**

4.50 OWNED

Subtotal **
4.50

FARM # 2
2.13 OWNED

Subtotal **
2.13

FARM fl 3
3.75 OWNED
2.17 RNTDOUT

Subtotal **

FARM # 8
3.64 OWNED
0.36 RNTDOUT
Subtotal **
4.00
FARM # 9
6.43 OWNED
Subtotal **
7.14
FARM # 10
1.43 OWNED
0.21 RNTD_IN
Subtotal **
1.64

*** Total ***

36.30

10200.
800.

11000.
60000.
60500.

3600.
375.

3975.
141419.

LEVEL

LEVEL
LEVEL

LEVEL
LEVEL

LEVEL

LEVEL
LEVEL

CLYLOAM

CLYLOAM
CLYLOAM

CLYLOAM
CLYLOAM

CLYLOAM

CLYLOAM
CLYLOAM

BLCK

BLCK
BLCK

BLCK
BLCK

BLCK

BLCK
BLCK

159

Table 5.10 dBASE III RESULTS- FARM ECONOMICS OUTPUT

* Subsubtotal *

TABLE.
INPT OUTPT QUANTITY UNITS UNIT PRICE TOTAL VALUE
** FARM fl 1 (100 rupiah )
x ACTIVITY RICE
GRSS_INCOM 19800.00 kg. 0.91 18070.00
MATRL_INPT 414.00 kg/ltr -0.79 -329.00
LABOR_INPT 2232.00 hrs. -2.47 -5512.50
MACHN_INPT 160.00 hrs -0.56 -90.00
RENTL_COST 0.00 ha. 0.00 0.00
TAXES 4.50 ha. -98.22 —442.00
* Subsubtotal *
11696.50
x ACTIVITY 2WTRENTL
GRSS_INCOM 5.00 ha. 193.20 966.00
MATRL_INPT 117.00 kg/ltr —1.40 -184.00
LABOR_INPT 230.00 hrs. -0.59 -135.00.
MACHN_INPT 0.00 hrs. 0.00 0.00
TAXES 0.00 ha. 0.00 0.00
* Subsubtotal *
667.00
x ACTIVITY OFFFRMJB
* Subsubtotal *
0.00
x ACTIVITY LANDRENT
* Subsubtotal *
0.00
** Subtotal **
12363.50
xx FARMW 2
x ACTIVITY RICE
GRSS_INCOM 9700.00 kg. 0.90 8730.00
MATRL_INPT 334.50 kg/ltr —0.79 -264.05
LABOR_INPT 896.00 hrs. -2.55 -2281.80
MACHN_INPT 95.00 hrs. -0.63 -60.00
RENTL_COST 0.00 ha. 0.00 0.00
TAXES 2.13 ha. -103.29 -220.00
* Subsubtotal *
5904 15
x ACTIVITY 2WTRENTL
GRSS_INCOM 4.30 ha. 209.53 901.00
MATRL_INPT 253.00 kg/ltr -1.15 -291.00
LABOR_INPT 240.00 hrs. -0.50 —120.00
MACHN_INPT 0.00 hrs. 0.00 0.00
TAXES 0.00 ha. 0.00 0.00

160

Table 5.10 (Cont’d)

---------—-------——-——-———-——--*-——-----—-——-—-—‘—-_—-------

** FARM 8 10

INPT OUTPT QUANTITY UNITS UNIT PRICE TOTAL VALUE
x ACTIVITY LANDRENT ( 100 rupiah )
* Subsubtotal *
0.00
** Subtotal **
6394 15
** FARM # 3
** FARM # 9
x ACTIVITY RICE
GRSS_INCOM 31200.00 kg. 0.81 25160.00
MATRL_INPT 1153.98 kg/ltr -0.75 -860.60
LABOR_INPT 2753.00 hrs. -2.67 -7356.00
MACHN_INPT 120.00 hrs. 0.00 0.00
RENTL_COST 0.00 ha. 0.00 0.00
TAXES 7.14 ha. -112.04 -800.00
* Subsubtotal *
16143 40
x ACTIVITY 2WTRENTL
GRSS_INCOM 7.00 ha. 210.00 1470.00
MATRL_INPT 234.00 kg/ltr -1.10 -257 00
LABOR_INPT 336.00 hrs. -0.73 —245.00
MACHN_INPT 0.00 hrs. 0.00 0.00
TAXES 0.00 ha. 0.00 0.00
* Subsubtotal *
968 00
x ACTIVITY OFFFRMJB
GRSS_INCOM 100 00 hrs. 11.00 1100.00
MATRL_INPT 0.00 kg/ltr 0.00 0.00
LABOR_INPT 0.00 hrs. 0.00 0.00
MACHN_INPT 0.00 hrs. 0.00 0.00
* Subsubtotal *
1100 00
* ACTIVITY LANDRENT
GRSS_INCOM 0.71 ha. 1126.76 800.00
MATRL_INPT 0.00 kg/ltr 0.00 0.00
TAXES 0.00 ha. 0.00 0.00
* Subsubtotal *
800.00
** Subtotal **
19011 40

161
5. 3 Results of Advanced Processing

The output tables and reports generated with FARMAP and
dBASE III PLUS provide data to needed establish a basis for
an information system. However, these data should be
processed and evaluated into a form which is meaningful to
decision makers. To illustrate, FARMAP and dBASE III PLUS do
not provide a built-in features to perform statistical and
some advanced economic analysis, which are required for
farming system analysis and decision—making process.
However, they provide means to export data files, to
complementary packages for further processing. It is
essential to link micro level data with the macro policy
analysis through processing these data into usable
information. Therefore, the following discussion will
illustrate means of interfacing FARMAP and dBASE III PLUS
with some complementary tools such as statistical and
economic packages. The economic analysis tools include
forward planning models (e.g., long-range financial
budgeting and total business linear programming), whole-farm
production function estimate, and multi-year variability
estimates for risk and uncertainty analysis. These tools are
often used to transfer micro level data into usable
information for the macro policy formulation.

The following discussion applies to both FARMAP and
dBASE III PLUS. However, some restrictions and differences

will be illustrated (see section 5.4).

162

5.3.1 Statistical Analysis

As mentioned earlier in Chapter 4 (see section 4.1.5),
program EXTRAC was used and command file EXTRAC1.cmf was
designed to extract means data of the rice activity for the
ten farms under study. The output obtained with some
modifications are shown in Table 5.11. These modifications
were done for statistical analysis purposes and because of
the operational error found in the command TITLE of program
EXTRAC (see section 5.4 for details). Eight variables were
obtained: farm number, number of persons per farm, age of
the head of household, maximum age of family members (the
oldest), number of parcels, area allocated for rice,
variable costs and gross income for the rice activity.

The output was exported to a commercial statistical
software, ABSTAT, for further analysis not supported by
FARMAP.

Table 5.12 describes the results obtained using ABSTAT.
The five variables processed were number of persons (NO.
PRS.), age of the head of household (AGE HH.), gross
income/hectare (G. IN. HA.), variable costs/hectare (V. CST.
HA.), and area allocated for rice for each farm (AREA).

Three sets of results were obtained for each of the
five variables. The first set provides mean values, standard
deviation, variance, standard error of the mean, and
coefficient of variation. For example, the mean rice area of

the 10 farms is 3.127 hectare and for number of persons is

163

5.6 person.

The second set provides the minimum, maximum, range,
and the total values for each variable. The third set of
results gives the median and mode of each variable. It also
provides the skewness and kurtosis of the error distribution
(either more or less peaked than a normal distribution).
These descriptive statistics can be used to analyze the
performance of each farm and describe factors affecting the
productivity of each farm. They also help to establish a
comparative analysis criteria among the performances of

different farms.

Gross
Income

Van
Costs

164

RESULTS OF PROGRAM EXTRAC

Table 5.11

0000000000
0000000000
6.1“19m61u4604
3310324536
0632891367
9913243036
WMBWOWO5O6
5868136064
L594062868
3034659718
9695276825
5273423683
0352655434
5174483646

1122211222

9065546188
3433422433

1065546188
3433422433

64M6444963

1234567890
1..

Table 5.12 RESULTS OF STATISTICAL ANALYSIS

165

ABSTAT 4.12
FILE: ALIl

THERE ARE 8 VARIABLES AND 10 CASES IN THE DATA SET

10 CASES (100.0%) ARE VALID

VARIABLE
2 NO. PBS.
3 AGE HH.
6 G.IN.HA.
7 V.CST.HA.
8 AREA

VARIABLE
2 NO. PRS.
3 AGE HH.
6 G.IN.HA.
7 V.CST.HA
8 AREA

VARIABLE
2 NO. PRS.
3 AGE HH.
6 G.IN.HA.
7 V.CST.HA
8 AREA

MEAN

5.60000
36.2000
4439.40
1577.20
3.21700

MINIMUM
3.00000
24.0000
3913.00
1223.00
1.64000

MEDIAN
5.00000
38.0000
4442.00
1477.50
2.94000

STD.DEV.

2.31900
6.52857
396.162
362.789
1.46877

MAXIMUM
10.0000
45.0000
4968.00
2188.00
6.43000

MODE
4.00000
38.0000

NONE

NONE

NONE

VARIANCE
5.37778
42.6222
156944
131616
2.15729

RANGE
7.00000
21.0000
1055.00
965.000
4.79000

SKEWNESS
0.851991
-0.841065
0.0566567
0.695571
0.983365

STD ERROR
OF MEAN
0.733333
2.06452
125.277
114.724
0.464466

TOTAL
56.0000
362.000
44394.0
15772.0
32.1700

KURTOSIS
2.47777
2.70464
1.44036
1.97076
3.22149

COEFF OF

VARIATION

41.4108
18.0347
8.92378
23.0021
45.6566

166
5.3.2 Forward Planning (Predictive Analysis)

Because of the continuous stream of changing
circumstances including price fluctuations, new technology,
change in input availability, and new marketing strategies,
forward planning in needed. Forward planning tools allow to
predict the expected outcomes of various adjustment under
different assumptions regarding the future.

There are seven interrelated steps in forward planning
(Harsh, et al., 1981): 1) appraisal of the goals and
objectives, 2) inventory of resource availability, 3)
selection of alternative to be analyzed. 4) selection of
input/output information to be used in the analysis process,
5) selection of prices to be used in the analysis process,
6) organization of input/output and price information into
an appropriate analysis structure, and 7) analysis of
various alternatives.

Identification of family-business goals and the
integration between them lie under management by objectives
approach. The availability of land, labor, capital, and
other resources have a major impact on the farming business.
Table 5.13 shows a worksheet designed to asses the
availability of these resources. As shown on the table
FARMAP and dBASE PLUS can provide the information needed to

identify resources currently available and to establish an

* this part draws heavily on Harsh et al., Managing the Farm
Business, 1981.

167

inventory of farm resources. Tabulating the future
availability of these resources can be done based on their
current availability. The boxes marked X show that these
information can. be provided by either FARMAP or dBASE.
Whereas, the boxes left blank show that these information
should be provided by other sources. Most of these data are

provided by FARMAP standard record type groups.

168

Tables 5.13 INVENTORY OF FARM RESOURCES WORKSHEET

 

Current Availability

 

Resource FARMAP dBASEI

 

Dollars Capital

Owner equity

Short-term debt

Intermediate debt

Long term debt

Land
Cropland (owned,rented-in,
rented-out, leased, communal)
Grazing land (owned, rented-in,
rented—out)

Labor(operator, family, permanent,

seasonal, contract labor)

Management Operator

Management services

(extension, consulting firms,

input suppliers)

Depreciable assets capacity
Equipment or machinery systems
(owned, rental, custom hire, lease-

purchase option, share purchase)
Improvements (owned, rental, lease-
purchase option)
Livestock (owned, leased)

Product markets (cash, contract
sales, vertical integration)

Purchased inputs markets(commercial,

cooperatives, feedstuffs, farm

produced, purchased) X X

>< ><>< >< >< ><3><><3><
>< ><>< >< >< ><3><><><

><><><><
><><><><

 

Source: adapted from Harsh, et al., Managing Farming
Business, 1981.

+ The user has to create database structures to incorporate
these data.

++The user has to define new records and/or data fields to
cover these topics which are not included in the standard
FARMAP record types groups.

169

FARMAP and dBASE III PLUS can be used to perform the
selection of input/output information, and prices to be used
in the analysis process. For example, data sources needed
for the development of input/output relation include the
farm’s own accounting system, experimental data, and farm
surveys can be included in FARMAP questionnaires (coding
sheets), and record type groups. Also, database structures
can be designed to cover the data needed for the selection
of input/output relationships and prices to be used in the
analysis process.

Forward planning techniques include: total business
budgeting and partial budgeting are used to organize input-
output and price information. Enterprise budgets are used
to simplify the process of doing partial and total business
budgets. An example of using FARMAP and dBASE III PLUS to
provide data needed to prepare enterprise budgets appear on
Table 5.14. The income, expenses, and resource needs of the
rice activity on a per unit basis. Most of these data needs
are included on the standard FARMAP record type groups.
However, database structures should be generated by the user
when using dBASE III PLUS to generate enterprise budgets.
Partial and total business budgets can then generated and an
analysis of various alternatives can be performed. These are
essential factors for eliminating risk and uncertainty

involved in decision making.

170

Table 5.14 AN EXAMPLE OF RICE ACTIVITY BUDGET *

 

 

 

 

Rice for Sale Enterprise FARMAP+ dBASE III+
(per acre figures)
Income
Cash rice sales (-Kg 0 —/Kg) X X
Total gross income X X
Cash expenses
seed X X
Fertilizer X X
Chemicals X X
Fuel and Repairs X X
Hauling Cost X X
Other Misc. cash Costs X X
Interest on current debt X X
Total cash expenses X X
Selected resource needs
Cropland X X
Labor X X

 

The body of the enterprise budget in adapted from Harsh,
et al., Managing the Farm Business, 1981.

The X’s denotes the availability of data needed for
different aspects.

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5.3.3 Whole-Farm Planning

Evaluating the impacts of possible adjustments in the
farming operation, whole-farm planning, is very important to
enhance the decision-makers ability to select the better
decisions. There are two techniques commonly used for doing
whole-farm planning: 1) long-range financial budgeting, and
2) total business linear programming. In this section, total
business linear programming will be discussed. However,
long-range financial planning will also be briefly
discussed.

The objective here is to study the usefulness of using
FARMAP and dBASE III PLUS to perform micro—level research in
developing countries. This will be done through illustrating
an example to explain the data needed to carry out linear
programming analysis and how these data can be provided by
FARMAP and dBASE structures.

Data needed to carry out long-range financial planning
can be extracted from the output tables and reports of
FARMAP and dBASE III. For FARMAP, the standard output tables
can provide most of the data needed, while for dBASE III,
the user has to design the output formats to get these data.
For example, to compare the base situation with the proposed
expansion of different enterprises, data are needed to cover
the following aspects:

1) projected enterprise mix,

2) projected beginning balance sheet which includes

172

assets, liabilities and net worth, and analysis

factors,

3) projected income statement which includes: income,

expenses, and net farm income,

4) projected annual flow of funds which includes:

source of funds, use of funds, and total cash outflow,

5) projected growth in net worth, and

6) projected profit and return analysis.

The data needed to calculate the above mentioned financial
statements can be provided from the standard FARMAP and
dBASE III output tables. For example, FARMAP activity mode
subtables provide the whole data for each enterprise
(activity). Of course, some calculation will be required to
get the best option. For instance, the analysis factors
(current, intermediate, and net capital ratio), must be
calculated.

Linear programming is a mathematical method to find the
optimal combination of activities to meet a specific
objective. It has three components: 1) a desire to maximize
or minimize some objective, 2) a set of activities or
processes available to accomplish this objective, and 3) a
set of constraints that limit the ability to achieve this
objective.

Table 5.15 is adapted from Ibnouf (1985), to show the
data needed to carry out linear programming analysis in
developing countries. One activity is selected (which is

sufficient enough) to illustrate data requirements and their

173

availability to be transferred from FARMAP or dBASE III into
linear programming package. The X’s appear on the table
denote the availability of these data on the standard FARMAP
output tables. For example, the resources (Bi’s) including
land, hired labor (by month), operating capital (by month),
and quantity of early sorghum are all covered under standard
FARMAP output tables. The six activities which are land
preparation, planting, two weeding activities, (first and
second weeding), and two crop harvesting activities are also
provided by the standard FARMAP output tables. Program
EXTRAC of FARMAP can be used to extract and export these
data into linear programming package. This can be done by
designing command file similar to that shown on Figure 4.6
with the commands required to obtain these data. For dBASE
III PLUS, the same discussion can be applied, however, the
user has to specify the data report formats for the output

required.

174

Table 5.15 EXAMPLE OF INTERFACING FARMAP AND dBASE III PLUS
WITH LINEAR PROGRAMMING DATA REQUIREMENTS

Early sorghum Sign RHS
Objective -------------------------------------
function (Cj’s) LNDP PLNT WEDl WED2 HVSTl HVST2
--------------- X X X X X X
Resource Units ---------------------------------------------
(Bi’s)
LAND ha 1 < X
ha -1 1 < 0
ha ' ' 1 < 0
ha -1 1 < O
HIRED LABOR
June MD X < 0
July MD X < 0
Aug. MD X < 0
Feb. MD ° x 2 0
March MD < 0
OPERATING CAPITAL
June 3 X < 0
July S X < 0
Aug. S X < 0
Feb. S ' x k 0
March 3 < 0
QU. E. SO. kg X < 0

Source: the body of the table is adapted from, Ibnouf, M. A. 0.
An Economic Analysis of Mechanized Food Production
Schemes in the Central Plains of the Sudan. Ph. D.
Dissertation, Michigan State University 1985.

Abbreviations:
LNDP: land preparation, PLNT: planting, WED1= lst weeding,
WED2: 2nd weeding, HVST1= lst harvest, HVST2: 2nd harvest,
RHS: right hand side, ha: hectare, MD: man-day, $= monetary
currency, QU. E. 80.: quantity of early sorghum, X’s: data
found on standard FARMAP output tables.

175

5.3.4 Whole-farm Production Function Analysis

In developing a farm plan there exist three basic
questions addressed by production economics: 1) what to
produce?, 2) how to produce?, and 3) how much to produce?.
One approach to the problem of optimum allocation is to
estimate a whole-farm production function from which the
elasticity coefficients of production and the marginal value
products of factors can be estimated. Different levels of
inputs combined in optimum proportions can be increased
until the ratio between marginal factor cost and marginal
value product for each variable input becomes equal to one.
Under this condition, the high profit point is reached and
the optimum level of resource use is determined.

There are several algebraic forms which can be used to
fit the production function including Cobb-Douglas,
spillman, quadratic, power and square root functions. To
illustrate, the Cobb-Douglas type functions (Heady and
Dillon, 1961) is shown because of its goodness of fit to the
data, efficient use of degrees of freedom and computational

feasibility. The equation is of the form

b1 b2 bn
1 X2 ...Xn 5.1)

where Y represents the dependent variable G(output),

Y = A X

XPVlPV,...,Xn represents the independent variables (inputs)
that determine the output, and the exponents bi (i =
1,. .,n) are the elasticities of the independent inputs Xi’s
with respect to the dependent variables (Y) (i.e., these

exponents indicate percentage change in output associated

176

with a one percent change in the respective input factors
while keeping all other inputs constant).

The above equation (5.1) can be expressed in
logarithmic form as follows:

Log Y = log A + b1 103 X1 + b2 10‘ X2 +"'+bn log x“ (5.2)_

After fitting the equation to empirical data by the least
squares regression technique the marginal value
productivities (MVP’s) for each factor input can be
estimated by using the following equation

MVP"i =bi Y/xi (5.3)

All the variables required to fit the production
function estimate can be extracted from FARMAP and dBASE III
output tables. For example, the following Cobb-Douglas model
can be fitted to empirical data by the ordinary least square

regression technique:

lbl 2b2 3b3 4b4 5b5

Y = a X X X X X ..(5.4).

where Y is the gross income (or the dependent variable), and

the independent variables are:

X1, land (acres),

X2, labor (months),

X3 operating expenses (monetary units),
X4 machinery investment (monetary units), and

X5, buildings (monetary units).

Data needed can be transferred using program EXTRAC of
FARMAP into complementary package to carry out that sort of
analysis. When using dBASE III PLUS the user can design the

required output report formats according to data

177

requirements.

5.3.5 Multi-Year Production Variability Estimate

There are various economic and statistical tools to
estimate multi-year production variability for risk and
uncertainty analysis. These tools include time-series
statistical analysis, quadratic programming, semivariance
programming, and so forth.

In this section, the MOTAD (Minimum of Total Absolute
Deviations) formulation (Hazell, 1971) is briefly discussed
as an example of the various tools used for farm planning
under uncertainty. This model uses the expected return and
the mean absolute income deviation for farm planning under
gross margin uncertainty (i.e., uncertainties in activity
costs, yields, and prices). The steps involved are: 1)
calculating the mean for each of the production activities
(X1, ..., Xg) across all years (t1, ..... ,tn), 2) calculating
the absolute deviations from the means for each year (t1,

., tn) for each of the activities (X1, ...., Xg), 3)
setting up n rows in the standard linear programming matrix
to keep track of the absolute variations across all
activities for each year, and 4) minimizing absolute
deviations across all years subject to a desired level of
income and other normal constraints.

Tables 5.16-17 describe numeric illustration of the
MOTAD model. Table 5.16 shows a time series of gross margins

for four vegetables (X1, ..., X4), and Table 5.17 shows a

178

tableau of constraints and activities for the MOTAD mOdel.
All of the data needed to perform this type of analysis can
be extracted from the FARMAP output tables for each farm by
using program EXTRAC of FARMAP. However, multi-year
questionnaire must be designed to get the required data
across all years (t1, ..., tn). The X’s appear in Tables
5.16-17 denote the availability of these data on FARMAP and
dBASE III PLUS output tables. However, the user has to

design the output tables format when using dBASE III PLUS.

Table 5.16 ACTIVITY GROSS MARGINS PER ACRE FOR EXAMPLE

PROBLEM.
Year X1 X2 X3 X4
t1 x x x x
t2 x x x x
t3 x x x x
t4 x x x x
t5 x x x x
t6 x x x x
Average X --------- X --------- X --------- X --------------

Source: adapted from Hazell, P. B. R. ”A Linear Alternative
to Quadratic and Semivariance Programming for Farm
Planning Under Uncertainty” Am. J. of Ag. Econ.,1971.

*The X’s appear in the table denote the availability of data
on FARMAP and dBASE III PLUS output tables.

179

Table 5.17 EXAMPLE OF INTERFACING FARMAP AND dBASE III PLUS

WITH MOTAD DATA REQUIREMENTS.

A (dollars)

b1
b2
b3
t1
t2
t3
t4
t5

t6

(acres)

(hours)

(dollars)
(dollars)
(dollars)
(dollars)
(dollars)
(dollars)
(dollars)
(dollars)

Minimize
< X
< X
< X
> 0
> 0
> 0
> O
> 0
> 0
= A

Source:

adapted from Hazell,
to Quadratic and Semivariance Programming for

P. B. R.

”A Linear Alternative

Farm

Planning Under Uncertainty” Am. J. of Ag. Econ.,1971.

*The X’s appear in the table denote the availability of data

on FARMAP and dBASE III PLUS output tables.

+The Y’s denote the availability of data on FARMAP and dBASE

III

PLUS output tables

which are needed to get these

activity gross margin deviations from their sample means in
the respective year.

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5.4 Features and Limitations

5.4.1 FARMAP Features and Limitations

FARMAP is a very powerful tool for rural surveys data
storage, processing and retrieval. It was _developed to
overcome slow manual processing of farm management survey
data. The package is designed for world wide application,
since numeric codes are used throughout the execution and
processing of data. Therefore, it is language independent
which is very convenient for farming system research
analysis in developing countries. The coding system is also
very logical.

FARMAP provide a unique tool to carry out micro level
research of rural data. The record type groups and the
coding system can be used to design a unified system for
rural data collection and analysis. For example, they can be
used to design questionnaires for a wide variety of rural
surveys. The record types groups and coding system comprise
an agricultural information coding system. This facilitate
the processing and analysis of rural survey data with the
pre-defined structures of these record types groups. The FAO
experience in farm management analysis is also reflected in
the package. For example, the package provides very useful
economic information regarding the consumer and labor units’
adult equivalent. The standard output tables also provide
very useful information needed to establish a basis for a

farm management information system. The output tables are

181

pre-defined and automatically generated by the package. They
cover many topics such as: household composition and
demographic information, land characteristics, crops and
animals productivity, cash and kind flow, net worth
statements, enterprise budgets, farm economics, and so
forth. FARMAP also provides a very convenient way to
generate user-defined output tables. This feature allows for
generating unique reports and tables which better meet the
objectives of the survey under consideration.

However, there exist some limitations and operational
difficulties which must be solved to fully utilize the
whole feature of the package. The following discussion
demonstrates the most important limitation which hampered
processing with FARMAP:

1. Accuracy: the size of the largest number that can be
handled by FARMAP micro version is up to 10 figures. This is
usually not convenient for small units of area and currency.
Also, rounding of the results on the output subtables is
somewhat misleading.

2. Trailing and leading zeros should be consistently
recorded (or omitted) for any particular variable. This
might cause errors or frustration during data entry process.
For example, the month-week code 032 which represents the
second week of March cannot be entered as 32 otherwise an
error will occur.

3. All keyboard entries should be in upper case. The package

does not recognize lower case characters.

182

4. Some commands of some programs are not operational or do
not operate correctly. For example, the command TITLE of
program EXTRAC is not operational, and program TRANSB does
not operate correctly (this program is used to combine
binary data files together), especially when merging
multiple files containing multiple farms’ data.

5. Data entry process, using program EDIT, of general farm
survey data is not convenient, because the user has to keep
track of columns and rows position. Also, it is not possible
to append or insert new data records when using program
ENTERD.

6. FARMAP was originally designed for use on mainframe
computers, and the micro version is still not developed
completely. For example, many modifications and new micro
versions are being developed to correct some programs and
commands which are not currently operational.

7. The User’s Manuals are not an easy task to be totally
understood which contain more than 800 pages. In addition,
the command files are not simply described.

8. Some difficulties arise when using program CORREC, since
a new file should be created in addition to the old file
being edited. Also, correcting data records should be in
ascending order (1. e., the first record cannot be edited
unless you were at the beginning of the file).

9. The user has a limited control over the standard pre-
defined output subtables produced during standard tabulation

stage (This can only be handled by performing advanced

183

tabulation, which is not easy for the novice users).

10. The total disk space required to run the package for
only 10 farms (of about 620 records) amounted to 5.6 mega
bytes which is not affordable by most microcomputers.
Therefore, a hard disk is highly recommended. To illustrate,
about 332.4 k bytes were required for each of TAB2.BIN,
TAB31A.BIN, TAB31F.BIN, TAB31Y.BIN, and TAB41.BIN files. In
addition, the message files, which have as their main
functions the listing of errors, also contained redundant
and unnecessary information about their associated command
files.

11. The package does not provide any statistical or linear
programming capabilities. Therefore, complementary packages
are required to carry out those features.

Finally, it is a challenge to utilize the full features
of FARMAP and a programming experience and data processing
background are required. However, it should be mentioned
that, many of the limitations and operational difficulties
were reduced or totally eliminated by using FARMAP menu-

driven version on a microcomputer with a hard disk.

5.4.2 Features and Limitations of dBASE III PLUS

dBASE III PLUS is powerful development tool designed
for microcomputer business applications. It can be used as a
stand-alone system for a single-user, or it can be networked
in a multiuser local-area network (LAN). dBASE III PLUS can

be used to conceptualize and create databases for numerous

184

types of applications. Editing and modifying of data fields
and records can be done with great level of flexibility.
Also, importing and exporting data files are provided by the
package. The database structures allow for a flexible
processing and updating of data. The file structures better
utilize the disk space available (e.g. only less than one
mega byte was needed to process the ten farm data which is
about one-fifth as the disk space with FARMAP). Although the
package is well-developed, some operational difficulties
arise especially when utilizing the command mode rather than
the menu-driven assistant feature. The followings are some
limitations encountered:
1. Unlike FARMAP, the package is mainly designed for
business applications as a relational database manager, not
for farm management analysis. So, the user has to design his
sort of analysis, the report forms, and the required output.
2. The technical manuals have proven frustrating. They
contain almost 1000 pages which is a difficult challenge to
accomplish.
3. The menu-driven Assistant feature of dBASE is supposed to
provide the novice with the ability to create data files and
other supporting files (e.g. format, index, sort, etc.).
However, the terminology shown at the various menu screens,
the ‘assistant‘ features, and the ‘application generators‘
features are some how confusing for the complete novice.

In addition, some features and capabilities of the

package require programming and using the dot prompt

185

commands. For example, COPY TO and APPEND FROM commands can
only be performed using the dot commands.

Therefore, programming knowledge is essential for the
full utilization of the package. Also, for complex or
sophisticated economic models, the ‘assistant‘ feature or
the ‘application generator‘ of dBASE III PLUS will not be
sufficient enough for developing such models. Programming
with dBASE III PLUS can provide that knowledge, but prior
knowledge of microcomputer operations and programming
techniques is required.

4. The width and content of any particular data field cannot
be changed at the same time, otherwise the content of that
data field will be lost. Therefore, two runs are required
for that purpose.

5. The cost of buying dBASE as a commercial package is about
US $400 which is somehow expensive for the researchers in
developing countries with the low salaries and standard of
living.

6. Regardless the speed of execution, the biggest
disadvantages of dBASE’ SORT program is that any changes or
alterations to the master file are not automatically
reflected in the sorted versions of the master file. Since,
a new sorted database file is generated which causes
problems of data inconsistency and redundancy.

7. While the built-in reporting feature of dBASE provides
great flexibility in creating report formats, there also

exists a set of restrictions, the user has to be content

186

with the format as presented by dBASE. Therefore, the same
reports were created by designing dBASE programs (without
the built-in reporting facility of dBASE). The same results
were obtained but with more user control on the required
output. The same commands were used, however, more freedom
in designing the report format was obtained by this
programming feature. Thus, knowing dBASE programming
techniques is essential.

8. The package does not provide statistical and linear
programming features. The sum, count and average of data
fields’ contents can be performed.

Additionally, programming experience and data
processing background are highly recommended to use the
package for micro level research and farming system analysis.

Table 5.18 illustrates a comparison between FARMAP and
dBASE III PLUS features. As discussed earlier, it is highly
recommended to use microcomputers with 10 mega hard disk
when executing FARMAP menu-driven version.

The maximum records per file for both programs are
unlimited. However, the computer capacity or the memory
space available will determine that number. The standard
FARMAP number of fields per record is 27, while it is up to
123 fields per any particular record in dBASE.

In FARMAP, the maximum number of characters per data
field is 10, however, that number is variable in dBASE. For
example, the data field width is 19 for numeric fields, 8

for date, 254 for character fields, 1 for logical, and up to

187

5000 characters for memo fields.

dBASE III PLUS is superior in terms of users’
- friendliness, importing and exporting data files, On-line
help, editing and modifying data fields/records, and level
of user’s control over data entry and processing.

Data entry process using the screen forms designed
using dBASE III were tested for comparison purposes and it
was found more easy and simple to be utilized. Also, errors
were eliminated to great extent. For example, after filling
the first data field the cursor moves automatically to the
next data field, and so on. Therefore, editing can be done
while entering data to correct any possible errors. Unlike
FARMAP, editing of any records at different positions can be
done (it is not possible to edit or correct data fields in
smaller record sequence numbers in the same run unless you

were in the beginning of the file).

188

Table 5.18 FARMAP AND dBASE III PLUS COMPARISON

 

 

Feature dBASE FARMAP
Cost $400 free
Hardware Requirements
RAM (bytes) 256 k 128 k
Disk (min. recommended) 360 k 10 mega bytes
Operating Systems MSDOS, CP/M MSDOS, CP/M
Maximums
Records/File unlimited unlimited
Characters/Record 4000 80
Fields/Record 128 27
Characters/Field up to 5000 10
Sorting and Indexing
Sorting on single field yes yes
Sorting on multiple fields yes yes
Indexing yes ‘ no

Table Conversion

DIF target yes no
ASCII target yes yes
LOTUS l 2 3 yes no
User Friendliness high low
On-line Help yes no

Edit Features

Browse and Edit yes no
Display yes yes
Record Deletion yes yes
Record Marking yes no
Insert Records at

specific location yes yes

Transfer Output

To Screen yes yes
To Printer yes yes
To Disk yes yes

Statistical and Linear
Programming features no no

Accuracy high low

 

189

5. 5 Conclusion

As mentioned earlier, it is essential to have reliable
and adequate data set to establish a basis for farm
management information system to support the operation,
management, and decision making functions in developing
countries. Harsh, et al., 1983, state that an information
system closely relating to the major functions of problem
definition, observation, analysis, planning, and decision
making contains the following four components: descriptive,
diagnostic, predictive, and prescriptive information.

There exist some limitations when using FARMAP and
dBASE III PLUS including hardware and software requirements.
For example, some programming experience and background in
microcomputers operations are required to generate advanced
user-defined tables and reports. However, the results
obtained are meaningful and promising. Learning and using
database management systems such as FARMAP and dBASE III
PLUS is a challenge and an essential task. This is especial-
ly true when considering the failure of the current system
to generate reliable data to perform micro level research
analysis which is needed to link micro farming system
research data with macro policy analysis. This linkage is
very crucial to achieve agricultural development in
developing countries.

FARMAP is a very powerful toolfor rural survey data
storage, processing, and retrieval. It can be used to

replace the tedious and slow manual processing of farm

190

management survey data. Many of the limitations (discussed
in section 5.4.1) can be eliminated or reduced to a great
extent by acquiring some programming experiences and
background about microcomputers operations.

The coding system and data record groups provide a
basis for establishing a unified system for rural data
collection and analysis. They can also be used to set a
basis for farm management information system by using pre-
coded questionnaires with the required data sets for various
farming system contexts.

The subtables of the farm and activity modes generated
by FARMAP cover a wide variety of information which include:
(1) Household Composition Subtables.

(2) Land Resources Subtables.

(3) Crops List Subtables.

(4) Net Worth Subtables.

(5) Economics Subtables.

(6) Cash-kind Flow Subtables.

(7) Power Subtables. .

The same sort of discussion can be applied on dBASE III
application except for the differences and comparisons
mentioned in Chapter 5, section 5.4. The same data were used
and almost similar analytical procedures were employed as
those done with FARMAP. However, more flexibility and
simplicity in data entry, and data editing processes were
obtained. In addition, a relatively higher level of user’s

control on the processing of data and generation of reports

191

were achieved when using dBASE III PLUS.

According to production economics theory, the three
basic decisions that must be made by all producers are: what
to produce, how much to produce, and how to produce various
products. Thus, production function analysis can be
performed to show the relationship between output of an
enterprise and the variable and fixed inputs needed to
achieve that output. Data about factors of production were
considered in the FARMAP and dBASE III data structures. For
example, FARMAP data records and output tables provide
information about levels of nitrogen fertilizer and their
associated levels of rice yield. Also, additional data '
requirements which were not available from the data set used
in the research, could be included in a future
questionnaires.

FARMAP and dBASE III PLUS do not provide statistical
and forward planning tools often needed for a thorough
farming system research and a sound decision making.
However, they provide means for exporting and interfacing
data files to other packages for further processing.
Descriptive statistics were obtained which can be used to
planning purposes.

Linear programming analysis can be used for whole farm
planning to evaluate the impacts of possible adjustments in
the farming systems employed in developing countries. Data
reflecting changing conditions such as price trends, new

technology employed, level of inputs availability and usage,

192

cost of production, and so forth, are provided or considered
within the data structures designed. Due to lack of
variability of data contained in the limited sample size of
the ten farms, and the farms only having single primary
enterprise, a linear programming analysis was not performed
on these farms. However, it would be able to use FARMAP and
dBASE III PLUS to generate the data needed to do linear
programming analysis.

Microcomputers can replace the tedious and slow manual
checking and manipulation of farming systems research data.
This research approach is commonly used by researchers in
developing countries. Microcomputers can play a vital role
in eliminating or reducing chances of computational errors
and the time required for data processing. In addition, the
difficulty and amount of effort required are greatly reduced
when microcomputers are used as a research tool.

To conclude, FARMAP and dBASE III PLUS represent two
examples of database management systems. They are very
powerful and useful tools to carry out micro level research
in ,developing countries. There exist some limitations
related to their full utilizations which can be solved by
acquainting some programming experiences and background on
microcomputers’ operation. However, the results obtained are
promising and these programs can be used to establish a
unified system for data collection and analysis, and to
structure a basis for farm management information systems

highly needed in developing countries.

CHAPTER 6

Summary, Policy Implications, and Suggestions

for Further Research

6.1 Summary

Agriculture plays a vital role in the economy of Egypt.
About 20 percent of the total GDP of Egypt is produced by
the agriculture sector, and almost 50 percent of the total
labor force is engaged in agriculture.

Egypt’s total area is about one million square
kilometers of which only 3 percent is under cultivation. The
population of Egypt was 45.2 million in 1983 with an average
population growth rate of 2.5 percent which adds over one
million newborns each year.

Thus, the country faces two fundamental problems; a
rapid increase in population growth and a severe shortage
cultivable land.

Egypt has become increasingly dependent upon imports to
meet its food needs. In addition, the agricultural sector
has shown a slow growth rate, about 2.5 percent. Services,
including housing, public utilities, tourism, and other
services grew by an average of more than 8 percent per year
and petroleum by 30 percent per year. Several factors have
contributed to the decline in the performance of the
agricultural sector and the widening gap between the

production and consumption of major agricultural commodities.

193

194

These factors include: a relatively low level of investment
in agricultural research and extension programs. Also, there
is a void of reliable micro and macro economic data. This
absence is compounded by a lack of quantitative analysis of
existing data. Other factors include: 1) the absence of the
use of microcomputers and database management system on
micro level research studies, and modern analytical
techniques, 2) the absence of records of production, income
and expenditures, and 3) the lack of a unified system for
data collection and analysis, and the lack of information
needed for the macro policy formulation.

The data collection and analysis problem has three
inter—related aspects: (1) insufficient economic data is
being collected, analyzed and fed into the decision-making
process; (2) the capacity to utilize whatever data and
analyses are available is insufficiently developed; and (3)
links which integrate the research and analysis process into
the decision-making process are weak or missing.

Thus, these factors led to the failure of the current
system to generate the economic data and analyses needed to
develop sound plans and rational policies for agriculture.
Data currently available do not provide the basis for
economic and financial analysis, and sound decision-making.
Thus, decision-makers are tempted to institute policies
which are politically popular because they do not comprehend
the economic consequences.

The purpose of the study is to design a framework for

195

quantitative micro-level data collection and analysis using
database management systems such as FARMAP and dBASE III
PLUS and establish a basis for farm management information
system with its four components: descriptive, diagnostic,
prescriptive, and predictive information.

This study addresses the data collection and analysis
problem of subsistence and semi-subsistence smallholder
agriculture in developing countries which commonly have
strong linkages between farm production and household
consumption, and to create a unified system of rural data
collection and analysis. Improvements in these areas should
strengthen planning and policy formulation.

The objectives of the study are: (l) to describe the
Egyptian agricultural sector and relate it to other
developing countries emphasizing the constraints facing the
agricultural development, (2) to develop an analytical model
to help economic analysts better utilize database management
systems in micro level research, (3) to evaluate the
usefulness of using DBMS such as FARMAP and dBASE III PLUS
in the quantitative analysis, and 4) to develop a database
for farm management and production research in farming
systems context, and, to evaluate the impact of using
microcomputers in data processing and analysis, and (5) to
design data structures that can be used for data collection
and analysis in rural areas.

Due to lack of reliable economic data, along with

limited funds and time constraints, secondary data were used

196

to carry out the research. In addition, sufficient economic
data from the Egyptian agricultural sector were not
available to satisfy data requirements to accomplish the
research. Therefore, secondary data were chosen from another
farm survey project of a developing country, Consequences of
Small Rice Farm Mechanization Project (IRRI/USAID contract
No. tac 1466). This project was conducted in three Asian
countries, the Philippines, Thailand, and Indonesia.

A modest sample size of 10 was considered adequate to
accomplish the objective of the study. Data, originally
stored on magnetic tapes on 80 column records, were
retrieved, manually interpreted, checked, validated, and
transferred to recording sheets. Data were then interpreted
and recorded using the FARMAP coding system. A microcomputer
was used for data entry checking, validation, and processing
using two database managers, FARMAP and dBASE III PLUS.

To determine the advantages and disadvantages of using
FARMAP and dBASE III PLUS to carry out micro level research
in developing countries, a set of comparison criteria was
developed. There exist common set of tasks which most
database management systems are able to perform. These tasks
include access specific information at random, sort and
index information, generate reports, create forms for ease
of data entry and validation, update and modify selected
records and/or fields, create new databases from existing
files; append data from other sources, and export data to

other statistical and planning models for further analysis.

197

Therefore, the comparison criteria between FARMAP and dBASE
III are based on the above mentioned common tasks
particularly as they can be used to generate economic data
for planning purposes. In addition, other factors are used
including cost, hardware and software requirements, user-
friendliness, level of support by other packages, online
support, and level of user’s control.

The structure of FARMAP and dBASE III PLUS are
described in Chapter 4. FARMAP data processing comprises
four different stages. These stages are: data storage, data
validation, data tabulation, and advanced processing. The
four stages were employed and executed to accomplish the
research. In data storage stage, data were transferred from
the coding sheets into the computer. General survey
qualitative data were entered using a word processor package
(i.e., WORDSTAR). Resource description information and
resource flow data records were entered interactively using
program ENTERD of FARMAP.

Checking and modification of data was done during
validation stage. The steps involved were checking single
records, performing range checks on the magnitude of inputs
and outputs, and doing multi-record consistency checks.

During the. tabulation stage, FARMAP tables were
produced in two different modes; farm mode and activity
mode. The farm mode is used to produce a summary for an
entire farm. In a second run with different sorting

requirements, the power use tables for an entire farm were

198

produced. The activity mode produces one table for each

activity on a farm.

Standard subtables cover the topics of household
composition, land resources, cropping patterns, animal
resources, net worth statement, economics, cash and kind
flow, and power use and types (i. e. human, animal, and
machine).

The advanced processing stage is used to design user-
defined tables other than those mentioned above. Also, it is
used to export FARMAP data files to other complementary
programs such as statistical and economic planning tools.
Data were exported to a statistical program called ABSTAT
and descriptive statistics analysis were performed. Other
economic tools (e.g., linear programming) are also examined
to show how FARMAP and dBASE III PLUS data can be extracted
and processed into these tools. The results of FARMAP are
presented in Chapter 5.

The same analytical procedures were performed using
dBASE III PLUS with the same data. dBASE III PLUS is a
relational database management system for designed for
microcomputer applications. Data files were transferred from
FARMAP to dBASE III using the programming features of dBASE.
However, the same data could have been entered directly into
dBASE III PLUS. The structure of dBASE III and the
analytical procedures employed are discussed in Chapter 4.
The results obtained are presented in Chapter 5, which are

similar to those of FARMAP. Some modifications were done on

199

data structures and the generated output reports which are
also shown.

To conclude, data requirements to carry out micro level
research in developing countries established the path and
analytical procedures of this study. With the limitations of
FARMAP and dBASE III PLUS discussed earlier, both programs
can be used to establish a basis for farm management
information system with its four components: descriptive,
diagnostic, predictive, and prescriptive information.

Descriptive analysis including accounting information
systems and farm records, and diagnostic analysis can be
performed using FARMAP and dBASE III PLUS. Also, FARMAP and
dBASE III PLUS provide means to export data needed to
perform predictive and prescriptive analyses. However, other
complementary packages such as linear programming and
statistical programs are needed to strengthen and complement
required processing tasks to accomplish micro level research
in developing countries.

The use of microcomputers can eliminate to a great
extent the involved and tedious process of data entry,
retrieval, validation, and tabulation. A framework of data
requirements was established for the farm management
information system to be utilized in developing countries.

Both FARMAP and dBASE III PLUS are very powerful and
excellent tools for carrying out micro level research in
developing countries. They provide a unified system for data

collection and analysis and a basis to establish a farm

200

management information system which is highly needed in
developing countries to correctly evaluate policy
alternatives. One should select dBASE III PLUS because it is
more flexible and more powerful than FARMAP. However, FARMAP

can be chosen if there is a budget constraint.
8. 2 P01 icy Implications

On the assumption that the data sources and the
analytical procedures have a reasonable degree of validity,
the results obtained can provide some insights and
guidelines to help policy makers and economic analysts carry
out micro level research in developing countries.

The data requirements to establish a basis for farm
management information system and to develop a unified
system of rural data collection and analysis can be
extracted from FARMAP data record groups, and data
structures created using dBASE III PLUS.

Database structures can be used to create production,
expenditures, and income records on the national and local
levels in developing countries where the absence' of these
records is common. Organized and easily accessible database
structures can contain accurate, and updated, economic
information for better agricultural policy planning and
decision making. These data may describe price series for
inputs and products, financial balance sheets, farm income,
and so forth. On the farm level, the use of microcomputers

may be adequate. However, it may require larger computers

201

such as mini, maxi, or mainframe computer systems to
process, store, and retrieve the data on the local,
regional, and national level.

The study calls for strengthen investment on
agricultural research and extension services based on more
accurate, more reliable, and more timely agricultural data.’
More effective research and extension programs are needed to
improve sector policies and programs.

The use of microcomputers is highly recommended to
replace slow and tedious manual data processing. Using
microcomputers substantially reduces chances of errors. It
eases the time and effort required for data manipulation.
The potential of using computerized database managers is
great and very promising in developing countries.

Interfacing databases with other economic and
statistical models is very essential to accomplish research
needs in developing countries. Modern quantitative analysis
tools including statistical, mathematical programming, and
other related economic analysis are needed. Good
coordination between data collection process and economic
analysis is also needed to ensure that data collected meets
the needs of policy analysis.

To conclude, using computerized database management
systems to perform micro level research in developing
countries are highly recommended to replace manual data
storage, retrieval, and validation which caused the failure

of the current system to generate reliable, accurate

202

economic data and perform quantitative analysis required for

better planning and a sound decision making process.

6.3 Limitations of the Study and Suggestions for Further

Research

The study is based on secondary data of West Java site
(Indonesia) with a modest sample size of 10 farms. This was
done mainly because of the limited funds, time constraints,
and lack of reliable economic data from Egypt.

The research was also performed for illustrative
purposes on a small scale to determine the usefulness of
using database management systems to carry out micro level
research in developing countries.

Therefore, the scope of the study should be expanded to
cover larger studies based on primary data sources to test
the validity of the models and database structures created.
Also, more attention should be given to solving the
operational errors found in the FARMAP system.

While means of interfacing both FARMAP and dBASE III
PLUS with complementary packages were introduced,v however,
more analysis should be made for a thorough and a complete
farming research system needed in developing countries. For
example, illustrated manuals and comprehensive analytical
summaries should be developed. Also, other planning tools
should be tested regarding the proposed interfacing of these
tools with FARMAP and dBASE III PLUS. These complementary

packages include statistical, linear programming analysis,

203

and other planning tools. Other database management systems
(e.g., Statistical Package for Social Science ‘SPSS’) should
also be examined to study their usefulness to carry out
micro—level research. Developing a national archive system
for rural data is another task which must be considered in
the future research.

Finally, more coordinated work is required to build a
basis for the proposed farm management information system

and the unified system of rural data collection and

analysis.

MIX

APPENDIX A
FARMAP GROUP MEANS TABLES

APPENDIX A - FARMAP GROUP MEANS TABLES

TABLE A.1 FARMAP STANDARD TABLE- FARM MODE- COMPLETELY
DISAGGREGATED- GROUP MEANS TABLE

**********¥********************¥**********************¥*¥***

FARM NUMBER 999.0 (MEANS FOR THE 10 FARMS)

TABLE A.l.1 ** HOUSEHOLD COMPOSITION **

**********¥*******************************************¥*****

TOTAL FAMILY PERMLAB OTHERS
AGED ADULT YOUTH CHILDREN

PERSONS 5.6 O 2.1 1.3 2.0 2 .0
FEMALE 1 . 9 0 l . l . 2 . 8 0 . 0
MALE 3. 7 0 l. O 1 . 1 1 . 4 2 . 0
CONSUMER
UNITS 4. 1 0 9 1. 0 1 0 2 0
LABOUR
SUPPLY 2.8 .0 2.1 .6 .0 .2 .0

HEAD SEX F AGE 36 YRS SCHOOLING 5.4 MTHS RESIDENCE 12.0

************X**************¥****¥*¥*¥**¥****#**************X

TABLE A. 1.2 *8! LAND USE!“

******************¥*****************************************

TOTAL ANN.RF. ANN. IRR PERM.RF PERM. IR PASTURE OTHER

OWNED AND
MANAGED 3.2 0 1.9 0 .0 O 1.3
RENTED IN
AND MANAGED .0 .0 .0 0 .0 0 .0
RENTED OUT
AND MANAGED .4 O .3 0 .0 0 .1
TOTAL 3.6 .0 2.2 .0 .0 .0 1.5

**************8********************************¥************

204

205

Table A.1 (Cont’d)

TABLE A.1.3 ** CROPS GROWN *8

*************¥*************¥******X*********¥***********X***

CROP COMPONENT PARCEL-PLOT VARIETY AREA
RICE 29. 1001. MODERN 2.98
RICE 29. 31001. MODERN .02

***********¥***********#************************************

TABLE A.1.4 ** NET WORTH STATEMENT **

**************¥*****8******¥*¥*¥¥*************¥***¥***¥¥¥***

LAND PH.ASSET PERCROP ANIMALS OTHERS -DEBTS TOTAL

CLOSING

INVENTORY 10913.8 732.0 .0 .0 .0 -34.0 11811.8
INCOMING AND

APPRECIATION .0 .0 .0 .0 .0 .0 .0
OUTGOING AND

DEPRECIATION .0 .0 .0 .0 .0 .0 .0

*************¥**********************************************

208

Table A.1 (Cont’d)

TABLE A.1.5 ** ECONOMICS **

******************¥*****************************************

( IN UNITS OF )

AREA 3.83 QUANTITY 1.000 PRICE 1.000 VALUE 10.000
ACT COMP RESOURCE ECONOMICS FARMER E C O N O M I C 8
QUANTITY VALUE QUANTITY PRICE VALUE ZCASH
RICE RICE 1403 1403 80
TWTRCTR TWTRCTR 245 94
OFFFRMJB OFFFRMJB 11 75
RNTINCOM RNTINCOM 38
GROSS INCOME 1403. 1697. 81
MATERIALS
RICE RICE 45 46 95
TWTRCTR TWTRCTR 45 100
LABOUR
RICE RICE 2258 446 2258 2.02 446 24
. TWTRCTR TWTRCTR 411 .97 40 94
DRAFT
POWER
RICE RICE 2 2 80
VARIABLE COSTS 494. 579. 41
GROSS MARGIN 910. 1118.
RICE RICE
USER-SUPPLIED TITLE 910. 1112.
NET RETURN BEFORE TAXES 910. 1112.

GENFARM GENFARM 43
NET RETURN AFTER TAXES 910. 1089.

100

***********¥***********************8******************¥*****

TABLE A.l.6 ** CASH KIND FLOW **

**********¥**********¥************************¥******¥*******

AREA 3.63

IN UNITS OF

10.00000

TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT

CASHFLOW 941. -31 -41 -12 189 837
KINDFLOW -19. -6 8 0 -3 -16
TOTALS 909. -37 -47 -12 186 821

0 0 0 0 0
0 0 O 0 0
0 0 0 0 0

O -l
0 -1
0 -2

************************¥******************************¥****

207

Table A.1 (Cont’d)

TABLE A.1.7 ** HUMAN POWER USE **

*************¥******************3****************************

MONTHLY DISTRIBUTION BY ACTIVITY AND COMPONENT

AREA 3.63 IN UNITS OF 1.00000
ACT COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
RICE RICE 1029. 255 469 117 16 168 9

TRAC TRAC 411. 23 324 65
TOTALS 1440. 274 795 179 16 168 0 0 0 0 0 0 8

*****************************************¥********************

TABLE A.1.8 ** MACHINE POWER USE *8

***************************************************¥*¥*****¥**

MONTHLY DISTRIBUTION BY ACTIVITY AND COMPONENT

AREA 3.63 IN UNITS OF 1.00000
ACT COMP TOTAL NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT
RICE RICE 112. 81 17 9 4 4

TOTALS 112. 80 17 8 4 0 0 0 0 O 0 O 4
***************************************************************

N.B.: Only FARMAP FARM MODE TABLES are presented here. For
more details see Chapter 5 (Section 5.1).

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