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A QUALITY CONTROL MODEL FOR OIL PALM
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Ernest Meshack-Hart
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A QUALITY CONTROL MODEL FOR OIL PALM FRESH FRUIT BUNCHES (FFB)
BV
.0
Ernest Meshack-Hart
A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Agricultural Engineering
1984
C>1985
ERNEST TAMUNO MESHACK-HART
All Rights Reserved
A QUALITY
CONTROL MODEL FOR OIL PALM FRESH FRUIT BUNCHES (FFB)
BY
Ernest Meshack-Hart
AN ABSTRACT OF A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Agricultural Engineering
1984
ABSTRACT
A.QUALITY CONTROL MODEL FOR OIL PALM FRESH FRUIT BUNCHES
(FFB)
BY
Ernest Meshack-Hart
The purpose of oil palm quality control is to make it
possible for management to take the right action at the
right time, so that the objective of the enterprise, to get
the highest quality of oil yield may be attained as well as
maintained. This is very important because negligence of
this matter may result in a financial loss.
Normally, harvesting is carried out in a cycle of
several days. Therefore, bunches of different stages of
ripeness will be found in the harvest. The harvest
composition, that is the proportion of the number of bunches
in each class of ripeness, will depend on the minimum
ripeness criterion and the harvesting cycle in force. The
appropriate criterion and harvesting cycle will depend,
among other things, on the rate of ripening, which itself is
Ernest Meshack-Hart
influenced by climate. Therefore, the criterion and
harvesting cycle should always be adjusted. To make the
right adjustment it is very helpful to analyze the crop
composition regularly, by sorting a number of harvest
samples using the model developed for this purpose.
This research focused on the quality of fresh fruit
bunches (ffb) as affected by field factors (harvesting).
There were three specific objectives:
1. To investigate the possibility of establishing a
ripeness criterion by color based on ffa content.
2. Evaluate field factors that affect oil quality with
ffa content as a primary assessment factor.
3. Develop an oil palm harvest analysis model which can
aid producers and processors to improve oil quality.
A systems analysis approach was used as the analytical
and problem evaluation technique. The resulting generalized
data were used for verification of the computer simulation
model.
The significant conclusions derived from the
statistical and computer simulation analysis were as
follows:
The control of percentage detached fruit alone, does
not affect the choice of appropriate premium substantially
and, therefore, has little or no effect on the revenue
Ernest Meshack-Hart
accruing to the farmer. The world market price of N800 per
ton of oil and the quality premium award of 1 percent for
every percent below 5 percent are not enough to encourage
Nigerian growers to produce high quality oil. To recapture
world market share, Nigeria must offer artificial
incentives, not based on world market premiums. The color
of the outer fruit should be at least 70% ripe color for
Tenera and Pisifera variety. However, color alone cannot be
used as a ripeness criterion, especially by inexperienced
harvesters because of the variations in color within bunches
in the same class of ripeness.
To obtain oil of low free fatty acid content from ripe
fruit, it is an important requirement to avoid bruising and
damage as far as possible at all stages from the time of
harvesting to the time of fruit sterilization. It is of
paramount importance to ensure that unripe and very ripe
bunches are kept to a ndnimum and that all loose fruit are
collected.
Approved' " ‘ Wait/mm
MajggrProfessor
Approved"
Department Cha rman
ACKNOWLEDGEMENTS
The author wishes to express his sincere gratitude to
his major professor, Dr. Ajit Srivastava for his guidance
and help throughout the course of this study. His continual
interest in this project during the problem formulation,
data collection and analysis provided the vital catalyst
which insured the successful completion of the research.
The helpful comments and guidance of Prof. Merle L.
Esmay, Prof. John B. Gerrish, Prof. Fred W. Bakker-Arkema,
Prof. R.D. Stevens, Prof. James H. Stapleton who served on
the author's graduate advisory committee are appreciated.
The research was possible due to the financial
assistance of the Thoman Fellowship. Special appreciation is
also extended to Nigeria Institute for Oil Palm Research
(NIFOR) for providing accommodation during the data
collection. The cooperation of the staff of the Institute
facilitated the process of data cllection. The author feels
deeply indebted to Dr. Onochie, the Director of the
Institute who made all this possible.
Thankful acknowledgement is also due to my wife
Constance whose mult-dimensional support has contributed
immensely to the success of this program.
ii
The understanding and patience of the author's parents,
Chief Peter Meshack-Hart and his wife, Ellen, are highly
appreciated. Their moral support and parental guidance
during the years have been invaluable.
iii
TABLE OF CONTENTS
Page
Chapter 1 - Introduction 1
1.1 Oil Palm Development in Nigeria 3
1.1.1 Policies and Programmes for Oil
Palm Development 12
1.1.2 Small Holders' Scheme 17
1.1.3 Small Holders' Unit, Ahoada,
Rivers State 17
1.1.4 The Problems of Small Holders 17
1.2 Objectives of the Study 18
1.2.1 Present Method of Valuating
Fresh Fruit Bunches Supplied
by Farmers 21
1.2.2 Problems Associated with the
Present Method 21
Chapter 2 - Review of Literature 23
2.1 Field Factors Affecting Palm Oil
Quality‘ 23
2.1.1 Effect of Age and Environment 23
2.1.2 Agronomic and Seasonal Effects 24
2.1.3 Genetic Effects 25
2.2 Harvesting Standards and Quality 25
2.2.1 Determination of Ripeness 26
2.2.2 Effect of Degree of Ripeness on
Quality and Quantity 27
2.2.3 Effect of Fruit Collection and
Transportation on Quality 30
2.3 Delay on Processing and Quality 31
2.4 Fruit Damage and Quality 32
2.5 The Color of the Palm Fruit 35
2.6 Economic Evaluation of Tree Crops 36
iv
Chapter
3.1
Chapter
4.1
4.2
Chapter
5.1
5.3
5.4
3 - Oil Palm Harvesting Operations
Present Harvesting Methods
3.1.1 Harvesting with Chisel
3.1.2 Pole-Knife Method (Malaysian
Knife)
3.1.3 Harvesting with Climbing Ropes
4 - Methodology, Data Collection and Analysis
Methodology and Data Collection
Statistical Analysis
5 - Model Development and Simulation for Oil
Palm Quality Control
Identification of System
Components
5.1.1 Systems Constraints and Desirable
Mbdel Characteristics
Input Data
5 2.1 % Detached Fruit
5.2.2 Quantity (Sum of Bunch weight)
5.2.3 Length of Time Delay in Days
5.2.4 Fruit Condition
5.2.5 Age of Palm Tree
Oil Palm Quality Control Model
5.3.1 An Economic Framework for Estimating
Annual Revenue from Oil Palm
Systems Simulation
5.4.1 Simulated Output and Discussion
Chapter 6 - Summary
Chapter 7 - Conclusion
Chapter 8 - Suggestions for Further Study
Appendices
References
38
39
39
39
43
47
47
51
69
69
71
75
75
77
73
78
79
80
92
95
96
146
150
152
154
215
1.1.1
1.1.2
1.1.4
4.3
4.4
LIST OF TABLES
Nigeria's Percentage Share in World
Production of Palm Kernel 1961—65,
1969-71 & 1978-80
Projected Figures for Palm Produce
Areas Under Major Oil Palm Plantations
1982
Oil Palm Planting Targets and
Achievements, 1975-1980 Smallholders'
Oil Palm Development Projects
Oil Palm Targets and Achievements, 1975-
1980 Estate Oil Palm Development
Projects
Rate of Acidification — Tenera
Linear Regression Analysis: Free
Fatty Acid and Ripeness (% wt. of
detached fruit)
Variation of Bunch Characteristics with
Ripeness (after Dufrane and Berger,
1957)
Rate of Acidification for Tenera,
Pisifera and Dura
Linear Regression Analysis of % FFA
and % Detached Fruit in the Three
Locations
Linear Regression Analysis of % FFA
and % Ripe Color in the Three Locations
Tenera: Simulated Fresh Fruit Bunches
at the Mill Reception, Sample #1
Tenera: Simulated Fresh Fruit Bunches
at the Mill Reception, Sample #2
vi
Page
13
15
19
20
31
55
55
56
64
65
99
100
5.3
5.4
5.5
5.6
5.8
5.9
5.10
5.11
5.12
5.13
5.14
5.14b
5.15
5.16
5.17
Tenera: Simulated Fresh Fruit Bunches
at the Mill Reception, Sample #3
Tenera: Simulated Fresh Fruit Bunches
at the Mill Reception, Sample #4
Tenera: Simulated Fresh Fruit Bunches
at the Mill Reception, Sample #5
Summary of Simulated Fresh Fruit
Bunches at Mill Reception: Tenera
Observation of Degree of Ripeness -
Tenera
Observation of Degree of Ripeness -
Pisifera
Observation of Degree of Ripeness -
Dura '
Revenue and Costs Per Hectare of Oil
Palms
Discounted Net Present Value of
Future Returns and Capital Profile
From One Hectare of Oil Palm Over
Its Life Span at 12%, 11%, 10.54%,
10% and 9.5%
Capital Profile for One Hectare of
Oil Palm Over Its Life Span at 12%,
10.54% and 10%
Valuation of One Hectare of 20-Year—Old
Palms in Naira (N)
Simulated Annual Revenue for One
Hectare of Oil Palm - Grower
Showing a Grower's Annual Gross
Revenue for One Hectare of Oil Palm
and the Associated Losses when Under-
ripe and Over-ripe FFB are Harvested
Simulated Annual Revenue for One Hectare
of Oil Palm - Processor or Both
Simulated Effect of Time Delay and
Fruit Condition on Fruit Quality and
Value - Tenera
Simulated Effect of Time Delay and Fruit
Condition on Fruit Quality and Value -
Dura
vii
101
102
103
107
111
112
113
121
123
124
125
127
128
132
134
135
5.18
Simulated Effect of Time Delay and Fruit
Condition on Fruit Quality and Value -
Pisifera
The Sensitivity Analysis Results
viii
136
143
1.1
2.1
LIST OF FIGURES
Map of Nigeria Showing the Nineteen
States and "Oil Rivers Protectorate"
Map of Nigeria Showing Soil Type Where
Oil Palm can be Grown and Also the
Oil Mill Locations and Two Large
Plantations Established by the
United African Company (UAC)
Oil Synthesis in Fruits of Tenera
Palms in Malaysia
Production of Free Fatty Acis in
the Mesocarp of Oil Palm Fruit
Following Bruising (Eek-Nielsen,
1969)
Oil Palm Fruit Color
Harvesting Chisel
Harvesting with the Chisel
Harvesting Hook
Harvesting with Knife Attached to
the Pole
Harvesting by Climbing
Map of Nigeria Showing "Oil Palm
Belt" and the Sampling Location
A, B and C
Tenera: % FFA Vs. Loose Fruit ABC
Tenera: % FFA Vs. % Detached Fruit -
Location ABC
Tenera: % FFA Vs. % Ripe Colour
Location ABC
Rate of Acidification - Tenera
ix
Page
29
34
37
40
41
42
44
46
49
52
53
54
58
5.2
5.3
5.4
5.8
5.9
5.10
5.11
Rate of Acidification - Dura
Rate of Acidification - Pisifera
Tenera: Mean Bunch Weight and Age
Dura: Mean Bunch weight and Age
Pisifera: Mean Bunch Weight and
Age
Blackbox Diagram for a Generalized
Oil Palm Quality Control Model
Simplified Oil Palm Quality Control
System
Field Factors Influencing Oil
Palm Quality
The Diagram of Oil Palm Quality
Control Concept
Simplified Flow Chart for Oil Palm
Quality Control Model
Relationship of Oil Per Mesocarp of
the Fresh Fruit Bunches (FFB) and
FFA Content with Degree of Ripeness
for the Five Harvest Compositions
Tenera: Relationship Between Loose
Fruit on the Circle Before Cutting
the Bunch and Percentage
Detached Fruit to Total Fruit
After Cutting the Bunch
Dura: Relationship Between Loose
Fruit on the Circle Before Cutting
the Bunch and Percentage Detached
Fruit to Total Fruit After Cutting
the Bunch
Pisifera: Relationship Between
Loose Fruit on the Circle Before
Cutting the Bunch and Percentage
Detached Fruit to Total Fruit
After Cutting the Bunch
Capital Profile for One Hectare of
Oil Palms
Discounted Net Present Value of Future
Returns from One Hectare of Oil
Palms Over Its Life'
X
59
60
61
62
63
72
73
74
81
86-90
110
114
115
116
119
120
5.12
5.12b
5.13
5.14
5.15
5.16
Simulated Annual Revenue for One
Hectare of Oil Palm Grower
Showing a Grower's Annual Gross
Revenue Per Hectare of Oil Palm
with and without Quality Control
Simulated Annual Revenue for One
Hectare of Oil Palm - Processor
or Both
Tenera: Simulated Effect of Time
Delay and Fruit Condition on
Quality and Value
Dura: Simulated Effect of Time
Delay and Fruit Condition on
Quality and Value
Pisifera: Simulated Effect of
Time Delay and Fruit Condition
on Quality and Value
xi
129
130
133
138
139
140
LIST OF APPENDICES
Input Format for the Oil Palm
Quality Control Model
Price of Fresh Fruit Bunches
Based on Bunch Code
Percentage FFA from Fruit Bunches
at Different Stages of Ripeness
from Different Estates - Tenera
Pisifera: Percentage Free Fatty
Acid from Fruit Bunches at
Different Degrees of Ripeness
Data Collected from Nigerian
Institute for Oil Palm Research
(NIFOR)
Dura: Percentage Free Fatty Acid
from Fruit Bunches at Different
Degrees of Ripeness Data Collected
from Nigerian Institute for Oil
Palm Research (NIFOR)
Mean Bunch Weight (kg) and
Corresponding Age for Different
Varieties - Dura, Tenera, and
Pisifera on an Inland Loan Soil
Mean Bunch Weight and Age of
Palm for Tenera, Pisifera and
Dura
Tenera: Simulated Harvest
Compositions with Associated
Percentage Detached Fruit,
Percentage Free Fatty Acid and
Percentage Oil Content and Revenue
(N/kg)
Pisifera: Simulated Harvest
Compositions with Associated
Percentage Detached Fruit, Percentage
Free Fatty Acid and Percentage Oil
Content
xii
Page
154
155
156
157
158
159
160
161
162
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Dura: Simulated Harvest Compositions
with Associated Percentage Detached
Fruit, Percentage Free Fatty Acid and
Percentage Oil Content
A Sample of a Monthly Report Sheet on
a Fresh Fruit Bunch Analysis
A Sample Sheet for Recording the Quality
of Fruit Delivered to a Mill
Pisifera: Simulated Fresh Fruit
Bunches at Mill Reception
Dura: Simulated Fresh Fruit Bunches at
Mill Reception
Analyses of Tenera Fruit Samples
Obtained in a Prospection of Eastern
Nigerian Groves
Tenera: % Detached Fruits Vs. % FFA
Location A
Tenera: FFA Vs. % Detached Fruit
Location
wdp
Tenera: FFA Vs. % Detached Fruit
Location
OdP
Tenera:
Location
FFA Vs. % Ripe Color
>60
Tenera:
Location
FFA Vs. % Ripe Color
U360
Tenera: % FFA Vs. % Ripe Color
Location C
Pisifera: % FFA & % Ripe Color
Pisifera FFA/% Loose Fruit
Dura FFA/No. of Loose Fruit
Subroutine to Store Data in Arrays
Subroutine to Echo Input to Check
Values
Subroutine to Convert Detached Fruit
in Weight to Number
xiii
163
164
165
166-170
171-175
176
177
178
179
180
181
182
183
184
185
186
187
188
28
29
30
31
32
33
34
35
36
37
Subroutine to Calculate Percent
Detached Fruit and Indicate Bunch
Code
Subroutine to Calculate Fatty Free
Acid
Subroutine to Calculate Standard
Premium
Subroutine to Calculate Quantity
Premium
Subroutine for Calculation of Loss
and Harvest Composition
'Palmkey' Subroutine to Aid in
Adjustment of Harvesting System
'Palmroyal' Subroutine for
Calculation of Internal Rate of
Return, and Equivalent Annual Cash
Flow
Distribution of Bunches by Weight
in Mature Dura and Tenera Areas
(After A. Southworth 1973)
Probable Relationship Between Oil
Content and Oil Quality and
Percentage Detached Fruit (After A.
Southworth 1973)
Oil Palm Quality Control Program
.xiv
189-190
191
192
193
194-197
198-199
200
201
202
203
Symbols
FFB
FFA
MSTD
LF
BW
AG
UR
RF
VR
W
NPV
IRR
Pm’
NAAC
Estate
TD
Pm
DF
Stantw:> <:or1w1Â¥
LIST OF SYMBOLS AND ABBREVIATIONS
Description
Fresh Fruit Bunches
Free Fatty Acid
Minimum Harvesting Standard
Loose Fruit
Bunch Weight
Age
Bunches Classified as Unripe
Bunches Classified as Ripe
Bunches Classified as Over-Ripe
Weight
Net Present Value
Internal Rate of Return
Free Fatty Acid Correction
Factor
National Agricultural Advisory
Committee
A Plantation of Palm Trees
with Integrated Farm
Settlement
Time Delay
Standard Premium
Average Percentage Detached
Fruit
Nigerian Currency — Naira
Original Loan or Principal
Interest Rate
Time Duration
Principal Plus Interest
After Time Duration
Equivalent Annual Cash Flow
Equivalent Annual Loan
Benefits in Each Year
Costs in Each Year
Margin Obtained at the End
of the Year
% Detached Fruit
% Oil Per Mesocarp Weight
Pa ent in Ni erian Currency
Ca led Naira N)
XV
Units
Kilograms, pounds
Percentage
Decimal
Kilogram
Kilogram
Years
Kilogram
Naira
Percentage
Days
Decimal
Percentage
Naira
Percentage
Years
Naira
Naira
Naira
Naira
Naira
Naira
Percentage
Percentage
CHAPTER 1
Introduction
The Portugese explorers Ca'da Mosto in 1455-1467, and Duarte
Pacheco Pereira in 1506-1508, made mention of oil palm
(Elaeis guineensis Jacq.) and its products in the reports of
their visits to the West African Coast (Crone, 1937; Mauny,
1956). In 1588 and 1590, small quantities of palm oil were
imported to England, but it was not until 1790 that the
first import of about 130 tons of palm oil was made from
Nigeria to England (Manny, 1956).
As a result of the dominance of the slave trade and
method of trading, the importance of palm products into
Europe did not attain much significance in the late
eighteenth and early nineteenth centuries. Even when the
slave trade was abolished in 1807, it was only from 1830
that the future trade of oil palm. was assured. by the
implementation of active measures to suppress the trade in
slaves, and by the encouragement given to the oil trade by
the British Government.
The fact that the oil palm has played an important part
in the Nigerian economy is reflected in the relatively high
value of exports of palm produce to the total national
1
exports. In 1900, when the total agricultural commodities
amounted to 95.6% of total exports, the contribution made by
palm. oil and palm ‘kernel alone was 81.6% or,£l,514,900
($2,242,052.00). This continued to be the pattern of export
trade until the mid 1920's, when increasing contributions
began to be made by cocoa and groundnuts. At this time oil
palm products accounted for 53.7% of export earning. The
relative importance of the crop in the economy dropped to
about 30% during the period 1925-1955 and from 1960-1965 it
varied from about 15 to 24% of total export revenue
(Oyenuga, 1967).
During the period 1959-65, commercial exports of palm
oil and palm kernels averaged 163,000 and 414,000 tons per
annum, respectively. Exports of palm produce from Nigeria,
therefore, constitute nearly 30% (palm oil) and 50% (palm
kernel) of the world trade in these commodities. Since the
exports of palm kernels greatly exceed those of palm oil,
the indication was that internal consumption per annum must
account for a large part of the palm oil produced.
Estimates have placed the amount utilized locally at 150,000
to 200,000 tons of oil annually (Oyenuga, 1967).
Palm oil contains large amounts of carotene from which
vitamin A is derived. Since it is used extensively in food
preparations in Nigeria as well as in other parts of West
Africa, where it is used in stews or eaten raw with yams,
plantains or other starchy foods, it is of great dietic
importance in reducing the incidence of diseases due to
vitamin A deficiency. In addition, palm oil and palm kernel
continue to be used in the manufacture of margarine,
compound cooking fats, soap, candles, cosmetics,
confectionaries and as a lubricant in the tin-plating
industry. Palm kernelcake, which is 21 byproduct from the
extraction of palm kernel oil, is a useful source of protein
(about 20%) and is widely used in the rations of dairy and
beef cattle, pigs and horses (Oyenuga, 1959).
Further uses of the oil palm include the production of
palm wine which is an industry of considerable economic and
nutritional importance. The wine which is obtained by
tapping excised male inflorescences is an important source
of vitaminB Complex. The leaves are used as thatch for
covering 'houses; the leaf rachises provide material for
fences and for reinforcing buildings; the midribs of the
leaflets can be made into brooms; the bunch refuse is used
locally for soap making and the fibre and shell produced
after oil and kernel production, are a source of fuel as
well as aggregate material for flooring.
1.1 Oil Palm Development in Nigeria
The production of palm oil in Nigeria reached its
lowest ebb during the Nigerian civil war (1966-70). As a
matter of fact, it was estimated in 1978 that Nigeria had
become a net importer of palm oil, to the tune of 30,000
metric tons worth N-ll million ($16 million). The forecast
for 1980 was 200,000 metric tons of oil and fat imports for
Nigeria.
The prediction by (The Standard Chartered Review, 1978)
about the decline in the production of palm oil was not
taken seriously because the past performance of Nigeria in
the world trade for palm oil had been excellent. (See Table
1.1) In 1961, Nigeria's percentage share in world production
was the highest (39.9%). Nigeria continued to be the world's
largest exporter of palm products till the inception of the
civil war in 1966 when she lost the leadership to Malaysia.
Surprisingly, Ivory Coast, which in the past was unknown in
palm oil international trade, became a net-exporter owing to
a well organized modern oil palm plantation system, for
example, "Sodepalm Palmivoire Abidjam Coted'lvoire.â€
The development of oil palm industry in Nigeria has
made some ‘progress but very slow' in. making the desired
impact. The trade in palm produce started in the Eastern
Nigeria as "Oil Rivers Protectorate," extending along the
southern part of the country, covering cross river, Rivers,
Anambra, Imo, Bendel and Ondo states. (Figure 1.1)
At the initial stage, the exportable palm produce
depended on wild oil palm groves. The "Oil Palm Belt"
developed with particular abundance in the Eastern region of
Nigeria, stretching from Calabar to the bank of the Niger
Ln
Table 1.1 Nigeria's Percentage Share in WOrld Production
of Palm Kernel 1961-65, 1969-71 & 1978-80
Producing
Region/Country Percentage Share of world Total
1961-65 1969-71 1978-80
Nigeria 39.9 24.4 ' 18.5
Zaire 10.4 8.4 4.3
Sierra Leone 15.3 5.1 1.7
Benin 3.5 3.5 2.7
Cameroon 3.5 3.5 2.7
Ivory Coast 1.7 1.6 1.7
Indonesia 3.2 4.2 6.7
Malaysia 2.8 8.4 28.0
Africa 76.0 62.0 41.1
Asia 6.0 15.1 37.6
South America 15.2 21.1 19.2
North Central America 2.8 1.8 1.1
Oceania 0.0* --- 1.0
World Average Annual
Production (Metric‘Tons) 1,050,400 1,178,651 1,658,345
*NOTE: Insignificant share in world total product tone.
SOURCE: Computed from F.A.O. Production Yearbook
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and to the forest areas of Western Nigeria. It was the oil
palm development in the Belgian Congo, Malaya, and the
Netherland East Indies Java and Sumatra in the Far East that
made it mandatory that if Nigeria's oil palm produce was to
survive, immediate steps must be taken to rehabilitate the
wild oil palm groves. The first oil palm replacement scheme
was launched in 1926. This started effective operation in
1928 and by 1935 only 21 acres (about 13 hectares) were
planted by six farmers.
It is important to note that during 1948/52, all the
revenue from oil palm products came predominantly from
exploitation of the uncultivated wild oil palm trees.
Therefore there has been the need for better quality product
and price differentials in order to sustain Nigeria's
revenue from oil palm produce.
The department of agriculture in the early 1920's and
1930's established demonstration oil palm plots to study the
yield potentials of selected oil palm trees. After the West
African Agricultural Conferences of 1920 and 1927, the Oil
Palm Research Station (OPRS) near Benin was established in
1939. In 1951, the station was transformed into the West
African Institute for Oil Palm Research (WAIFOR) and in
1964, to the Nigerian Institute for Oil Palm Research
(NIFOR).
The United African Company was in the forefront in the
palm produce trade in Nigeria. This company provided the
bulk oil plants set up at Portharcourt, Burutu, Abonema,
Koko, Opobo and Calabar where palm oil was refined before
shipment. (Figure 1.2) In addition, the U.A.C. established
two large oil palm plantations, the Pamol Ltd., at Ikot Mbo
near Calabar and Cowan Estate at Ajagbodudu near Sapele. The
company also introduced hydraulic stork mills to process
palm oil and kernels at these planations.
In 1945, the colonial Government of Nigeria requested
the UAC to design Pioneer Oil Mills, for it had become
evident that the crude local method of processing could not
compete with the product of modern oil palm plantation of
Belgian-Congo and Malaysia.
Before this request, the hand Screw Press (Dutscher
Press) was introduced to Nigeria in 1932 in order to improve
the quality of palm oil produced in the country (Hartley,
1965). By 1938, about 834 farmers had purchased the Hand
Press for their operation. It must be noted that by the
native method the oil extraction is about 45% while the Hand
Screw Press has 65% extraction of palm oil.
Eventually, the Government introduced the Pioneer Oil
Mills in 1946. By this Pioneer Oil Mill process, the fibre
and nuts are separated by hand as well as the separation of
shell and ‘kernels. The reason for not completely
medhanizing the process was to adopt an appropriate
technology that would provide employment for men, women, and
children in the oil palm 'belt area. The Pioneer Mill
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10
required a staff of about 30 persons to run. It could
process 100 tons of fruit per month in 8 hour shifts. The
extraction rate of oil by the Pioneer Mill was about 85%,
whereas the UAC owned automatic stork mills at Ikot and
Ajagbodudu had 95% extraction of oil.
At the end of the Nigerian civil war in 1970, there
were 26 of these mills salvaged in the former East Central
State. Of these, four are located in Cross River and River
States. Many of these mills have since been in a state of
disrepair, although the agricultural development authority
did reactivate 26. In Orlu areas of Imo State, the Dutscher
Press (Hand Screw Press) is still very popular. This is
also the case in many palm oil producing areas.
The stork hydraulic hand press developed by the
Nigerian Institute for Oil Palm Research (NIFOR) was
introduced to increase palm oil extraction by small-scale
producers but this has not gained as much universal
acceptance as the Hand Screw Press. The oil extraction of
the stork hydraulic press is estimated at 90 percent.
By 1956, the grades of palm oil for export were: (1)
Special Palm Oil, 5% ffa or over but not more than 9% ffa;
(2) Technical Palm Oil Grade II over 9% but not more than
18% ffa; (3) Technical Oil Grade III over 18% but not more
than 30% ffa.
By 1980, the grades of palm oil for export were: (1)
Special Palm Oil; not more than 3.5% ffa, (2) Technical Palm
11
Oil Grade I over 3.5% to not more than 18%. Technical Palm
Oil Grade III was eliminated.
Palm oil and palm kernel of "first quality" are the
exportable palm oil and palm kernel. A "first quality" palm
kernel is that which is dry and hard while a “first quality"
palm oil is that which contains not more than 5% ffa, less
than 2% by weight of dirt and water, and is not
adulterated. The concept of the "first quality" is to have
palm produce which is safe for human consumption and which
can compete favorably in the world market.
Historically, palm oil has been considered less
attractive than many other oils. This, however, dates from
a period when palm oil reached Europe with a free fatty acid
(ffa) of 20-25% while also highly oxidized. Modern
cultivation and processing have improved the image of palm
oil for shortenings, deep frying and production of margarine
(Hartley, 1967).
The future growth of the use of palm oil in fat blends
and cooking oil will depend on how well Nigerian growers can
meet the need of the edible oil refining industry. In this
direction, this project highlights criteria and other
various field factors which will promote the production of
high quality palm oil with low content of ffa and a low
content of moisture, impurities, and of heavy metals, low
oxidation value and excellent bleachability.
There is need for Nigerian producers of palm oil to
12
become aware that a low ffa is the first characteristic to
which edible oil refiners pay attention and hopefully it
will be a good ambition to create a special Nigerian
identity for its palm oil so that it will hold great
attraction for edible oil refiners not only in Nigeria but
even more in the overseas market.
1.1.1 Policies and Programmes for Oil Palm Development
in Nigeria
The special importance of the oil palm as a crop lies
in the fact that palm oil is the main source of fat for the
Nigerian population. Palm produce provides raw materials
for industries, plays a vital part in exports, can absorb a
large number of the working population and can provide a
substantial share of the capital necessary for development.
In view of the role of palm produce, recent policies have
been re-orientated towards an increase in productivity and
area. The report of NAAC (National Agricultural Advisory
Committee) contains a table of production. to :match the
estimated value of N600 million ($882 million) of
export—earning schedules for 1980. The NAAC admits that
because of probable falling prices, it is necessary to more
than double the present area to meet the required export
goals.
The projected figure for palm: produce is given in
(Table 1.1.1). In the table, it is assumed that the areas
planted in 1975-80 will produce by 1985 while 1980-85
13
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plantings will not contribute yield worthy of inclusion in
the total yield figures. By 1985, a total of 951,644 tons
of oil and 518,864 tons of kernel will be produced.
Although the production has dropped over the past few
years as a result of the Nigerian civil war and the world
market situations, there are major oil palm plantations in
the following states, Anambora, Bendel, Cross River, River,
etc. (Table 1.1.2) The Western, Bendel, and Lagos States
account for 30%; the remaining 5% is being produced in the
Northern States by Kwara, North-Western and Benue-Plateau
States.
About 90% of the total production of palm produce was
from semi-wild groves of which there was an estimated area
of 6 million hectares. These wild groves were either in
smallholdings or free. The remaining prOportion was
obtained mainly from. estates and from settlements. The
country has improved in her "Tenera" hybrid palm whidh can
produce at least six tons of bunches per hectare as against
three tonnes per 'hectare of wild groves. The improved
Tenera hybrid palms were adopted in the 1950's by the former
West African Institute for Oil Palm Research (WAIFOR) now
NIFOR. The Institute has also advanced in agronomic
practices for producing palm seedlings and established
fertilizer response coefficients under field conditions.
Processing of palm oil is accomplished through mechanized
mills and traditional processes. Marketing of produce for
Table 1.1.2 Areas Under Major Oil Palm ‘Plantations, 1982
IState. .1 p ' Area Under Major Oil Palm Plantation (ha.)
Anambora 2,572
Bendel 13,786
Cross River 15,888
Rivers 8,814
Imo 5,811
Lagos 108
Ogun 1,667
Ondo 11,593
Oyo 60
60,299 ha.
Source: NIFOR, Progress Report on the 1982 Annual Research
Programme -- Oil Palm Programme
16
export is carried out by the State marketing Board through
licensed buying agents. Local markets for palm produce are
concentrated in the hands of middlemen and palm produce
dealers.
Most of the programs are carried out on a State level.
The State programs involve expansions in output through an
increase in area. The increase in area takes the form of
rehabilitation of wild palm groves as output per hectare of
an estate or rehabilitated grove is assumed to be higher
than that of wild grove. It was estimated that by 1985, the
field of all palm areas will be improved by approximately
13%, equivalent to an area estimated at 337,000 ha. out of
which the Smallholders Development Scheme will account for
70 percent.
The States mainly affected are Imo and Anambra States,
Cross River State, Rivers, Oyo, Ondo States, and Bendel
State. However, Kwara and Niger States proposed to develop
Smallholder Development Schemes of 166 and 42 hectares,
respectively.
The consortium for the study of Nigeria's Rural
Development (CSNRD) suggested: (i) priority to be given to
Smallholders in the expansion of oil palm production, (ii)
deve10pment of State governments oil palm campaign for
1970-75 period through subsidy' and loan components; and
(iii) loan program for 1975-80 for the financing of oil palm
expansion after eliminating marketing Board and exporting
17
taxes on oil palm.
1.1.2 Smallholders Scheme
This is one of the programs geared towards increasing
oil palm production. In all the oil palm producing states,
the program is emphasized.
1.1.3 Smallholder's Unit - Ahoada
A good example of the Smallholders' Scheme is located
in Ahoada, Rivers State of Nigeria. Similar schemes are
scattered all over oil palm producing areas. For a farmer
to be eligible to participate in Ahoada Scheme, the farmer
must own a minimum of one hectare. The State government
makes a cash advance of N300.00 ($441.00) in four
installments. The first. year, $180.00, the second. year,
1150.00, third year, 1340.00 and fourth year, N30.00. This
loan is repaid with a 9.5% interest and has a 7 year grace
period.
The government further supports the farmers with free
fertilizer, seedlings, transportation of seedlings and
fertilizer and protection of the plantings. When the palm
fruit mature and are harvested, the fresh fruit bunches
(ffb) are bought by the government for processing into palm
oil. The current (1984) rate is N75.00/ton ($110.215) of
ffb.
1.1.4 Smallholders' Problems
The main problems of the Smallholders in Rivers State
are very much conunon to other Smallholders in the other
18
States. These are:
i. Poor funding - whereby the loans are not paid at
regular or stipulated periods.
ii. The farmers feel cheated - the government clerks
use faulty scales in weighing their palm produce.
iii. The payment for the supply of fresh fruit bunches
is unduly delayed.
iv. Lack of good access roads to the plantations and
also inadequate transportation system to move the palm
produce to the mill for processing.
v. Lack of incentives for the growers.
vi. Shortage of labor, especially harvesters, when the
tree is tall.
All of these problems, coupled with poor field
supervision, are responsible for the low productivity and
poor quality oil. (Table 1.1.4, 1.1.5)
1.2 Objectives of the Study
The general objective is to obtain information on the
changes in ffa content with fruit bunch ripeness and color
and then translate this information into practical use to
improve oil quality. The specific objectives were:
(i) To investigate the possibility of establishing a
ripeness criterion by color based on ffa content.
(ii) Evaluate field factors that affect oil quality
19
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21
with ffa content as a primary assessment factor.
(iii) Develop an oil palm harvest analysis model which
can aid producers and processors to improve oil quality.
1.2.1 Present Method of Evaluating Fresh Fruit Bunch
Supply by Farmers
In the past, there was no standard method of evaluating
the quality of ffb supplied by the farmers or delivered at
the Mill for processing. The general method common to all
.the major processing mills was evaluation by visual
inspection. The payment for bunches was based on distance
only, in which case it was assumed that all the bunches were
in optimum condition, except the green and rotten bunches
which were rejected.
The NIFOR Mill Company has the following rates for ffb:
From 0 to 50 km, a metric ton is £67.00
55 to 100 km, a metric ton is £70.00
greater than 100 km, a metric ton is 75.00
The farmgate price for locations within 50 km from the
Mill is N50.00/ton.
1.2.2 Problems Associated with the Present Method
Some of the problems associated with the present method
of assessing fresh fruit bunches were:
(i) It was based on distance only. The majority of the
farmers have acute transportation problems. When the
bunches were collected by the Mill Company, they were
purchased at farmgate price.
22
(ii) The quality of the bunches was not given a serious
consideration during the evaluation process, except the
green and rotten bunches were discarded. No thought was
given to the fact that the most efficient processing
facility cannot guarantee high quality oil, if the quality
of the fruit arriving at the factory was poor. Therefore,
quality control begins in the field.
(iii) The use of visual inspection is not only
unreliable but also deceptive. If a black or green immature
fruit bunch is harvested and left on the ground, it will
show symptoms of ripeness after a few days (Arokiasamy, M.,
1969) but its oil content will be low. This phenomenon
could be exploited by dishonest farmers.
(iv) The small farmers who constitute the bulk of the
producers are exploited and incapacitated in the repayment
of their loans because the present method tends to be
arbitrary and subjective. The fate and future of the
farmers, therefore, lie in the hands of the unscrupulous
assessment officers.
(v) The present method breeds malpractice and
corruption because there are no set procedures and standards
for assessing the quality of the fresh fruit bunches. As a
result of these malpractices, rotten and severely bruised
bunches are passed as good and paid for. This results in
poor quality oil.
CHAPTER 2
Literature Review
pg;1 Field Factors Affecting Oil Palm Quality
Knowledge of the field factors which affect oil quality
is inadequate (Gray and Bewan, 1969). The fact remains, that
efforts to improve processing, storage and shipment of oil
are to no avail if the quality of the fruit arriving at the
mill is poor. Some of the factors which may influence oil
quality are genetical, agronomic, environmental, palm age
and are due to improper harvesting techniques.
2.1.1 Effects of Age and Environment
A general field observation has been that the age of
the palm is of some significance because fruit ripening
seems to be faster on young palms just coming into bearing.
Age also affects ffa level through the height factor, with
falling fruit being damaged to a greater extent in older-
taller palms (Gray and Bevan, 1969). In addition, visual
assessment of ripeness becomes more difficult as the palms
23
24
grow taller.
There is little evidence that the chemical composition
of palm oil may be influenced by environment. Geographical
variations have been noted in the content of unsaturated
acids (Eckey, 1954) and Jacobsberg, 1969). The soil
characteristics affect the chemical composition of palm
fruit and the oil paln- quality (Arokiasamy, 1969). The
observations made so far have been totally subjective
(Richards, 1969).
2.1.2 Agronomic and Seasonal Effects
There are no published data concerning the effects of
agronomic factors, such as the effect of fertilizer type and
amount on oil quality. Since bunches with poor fruit set
tend to be partly rotten at harvest time, assisted
pollination may have a beneficial effect on oil quality
(Gray and Bevan 1969).
The rate of ripening is known to be affected by
seasonal variations (Broekmans, 1957), (Hartley, 1967) and
differences have been noted in composition and plasticity
where wet and dry seasons are sharply distinct (Loncin, and
Jacobsberg, 1965). Sunshine may influence carotene levels,
whilst rainfall may cause bruising where the exocarp has
become soft with ripening (Bunting, et a1., 1934) although
this is probably not of considerable practical significance
where climate is reasonably constant (Hartley and Nwanze,
1965). Low temperatures have also been implicated in high
25
ffa levels (Wolvesperges, 1969).
2.1.3 Genetical Factors
Little is known of any variations in oil quality which
might be related to particular types of planting materials.
Differences both between and within progenies have been
recorded in carotene content by Ames, Raymond, and Ward,
(1960) and Arnott, (1966) and Purvis, (1957), although this
appears to have little effect on bleaching. Bleaching is
significantly impaired only when high temperatures have
resulted in the formation of oxy-carotenes (Hiscocks and
Raymond, 1964).
2.2 Harvesting Standards and Quality
No matter how good the processing or shipping, these
are immaterial if the quality of the fruit arriving at the
factory is poor. Hence, quality control begins while the
fruit is still on the palm and is very closely connected
with 'harvesting standards and ‘practice (Gray and Bevan,
1969). This aspect of quality control cannot be
over-emphasized, especially since oil quality and ffa
content are affected (Hartley, 1967) and Gebr, Stork and Co.
1960). One week before ripening, the oil content may have
reached 80% of the final amount (Arnott, 1966, Bunting,
Georgi, and Milsum, 1934; Crombie, 1956). During periods of
low oil content, palmitic and linoleic acids predominate,
with oleic acid occurring only in very small quantities.
During the final week of ripening all oils increase in
26
amount, but that of oleic acid shows the greatest increase
to become second to palmitic acid in quantity (Crombie, and
Hardman, 1958). The final oil change occurs simultaneously
with color change and the exocarp becomes softer on ripening
(Arncott, 1966), (Bunting, et a1., 1934).
The objective of harvesting is to combine maximum oil
yield with an acceptable ffa level. At an estate where the
annual crop is 50,000 tons of fresh fruit bunches (ffb),
harvesting under-ripe fruit has been estimated to result in
an oil loss of 900 tons (Speldewinde, H.V. 1968). Under-ripe
fruit contains less ffa but also less oil: overripe fruit
has a much higher ffa content and bleachability is also
impaired (Jacobsberg,-1969). In addition to oil quantity,
'changes occur in the chemical composition of the oil during
the final phase of ripening.
2.2.1 Determination of ripeness
There is little experimental evidence on which to base
an exact criterion of ripeness (Bevan and Gray, 1969) and
this one aspect of quality control which is_urgently in need
of full and accurate investigation. One of the major
difficulties in this determination is the interval between
the time when the first fruit ripens on a. bunch and the
last, which can be as long as 16-20 days (Bevan, Fleming,
and Gray, 1966), Gebr, Stork and Co. (1960), Grut (1966).
The commonly used determining factor of bunch ripeness
is the percentage of detached fruit from a bunch. This
27
measure of ripeness is used by harvesters and also applied
by supervisors in the control of harvesting, but the true
test of its validity as a ripeness indicator is whether or
not it relates to oil quality and quantity (Southworth
1976). Southworth further elaborated on the best definition
of a detached fruit, as one that can easily be removed from
the bundh by hand, this is not the same as a fallen fruit,
though it is closely related.
Studies in the Congo showed that the maximum oil
percentage occurred at 50% loose fruit, but the increased
oil must be balanced against a reduction in price due to
higher ffa content (Dufrane and Berger, 1957). Other
criteria 'have been suggested (Gerard, Renault, and
Chaillard, 1968). With the close relationship between ffa
level. and. loose: fruit. number under’ normal conditions of
estate practice, it could well be preferable to collect and
process loose fruit separately. Harvesting interval is of
obvious importance in quality control since the larger the
interval the greater will be the number of loose fruit and
hence a ‘higher ffa level. An interval of 7 days and
certainly not exceeding 10 days would seem desirable (Turner
and Gilbanks, 1974).
2.2.2 The Effect of Degree of Ripeness oniguality and
Quantity
The number of detached fruit and the ffa content of oil
have been correlated. Dufrane and Berger (1957) showed a
28
linear relation between ffa and percentage detached fruit.
Ng. and Southworth (1973) confirmed a linear relationship
with a slope of) 0.1262. Thus, for every 1% change in
percentage detached fruit, ffa increases by .13%. There have
been many studies of changes in oil content with time. For
example, studies by Rajaratnam and Williams (1970) and
Thomas Phang Sew, Chan, Easua and N9. (1971). Figure 2.1
shows a typical curve of oil content against time after
anthesis.
According to study, by Rajaratnam and others, oil
content as measured by percent oil/dry mesocarp starts to
accumulate rapidly 110 days after anthesis. At 150 days
after anthesis (which corresponds to the first fruit
becoming detached) oil content levels off to some extent
although it still continues to increase until all the fruit
are detached.
The important period of time with respect to harvesting
is the time from the first fruit detachment onwards.
Unfortunately, evidence in the literature on oil
accumulation during this' period conflicts. Dufrane and
Berger (1957) showed. that. percent oil/mesocarp increases
linearly until at least 50% of the total fruit are
detached. They used oil fresh mesocarp as a measure of oil
accumulation, but with a different sampling procedure as
that of Desassis (1957). They analyzed 400 Tenera bunches,
at different stages of ripeness, at Bokonje in the Congo.
I‘d
k0
I
70'
Q.
a
t 50' g ’ .
3. 340- .-
> a P ..
s @20' e.
=- 5 _
o 30L .2 ,o'
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IO-
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days after an thesis
FIGURE 7.1 OIL SYNTHESIS IN FRUITS OF TENERA PALMS IN MALAYSIA.
(AFTER RNARATNAM & WILLIAMS 1970)
30
After normal harvesting each bunch and its loose fruit were
analyzed separately.
Ng. and Southworth (1973), however, demonstrated a
curvilinear increase in percent oil/mesocarp: the rate of
increase slowing down after 20% of the fruit has become
detached from the bunch. Maximum percent oil/mesocarp
occurs at about 30% detached fruit to total fruit.
Wuidhart (1973) showed that percent oil/mesocarp and
percent oil/dry mesocarp increase up to 6% detached
fruit/ffb. This corresponds to approximately 9-11% detached
fruit to total fruit. The reasons for the apparently
contradicting evidence in past studies are either in the
methods of sampling or in the method of expressing oil yield
or both.
2.2.3 The Effect of Collection and oerransportation on
992.1121
Rapid movement of fruit to the loading points as early
as possible is very important for efficient factory
operation as well as for quality control. It is well
recognized that collection and transportation of fruit must
take place as quickly as possible (Bevan, Fleming, and Gray,
1966), (Coursey, 1965), (Olie, 1969). Mechanized means of
picking up loose fruit would greatly accelerate collection
and if properly designed, could also reduce both damage and
dirt contamination.
Roadside collection points also need to be arranged so
31
as to step dirt collection, and apparent increase in ffa
content of fruit kept in the sun for a long time.
(Eek-Nielsen, 1969) Considerable interest has been generated
in Malaysia in the use of containers for fruit transport
(Cunningham, 1969) which could reduce both dirt and damage.
After the initial sharp rise in ffa levels following damage,
later increase is comparatively slow but still significant.
For this reason every effort should be made to process all
fruit harvested on the same day.
2.; Delay on Processing and Quality
There is not enough evidence to show that there is a
relationship between speed of fruit transport to the Mill
and processing (Gray and Bevan, 1969) although there are
"strong indications that rapid collection and transport are
necessary for the production of high quality oils.
It is still doubtful if oil with very low ffa content
can be obtained where fruit is processed on the same day as
bunch cutting. A plantation trial in Malaysia, concerning
Ithe relationship between delay in processing and ffa level
gave the following results:
Table 2.1 Rate of Acidification - Tenera
No. of days between ffa levels (percent)
harvesting and processing Batch 1 Batch 2 Batch 3
0 1.80 1.96 2.04
1 2.32 -- --
2 -- 2.89 2.13
4 3.31 3.46 2.23
Source: Gray and Bevan (1969)
b)
I‘ -)
Rapid processing of fresh fruit bunches is necessary in
order to eliminate the delay factor in quality deterioration
and this requires close coordination between field and
factory operations or growers and processor (Fleming, 1969
and McCulloch, and Anderson, 1969). In peak seasons it is
preferable to leave fruit on the palms rather than harvest
it and leave it lying around, since there will be a slower
rise in ffa- content in unharvested fruit (Bevan, Fleming,
and Gray, 1966 and Hartley, 1967).
- 2.4 Fruit Damage and Quality
The amount of bruising or damage to fruit influences
quality, especially in the amount of ffa and perhaps
oxidation in the oil. Some damage is unavoidable, but much
could be reduced, both during handling in the field and
transport to the Mill (Wolvesperges, 1969). The palm fruit
contain an enzyme, lipase, which causes the breakdown of oil
into fatty acids and glycerol after the vacuolar' membrane
around the oil constituents in the cell has been broken,
either through damage or decay (Gray and Bewan, 1969). The
rate of enzymic conversion of fats into fatty acids is very
33
high and it has been shown that the ffa content of fruit
rises from below one percent to over six percent within 20
minutes of bruising (Bek-Nielsen, 1969), with slower but
steady and significant rise with time after this. (Figure
2.2) This is of considerable practical significance. It is
not practically possible to step the enzymic reaction until
this is brought about by high temperature during the
sterilizing process. Therefore, it is apparent from these
basic considerations that the most important requirements
for obtaining oil of low ffa content from ripe fruit is to
avoid bruising and damage as far as possible at all stages
from the time of harvesting to the time of fruit
sterilization.
Damage'occurs at various stages during harvesting and
mill handling and some of the sources of damage are listed
below: (Gray and Bewan, 1969)
(i) The fall of loose fruit to the ground. The level
of ffa in this connection will be related to the number of
loose fruit used as the harvesting criterion.
(ii) The fall of fruit bunches after harvesting, with
much depending on palm height.
(iii) The way fruit and bunches are handled prior to
collection: fruit thrown into baskets and onto the ground.
(iv) Transfer of fruit from collection points. Rough
handling (long-distance throwing into trucks with associated
misses, etc.).
34
mt-
80 (00 I20
nouns 2.2 moot-enou- or tars nm was m we «assoc-nag 3r OIL PALM
sau-r FOLLOWING saws-Ne (Eek-Nielsen . 19°
0
35
(v) Dirt ground into the fruit surface, damages
tissues, influencing both dirt and ffa content.
(vi) Rough roads over which fruit is transported.
(vii) Tipping from transport onto ramps and into
storage compartments or sterilizer cages.
(viii) Vehicles run over loose fruit on ramps, etc.
and also labor tread onto piles of loose fruit. This would
seem to be a particularly bad source of ffa increase.
Mechanized ‘harvesting and interrow collection could
well be of value in reducing damage.
2.5 The Color of the Palm Fruit
The varieties of oil palm distinguished by the color of
the fruit have long been recognized by Chevalier (1910), and
Vanderweyen (1952). The later listed the following types:
Nigrescens
Albo-Nigrescens
Virescens
Albo-Virescens
In Vanderweyen system, the term Poissoni is prefixed if
a 'mantle' (a ring of supplementary carpets) is present, and
the terms Dura, Tenera, or Pisifera may be added to
designate the internal form of the fruit. At full maturity,
Nigrescens has been described to have a reddish orange color
of varying intensity. While Virescens is green before
36
ripening, but at full maturity the color is light reddish
orange (Figure 2.3).
Some studies (Purvis, 1957) at NIFOR have shown that
both the Nigrescens and Virescens types can be divided into
subtypes. In Ghana, West Africa, a clear distinction is
made in the Nigrescens types, 'Abepa' typically red fruited
and "Abetuntum" typically orange fruited but in Nigeria no
such distinctions are made (Purvis, 1957).
2.6 Economic Evaluation of Tree Cropg
The returns from tree crops are best evaluated by using
discounting techniques (Upton, 1973). 2h1 discounting, tree
crops are treated as a long term investment involving
deferred returns. The delay between the input of capital
and the receipt of its products complicates the estimation
of return on capital. The trace of the pattern of capital
values over the life of a project, an asset or an enterprise
is known as 'Capital Profile' (Harrison, 1956). The total
capital requirement is determined by the peak requirement.
In the use of the discounting technique, the
determination of the discounting* rate is very important
because the profitability of any project or investment is
highly dependent on the interest rate.
37
NIGRESCENS
Rubto- Nigrescens Rut-Io- Nigrescens
Reddish
Orange ‘
Light Yellow
VIRESCENS
Light Beddish
Orange
FIGURE 2.3 OIL PALM FRUIT COLOR
Chapter 3
Oil Palm Harvesting Operations
Harvesting operations can. be classified under three
broad headings:
(a) finding and cutting ripe bunches,
(b) collecting the bunches and loose fruit and carrying
them to the collection point, and
(c) loading into vehicles for transport to the mill.
The methods by which fruit bunches are harvested and
their organization have been subject of some study (Turner
and Gillbanks, 1974). In Malaysia, there are some data
available on the time required for each section. of the
harvesting process mentioned above. The time for each
component varies, depending on such factors as age, yield,
the harvesters' experience, etc. Such information is very
relevant both for estimating potential work output per
harvester and in determining where possible changes can be
made to improve efficiency, economy, and quality control.
38
39
3.1 Present Harvesting Methods
A palm bunch is ready to be harvested when it has just
a few' loose fruit. It is essential that each palm. be
inspected at regular intervals for ripe bunches since
over-ripe fruit produces low quality palm oil.
There are three methods of harvesting palm bunches
recommended by the Nigerian Institute for Oil Palm Research
(NIFOR). In each method, it is essential that only the fresh
leaves which hinder removal of the bunch should be cut off.
3.1.1 Harvesting with Chisel
This method involves the use of a piece of flat iron 23
cm long, with one end rounded off and well sharpened (Figure
3.1). The other end is bolted to one end of a metal water
pipe 23 cm long. Inside the hole at the other end of the
water pipe is fixed a wooden handle up to 3/4 meters long
after fixing. This implement, called a harvesting chisel.
can be made by a village blacksmith. The harvesting chisel
is used for harvesting bunches from young low palms. A good
harvester needs only one strike and by careful manipulation.
of the implement can have the stalk cut and the bunch pushed
out (Figure 3.2). To avoid inflicting injury on the stem of
the palm, much care is required in the use of the harvesting
chisel.
3.1.2 The Pole-Knife Method
This implement usually referred to as the harvesting
j/‘m *“ 23"" {‘A‘ 2"†—‘:‘
0
Wooden handle . 32 cm- I
galvanised Bolts
Iron pipe
FIGURE 3.1 HARVESTING CHISEL
FIGURE 3.2 HARVESTING WITH THE CHISEL
42
Knife
Binding wt re Pole
.-‘
~ 1' a I‘VN'IBII.’
FIGURE 3.3 HARVESTING HOOK
43
hook or the Malaysian Knife, (Figure 3.3) is used in
harvesting bunches from palms which have become too tall to
be ‘harvested with the chisel. The Malaysian Knife, is
sickle-shaped, and is firmly tied on to a pole (Indian
bamboo or any strong "bush" pole) with binding wire. The
length of the pole depends on the height of the palms to be
harvested. The knife is usually well sharpened, and a
sheath is provided to cover the knife when the implement is
carried along the road.
When harvesting with a harvesting hook, the harvester
stands at a convenient spot to enable him get at the stalk
of the bunch (Figure 3.4). If the bunch to be harvested is
subtended by one or more leaves, which prevent access to the
stalk, the leaves are cut off close to the trunk with the
knife. The harvester with the use of the harvesting hook
severes the bunch from the crown with a downward pull. A
goodharvester usually succeeds in getting the bunch down
with one pull. Sometimes he may, after the cut, hook the
top of the bunch and then pull downwards so that the bunch
falls to the ground. A well trained Operator using the
pole-knife method can harvest palms of about 8 meters
height.
3.1.3 Harvesting with Climbing Ropes (single and
double)
The method of harvesting palm bunches by climbing the
palm tree with a rope (single or double) is very popular in
44
â€I- I ’
\ . "1,," 117/
\ I“, I
’ III .1/
,\ A . 'il ‘ e
H ' 4
. . ‘ '-
\ . i , ’
--|
7‘ .I
. . I
'I I
a . I‘ ' ‘ ° -’
A ~ 9 . ‘ . I
I’\ A x .- _'
FIGURE 3.4 HARVESTING WITH KNIFE ATTACHED TO THE POLE
45
Nigeria, especially in the Eastern States -- Rivers, Cross
River and Imo State.
The rOpe system (see Figure 3.5) has the advantage of
placing the harvester very close to the bunch but it is a
very dangerous and slow method. It does not only expose the
harvester to the hazards of being attacked by snakes and
other harmful insects, but the harvester also runs the risk
of falling from the top of the tree. The usual advice and
practice is to cut excessively tall palms and replant the
area e
r
C‘s
A
FIGURE 3.8 HARVESTING BY CLIMBING
Chapter 4
Methodology, Data Collection and Analysis
The basic factor that determines the quality of oil
from an oil palm fruit bunch is the degree of ripeness at
the time of harvest. For good quality oil, the fresh fruit
bunches must be harvested in good time and with as little
damage as possible to the fruit in order to keep the free
fatty acid (ffa) to a minimum.
4.1 Methodology and Data Collection
A three month field investigation and data collection
was undertaken in Nigeria to determine the influence of
harvest operations on oil quality. Data and information
regarding quality control measures were collected with
reference to their relevance to the research objectives.
There were three sources of data and information. (1)
Direct field measurements during the harvesting operation.
(2) Interviews with oil palm farmers participating in
smallholders' scheme. (3) Documentation available at the
research institute, eg. Nigeria Institute for oil palm
research for supplemental data.
47
48
Random. samples of fruits from ‘bunches at different
degrees of ripeness ,were obtained from the field and
subjected to ffa analysis. Three locations within the oil
palm belt region were selected (Figure 4.1). The data were
more concentrated on Tenera, the popular variety, than on
Dura and Pisifera. The fresh fruit bunch classification was
based on the number of detached fruit. No special
harvesting was organized for this purpose. Rather the usual
harvesters were accompanied and numbers of detachedfruit
before and after cutting the bunches were recorded. The
.harvesters cut the bunches as 'ripe' according to the
harvesting standards laid down by the management. The
bunches were examined and classified based on the number of
detached fruit. For the purpose of this study, detached
fruit were the total of those that had dropped out of the
bunch or could be detached by hand. The ripeness
classification on which the study was based is as follows:
Code % Detached Fruit Degree of Ripeness
0 None ~ . Very Unripe
1 One loose fruit to 10% Unripe
2 10% to 20% of outer fruit Under ripe
3 20% to 40% of outer fruit Just ripe
4 40% to 60% of outer fruit Ripe
5 60% to 80% of outer fruit Over ripe
6 80% to 100% of outer fruit Very over ripe
Very Unripe: No loose fruit before and after cutting the
bunch. It is impossible to loosen any of the
outer fruits by hand.
Unripe: One loose fruit to 10% of the outer fruits
.U 8:8 .m 2 8.33803 9:89.88
85 .888 .8888 S88 888.. 88328 8888882 88 as. 8.8 88:88.8
8.08:: .8
n U .88
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a u 8 .88 88888 a a.
888cc... «3888888 - 808958 883803 0 m e 8885:. mummy 33m 888
8888888888 .8888 8889
49
50
detached or detachable by hand.
Under-ripe: 10% to 20% of the outer fruit detached or
detachable by hand.
Just ripe: 20% to 40% of the outer fruit are detached or
detachable by hand. A large proportion of the
fruits could be detached by hand with little
difficulty.
Ripe: 40% to 60% of the outer fruits are detached or
detachable by hand. A large proportion of the
fruits could be detached by hand with little or
no difficulty.
Over-ripe: 60% to 80% of the outer fruit detached or
detachable by hand. A large proportion of the
fruit could be detached by hand with no difficulty.
Very over-
ripe: 80% to 100% of the outer fruit detached or
detached by hand. Some inner fruit are also
detachable by hand at this stage.
One hundred and thirty-two tests were carried out in
(NIFOR) Nigeria Institute for Oil Palm Research, using fruit
harvested from mature Tenera palms on three estates. The
mean values of (1) number of loose fruit, (2) percentage
ripe color, (3) percentage free fatty acid and (4)
percentage detached fruit for the seven classes of bunches
from the three estates are presented in Appendix 3.
The data obtained were analyzed using the SPSS
Statistical Package. The analyses of variance, linear
regression and correlation analyses were carried out. A
systems analysis approach was used as the analytical and
problem evaluation technique. The resulting generalized
data were used for verification of the computer simulation
model. Details of the statistical analysis are discussed in
51
the next section and the model development is presented in
Chapter 5.
4.2 Statistical Analysis
The SPSS statistical package was used in the analaysis
of the data. A forward (stepwise) inclusion multiple
regression analysis was used. This is essentially a search
method which computes a sequence of regression equations, at
each step adding or deleting an independent variable until a
reasonably. good "best" set of independent variables are
obtained. The criterion for adding or deleting an
independent variable can be stated equivalently in terms of
error sum of squares reduction, coefficient of partial
correlation or F-statistic.
An investigation of the relationship between the
dependent variable and other independent variables by means
of graphs was made. The plots of mean free fatty acid
with the corresponding means iof number of loose fruit,
percentage ripe color and. percentage detached fruit for
bunches of the same class were made. The plots for the
three locations were found to be linear and highly
correlated (Figures 4.2, 4.3, 4.4).
The percentage detached fruit explains 99% of the
change in percent free fatty acid (ffa) content with degree
of ripeness. Table 4.1 summarizes the regression analysis
of the three varieties, with respect to free fatty acid and
ripeness (percent weight of detached fruit at 95% confidence
92 SEE $08 .m> «E w â€388. «J $ng
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Table 4-1 Linear Regression Analysis: Free Fatty
Acid and Ripeness (% wt. of detached fruit)
2
variety Regression Intercept R C.1
Coefficient
Tenera .0184 - .613 .998 95%
Dura .0194 .601 .953 95%
Pisifera '.oi73 _ _ .601. .997_ 95%
C. l -' Confidene Interval
Table 4.2 variation of Bunch Characteristics with Ripeness
(after Dufrane and Berger, 1957)
Correlation
with 8
.detached
fruit ’
Detached
fruit 6 2.2 7.1 11.7 18.6 21.5 34.9 35.1 45.8 --
Mean bunch - A
wt. (kg) 4.8 5.4 4.7 4.5 4.8 4.2 4.0 3.7 -0.9l**
Pruit/ .
bunch (t) 68.8 66.4 67.4 63.6 62.6 58.8 62.4 55.0' -0.95***
Oil/fresh
mesocarp- _
(i) 45.7 47.5 46.1 48.0 48.1 50.7 50.7 51.0 0.90***
Number of
bunches
analyzed 94 70 80 - 63 46 22 19 6 --
56.
Tablel4.3 Rate of Acidification for Tenera, Pisifera
and Dura
No. of Days Between % FFA
Harvesting and Tenera Pisifera Dura
Processing
o .69 .67 .71
1 1.39 1.34 1.37
2 2.09 2.00' 2.06
3 2.76 -- --
4 3.32 2.77 3.32
57
level.
In the analysis, one of the independent variables that
has an insignificant effect on free fatty acid was the tree
height. This is contrary to the bunch damage analysis by
Clegg (1973). He; related damage entirely to the height of
the drop and reported that a drop of 20 feet resulted in an
ffa rise of .26%. A possible explanation for the difference
in the effect of height, might be due to the cushioning
effect from the soil texture and probably the weeds around
the tree. This explanation seems very obvious because if a
bunch drops 20 feet on a bed of cotton wool it would have a .
different amount of damage and effect on ffa than if it
dropped 10 feet on a hard surface, stones or ground.
In the analysis of the effect of delay in processing
fresh fruit bunches (ffb) after harvesting, on an oil
quality was found to be linearly correlated (Figures 4.5,
4.6, 4.7). This means that the longer the delay the more the
deterioration of the oil quality because of the increase in
the free fatty acid. The mean bunch weight is highly
.correlated with the age of the palm (Figures 4.8, 4.9,
4.10).
The results of the analysis of samples from the three.
different locations, - NIFOR, Cowan Estate, Elele Estate
were not remarkably different. The slight difference might
be due to the environmental factors, such as soil type and
climate (Tables 4.4 and 4.5).
58
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60
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38me I coï¬umoï¬udgom no 35. We 3:03
mada mo .02
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64
Table 4.4 Linear Regression Analysis of % FFA and %
Detached Fruit in the Three Locations
Location A Location 8 Location C
N 35 47 50
a 7.078 x 10'1 5.732 x 10'1 5.650 x 10'
b .01660 .01891 '.01990
sb .00043 .00038 .00033
5; (.4985)2 (.5586)2 (.6037)2
r2 . .9864 .98839 . .98688
7 1.3503 1.2087 1.2756
2 38.6857 33.7660 35.74
52 .0055185 .00554242 .004884529
5: (29.6228)2 _(29.1914)2 (30.0298)2
1
Loo. A I Elele, Loc. 8 - Cowan Estate, Loc. C - NIFOR
(Nigerian Institute for Oil Palm Research)
a - intercept, b a slope, s - std. deviatign, S -
variance,13- coefficient ofbdeterminition, Y a mean of %
ffa, z - mean of a detached fruit, 5 - variance of %
detached fruit, 8 =- variance of % ffa, N =- number of
observations, NS Y. Not Significant
65
Table 4.5 Linear Regression Analysis of 8 FFA and 8 Ripe
Color in the Three Locations
Location A Location 8 Location C
N 35 47 50
-1 -1 -1
a 4.579 x 10 2.501 x 10 2.216 x 10
b .01439 .017310 .017620
sb .00144 " .00144 .0141
s; (.4985)2 (.5586)2 (.6037)2
r2 .89649 .89519 .90181
8 1.3503 1.2087 - 1.2756
2. 63.1429 . 58.9362 62.06
5: (30.0521)2 ' (29.5469)2 (30.5924)2
52 .06662001‘ . .08298393- .09164438
Loc. A IIElele Loc. 8 a Cowan Estate, Loc. C = NIFOR (Nigeria
Institute for Oil Palm Research) 2
a - intercept, b 3 slope, S 8 standard deviation, 8 -
variancg,r I coefficient 0 determination, Y a mean of
8 FFA, x2- mean of 8 ripe color, 8 - variance of 8 ripe
color, 5 I variance of 8 ffa, N a number of observations,
location effects significant at .05 level. '
66
For very high accuracy, free fatty acid content and
percentage ripe color should be estimated on location. For
the purpose of a rough estimate, a generalized model could
be used to represent the locations within the "oil palm
belt“ because of the minor location effect.
An investigation into the loss of loose fruit, during
the field trip in Nigeria revealed that many farmers failed
to ensure that .all loose fruit were collected after
harvesting the bunch. This is a potential (source of
financial loss that should be controlled. At an oil price
of 3800 and kernel price of N400 per ton, the loss of one
loose fruit for every bunch harvested represents an annual
loss per acre of 32.15 or 35.38 per hectare. (Assuming 10
ton ffb per acre,bunch. weight 40 lb, fruit ‘weight 12g,
oil/fruit 36.7 and kernel/fruit 7.5%.)
Analysis: For every bunch, l loose fruit lost
worth of one fruit: (12 x 0.367)g of oil
800/ton [(129 x 0.367) lOGg/ton] =_3
3.52 x 10 H/fruit
129 x .075 of kernel
400 R/ton [(12 x .075) 106 g/ton_3=
. 0.36 x 10 NÂ¥fruit
Each fruit value is 3.88 x 10‘é3H/fruit
An acre harvested:
10 ton [(40 x .45 kg/lb) (1000 kg/ton)] =
555.55 bunch/acre
67
With a loss of one fruit/bunch, a loss of
555 fruit
acre is sustained loss N/acre = 3.88 x 10
H/fruit x 555
fruit/acre
I 2.15 N/acre or
35.38/hectare
For Growers:
One fruit is worth: 75 ï¬/ton (129 lOEg/ton) I
.9 x 10 H/fruit
for 550 fruit/acre
-3
.9 x 10 R/fruit x 550 fruit/acre I
.495 H/acre
approx. .50 N/acre or 31.25/
hectare
It takes an extra effort on the parts of the harvesters
to pick up the loose fruit at the base of the palm tree and
collection points.
An interview with harvesters revealed that picking up
the loose fruit was not only tedious but also
back-breaking. The harvesters also complained of poor wages
and so strongly objected to any harvesting instruction that
demanded extra effort and energy on them. This feeling of
poor salary structure amongst the field staff (harvesters
and carriers) was counter productive. If farmers are to cut
down on the field losses, it is necessary that they consider
an upward review of the field workers' wages.
68
The need for a complete collection of loose fruit was
emphasized by Turner and Gillbanks (1974). The above
analysis confirms this need and demonstrates the importance
of ensuring that all loose fruit are collected.
Chapter 5
Model Development and Simulation of a Farmer Supply
Processing System
The visual inspection is not sufficient to deal with
the problems of quality control as practiced presently. A
computer assisted systems analysis approach can provide
an effective' means for dealing with the problem.
This chapter presents a computer aided system
analysis approach for the analysis of the harvest
composition and prediction of the quality and quantity of
the oil in the "oil palm belt" of Nigeria. Traditionally,
researchers have relied on conventional, large computers for
such analyses, but they are not always readily available in
many developing countries. For this reason, a micro
computer with BASIC language was used for analysis and
.simulation of quality control technique in the present
research.
5.1 Identification of System Components
This forms a link between the statement of needs and a
specific statement of the problems that. must be solved in
69
70
order to satisfy those needs (Manetsch and Park, 1977). At
this point, the oil palm quality control system was viewed
as a "black box" (Figure 5.1). 6
Weather, prices, population, inflation, pests and
diseases, and availability' of labor were defined as
exogenous components. The input parameters have an impact
upon the desired system output: these parameters tend to be
fixed and are as important as decision variables (Manetsch
et al., 1974). In the present (case, the establishment
policy was classified as an input parameter: it consists of
such elements as ‘subsidization, _discount, 'standard, and
quantity premium. Production function levels such as
fertilizers, pesticides, oil palm land, etc. were held.
constant during the simulation process. The controllable
inputs were number of detached fruit, length of time delay
before processing, area ‘harvested, etc. The number and
combinations of these controllable input levels were changed
during the simulation of the system model to alter the
performance and results.
The desirable system output for oil palm quality
control consists of high quality palm oil with desired ffa
level, high market and nutritional value; In addition, high
premium. and returns accrue .to the farmer because of
increased production of required standard of fresh fruit
71
bunches. The undesirable systems output are, no premium for
the farmer, high loss and poor quality fresh fruit bunches,
and loss of interest in oil palm enterprise (see Figure
5.1). These undesirable factors are used to stimulate
farmers to readjust their harvesting system and management,
thus leading to a feedback mechanism.
The linkages and interactions between identified
components in oil palm quality control are illustrated in
the simplified model presented in Figure 5.2, and the field
factors influencing oil palm quality control are shown in
Figure 5.3.
5.1.1 System Constraints and Desirable Model
Characteristics:
1. The model should be able to represent a wide range
of regional conditions, in this case, the environmental and
agronomic conditions prevalent in the "oil palm belt."
2. The model assumes that the probabilistic nature of
the weather factors that have deleterious effects on the
rate of fruit detachment can be minimized by good
supervision and discipline.
3. The model should be able to handle any practical mix
of harvest composition and attend to as many farmers as
possible per day, with maximum of five hundred bunches
each.
4. The model should be of practical application to
growers and/or processors in Nigeria.
72
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1 III:
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75
5. The :model assumes that the method. of .production
throughout the "oil palm belt" is the same, especially the
regular maintenance of the plantation.
5.2 Input Data
There were six input data required for the palm oil
quality control model. These are:
(i) Percentage detached fruit
(ii) Quantity of fresh fruit bunches
(iii) Length of time delay in days
(iv) Fruit condition (eg. degree of damage, diseased or
rotten)
(v) Age of the palm tree, and
(vi) Variety (Tenera, Dura, Pisifera).
5.2.1 Percentage Detached Fruit
This is the percentage weight of the detached fruit
with respect to the whole fresh fruit bunch. This is
obtained by counting the number of detached fruit before and
after cutting the bunch. The bunches are coded according to
the percentage detached fruit, ranging from 0 to 6. The
different codes represent different levels of ripeness.
The percentage detached fruit is very highly correlated
to the free fatty acid of the fresh fruit bunch as shown by
the regression analysis in the previous chapter (Figure
4.3). This confirms the work of Dufrane and Berger (1957).
76
Thus:
FFA = a + b (R) (5.1)
where R = % detached fruit
b = slope
FFA = estimated free fatty acid
a = intercept
LF = f (BW,AG) (5.2)
where LF = loose fruit before cutting
BW = average bunch weight
AG = age of palm tree
LF = MSTD * BW (5.3)
where
MSTD = minimum harvesting standard
Since average bunch weight is linearly correlated to age
(see Figure 4.5).
BW = a + b(AG) (5.4)
Substituting (5.4) in (5.3)
LF = MSTD * (a + b*AG) (5.5)
Equation (5.5) is used in the model to help the farmer
adjust the harvesting system. The number of loose fruit on
the ground before harvesting the bunch is the most commonly
used measure of ripeness by the harvesters in the field.
The percentage of detached fruit of a bundh is the
percentage of all the detached fruit after the bunch has
been harvested. The definition of a detached fruit in this
context is one which has either fallen from its bunch or can
77
be detached by hand. In the analysis in the previous
chapter, the number of loose fruit as it is used by the
harvesters, and the percentage detached fruit are highly
correlated and predict the degree of ripeness very
effectively. The percentage detached fruit is frequently
used in predicting the free fatty acid in the model because
it explains 99% of the change in percentage of free fatty
acid (ffa) and has less loopholes than the number of loose
fruit techniques. Some of the limitations of the loose
fruit on the ground as indicator of ripeness are:
1. The activities of rodents, squirrels, and other
pests tend to increase or reduce the number of loose fruit
on the ground, thereby misleading the harvesters.
2. Heavy storm a day before harvesting also affects the
accuracy of loose fruit on the ground as a measure of
ripeness.
3. Loose fruit dropping into the palm fronds or
epiphytic growths on the palm trunk are never seen or
available and therefore makes the ripeness measurement less
accurate.
4. Guessing the weight of bunch when it is up high on
the tree is not an easy task for the harvesters and so error
of judgement is inevitable.
5.2.2 Quantity
This is the total weight of all the ‘bunches of a
particular variety. The quantity' premium. calculation is
78
based on the bunch code and quantity.
5.2.3 Length of Time Delay in Days
There is still rather inadequate evidence of the
relationship between speed of fruit transport to the mill
and processing, although there are strong indications that
rapid collection and transport are necessary for the
production of high quality oils. An estate trial concerning
the relationship between delay in processing and free fatty
acid level gave the results for three varieties (Table 4.3).
5.2.4 Fruit Condition
This is an evaluation of the condition of the fresh
fruit bunches (FFB) on arrival at the mill. For more
accurate assessment, the bunches are classified according to
the severity of bruises they receive at different stages of
handling. Clegg (1973), in his analysis of damage incurred
by oil palm bunches during handling and transport,
classified impacts in terms of their effect on free fatty
acid of the bunches. In this model, there are five
categories of bruised condition of bunches.
These are: (i) unbruised
(ii) moderately bruised
(iii) severely bruised
(iv) very severely bruised
(v) extremely bruised
A bunch is classified as unbruised if there are no
bruises at all. Moderately bruised if less than 20% is
79
bruised. Severely and very severely bruised between 20 and
50% and greater than 50%, respectively. A bunch is very
extremely bruised when it is more than 75% bruised. The
extent of damage on bunches depends on the stage of
ripeness. Over-ripe fruit is more prone to bruises because
of the soft membrane.
5.2.5 Age
The age factor ‘has an effect on the rate: of Tbunch
ripening. The rate of ripening is most rapid in the
youngest material and decreases with an increase in the age
of the palm. Within each age group, the smaller the
bunches, the faster the total fruit detachment (NG and
Southworth, 1973).
The average bunch weight increases with the age of the
palm. This was shown in the analysis in the previous
chapter (Figure 4.8). For the variety,
DURA -
BW = .1779 + 1.233AG (5.6)
MSTD = LF/(.l779 + 1.233AG) (5.7)
from Equa. 5
TENERA -
EN = -.0257 + 1.291AG (5.8)
MSTD = LF/(-.0257 + 1.291AG) (5.9)
PSIFERA -
BW = 0.7326 + 1.192AG (5.10)
80
MSTD = LFflp.7326 + 1.192AG) (5.11)
5.3 Oil Palm Quality Control Model
Systems researchers treat models as an abstraction of
the real. world. For' a good representation of the real
world†an. effective evaluation. of the many linkages and
factors that constitute the system is a sine qua non. The
simplified model is shown in Figure 5.2.
The diagramatic illustration of the concept of oil palm
quality control is shown in Figure 5.4. The quality control
program defines free fatty acid as the main component of
quality; it is affected by the field factors (harvesting).
'The next step is the development of techniques for measuring
this quality through sampling and laboratory tests to
cross-check and confirm percentage free fatty acid results.
With a predetermined free fatty acid level, upper and lower
limits are'established. The core of the entire system is
the system of economic factors which are very important to
growers, processors and the final consumers.
Among all the field factors that affect the free fatty
acid of oil palm fruit, the most critical is the percentage
of detached fruit. The relationship between the percentage
of detached fruit and percentage of FFA is represented in
the following equations:
81
SAMPLES OF\ DESIRED LEVEL
HARVEST AND\ OF PERCENTAGE
LABORATORY FREE FATTY
ANALYSIS] Consumer ACID
Grower ‘ Processo
I
I
C ' j i
~u— Economics ,
923cm m UPPER AND mm
mm “CID comm Lmrr
calm: or FFA
f
‘ OIL PALM QUALITT CONTROL 1
Figure 5.“ The
diagram of oil palm quality control concept.
82
Tenera: FFA = .614 + .0184R (5.12)
Dura: FFA = .614 + .0195R (5.13)
Pisifera: FFA = .601 + .0173R (5.14)
where FF is percentage free fatty acid and R is percentage
detached fruit.
Time delay has an effect on the fruit quality, as shown
in the data analysis. The effect is expressed in the
following equations:
Tenera: FFA = .724 + .663TD (5.15)
Dura: FFA = .726 + .654TD (5.16)
Pisifera: FFA = .784 + .521TD (5.17)
Where TD is time delay in days.
The effect of bruises or damage on fresh fruit bunches
was analyzed by Clegg (1973) who classified the impact
according to the rise in ffa resulting from the damage on
the fresh fruit bunch. In this model, the bruised bunches
are classified according to the degree of severity of the
bruises. The model rejects very severely bruised, diseased
or rotten bunches.
Dufrane and Berger (1957) concluded that oil/fresh
mesocarp increases linearly with increasing percentage
detached fruit, and from Table 4.2 the linear relationship
was developed into the equation:
83
0PM = 45.59 + .13R (5.18)
where 0PM is the percentage oil per mesocarp weight.
Conversion of Equation 5.18 to kilogram oil per mesocarp =
.0456 + .OOl3R
Equation 5.18 ‘was used to calculate percentage oil per
mesocarp at different degrees of ripeness.
With an oil palm plantation where the annual crop is
50,000 tons of ffb, harvesting unripe fruit has been
estimated to result in an oil loss of 900 tons (Speldewinde,
1968). This relationship is used to compute the loss due to
the harvesting of unripe bunches.
Commercial harvesting’ will continue 1x) result in a
mixture of bunches at various levels of ripeness of
under-ripe, of ripe and of over-ripe. The important
practical issues are the establishment of a more
satisfactory definition of ripeness and how to control the
level and range of bunch ripeness to maximize oil yield, oil
quality and minimum loss. For this reason, the harvested
fresh fruit bunches are classified under three main degrees
of ripeness -- under-ripe (UR), ripe (RF), and over-ripe
(VR). The corresponding percentages of the detached fruit in
a typical harvest composition are denoted by P1, P2, and P3,
respectively. An average percentage detached fruit defining
such a harvest composition is expressed by the equation:
DF = (WUR x Pl) + (WRF x P2) + (WVR x P3) (5.19)
84
where W represents weight of bunches in a particular
ripeness category.
DP - Average percentage detached fruit
The above equation in conjunction with equation 5.18 is
important in computing the percentage free fatty acid and
percentage oil per mesocarp.
The award of standard premium is represented in the
following expression:
pm’ . 1 + Pm (2 - FFA) , (5.20)
where Pm is standard premium, FFA is free fatty acid
percentage and Pm’is free fatty acid correction factor.
For the choice of approPriate premium based on the
percentage detached fruit and market price, we should
maximize the function:
Pmt . K (N/kg. Oil) [1 + Pm (2-FFA)] [0.456 + 0.00133]
kg. oil (5.21)
where Pmt is the payment in Nigerian currency
called Naira and R is percentage detached fruit,
K.- market price for one kilogram oil. K is modified
by the free fatty acid premium. -
For the variety, Tenera, substitute equation 5.12 in equation
5.21 to obtain
Pmt . K [1 + 1.386 Pm - 0.0184 meJ [0.456 + 0.00133]
det
____. . 0 . K [(-0.0184Pm) (0.456 + 0.0013R) + (1 + 1.386Pm -
an 0.0184PmR) (0.0013)]
' -2
1.3 I Pm(6.59 + 4.78 x 10 R)
1.3 ' _ 1
Pm a
6.59 + 4.78 x 10"2 R a 5.06 + 3.67 x 10‘2a
85
It is assumed that K cannot be zero, else there will be no
pricing policy.
1
Pm =
5.06 + .0367R (5.22)
from equation 5.22, the control of percentage detached fruit
alone, does not affect the choice of appropriate premium
substantially and, therefore, has little or no effect
on the revenue accruing to the farmer.
The logical flow chart describing the model is
presented in Figure 5.5. The model consists of a main
program and ten subroutines. The program prompts the user
to enter the day's premium, the name of the farmer,
Plantation, State of origin, the variety of the bunch, the
number or weight of detached fruit, time delay in days, the
fruit condition (bruised or unbruised), and the age of the
palm. The percentage detached fruit determines the bunch
code and the percentage free fatty acid. The final level of
free fatty acid is influenced by' the factors like time
delay, and the condition of the fruit. Using the bunch
code, the program searches a table of information to find
the pricing data appropriate to that bunch code. The price
of the bunch is modified by the free fatty acid correction
factor. Bunches with code numbers 3 and. 4 are awarded
standard premium. The standard premium is either positive
or negative, depending on the percentage free fatty acid
fixed by the establishment. The day's premium is used to
calculate quantity premium. If this quantity of bunch code
numbers 3 and 4 are equal to or greater than the thresholds
86
(â€I“)
mun.
mcmmAuou
I
/:A! s PREMIUH/
STORE DATA
IN AREA!
:m's um:/
INPUT: HEIGHT 08
NUMBER OF DETACHED
FRUIT. TIME DELAY,
FRUIT CONDITION,
AND AGE
I! FRUIT
COIUITIOK IS
â€HELP"
88
C0030! cmrxon ETD
Gmmnm :10quch
‘ [
ADJUSTMENT
SUBTOTAL FOR
VARXINO LEVELS
OR
FRUIT CONDITION
FOR PRINTING
LATER
89
90
PRINT
PROGRAM NNB
' l
C D
Figure 5.5 Simplified Flow Chart for Oil Palm Quality Control
Model.
91
for those bunch coded numbers, a quantity premium is
awarded. It is important to note that the standard premium
(if applicable) is applied first to calculate the bunch
price, then the quantity premium if applicable is applied to
this price. The table of bunch information is stored in the
form, of data statements in array. The ,subroutine flow
charts are in Appendices 25-34. For detailed in-put format
see Appendix 1. I
The main functions of the model are:
1. To encourage growers through award of premium to
produce and harvest only ripe fruit.
2. Carry out a harvest composition analysis and make an
estimate of the percentage free fatty acid and
oil/mesocarp.
3. If the harvest composition is high in either unripe
or over ripe or both, an appropriate estimate of annual loss
in ï¬/hectare was made.
4. It is not only a useful decision making toolin the
hands of a plantation manager but also helpful in assisting
a grower make necessary adjustment needed to attain and
maintain maximum production of good fresh fruit bunches.
5. The model is also a useful instrument in the hands
of policy makers who require direction and guidelines during
the process of policy making.
92
5.3.1 An ‘Economic (Framework for Estimating Annual
Revenue from Oil Palm
The discounting techniques were useful because they
capture the delay between the initial investment' and the
return (Upton, 1973). This delay involves cost which should
be taken into account in estimating net return. In
practice,' discounting is generally more (useful than
compounding because the farmer is concerned with present
values, rather than future values in making current
decisions. Thus, it is not very helpful to know what the
future value of a profit will be in 20 years time..
The process of estimating the present value of future
cash flows is known as discounting and is the opposite of
compounding.
P - original loan or principal.
r - interest rate
vn - principal plus interest after year n
n t number of years
for compounding: Vn :- P(l + r)n (5.23)
If both sides are divided by (l + r):1
Vn
(I+r)h (5.24)
P a
The discounting is for time, and not other factors likev
risk, etc. However, it is possible to add a risk-discount
and poor harvest-discount to the rate of interest (r) in
order to capture the effect of these factors on returns.
To convert a stream of irregular cash flows, such as
93
may be obtained from a tree crop, into an equivalent
annuity, the following formula is used (Upton, 1973):
Pan r (1+r) (5.25)
A =
(1+r) - 1
Where A is the equivalent annual cash flow and Pan is
the equivalent annual loan.
The net present value is calculated with the formula
(Gittinger, 1982)
n -
2: B“ C“
t=l
(l+r)n (5.26)
where Br1= benefits in each year, Cn = costs
in each year, n = number of years, r = interest
rate.
The internal rate of return (IRR) which is the yield of
the investment or the marginal efficiency of capital can be
obtained by calculating NPV of a range of different interest
rates and finding at what value, the net present value is
equal to zero.
The net present value is determined as follows
Pn = 0 (5.27)
Mn
Pn-l =
l+r (5.28)
Mn-l + Pn-l
Pn—2 =
l+r (5-29)
94
M2 + P2
P1 =
l + r (5.30)
M1_+ P1
P2 =
l + r (5.31)
Where P = net present value of future returns at the end of
year i and M- = the margin obtained at the end of that year,
r = rate of interest and n = crop life in years.
To obtain the Net Present value of cash flow accruing
from an oil palm plantation, these must all be discounted to
year zero, that is to a point in time before any investment
is made (Upton, 1966). The value of the trees can be
estimated at any stage of their life either in terms of the
total cost of establishment including compound interest, or
in terms of the discounted value of expected future returns
(Upton, 1973). The former represents the cost and the
latter the expected benefit.
Another approach of valuing capital assets such as tree
crops is known as the capital profile. The capital profile
traces out the pattern of capital requirements of a single
activity or a combination of activities over time (Harrison,
1956). Although this approach might lead to some
unacceptable conclusion, yet for the purpose of making
annual valuation in terms of capital cost, or other costs
due to good or bad operational techniques, it is very
illustrative. For the activity of producing a hectare of
oil palms a capital profile may be established in terms of
cumulative costs of establishment. These costs appear as
95
the negative margins in the first four years of the life of
the oil palm plantation given in Table 5.10. Thus, the
valuation in terms of capital cost of a one year old hectare
of oil palms is $1067.45. By the end of the second year, the
cumulative cost is the cost in the second year plus interest
on the cost in the first year. For example, for a ten
percent rate of interest then the capital cost by the end of
the second year would be 220.99 + 1067.45 (1.10) = 1395.19
(Figures from Table 5.10).
Thus, the calculation of the capital profile proceeds
as follows:
Ci as - Mi (5.32)
C2 8 "M2 '4' Cl(1+r) (5.33)
C3 3 " M3 + C2(1+r) (5.34)
C - - M
Tl n + Cn_l (1+r) (5.35)
where Ci: capital valuation at the end of the year i and Mi:
the margin obtained during that year, r = rate of interest
and n = crop life in years.
5.4 Systems Simulation
Simulation has been defined by Naylor (1960) as the
operation of a model that represents a real world system.
Manipulation of the system inputs makes it possible to
96
simulate the systems behavior under a given set of
assumptions. Simulation models are best at providing an
optimal range of information rather than a single optimal
point. In the quality control model, the oil palm
plantation. estates sampled. are all ‘within the '0il Palm
Belt.‘ The generalized simulation output should be
interpreted as an actual representation of the values of
variables obtainable in the oil palm belt because the
location effect was found 1x) be statistically
insignificant. After the simulation model is verified, the
sensitivity analysis can be performed using various levels
of the controllable input data.
The weather effect on quality was not considered far
reaching because the indirect effect of weather on ripening
rate could be effectively handled by harvesting discipline.
There are basically two seasons in Nigeria and these are dry
and rainy seasons. The weather condition does not interrupt
the harvesting schedule, especially in the dry season.
5.4.1 System Simulation Output and Discussion
The Systems Simulation output of farmers' supplies of
fresh fruit bunches (ffb) or deliveries at the Mill was: (1)
the stage of ripeness of the bunches measured in terms of
percentage detached fruit, (2) the quality of the bunches,
determined by the level of percentage free fatty acid
content, (3) the premium award based on the quality of the
bunch determined primarily by the level of ffa, which is
97
influenced by the degree of ripeness, handling and other
field factors, and (4) the award of quantity premium based
on the ability to supply or deliver a predetermined quantity
of fresh fruit bunches (ffb) with the specified degree of
ripeness and the percent free fatty acid (ffa) content
within the desired limit. The quantity premium varies from
day to day depending on the establishment's goals and
objectives, and (5) The price for each bunch depends on the
bunch's degree of ripeness, and is coded from zero to six
(See Appendix 2). The probable color of the bunch could be a
helpful indicator of ripeness stage when in doubt. For a
broader perspective, percentage of ripe color is used.
Some additional outputs are the total amount due to
farmer based on the number of bunches sampled and
simulated. The harvest composition relates the different
proportions of ripeness stages of the bunches in terms of
unripe, ripe and very ripe. The net present values on
annual and per hectare basis are also obtained for a planned
oil palm life span of thirty-five years. At different
interest rates, other factors like risk could be
incorporated if necessary. The equivalent annual cash flow
and the internal rate of return on capital can also be
obtained from the output if requested. The capital profile
technique is an additional output designed not only to
regulate the growers, especially the smallholders, but also
serves as an aid in the successful planning of oil palm
98
plantation schemes. The suggested weekly and monthly
records (See Appendix 11 and 12) are necessary in order to
make adjustments necessary for attainment and maintenance of
maximum quality and quantity of oil in an economic way.
Tables 5.1—5.5 are five samples of simulated fresh
fruit bunches at the Mill reception. In Sample #1, the five
bunches have different percentages of detached fruit and,
therefore, are at different levels of ripeness. The age and
the time delay are common to all bunches because all the
bunches are expected to have come from the same field or
block where all the palm trees are of the same age. The
weight of the bunches might not necessarily be the same.
In harvesting operations, field or blocks are harvested
in rotation and in each block, section or field, the palm
trees have the same planting date and are, therefore, the
same age. Each bundh is valued on its merits, suCh as the
stage of ripeness, the percentage free fatty acid, the
visual assessment of rotten, green or diseased. The
subtotal is the current worth of the bunch at the Mill
reception. The number of bunches to be sampled will depend
on the overall number of bunches supplied and the sampling
should be random enough to be representative of the quantity
supplied or delivered. A suggested sampling procedure is as
follows:
In the estate or plantation environment, for a grower
and processor, the following sampling procedure should be
Table 5.1 Tenera:
99
Simulated
Fresh Fruit Bunches at the
. â€_MillReception, Sample #1
Sample #1 Bunchï¬
l 2 3 4 5
1. Detached Fruit:
(Wt. or Number) 1(W) 2(W) 4(W) 6(W) 8(W)
2. Wt. of Bunch (kg) 18 18 18 18 18
3. Time Delay in
Days 0 0 0 0 0
4. Condition of
Bunch (U) (M) (S) (S) (S)
5. Age of Palm. 16 16 16 16 16
(a) Degree of Under Just Over
Ripeness Unripe Ripe Ripe Ripe Ripe
(b) % FFA .78% 1.35 1.89 2.23 2.57
(c) Probable Yellow Yellow Orange Red Red
Color (Green or Orange or 70% Orange or 90%
40% Ripe or, 50% Ripe or 80% Ripe
Color Ripe Color Ripe Color
Color Color
‘9’ Std- Premium 9 5 a .noos-u.001 -K.002
(e) Subtotal 8 .18 N .72 81.36 31.29 H-68
(f) Extra Amt. Due
to Premium N .015 -ï¬.03 -u,043
(g) Total Amount Due Farmer 84.23
(h) Quantity Premium.a
(i) Harvest Composition 40:40:20
(j) Oil Per Mesocarp 48.84: Overall % FFA a 1.073%
(k) Loss Due to Unripe Harvest: u129.60/Acre Of
(1)
(m)
N324/ha. per ann.
Loss Due to Over Ripe Harvest
Grand Total Amount Due Farmer:
N4.23
100
Table 5.2 Tenera: Simulated Fresh Fruit Bunches at the
.Mill Reception, Sample #2
(1)
(m)
Sample #2 Bunch #
1 2 3 4 5
1. Detached Fruit:
(Wt. or Number) 2(W) 4(W) 6(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 20 20 20 20 20
3. Time Delay in
Days 2 2 2 2 2
4. Condition of
Bunch _ (U) (M) (S) (S) (S)
5. Age of Palm 19 l9 19 19 19
(a) Degree of . Under Just Over very
Ripeness Ripe Ripe Ripe Ripe Over
Ripe
(b) % FFA 1.485 2.23 2.39 2.54 2.69
(c) Probable Yellow Orange Red Red Red
Color 'Orange or 70% Orange or 90% or
or 50% Ripe or 80% Ripe 100%
Ripe Color Ripe Color Ripe
Color Color Color
(d) Std. Premium. 9 -H.0003 -H.003 -R.002-R.0007
(e) Subtotal N .8 31.49 81.43 N .75 a .18
(f) Extra Amt. Due
. to Premium. -N.006 -R.06 -R .04 -N .01
(g) Total Amount Due Farmer N4.65
(h) Quantity Premium N.6
(i) Harvest Composition 20:40:40
(3) Oil Per Mesocarp 49.75: Overall % FFA a 1.20
(k) Loss Due to Unripe Harvest
Loss Due to Over Ripe Harvest: H2.lS/Acre or N5.37/ha.
(If Area and Quantity Harvested are 1 hectare and 10
tons , respectively)
Grand Total Amount Due Farmer: 85.25
101
Table 5.3 Tenera: Simulated Fresh Fruit Bunches at the
. ...Mill,.Rec.eption._ Sample #3
Sample #3 Bunch 4
l 2 3 4 s
1. Detached Fruit: ,
(Wt. or Number) 3(W) 4(W) 6(W) 8(W) 10 (W)
2. Wt. of Bunch (kg) 18 18 18 18 18
3. Time Delay in
Days 1 1 1 1 1
4. Condition of
Bunch (U) (M) (S) (S) (S)
5. Age of Palm 15 15 15 15 15
(a) Degree of Just Just Over Over
Ripeness Ripe Ripe Ripe Ripe Ripe
(b) 8 FFA 1.256 1.741 2.11 _ 2.28 2.45
(c) Probable Orange Orange Red Red Red
Color or 70%, or 70% Orange or 90% or
Ripe Ripe or 80% Ripe 100%
Color Color Ripe Color Ripe
Color Color
(d) Std. Premium 3.005 a .002 -N .0008 -N.001 -N.0004
(e) Subtotal 81.45 N1.38 81.33 H.69 N .17
(f) Extra Amt. Due
to Premium 8 .10 s .03 41.01 4.02 41.008
(9) Total Amount Due Farmer N5.02
(h) Quantity Premium.s.54
(i)
(j)
(k)
(1)
(m)
Harvest Composition 0:60:40
011 Per Mesocarp 50.27: Overall % FFA a 1.275
Loss Due to Unripe Harvest
Loss Due to Over Ripe Harvest: 82.15 or 85.39/ha.
Grand Total Amount Due Farmer: N5.56
Table 5 . 4 Tenera :
10
Mill Reception, Sample #4
Simulated Fresh Fruit Bunches at the
Sample #4 Bunch!
1 2 3 4 5 6
1. Detached Fruit:
. (Wt. or Number) 2(W) 3(W) 4(W) 6(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 22 22 22 22 22 22
3.L Time Delay in
Days 2 2 2 2 2 2
4. Condition of
Bunch (U) (M) (S) (S) (S) (S)
5. Age of Palm 24 24 24 24 24 24
(a) Degree of Under Just Just Over Over
Ripeness Ripe Ripe Ripe Ripe Ripe Ripe
(b) t FFA 1.47 1.94 2.21 2.35 2.48 2.62%
(c) Probable Yellow Orange Orange Red Red Red'
Color Orange or 70% or 70% Orange or 90% or 90%
or 50% Ripe Ripe or 80‘ Ripe Ripe
'Ripe Color Color Ripe Color Color
- Color Color
(d) sea. Premium ' 9 “.0004 -u.001 -s.002 -u.002 -n.002
(e) Subtotal N .88 N1.66 N1.61 N1.58 N .83 N .82
(f) Extra Amt. Due
to Premium N .01 -N .03 4| .06 -!l .04 -N .05
(g) Total Amount Due Farmer N7.38
(h) Quantity Premium N.99
(1) Harvest Composition 17:50:33
(j) 011 Per Mesocarp 49.69: Overall : FFA . 1.19
(k) Loss Due to Unripe Harvest
(1) Loss Due to Over Ripe Harvest
(m)
Grand Total Amount Due Farmer:
$8.37
Table 5.5 Tenera:
Mill Reception. Sample #5
103
Simulated Fresh Fruit Bunches at the
Sample #5
Buncht
, 1 2 . 3 ‘4 5
1. Detached Fruit: .
(Wt. or Number) 2(W) 4(W) 8(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 20 20 20 20 20
3. Time Delay in
Days 0 o o o 0
4. Condition of
Bunch (U) (U) (U) (U) (U)
5. Age of Palm 19 19 19 19 19
(a) Degree of Under Just Over Over very
Ripeness Ripe Ripe - Ripe Ripe Over
_ Ripe
(b) a an .92 1.22 1.84 1.84 2.14
(c) Probable Yellow Orange Red Red Red
Color Orange or 70% or 90% or 90% or
or 50% Ripe Ripe Ripe 100%
'Ripe Color Color Color Ripe
Color Color
(d) Std. Premium 0 a .006 0 0 N .0001
(e) Subtotal 8.8 N1.62 N .8 H .8 N .19
(f) Extra Amt. Due
to Farmer N .12 -N .003
(g) Total Amount Due Farmer 84.21
(h) Quantity Premium .3
(i) Harvest Composition 25:25:50
(j) Oil Per Mesocarp 49.82: Overall % FFA = 1.21
(k) Loss Due to Unripe Harvest
(I)
(m)
Loss Due to Over Ripe Harvest:
Grand Total Amount Due Farmer:
N2.15/Acre
N4.Sl
104
considered. Sampling should be carried out so that results
of the analysis are representative of the whole harvest. In
which case the sterilizer cage may be taken as a sample
size.
(1)
(ii)
(iii)
(iV)
As
farmers,
The number of sample cages for daily analysis
should not be less than the number of fields,
blocks or divisions harvested.
The bunches sampled should be at random and
and as much as possible harvests should be
grouped according to plantings of the same
year or age.
Bunches to be sampled should still have
stalks with a white (fresh) cut surface,
otherwise,the time delay in days should
be indicated. If the bunches have
very dry, moldy or rotten cut surfaces,
they should be discarded and classified
as "not codeable." The same applies to
bunches in advanced stages of rot
pest-damage or disease.
The sampling exercise should be carried
out according to a program prepared at
the beginning of the year. This is to
simplify the control of the representative
sampling. The number of samples
for the whole year should be equitably
distributed among the years of planting
divisions.
a processor, receiving fresh fruit bunches from
the following sampling jprocedure Should 'be
considered:
(1)
For the analysis, the fresh fruit bunch
may be taken as the smallest sample size.
The number of bunches to be sampled will
105
depend on the total bulk supplied. For
example, with the supply of forty
fresh fruit bunches, a sample size of
fifteen to twenty will be considered
adequate.
(ii) The bunches sampled are taken at random
and accordingly coded based on percentage
detached fruit and resultant level of
free fatty acid.
(iii) Visual inspection of the bunches is
important to ensure that the bunches
are not rotten, moldy, or diseased.
Bunches that are excessively damaged
by bruising or delayed should be
noted. Diseased and rotten bunches
should normally be classified as
"not codeable" and discarded.
(iv) Only fresh fruit bunches are
considered for analysis and
coded. Abnormal bunches,
not fully developed, dry and
very wrinkly should not be considered.
(v) Accurate and consistent assessment
of degree of damage or bruising,
proper record of year of planting,
weight or number of detached fruit
and time delay are important in
order to obtain approximate harvest
composition of the grower's supply.
In all cases, a laboratory test for free fatty acid
content is necessary.
In SampLe #1 (Table 5.1), the grand total amount due
the farmer is 34.23 which is 4 percent higher than what he
would receive in the absence of any control measures. This
would have been £4.05. The model has warned the farmer
because of the high percentage of unripe fruit which gives
rise to the low oil content and the low extraction ratio.
Although the overall percentage of free fatty acids is low,
106
the premium obtained for such low free fatty acid cannot
offset the loss resulting from harvesting unripe bunches.
In this case, a loss of N1.29.60/acre or N324/ha.per annum
will be suffered if in a year nine tons of fresh fruit
bunches with the same harvest composition is supplied. In
Tables 5.2, 5.3 and 5.5 the amounts due the farmer are
105.25, 245.56 and £4.51, respectively; instead of 144.50,
145.40 and 51.50. The record of the loss due to unripe
harvest is necessary and serves as a timely warning for the
farmer to do something about the harvesting system. In this
case, there is need to either increase the minimum
harvesting standard or the harvesting circle, or both. A
constant check on the harvesting discipline is also
important.
In Sample #4, Table 5.4, the grand total amount due the
farmer is 198.37. This amount is 25% higher than what the
farmer would otherwise receive because he is compensated by
an award of standard and quantity premium for producing
fresh fruit bunches of high quality and quantity. From
Table 5.6, it is clear that the number of unripe and over
ripe bunches in a harvest, or in other words, it is the
harvest composition that determines the free fatty acid
content and the percentage oil content. The more the unripe
and over-ripe bunches are minimized, when compared to the
proportion of the ripe bunches, the better the compromise
between free fatty acid and the quantity of oil. Sample #4
107
umm>umo Gowns:
as one macs messes: sm.ez No.84 -.a m.~m emnmuumm
Am.mz me.ae a~.~ mm.am mmuem.s~
ume>umo Oman ue>o
as one macs magnum: em.mz A~.cm A~.H. on 64.66.:
une>uoo moan ue>o
o» one «mos sewage: mm.mz ms.ae o~.~ «n ee.ee.e~
umo>umo Omens:
0» use once magnum: mm..z ca.mv no.4 m~ emuequee
museum uoauem outcome: uceucou uqsum ocuuqmooeoo
use unease uoa duo can, eorouuoo . uuo>un=
nausea “nodumeoem Add! as aeoocsm Dasha omeuh caucusewm uo aueeesm e.m muons
108
(Table 5.4) has a crop composition that tends towards the
right combination of the different degrees of ripeness. The
resultant high revenue is due to the standard and quantity
premium intended to encourage the growers to adjust their
harvesting system in order to come up with similar harvest
composition in which unripe and over—ripe bunches are at the
minimum in relative proportion to the ripe bunches.
The oil content of under-ripe bunches is low and this
increases as the bunches move to the stage of optimum
ripeness through increased percentage of detached fruit.
The free fatty acid (ffa) percentage also increases with the
increased detached fruit. The control of percentage
detached fruit alone, does not affect the choice of
appropriate premium substantially and, therefore, has little
or no effect on the revenue accruing to the farmer. In
commercial practice, the objective should, therefore, be to
get as many bunches as possible within the range of desired
percentage detached fruit to total fruit. The use of the
world market price for oil and the FFA premium award in
formulating quality control policy will not be effective
because of the little difference in revenue between the
maximum detached fruit and zero detached fruit. The farmer
or management can vary the harvesting by changing the
harvesting interval and the minimum harvesting standard.
The influence of these factors have been shown by
Southworth, 1973. The harvesting interval determines the
109
spread of degree of ripeness in the crop, while the minimum
standard determines the minimum level of ripeness. Since
the change in free fatty acid with respect to detached fruit
is linear, the ‘harvesting circle and harvesting minimum
standard can be varied to any combination which will give
the number of detached fruit per bunch appropriate for the
required ffa. However, in the case of oil yield, the
relationship between the harvesting system is not very
straightforward and simple because of the discrepancies as
to when the oil synthesis in the bunch terminates. From the
simulation, any bunches having less than 25% detached fruit
to total fruit will contain less oil than those with greater
than 25% detached fruit to total fruit. The closer a bunch
is to zero detached fruit, the lower will be its oil content
(Figure 5.6).
A simulated observation of degree of ripeness based on
percentage detached fruit of the 'harvest composition is
shown in Tables 5.7, 5.8, 5.9, for the varieties Tenera,
Pisifera and Dura, respectively. For Tenera, to harvest
sufficiently ripe bunches, the number of loose fruit on the
circle before cutting of the bundh should be at least half
loose fruit per 1 kilogram of bunch weight. A bunch is also
considered unripe when less than 50% of the outer fruit are
ripe colored.
As a compromise, (see Figures 5.7, 5.8 and 5.9) for
simplicity of the harvesting instruction, a minimum ripeness
llO
BOF‘ . W250
’ H%FFACONTEN1‘
0—0 %OILINMESC£ARP
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20 25 30 35 40
% DETACHED FRUIT
Figure 5.6 Relationship of oil per mesocarp of the fresh fruit
bunches (FFB) and FFA content with degree of ripeness
for the five harvest canpositions.
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112
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113
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114
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117
criterion of twelve loose fruit may be suggested for Tenera
which may be combined with the assessment of ripeness by the
consideration of color. The color of the outer fruit should
be at least 70% ripe color for Tenera and Pisifera. Thus
besides counting or weighing loose fruit; the harvester must
also consider the color of the bunch; especially when in
doubt. However; to judge the color of the bunch from the
ground before cutting is rather difficult; especially when
the tree is high. A harvesting interval of seven days;
which is common throughout the palm belt; is considered
practical.
When the criterion chosen is twelve loose fruit for any
size of Tenera bunch; the result will be as shown below:
Bunch Weight(kg) No. of Loose Fruit per Bunch
kg of Bunch Weight Code
10 1.2 6
12 1.00 6
14 .85 6
18 .66 4
22 .54 3
26 .46 2
3O .40 2
34 .35 l
38 .31 1
42 .28 O
118
With this criterion. when the harvesting interval is 7
days; it is found that very over ripe bunches with the bunch
code #6 are usually consisting of small bunches which are
less than 18 kilogram weight. It is} therefore; better to
have different criteria for different fields or blocks
within an estate.
The simulated capital profiles for interest rates of
9.5%. 10%; 10.54%. 11% and 12% are illustrated in Figure
5.10. It is noteworthy that although positive margins are
obtained from the fifth year onward (Table 5.10); when
interest is charged at 10% the crop does not pay for itself
until the end of twenty-seventh year (Table 5.12). In other
words, up to this time; there is a positive capital
investment. At 11%; the revenue is never sufficient to
cover total capital investment} meaning that there is no
breakeven point. At 12%; the capital investment is
increasing throughout the crop life.
At the interest rate of 10.54% which is the "internal
rate of return" or "yield" the capital investment will just
be recovered at about the end of the productive life of the
investment.
The simulated discounted future returns are illustrated
in Figure 5.11. The profiles of NPV for interest rates of
9.5%; 10%; 10.54%. 11% and 12%. These curves are more
closely’ similar than. equivalent. capital ‘profiles; so ‘the
effect of interest rate on valuation; especially in the
119
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Table 5.10 Revenue and Costs Per Hectare of Oil Palms
N
I II III IV V VI
Year Number Yield Revenue Costs Margin Cumulative
(kg Cost
~ fruit)
1 NIL NIL 1067.45 -1067.45 ~1067.45
2 NIL NIL 220.99 - 220.99 -1288.44
3 NIL NIL 128.09 6 128.09 -l4l6.53
4 560 42 78.47 - 36.47 -1453.00
5 1680 126 100.98 + 25.02 -l427.98
6 2800 210 100.98 + 109.02 -1318.96
7 3360 252 117.48 + 134.52 -1184.44.
8 4480 336 117.48 + 218.52 - 965.92
9 5040 378 117.48 + 260.52' - 705.40
10 5040 378 117.48 + 260.52 - 444.88
11 5040 378 117.48 + 260.52- - 184.36
12-35 5040 378 117.48 + 260.52 Positive
- balances
Note: The original estimates prepared by an F.A.O. advisory
team.for the Ministry of Agriculture and Natural
Resources of Western Nigeria (F.A.O. Team 1974) for
use in planning farm settlements. These estimates
have been updated using current costs and prices. The
third column is obtained by multiplying the yield from
the second column by 75/1000 because the current
price is.N75 per ton. The margin in the fifth column
‘is simply the revenue (column III) minus the costs
-(column IV). The cumulative cost in column VI is the
total of all the annual margins up to and including
that year. .
122
latter years; is relatively small. Using the data given in
Table 5.10 and assuming that the margin accrues at the end
of the year} the value of a hectare of oil palms at the end
of its thirty-fourth year would be the discounted margin for
the thirty-fifth year. At an interest rate of 10%. this
would be'ï¬236.84 (see Table 5.11).
As when compounding costs; the interest rate when
discounting returns represents the opportunity cost of
postponed receipts of money and will incorporate an
allowance to cover risk; poor harvesting (too early or late
harvesting). Farmers who are abiding with the quality
control specifications are encouraged by using a rate
equivalent to opportunity cost of capital. In which case.
the benefit lost by farmers is the Opportunity cost of oil
quality and quantity improvement.
Valuation by this method are only negative in the early
years of an investment which is uneconomic to start with.
For the oil palm crop at an interest rate of 12%. Which is
higher than the internal rate of return; the valuation is
negative at the end of the first year (Table 5.11).
The difference in valuation obtained by the two methods
may be illustrated by considering the value of one hectare
of oil palms which have been established for 20 years (See
Table 5.13).
From this table it is clear that if the Opportunity
cost of capital is less than the internal rate of return,
1J23
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....._. ....~... ......._ ~....... n...~..~ ....._.. .s...~._ _~
p...... .o...... .m....._ _~.._~.. ~.n...n~ m....... .......~ .~
._....._ ......_~ ~.~....~ ........ .m.....~ ....-.. ........ .—
...._.~_ n......~ .......~ .....~.~ .e.....~ __...... .e...m.. .2
~eo.mnn~ .na.ee- unw.en_~ vse.voo~ nn_.emn~ m~v.ooo~ v.0.ao¢~ .—
seo.smv~ ~on.nm- e-.oh_~ cov.no_~ nv—.amn~ ~a~.~vo~ ~no.c—a~ em
.e.....~ _c....- .m....- .o.....~ .o.....~ .a...... .m....._. m.
.N....._ .......~ .m...m- a......~ .......~ .u...... n....... ..
.~....._ ._.....~ _.....- .oc..._~ .«m....~ .....~.~ n....... ..
omn.c.a_ o-.~ov~ em~.v_n~ «ao.m-~ ~a~.~wn~ ohm.nm.~ uoo.o.o~ ~—
aea.m~a_ ~ns..n.~ men.c.n~ aoo.a.- vaa.~en~ on...s_~ aoa.n~e~ _—
........ .p...m.~ .m.....~ n.....- .m...... ~n.....~ .o.....~ ..
.«.....~ .......~ .e.....~ .~_...- .«.....~ ....._- .p.....~ .
.....~_~ ~......~ ..~....~ ..m...- .o.....~ mno....~ .m.....~ .
.......~ .._....~ ..~...- ~n...._~ .~...~n~ ~.~....~ ........ .
._.....~ a.....- m....._~ ~p~.m..~ ....._- ........ _na..~.. .
n.~.n... .m.....~ .m...e._ ~......_ ........ ........ .._..... .
......._ .N...... .o...... ...H..._ .e...... a......_ ......._ .
n......_ p....... .~...... ... ..._ ........ .~...~.. .~..~._. .
......._ .m...... ....n.__ ..~..... .m...... .~..... ....... ~
....... .~m.m.~ .~.... .~.. ......~ ...... u ..~.... u n
2...... Eu... . 8.. kg .. E
a... a... .e. ....e_ ..~ .__ .m.
o...o.a acououa no: acoaote so: ago-0.: um: o...m.. scene». so: econ... no: use»
~euudeo ueuuceo
ICU: UIOHOU-un
.m.. can .e. ...m... .._. ..~. a. coma a...
nap uo>o l.ea _3c .0 0.0.00: osc lot. a...o.s ueu.aeu
0:0 ususuos oususm no osue> sconce; .02 ecucsooa.: _..m 3.26%
Table 5.12 Capital Profile for One Hectare of Oil Palm
Over Its Life Span at 12%, 10.54% and 10%
Interest Rates
Year 12% 10.54% 10%
1 1067.45 1067.45 1067.45
2 1416.534 1400.949 1395.18
3 1714.608 1676.699 1662.79
4 1956.831 1889.893 1865.54
5 2166.630 2064.068 2027.07
6 2317.606 2172.600 2120.76
7 2461.199 2267.073 2198.32
8 2538.023 2287.502 2199.63
9 2582.065 2268.085 2159.07
10 2631.393 2246.621 2114.46
11 2686.641 2222.895 2065.39
12 2748.518 2196.668 2011.41
13 2817.820 2167.677 1952.03
14 2895.438 2135.630 1886.71
15 2982.371 2100.206 1814.86
16 3079.735 2061.047 1735.83
17 3188.784 . 2017.762 1648.89
18 3310.918 1969.914 1553.26
19 3447.708 1917.023 1448.07
20 3600.913 1921.927 1332.35.
21 3772.503 1793.929 1205.07
22 3964.683 1722.489 1065.06
23 4179.925 1643.520 911.04
24 4420.996 1556.227 741.63
25 4690.995 1459.733 555.27
26 4993.395 1353.069 350.28
27 5332.082 1235.163 124.79
28 5711.412 1104.829 - 123.24
29 6136.262 960.758 - 396.09
30 6612.093 801.502 - 696.22
31 7145.025 625.460 -1026.36
32 7741.908 430.864 -1389.52
33 8410.417 215.757 -1788.99
34 9159.147 - 22.021 -2228.41
35 9997.725 - 284.862 -2711.77
125
Table 5.13 Valuations of One Hectare of 20-Year-Old Oil
Palms (N)
Rate of Interest/Opportunity
’ ' cost-of Capital
Yield*
10% ' 10.54% 12%
Cumulative Expenditure 1332.35 1921.92 3600.91
Discounted Future .4
Returns 1981.53 1921.92 1774.36
(Net Present Value) ‘ ‘
Yield Value
* The valuation obtained by compounding or discounting at
the rate of the internal rate of return is referred to
as the â€yield value.†'
126
for example at the rate of 10%, the net present value is
greater than the yield value and the cost is lower.
Conversely; if the opportunity cost of capital is greater
than the internal rate of return; for example at the rate of
12%. the net present value is less than the yield value and
the capital cost is higher. This is a useful guideline in
the choice of interest rates and payment of compensation as
an incentive for the improvement and expansion of oil palm
production.
Table 5.14 illustrates the different annual revenues
from different harvesting procedures. For example} in the
first year of harvest; if normal harvesting is done; the
annual revenue per hectare would be N42 but if unripe fresh
fruit bunches are always harvested; the annual revenue would
be 821.84; which is about 50%. loss. A loss of this
magnitude should be of great concern to any serious farmer
or grower.
Table 5.14b shows higher gross revenue of 12%. under
quality control measures. The increase is due to the award
of standard premium. Although it assumed that there is no
increase in cost in order to adopt the quality evaluation
process; the fact remains that growers are offered the
opportunity to increase their revenue through the award of
standard and quantity premium. This is more vividly
illustrated in Figure 5.12b.
Figure 5.12 is a graphical explanation of the farmers
127
Table 5.14 Simulated Annual Revenue for One Hectare
of Oil Palm - Grower
Revenue
Year Revenue Harvesting Revenue Harvesting Over Ripe
Unripe Fruit» _ Fruit
‘ ' LP LP LF
1 NIL -- 12 29 29
2 NIL -- -- -- --
3 NIL -- --g -- --
4 42 21.84 41.28 28 21
S 126 65.52 123.90 121.80 119.70
6 210 109.20 206.50 203 199.50
7 252 131.04 247.80 243.60 239.40
8 336 174.72 330.40 324.80 319.20
9 378 196.56 371.70 365.40 359.10
10 378 196.56 371.70 365.40 359.10
11 378 196.56 371.70 365.40 359.10
12-35 378 196.56 371.70 365.40 359.10
LP - Loose Fruit
128
Table 5.143 Showing a Grower's Annual Gross Revenue
for One Hectare of Oil Palm and the
Associated Losses when Under-ripe and
Over-ripe FFB are Harvested
Gross Revenue without
Quality Control
Gross Revenue with
Quality Control
Year Unripe Normal . Over-ripe Normal
1 -- -- -- --
2 -- -- -- --
3 -- -- -- --
4 21.34 42 21 47.32
5 65.52 126 ’119.70 141.96
6 109.20 210 199.50 236.00
7 131.04 252 239.40 233.92
3 174.72 336 '319.20 378.56
9 196.56 373 425.33
359.10
129
1"
83... 11.8.... 01.823. .3 838.5. 3 nu
p333 . 24¢.— .33 3 HES—Um: Us 236 §u>u¢ as; 3Uh<d32ra N. d 2.33....
a a- flap
. .. ..
1- d 1d!
3.23...— 8 I. 33 1- 3
.03-83833333381892; 33
dues-5.8.3.. an .33. 8.. 8.1.3.31...
13.32.03.333 .8 8.3!... 1:33 I 0
38.3.. a. 33s. 1.. 2).-{Isa
83.82. 33.8914 .3 III... 3 n a .
is.
i!!§§i.ls¢s<u<
130
. . .2230 >525
59...! .3... 5.3 .52. .3 .0 93.8.. 8.. 3:26. .35 .81.:- ..Ia§& a 3.39.... gm. .m 2.6:.
803525...
a v . n
1E.
.9250 2:25 5:3 3:26. .35
.3230 >536 .39...) 3:98. .35 -
8—
cm“
can
8..
8e.
'Wl/ w/ (#1.) annual 9049
131
annual income based on the harvesting performance.
Similarly; Table 5.15 and Figure 5.13 are for processors or
both. For the processor or both; the low annual revenue as
a result of harvesting over ripe freSh fruit bunches (ffb)
are more obvious because the loss is not only due to
increased loose fruit but also due to the poor quality of
the oil resulting from the increased free fatty acid (ffa)
content.
Tables 5.16; 5.17; 5.18 illustrate the effect of time
delay and the fruit condition on quality and value for the
varieties; Tenera; Dura and Pisifera; respectively. These
tables are simulated effects of time delay in days on
unbruised; moderately and severely bruised fruit in terms of
quality and value. Although the two factors} quantity and
quality are inextricably related; the harvesting criterion
is directly most important in oil quantity and indirectly in
quality. This is because of the damage factor in quality
deterioration that undamaged ripe fruit contains low free
fatty acid. For example in Table 5.16; the variety-Tenera
contains as low as 1.29% free fatty acid when unbruised and
processed the same day and the fresh fruit bunch is valued
at one naira; sixty-six kobo (Nl.66). When moderately
bruised and delayed for three days; the free fatty acid
rises to 2.40% and the bunch value drops to one naira; forty
kobo (31.40). If it were possible to avoid damage entirely;
then maximum oil quantity could be obtained without quality
132
Table 5.15 Simulated Annual Revenue for One Hectare of Oil
Palm - Processor or Both
Revenue
Year Revenue Harvesting Revenue Harvesting Over Ripe
“Unripe Fruit. Fruit
1 NIL -- 19 29 29
2 NIL -- -- -- --
3 NIL -- -- -- --
4 42 21.84 39.00 36.00 33.00
5 126 65.52 117 108 99
6 210 109.20 194.95 179.90 164.85
7 252 131.04 233.94 215.88 197.82
8 ‘336 174.72 311.92 287.84 263.76
9 378 196.56 350.91 323.82 296.73
10 378 196.56' 350.91 323.82 296.73
11 378 196.56 350.91 323.82 296.73
12-35 378 196.56 350.91 323.82 296.73
133
a .2 a. a.
2...... 86 3.5.38... II 3..<.. .5 3 2.353.. BIG .3“. 032945. .<:ZZ< n.3n<....3.a m .é 32......
gm» 8— 3.:
. q 4
£333.33; 3 ... S... a... 8..)
3...... 03.3.... 0......333 3. 03.3.... 1......ZIU
9.3.2.2... 33...... .3. 3.8.5. .3333 I A.
39.32.3333: 3 ... .3. o... .3... .9...........
13.3.... 3.3.3:... 2.. 3.3.5. .a......< I 0
3.8.3.. 3 ... .3. a... .3! .323...
03.8.... 0.....334 3. 1.3!... 3.333 I a
433......
2...... 3.333%.13.‘
Oh
mm
134
Table 5.16 Simulated Effect of Time Delay and Fruit
Condition on Fruit Quality and Value
Variety: Tenera
Fruit Condition Length of Delay % FFA
Amount Due
(Degree of (Days) Content Farmer (ï¬/FPB)
Bruising) ‘-
U 0 1.29 1.66
M l 1.74 1.56
M 2.07 1.47
M 3 2.40 1.40
S l 1.94 1.51
s 2 2.27 1.43
S 3 2.60 1.35
U - Unbruised
M - Moderately bruised
S - Severely bruised
135
Table 5.17 Simulated Effect of Time Delay and Fruit
Condition on Fruit Quality and Value
Variety: Dura
Fruit Condition Length of Delay % FFA Amount Due
(Degree of (Days) Content Farmer (ï¬/FFB)
Bruised)
U o 1.26 1.68
M 1.72 1.57
M 2 2.05 1.48
M 3 2.37 1.41
S 1 1.92 1.52
S 2 2.25 1.44
S 3 2.57 1.36
U a Unbruised
M - Moderately bruised
S . Severely bruised
136
Table 5.18 Simulated Effect of Time Delay and Fruit
Condition on Fruit Quality and Value
Variety:
Pisifera
Fruit Condition
Length of Delay % FFA
Amount Due
(Degree of (Days) Content Farmer (N/FFB)
Bruised)
U 0 1.22 1.69
M 1 1.66 1.58
M 2 1.92 1.52
M 3 2.18 1.45
S 1 1.86 1.53
S . 2 2.12 1.47
S 3 2.38 1.40
U - Unbruised
M . Moderarely bruised
S I Severely bruised
137
degradation through free fatty acid increase; but at present
the greater mount of oil derived from fruit is accompanied
by higher free fatty acid values. The higher the loose
fruit number; the higher is the free fatty acid (ffa)
content. The exocarp becomes softer as the fruit ripens; so
the fruit is damaged more readily.
Figures 5.14. 5.15; 5.16 further convey the idea that
quality control begins in the field because as far as free
fatty acid content. is concerned; the influence of
harvesting; handling standards and timing of fruit flow to
the mill are of paramount importance if oil of low free
fatty acid is to be produced. The effect of time delay on
the fruit becomes critical when the bunch or fruitlets have
suffered some degree of brusing or damage. For example; in
Figure 5.14 the severely bruised fruit delayed for three
days has 20% quality degradation than the moderately bruised
fruit delayed for the same number of days.
The objective of oil palm cultivation is to produce the
highest yield of good quality palm oil per unit area in the
most economical way. The last consideration requires
harvesting with an interval of several days. Consequently
the crop will consist of bunches at different stages of
ripeness. Therefore; the aim of harvesting is to get a crop
with a composition as near as possible to ideal; that is the
largest number of exactly ripe bunches. Regular analysis of
crop composition is a means of crop quality control. The
REVENUE 111:8: IBUNCH
138
1.80 - " 2-75
101° - 2050
1.60 «2.25
1.50 - .2.00
1.40 " "75
1.30 ‘ 35°
I :mnms-
I 111-110an! 8801880 I
s=ssvsmx BRUISED I
T° 4’ 1.29
O 1 2 3 I 5
mm In mus
FIGURE 5. l4 TENERA. SIMULATED EFFECT OF TIME DELAY AND FRUIT
mITION ON QUALITY AND VALUE.
1.80.
1.70
'8
'3
REVENUE INN/BUNCH
139
2.75
2050
2.25
10% 4 ‘°75
--- REVENUE,=N=/BDNCH
1.30 - — 1 m 0011mm 1 1-50
uwunmnsen
,/ swam-31111131.! 31101330 /’
’I: szsmmx 311111530 1
g V1.26
0 1 2 3 ll 5
mm IN nus
FIGURE 5. l5 DURA:
SIMULATED EFFECT OF TIME DELAY AND FRUIT
CONDITION ON QUALITY AND VALUE.
IFFACONTENT
REVENUE IN:N=/BUNCH
FIGURE 5. I6 913mm:
1.“
1.70
1.60
1.50
1.40
1'30 "' :mauxssn .
:mnmmx 31101330
1,, S:SEVERELY 311015.
e
l l 1 l
0 1 2 3 11
me: II was
140
4?
«9
\
"-’ REVENUE,=Nr/EUNCE
'-- f FFA CONTENT
CONDITION 0N QUALITY AND VALUE.
2.50
2.25
2.00
1.75
SIMULATED EFFECT OF TIME DELAY AND FRUIT
I FFA CONTENT
141
model achieves this by sorting samples of harvest according
to degree of ripeness. The most convenient place to do this
is at the reception station of the Mill. Under estate or
plantation environment; the control can be effected at the
collection points in the field in order to check the
discipline of the harvesters; and ensure that the minimum
ripeness critera are being obeyed.
The assessment of the incoming crop quality on daily
basis and the analysis of a farmer's supply by this model is
very useful both to the Mill manager and the grower. The
data collected in the analysis using the forms designed for
that purpose (see Appendix 11 and 12). could be periodically
compared with that from previous analyses. Under estate or
plantation environment by following these data every day.
one may know whether because of change in climate or other
factors. crops are becoming more unripe or ripe} whether
minimum ripeness criteria and harvesting cycle should be
adjusted or not and whether loose fruits are collected
completely or not. One may also account for changes in oil
and free fatty acid content; whether oil content is
declining because of an increase of losses in the processing
mill or because of an increase in quantity of unripe bunches
or because of incomplete fruiting and whether free fatty
acid level is increasing because the number of over ripe
bunches is increasing or not. Similarly; compiled monthly
report to a farmer or supplier could also be of assistance
142
in making adjustments where necessary.
Since there are many variable conditions, such as
variety of planting,age of planting; climate; discipline and
intelligence of the harvesters} etc.} it is difficult to get
experimental evidence upon which to base a definite ripeness
criterion. Therefore; a decision on the minimum ripeness
criterion and harvesting cycle for a certain planting should
be made by trial and error. For this; harvest analysis data
may be used. The right combination of minimum. harvest
ripeness criterion and harvesting interval is the one that
will yield a crop composition as shown by harvest analysis
data; which is in accordance with quality and quantity
requirements. Examples of such harvest analysis are shown
in the appendices. Once the right criterion and cycle are
found; these may be adjustd later when required as indicated
by harvest analysis data. An unripe harvest which has too
many unripe bunches; is not only the result of a too low
criterion or too short harvesting rounds; but also the
result of low discipline of harvesters. In which case; the
farmers should also learn to set up high incentives for the
harvesters; if they hope to enjoy the benefit of the quality
control measures.
The sensitivity tests were made to identify the degree
of this model response to various input variables. The
examples of these test results are presented in Table 5.19.
From Table 5.19; the first sensitivity analysis; with a
143
Table 5.19 The Sensitivity Analysis Results
Item _ Sensitivity Analysis
1 2 3
Harvest Composition 25:50:25 20:60:20 15:70:15
% detached
fruit 28.75 29 29.25
% FFA : 1.14 1.14 1.15
3 oil in
mesocarp 49.32 49.36 49.39
Net Present Value Int.
Rate/
Opp.
Costs
of Capital 10 yrs. 20 yrs. 33 yrs.
10.54% 2269.88 1921.92 448.88
11% 2194.03 1873.36 446.14
12% 2043.29 1774.36 440.29
*Quality and Value '
Time
Delay
in Days- -
. U M S
.5 1.09 1.49 1.69
+(s1.54) <s1.45) (31.41)
1 1.25 1.65 - 1.85
(u1.51) (31.42) (u1.3a)
2 1.58 1.98 . 2.1a
<n1.44) (31.35) (u1.30>
*The first sensitivity analysis was for unbruised bunch,
while the 2nd and 3rd runs were for bunches with different
degrees of damage. in this case moderately and severely
bruised. respectively.
1 Value of bunch.
M Nigerian currency (1 M a $1.33)
144
harvest composition of 25:50:25, the percentage detached
fruit is 28.75, the percentage free fatty acid is 1.14 and
the percentage oil in. mesocarp is 49.32. In the second
analysis, with harvest composition 20:60:20, the percentage
fruit is 29.00, the percentage free fatty acid is 1.14 and
the percentage oil in mesocarp is 49.36, and in the third
analysis with harvest composition 15:70:15, the percentage
detached fruit is 29.25, percentaged free fatty acid is 1.15
and percentage oil in mesocarp is 49.39. The table shows
that 20% increase in the proportion of ripe fruit and the
same percentage decrease in the proportion of unripe and
over ripe fruit, there is little or no effect on the
quality, in terms of free fatty acid content (1.14%). But
with 40% increase in the proportion of ripe fruit and the
same percentage decrease in the proportion of unripe and
very ripe fresh fruit bunches, there is an increase in free
fatty acid content from 1.14% to _1.15%. The model,
therefore, is not very sensitive to small changes in a
harvest composition. especially when the ripe proportion is
double or" more than double the unripe and very ripe
proportion. .
The net present value at the interest rate of 10.54% is
32269.88 at the tenth year but with 11% interest rate, for
the same year, there is a decrease of 3% (32194.03) and with
12% interest rate for the same year, there is a decrease of
7% (32043.29) but at the later year, for example 33rd year
145
there is only 1% decrease irrespective of the rate of
interest. The model is, therefore, more sensitive to the
effect of interest rate, inflation, etc. at the, early life
span of the palm tree and very insensitive to the effect of
interest rate on net present value in the later years of the
crOp. This means that the effect of interest rate on the
valuation especially in the later years is relatively small
(see Figure 5.11).
The model is sensitive to the effect of time delay on
quality and value of the fresh fruit bunches. The
sensitivity of the effect of the bunch condition on its
quality and value is very high and has an overriding effect
on the time delay per se. For example, the table shows that
an unbruised fresh fruit bunch delayed for two days has
about the same value ($11.44) as moderately bruised fresh
fruit bunch delayed for only half a day. In addition the
quality of severely bruised fresh fruit bunch (ffb), delayed
for just half day is very much lower than the quality of an
unbruised fresh fruit. bunch. delayed for two days. This
means that much as it is important to transport the fresh
fruit bunches to the Mill as soon as possible, it is more
important to avoid bruising and damage as far as possible at
all stages from the time of harvesting to the time of the
fruit sterilization.
CHAPTER 6
Summary
The objective of oil palm cultivation is to produce the
highest yield of good quality palm oil per unit area at the
farmer's level. This last consideration requires harvesting
with an interval of several days. Consequently the crop
will consist of bunches at different stages of ripeness.
Therefore, the ultimate goal of harvesting is to get a crop
with a composition that has the largest number of exactly
ripe bunches. Regular analysis of crop composition is a
means of crop quality control. A system analysis approach
was used in the analysis of the harvest composition and
prediction of the quality and quantity of the oil from fresh
fruit “bunches (ffb) supplied in the "oil palm. belt" of
Nigeria.
The assessment of the incoming crop quality on daily
basis and the analysis of a farmers supply by this model is
very useful both to the mill manager and the grower. The
data collected in the analysis could be compared with data
from previous analysis periodically. Under estate or
146
147
plantation environment, by following these data every day,
one may know whether because of change in climate or other
factors, crops are becoming more unripe or ripe, whether
minimum ripeness criteria and harvesting cycle would be
adjusted or not and whether the loss due to increased loose
fruit are prevalent or not. One may also account for
changes in oil and free fatty acid content, whether oil
content is declining because of an increase of losses in the
processing mill or because of an increase in quantity of
unripe bunches or because of incomplete fruiting and whether
free fatty acid level is increasing because the number of
over ripe bunches is increasing or not. Similarly, a
compiled monthly report to a farmer or supplier could also
be of assistance in making adjustments where necessary.
The discounting technique and capital profile method
are used as the economic framework for estimating returns
from the oil palm trees and tracing the pattern of capital
requirement throughout the economic life of the tree.
Crop quality control by harvest analysis, using this
model, which sorts the fresh fruit bunches into different
bunch codes or classes according to different degree of
ripeness, as described in the main text, is very useful both
for the mill manager, field manager and the grower. By this
means the manager may account for processing results,
especially a decrease of oil yield and increase of free
fatty acid caused by field factors. The field manager or
‘
148
grower may decide at the right time to adjust the minimum
harvest ripeness criterion and harvesting cycle to changing
outside factors, like influence of climate, age and others.
For the 'oil palm belt' area, a minimum harvesting
standard of twelve loose fruit is suggested if 30% detached
fruit is the target. At the mill end, the management should
abm at having as many bunches as possible within the range
of 28% to 31% detached fruit to total fruit. The color of
the outer fruit should be at least 70% ripe color. However,
color alone cannot be used as a ripeness criterion,
especially by inexperienced harvesters because of the
variation in color within bunches in the same class of
ripeness. These variations suggest that visual symptoms of
ripeness can indicate either apparent or optimum ripeness of
a bunch and harvesting the bunches on the basis of visual
assessment only can lead to either high or low free fatty
acid (ffa). In addition when the harvester has to harvest a
bunch from. a 'height, particularly' When it is partially
hidden from view by the subtending fronds, a certain amount
of error in judgement is unavoidable. Although the change
in color is highly correlated to change in percentage free
fatty acid, for more accurate assessment of ripeness, other
factors in addition to color changes should be used. The
use of color as an indicator will be more accurate if the
harvesters are given some special training.
It is apparent from all these basic considerations that
149
the most important requirement for obtaining oil of low ffa
content from ripe fruit is to avoid bruising and damage as
far as possible at all stages from the time of harvesting to
the time of processing. The management or grower should
ensure that unripe and very ripe bunches are kept to a
minimum and that all loose fruit are collected.
CHAPTER 7
Conclusions
The number of loose fruit as it is used by the
harvesters in the field and the percentage detached
fruit are highly correlated and predict the degree of
ripeness very effectively. Therefore, the quality
control measures can be enforced from the Mill
reception end by relating the percentage detached
fruit at the Mill to the number of loose fruit at the
base of the tree.
The control of percentage detached fruit alone does
not affect the choice of appropriate premium
substantially and, therefore, has little or no effect
on the revenue accruing to the farmer. The world
market price of 13800 per ton of oil and the quality
premium award of 1 percent per every percent below 5
percent are not enough to encourage Nigeria growers to
produce high quality oil. To recapture market share,
Nigeria must offer artificial incentives not based on
150
151
world market premiums.
Although the change in color is highly correlated to
change in percentage free fatty acid, color alone
cannot be used as a ripeness criterion, especially by
inexperienced harvesters because of the variation in
color within bunches in the same class of ripeness.
The quality control model can supply information
necessary to take management decisions related to
adjustment of harvesting system. The model can
predict a yield in terms of quantity (oil in
mesocarp), and quality (percent free fatty acid
content).
Based on the simulation results the most important
requirement for obtaining oil of low free fatty acid
content from ripe fruit is to avoid bruising and
damage as far as possible at all stages from time of
harvesting to the time of processing.
CHAPTER 8
Suggestions for Further Study
In this study, it is assumed that the effects of
weather can be minimized by good supervision and
discipline. The effect of exogenous variables like weather
effects, soil fertility and other environmental factors on
different types and ages of palm should be studied.
The model developed in this way should further be
expanded to include labor availability and its relation to
fruit quality.
In this study, it is assumed that within broad limits,
changes in harvesting systems do not significantly affect
the harvesting cost. This is not necessarily true. So an
in-depth study is required to assess and put cost to any
combination of harvesting interval and harvesting standard
by considering the amount of loose fruit and the walking
time between palms containing ripe bunches. This cost can
then be used in conjunction with the oil yield and oil
quality data to obtain the optimal harvesting system.
The effort required to develop a workable model, from a
152
153
measurement concept, in the absence of instrumentation
(spectrophotometers) to follow changes during maturation of
the oil palm 'bunches, can indeed be quite substantial.
However, the development of effective instrumentation and
measurement techniques for quality evaluation and quality
control is one of many important challenges for scientists
and engineers in the oil palm industry. The future need for
such instruments will become even more important in view of
increasing labor costs and the trend toward mechanical
harvesting and handling of oil palm bunches.
APPENDICES
154
Appendix 1 Input Format for the Oil Palm Quality Control
Model. . . .
~Input
Premium:
Farmers Name:
The State Name:
The Name of Plantation:
The variety:
Number of Detached
Fruit:
Bunch weight:
Time Delay:
Condition of the Fruit:
Description
This is the quantity premium
which could vary on a daily
basis.
The name of the farmer or field
supervisor's name in the case
of a government estate.
The State of origin where the
plantation is located.
The name of the plantation, but
in the case of a government
owned estate, the field number
should be entered.
Three varieties are considered
and these are (l) Pisifera,
(2) Tenera and (3) Dura.
N or W. This is entered either
in number or weight.
The weight of the bunch is
entered in kilograms.
Time delay is entered in days.
This is the time delay between
the time the fresh fruit bunches
(ffb) are harvested and their
delivery at the mill for
processing.
U - unbruised
M a moderately bruised
S a severely bruised
V u very severely bruised
x - extremely bruised
Help - gives more detailed
explanation to aid
classification of degree
of bruising
The age of the palm tree in years.
155
Appendix 2 Price of Fresh Fruit Bunches Based on Bunch
Code
Bunch Code Price (fl/Ton)
Green Bunches Rejected
10.00
40.00
75.00
75.00
40.00
mU‘chUNl—‘O
10.00
'The above is the suggested computer simulation prices
based on degree of ripeness. Current price is N75/ton
for any fresh fruit bunch accepted irrespective of the
degree of ripeness.
156
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he as. u. an mass .656 sso> m. a.
as as on» mm.— o. as 6a.. .656 m. n.
~m e. cognac no. se.. a. ea «a.» e.
.n as cognac m~._ ow a. oa.s_ua:e a.
mu en cognac sou—ea no. u« o mass nose: m.n.
p e. goose so..ua an. e. a «disc: m.
e a goose no. a. e masses suo> a. zen:
me as. out -.~ e. an odes nose auo> en
en «a out me»« a ca 08.. .o>o as
am .. «sense on. no.. e a. as.- e.
mm an avenue an. s as a. cash bass 9.
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: nv combo soudo> so. a n Odour: w. .0.
e e. guano em. a e oa_u:: >.o> n_ mom—z
ea cod no. .~.~ m an mass has: sum: m.p.
an as so. .a.. m an ass. uo>o «.9.
mm «a cos-so so. on. . m "a cos. a.
on as mature _~. . a. .. oa.u swan a.
n_ on caccuO 30..o> on. on a onus uses: m— .
5 av coouo 30~_o> me. o v moans: m— .m.
o o cacao em. a 9 causes >uo> Nu :eJOU
me as. not n..~ m on as.» uu>o >.o> «-
as as so. ea.— m an was. seat 9.
.953
on as mosque to. a... m ea ca.s a.
an as .ooaga. avenue e~.e o a. se.. snag m.
.uequ. .
m— an cognac gasses .e.. o e «a.» soot: m.v.
e um combo segue» us. v n ea_uc: ea .c.
o c cacao as. v .9 causes >u0> Au o—o—m
.u. .x. .3. .2.
magnum houoo .OO. .r. eueeb evasuh cascades 330.03
pogoeuoa . on“: souoo tub a .uO cocoa uO goes: .4.
a no a 30:3: .o: no .0: seamen secs: ooouo>< museum
.....
loan nauseous no season aceueuuuo as occurs: aqsuh lean can senescence n nupcodac
Appendix 4
157
Pisifera: Percentage Free Fatty Acid from Fruit
Bunches at Different Degrees of Ripeness
Data Collected from Nigerian Institute for
Oil Palm Research (NIFOR)
Bunch Bunch % % Bunch % Ripe
Code- weight Detached FFA Color Color
(CO) (K) Fruits (Y) (BC) ()0
. (2.) . . .....
0 14 0 .60 Green 0
1 14.5 6 i .70 Yellow Green 35
2 16 12.5 .82 Vellow Orange so
3 17 32 1.2 Orange 70
4 17 56 1.5 Red Orange 90
5 16.5 75 1.9 Red 95
6 17-5
88 2-15
Red
100
153
Appendix 5 Dura: Percentage Free Fatty Acid from.Fruit
Bunches at Different Degrees of Ripeness
Data Collected from Nigerian Institute for Oil
Palm Research (NIFOR)
Bunch Bunch % Detached % FFA Bunch
Code weight Fruits (Y) Color
(CO) f , (3).. , _’ (Z)~ ‘ (BC)
0 10 0 .37 Black
1 14 ‘ 9.5 .73 Black
2 14 16 1.06 Black
3 14.5 30 1.30 Black (shiny)
4 12 . 45 1.65 Black (shiny)
5 15 ' 75 - 2.05 Black (shiny)
6 16 94 2.30 Black (shiny)
' 159
Appendix 6 MBan Bunch Weight (kg) and Corresponding
Age for Different varieties - Dura, Tenera,
‘ 4 . ; '. g. ; 599:?â€15655 on: an:Inzl'aneroan;$91.1: ........
Pisifera
Age
._ weight: ...... Weight ; weight
2-3 -- 4.05 4.65
3-4 5.18 5.10 5.63
4-5 6.53 6.01 5.77
5-6 6.76 7.52 7.50
6-7‘ 8.71 9.19 9.20
7-8 10.36 10.81 11.49
8-9 11.71 11.26 11.89
9-10 12.69 NA 11.49
10-11 13.36 NA 14.08
Mean .
weight 9.41 7.71' 9.07
Variance 9.46 7.92 11.18
Standard ’
Deviation 3.07 2.81 3.34
Coefficient
Variation 33% 36% 37%
Source: Cowan Estate. Ajagbodudu, Bendel State.
160
Appendix 7 Mean Bunch Weight (kg) and Age of Palm for
Tenera, Pisifera and Dura
Linear Regression Analysis
Tenera Y a A + 3*x
Intercept, A - -5.785656E-02
Slope, B - 1.293929
Source Sum of Sq. Deg. Freedom
Regression 46.87901 1
Residual .6575241 5
Total 47.53653 6
F. 356.4813
Coeff. of Determination e .98
Coeff. of Correlation - .99
~Standard Error of Estimate - .3626359
Linear Regression Analysis
Pisifera ~Y - A + 3*x
Intercept, A - .7326107
Slope, B - 1.192167
Source Sum of Sq. Deg. Freedom
Regression 85.27579 1
.Residual 4.230744 ' 7
Total 89.50653 8
F- 141.0935
Coeff. of Determination a .95
Coeff. of Correlation 8 .97
Standard Error of Estimate a .7774265
Linear Regression Analysis
Dura ' Y - A + B*X
Intercept, A - 7.6786042-02
Source ' Sum of Sq. Deg. Freedom
Regression 65.07619 1
Residual 1.177048 6
Ft 331.7258
Coeff. of Determination a .98
Coeff. of Correlation a .99
Standard Error of Estimate = .4429161
Mean Sq.
46.87901
.1315048
Mean'Sq.
85.27579
.604392
-Mean Sq.
65.07619
.1961746
Appendix 8 Tenera:
Content and Revenue (ii/kg)
161
.0
Simulated Harvest Compositions with
Associated Percentage Detached Fruit,
Percentage Free Fatty Acid and Percentage Oil
Harvest % Detached % FFA % Oil Pevenue
Composition Fruit Content Content (u/kg)
0:100:0 30 1.16 49.49 0.0848
5:95:0 29 1.14 49.36 0.0853
10:90:0 28 1.12 49.23 0.0853
15:85:0 27 1.10 49.10 0.0858
20:80:0 26 1.09 48.97 0.0858
25:75:0 25 1.07 48.84 0.0862
30:70:0 24 1.05 48.71 0.0863
35:65:o 23 1.03 48.58 0.0866
40:60:0 22 1.01 48.45 0.0868
50:50:0 20 .98 48.19 0.0872
0:95:5 30 1.17 49.58 0.0850
0:90:10 31.5 ~1.19 49.68 0.0847
0:85:15 32.2 1.20 49.78 0.0846
0:80:20 33 1.22 49.88 0.0844
0:75:25 33.7 1.23 49.97 0.0842
0:70:30 34 1.24 50.08 0.0841
0:65:35 35.2 1.26 50.17 0.0839
0:60:40 36 1.27 50.27 0.0838
0:55:45 36.7 1.28 50.36 0.0836
0:50:50 37.5 1.30 50.46 0.0834
25:50:25 28.7 1.14 49.32 0.0853
20:60:20 29 1.14 49.36 0.0853
15:70:15 29.2 1.15 49.39 0-0338
10:80:10 29.5 1.15 49.42 0-0838
s:90:5 29.7 1.16 49.45 0-0851
5:85:10 30.5 1.17 49.55 0.0850
15:75:10 28.5 1.13 49.29 0-0854
5:00:15 31.2 1.18 49.65 0-0848
20:40:40 32 1.20 49.75 0-0846
40:40:20 25 1.07 48.84 0-0362
20:70:10 27.5 1.11 49.16 0:085?
10:85:5 28.7 1.14 49.32 0.0853
50:25:25 23.7 1.05 _48.67 0.0353
25:25:50 32.5 1.21 49.81 0.0845
60:10:30 22.5 1.02 48.51 0.0868
30:10:60 33 1.20 49.88 0.0846
10:75:15 30.2 1.16 49.49 0.0848
162
Appendix 9 Pisifera: Simulated Harvest Compositions with
Associated Percentage Detached Fruit,
Percentage Free Fatty Acid and Percentage Oil
Content
Harvest % Detached % FFA % Oil
Composition Fruit Content Content
0:100:0 30 1.12 49.49
5:95:0 29 1.10 49.36
10:90:0 28 1.08 49.23
15:85:0 27 1.06 49.10
20:80:0 26 1.05 48.97
25:75:O 25 1.03 48.84
30:70:0 24 1.01 48.71
35:65:0 23 .99 48.58
40:60:0 22 .98 48.45
' 50:50:0 20 .94 48.19
0:95:5 30.7 1.13 49.58
0:90:10 31.5 1.14 49.68
0:85:15 32.2 1.15 49.78
0:80:20 33 1.17 49.88
0:75:25 33.7 1.18 49.97
0:70:30 34.5 1.19 50.07
0:65:35 35.2 1.21 50.17
0:60:40 36 1.22 50.27
0:55:45 36.7 1.23 50.36
0:50:50 37.5 1.24 50.46
25:50:25 28.7 1.09 49.32
20:60:20 29 1.10 49.36
15:70:15 29.2 1.10 49.39
10:80:10 29.5 1.11 49.42
5:90:5 29.7 1.11 49.45
5:85:10 30.5 1.12 49.55
15:75:10 28.5 1.09 49.29
5:80:15 31.2 1.14 49.65
10:60:30 32.5 1.16 49.81
20:40:40 32 1.15 49.75
40:40:20 25 1.03 48.84
30:60:10 25.5 1.04 48.90
20:70:10 27.5 1.07 49.16
10:85:5 28.7 1.09 49.32
50:25:25 23.7 1.01 48.67
25:25:50 32.5 1.16 49.81
60:10:30 22.5 .99 48.51
30:10:60 33 1.17 49.88
10:75:15 30.2 1.12 49.52
163
Appendix 10 Dura: Simulated Harvest Compositions with
Associated Percentage Detached Fruit,
Percentage Free Fatty Acid and Percentage
Oil Content
Harvest % Detached % FFA % Oil
Composition Fruit Content Content
0:100:0 30 1.18 49.49
5:95:0 29 1.16 49.36
10:90:0 28 1.14 49.23
15:85:0 27 1.12 49.10
20:80:0 26 1.10 48.97
25:75:0 25 1.08 48.84
30:70:0 24 1.06 48.71
35:65:0 23 1.04 48.58
40:60:0 22 1.02 48.45
50:50:0 20 .98 48.19
0:95:5. 30.7 1.19 49.58
0:90:10 31.5 1.21 49.68
0:85:15 32.2 1.22 49.78
0:80:20 33 1.24 49.88
0:75:25 33.7 1.25 49.97
0:70:30 34.5 1.27 50.07
0:65:35 35.2 1.28 50.17
0:60:40 36 1.29 50.27
0:55:45 36.7 1.31 50.36
0:50:50 37.5 1.32 50.46
25:50:25 28.7 1.15 49.32
20:60:20 29 1.16 49.36
15:70:15 29.2 1.16 49.39
10:80:10 29.5 1.17 49.42
5:90:5 29.7 1.17 49.45
15:75:10 28.5 1.15 49.29
5:80:15 31.2 1.20 49.65
10:60:30 32.5 1.23 49.81
20:40:40 32' 1.22 49.75
40:40:20 25 1.08 48.84
30:60:10 25.5 1.09 48.90
20:70:10 27.5 1.13 49.16
10:85:5 28.7 1.15 49.32
50:25:25 23.7 1.06 48.67
25:25:50 32.5 1.23 49.81
60:10:30 22.5 1.03 48.51
- 10:75:15 30.2 1.18 49.52
164
Appendix 11 A Sample of a Monthly Report Sheet on a
Fresh Fruit Bunch Analysis
Estate: Month: Year:
Satisfactory
Day FFB%
Bruised
Diseased F
Bunches
%
Rotten
Bunches
%
sun
%RF
%VR
\DGQO‘UI-ï¬uNH
hue
rho
HHHHHHH
mummbuu
UR - Unripe Fruit, FR 8 Ripe Fruit, VR 2 Very Ripe Fruit,
FFB - Fresh Fruit Bunches
165
Appendix 12 A Sample Sheet for Recording the Quality of
Fruit Delivered to a Mill 7
Estate: Farmer's Name Source:
Lorry/ Date' Field Date 5 Date 8 Time
Tractor No. Harvested Number Ex-Estate at Factory
Fruit Bunch Information:
(a) ‘Variety
(b) No. of detached fruits before cutting
the bunch
No. of detached fruits after cutting
the bunch
Total No. of detached fruits/weight in kg =
(c) Bunch weight in kilograms a
(d) Bunch Conditions: Bruised Unbruised
Diseased No. of
bunches
Rotten No. of
bunches
(e) Height of tree (measured from the crown) ft:
Age (yrs.)
(f) Estimate of time delay (in days)
Remarks:
Appendix 13 Pisifera:
166
Mill Reception
Simulated Fresh Fruit Bunches at
Sample *1 Bunch # p
1 2 3 4 5 .
(Wt. or Number) 1(W) 2(W) 4(W) 6(W) 8(W)
2. Wt. of Bunch (kg) 18 18 18 18 18
3. Time Delay in
Days 0 0 0 O 0
4. Condition of
Bunch (U) (U) (U) (U) (U)
5. Age of Palm 16 16 16' 16 16
(a) Degree of Under Just Over
Ripeness Unripe Ripe Ripe Ripe Ripe
(b) % FFA .75 .91 1.22 1.53 1.84
(c) Probable Yellow Yellow' Orange Red Red
Color Green Orange or 70% Orange or 95%
-or 50% or 50% Ripe or 70% Ripe
Ripe Ripe Color Ripe Color
Color Color Color
(d) Std. Premium 0 0 N .006 H .003 0
(e) Subtotal 8 .18 x .72 1.45 31.41 s .72
(f) Extra Amt. Due I
' Premium :4 .10 if .06
(g) Total Amount Due Farmer N4.48
(h) Quantity Premium 0
(i) Harvest Composition 40:40:20
(j) Oil For Mesocarp 48.84: Overall % FFA = 1.03%
(k) Loss Due to Unripe Harvest: N144/Acre or 3360/ha.
(1)
(m)
Loss Due to Over Ripe Harvest:
Grand Total Amount Due Farmer:
$4.48
167
Appendix 13 (cont'd.)
Sample #2 ,Bunch #
1 2 3 4 5
1. Detached Fruit:
(Wt. or Number) 2(W) 4(W) 6(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 20 20 20 20 20
3. Time Delay in
Days 2 2 2 2 2
4. Condition of
Bunch (U) (M). (S) (V) (X)
5. Age of Palm 19 19 19 19 19
(a) Degree of Uhder Just Over Very
Ripeness Ripe Ripe . Ripe Over
Ripe Ripe
(b) % FFA 1.35 1.89 2.23 2.57 2.
(c) Probable Yellow Orange Red Red Red
Color .Orange or 70% Orange or 95% or
or 50% Ripe or 70% Ripe 100%
Ripe Color Ripe Color Ripe
Color Color Color
(d) Std. Premium 0 N .0003 -N.001 -N.002 -N.0009
(e) Subtotal 3 .8 31.51 31.46 a .75 a .18
(f) Extra Amt. Due
Premium 8 .01 48.03 -N.04 46.01
(g) Total Amount Due Farmer 84.70
(h) Quantity Premium H.60
(i) Harvest Composition 20:40:40
(j) Oil Per Mesocarp 49.75: Overall & FFA a 1.15%
(k) Loss Due to Unripe Harvest:
(1) Loss Due to Over Ripe Harvest: 82.15/Acre or 35.39/ha.
(m)
Grand Total Amount Due Farmer:
35.30
168
Appendix 13 (cont'd.)
Sample #3 Bunch #
1 . 2 3 4 5
1. Detached Fruit:
(Wt. or Number) 3(W) 4(W) 6(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 18 18 18 18 18
3. Time Delay in
Days 1 l l 1 1
4. Condition of
Bunch (U) (M) (S) (V) .(X)
5. Age of Palm 15 15 15 15 15
(a) Degree of Just Just Over Very
Ripeness Ripe— Ripe Ripe Over
Ripe Ripe
(b) % FFA 1.18 . 1.66 2.01 2.37 2.72
(c) Probable Orange Orange Red Red Red
Color or 70% or 70% Orange or 95% or
'Ripe Ripe or 70% Ripe 100%
Color Color Ripe Color Ripe
Color Color
(d) Std. Premium H.006 H.002 -N.0001 -N.001 -H .0007
(e) Subtotal 31.46 1.39 1.34 3.69 8.16
(f) Extra Amt. Due
Premium N .11 H.04 -§.002 -H.02 -H.01
(g) Total Amount Due Farmer H5.04
(h) Quantity Premium :4. 54
(i) Harvest Composition 0:60:40
(j) Oil Per.Mesocarp 50.27: Overall % FFA = 1.22
(k) Loss Due to Unripe Harvest:
(1)- Loss Due to Over Ripe Harvest: 32.69/ha if 5 tons
were harvested
(m) Grand Total Amount Due Farmer: N5.58
169
Appendix 13 (cont'd.)
Sample #4 Bunch #
1 2 3 4 5 ~ 6
1. Detached Fruit: ’
(Wt. or Number) 2(W) 3(W) 4(W) 6(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 22 22 22 22 22 22
3. Time Deiay in
Days 2 2 2 2 2 2
4. Condition of -
Bunch (U) (M) (S) (S) (S) V)
5. Age of Palm 24 24 24 24 24 24
(a) Degree of Under Just Just Over
Ripeness Ripe Ripe Ripe Ripe Ripe Ripe
(b) % FFA 1.34 1.80 2.06 2.19 2.32 2.64
(c) Probable Yellow Orange Orange Red Red Red
Color Orange or 70% or 70% Orange Orange or
or 50% Ripe Ripe 'or 70% or 70% 9" '
Ripe Color Color Ripe Ripe Ripe
. Color Color Color Color
(d) Std. Premium. 0 a .002 -n.000 -u.002 -u.00 . -u.002
(e) Subtotal “.88 l1.68 N1.63 l1.6l l1.59 n .83
(6) Extra Amt. Due
Premium u .03 «.01 -l.03 «.06 40.04
(g) Total Amount Due Farmer H8.22
(h) Quantity Premium H1.32
.(i) Harvest Composition 16:67:17
(3) Oil Per Mesocarp 49.41: _Overall % FFA - 1.10 '
(k) Loss Due to unripe Harvest: H144/Acre
(1) Loss Due to Over Ripe Harvest: u2.15/Acre
(m) Grand Total Amount Due Farmer: R9.54
170
Appendix 13 (cont'd.)
Sample #5 Bunch #
1 2 3 4 5
1. Detached Fruit:
(Wt. or Number) 2(W) 4(W) 8(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 20 20 20 20 20
3. Time Delay in ;
Days 0 .0 0 0 O
4. Condition of i '
Bunch (U) . (U) (U) (M) (U)
5. Age of Palm 19 19 19 19 19
(a) Degree of Under Just Over Over Very
Ripeness Ripe Ripe Ripe Ripe Over
. Ripe
(b) % FFA .88 1.15 1.71 1.71 1.99'
(c) Probable Yellow Orange Red Red Red
Color Orange or 70% or or or
,or 50% Ripe 90% 90% 100%
Ripe Color Ripe Ripe Ripe
Color Color Color Color
(d) Std. Premium 0 N .006 0 0 0
(e) Subtotal N .8 Nl.63 N .8 N .8 N .2
(f) Extra Amt. Due
Premium N .13
(g) Total Amount Due Farmer N4.23
(h) Quantity Premium N.3
(1) Harvest Composition 20:20:60
(j) Oil Per Mesocarp 50.14: Overall % FFA 1.20
(k) Loss Due to Unripe Harvest:
(l)
(m)
Loss Due to Over Ripe Harvest:
Grand Total Amount Due Farmer:
$4.53
N2.15/Acre or N5.39/ha.
171
Appendix 14 Dura: Simulated Fresh Fruit Bunches at
Mill Reception
Sample #1 Bunch #
1. Detached Fruit:
(Wt. or Number) 1 (W) 2 (W) 4 (W) 6 (W) 8 (W)
2. Rte of Bunch (kg) 18 18 18 18 18
3. Time Delay in .
Days 0 0 0 0 0
4. Condition of
Bunch (U) (M) (S) (V) (V)
5. Age of Palm 16 16 16 16 16
(a) Degree of . Under Just . Over
Ripeness Unripe Ripe Ripe Ripe Ripe
(b) % FFA .76 1.33 1.86 2.40 2.73
(c) Probable ' Shiny Shiny Shiny
Color Black Black Black Black Black
(d) Std. Premium - 0 0 N .001 -N.003 -N.003
(e) Subtotal .18 .72 N1.36 N1.29 N .66
(f) Extra Amt. Due .
Premium N .01 -N.05 -N.05
(g) Total Amount Due Farmer 84.21
(h) Quantity Premium 0
(i) Harvest Composition 40:40:20
(j) Oil Per Mesocarp 48.84: Overall % FFA N1.08
(k) Loss Due to Unripe Harvest: N144/Acre
(1) Loss Due to Over Ripe Harvest:
(m) Grand Total Amount Due Farmer: H4.21
172
'Appendix 14 (Cont'd.)
Sample #2 Bunch #
1 2 3 4 5
1. -Detached Fruit:
(Wt. or Number) 2(W) 4(W) 6(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 20 20 20 20- 20
3. Time Delay in
Days 2 2 2 2 2
4. Condition of
Bunch (U) (M) (S) (V) (X)
5. Age of Palm 19 19 19 19 19
(a) Degree of Under Just Over Over
Ripeness Ripe Ripe Ripe Ripe Ripe
(b) % FFA 1.46 2.01 2.36 2.71 3.06
(c) Probable Shiny Shiny Shiny Shiny
Color Black Black Black Black Black
(d) Std. Premium . a -s.0001 -N.002 -N.002 -a.004
(e) Subtotal .8 1.49 1.44 8.74 .71
(f) Extra Amt. Due _
Premium ~H.002 -N.05 ~N.05 -N.08
(g) Total Amount Due Farmer N5.18
(h) Quantity Premium 3.6
(1) Harvest Composition 20:40:40
(j) Oil Per Mesocarp 49.75: Overall % FFA 1.22
(k) Loss Due to Unripe Harvest:
(1)
(m)
8988 Due.to over 5389 Harv585=
Grand Total Amount Due Farmer:
£2.15
N5.78
173
Appendix 14 (cont'd.)
Sample #3- Bunch #
1 2 . 3 4 5
1. Detached Fruit:
(Wt. or Number) 3(W) 4(W) 6(W) 8(W) 10(W)
2. Wt. of Bunch (kg) 18 18 18 18 18
3. Time Delay in
Days 1 1 1 1 1
4. Condition of
Bunch (U) (M) (S) (V) (X)
. 5. Age of Palm 15 15 15 15 15
(a) Degree of Just Just Over very
Ripeness Ripe Ripe -Ripe Ripe Over
. _ Ripe
(b) % FFA 1.24 1.72 2.09 2.45 2.82
(c) Probable Shiny Shiny Shiny Shiny Shiny
Color Black Black Black Black Black
(d) Std. premium '1: .005 a .002 -:: .0007 «.001 «.0008
(e) Subtotal . N1.45 H1.38 81.33 N .68 H .16
(f) Extra Amt. Due
Premium :7 .lo :4 .03 «.01 «.03 -w.01
(g) Total Amount Due Farmer 35.00
(h) .Quantity Premium 3.54
(i) Harvest Cemposition 0:60:40
(j) Oil Per Mesocarp 50.27: Overall % FFA 1.29
(k) Loss Due to Unripe Harvest:
(1) Loss Due to Over Ripe Harvest: N2.15/Acre or N5.39/ha.
(m)
Grand Total Amount Due Farmer: 35.54
174
Appendix 14 (cont'd.)
Sample #4 Bunch #
1 2 3 4 5
1. Detached Fruit:
(Wt. or Number) 2(W) 3(W) 4(W) 6(W) 8(W)
2. Wt. of Bunch (kg) 22 22 22 22 22
3. Time Delay in
Days 2 2 2 2 2
4. Condition of
Bunch (U) (M) (S) (S) (S)
5. Age of Palm. 24 24 24 24 24
(a) Degree of Under Just Just
Ripeness Ripe Ripe Ripe Ripe Ripe
(b) % FFA 1.45 1.92 2.19 2.32 2.46
(c) Probable Shiny Shiny Shiny Shiny.
Color Black Black Black Black Black
(d) Std. Premium . 0 N .0006 -H.001 -H.002 -N.003
(e) Subtotal H.88 Nl.66 31.61 N1.59 al.57
(f) Extra Amt. Due
Premium N .01 -H.03 -H.05 -H.o7
(9) Total Amount Due Farmer H7.31
(h) Quantity Premium N1.32
(i) Harvest Composition 20:80:0
(j) Oil Per Mesocarp 48.97: Overall % FFA a 1.105
(k) Loss Due to Unripe Harvest:
(1) Loss Due to Over Ripe Harvest:
(m) Grand Total Amount Due Farmer: N8.63
175
Appendix 14 (cont'd.)
(1)
(m)
Sample #5 Bunch #
1 2 3 4 5
1. Detached Fruit:
(Wt. or Number) 2(W) 4(W) 8(W) 8(W) 10(W;
2. Wt. of Bunch (kg) 20 20 20 20 20
3. Time Delay in
Days 0 0 0 0 0
4. Condition of
, Bunch (U) (U) (U) (M) (U)
5. Age of Palm 19 19 19 19 19
(a) Degree of Under Just Over Over Over
Ripeness Ripe Ripe Ripe Ripe Ripe
(b) % FFA .90 1.20 1.80 2.20 2.10
(c) Probable Shiny Shiny Shiny Shiny
Color Black Black Black Black Black
(d) Std. Premium ' 0 N .006 a -H.0008-N.0004
(e) Subtotal a .8 N1.62 .8 a .78 a .79
‘(f) Extra Amt. Due
Premium 3! .12 -N.01 -N.008
(g) Total Amount Due Farmer 84.79
(h) Quantity Premium 3.3
(i) Harvest Composition 20:20:60
(3) Oil Per Mesocarp 50.14: Overall % FFA = 1.28
(k) Loss Due to Unripe Harvest:.
Loss Due to Over Ripe Harvest: H2.15/Acre or 35.39/ha.
Grand Total Amount Due Farmer:
N5.09
1f76
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APPENDIX 16’
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178
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APPENDIX 18
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APPENDIX
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APPENDIX
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APPENDIX 20
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APPENDIX 21
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APPENDIX 22
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APPENDIX 22
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APPENDIX 23
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185
APPENDIX 24-
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Appendix 25
PUB
IaOTO‘
Subroutine to store data in arrays.
187
Appendix 2 6
. Naggiighgégé
£03.: .039 o. 2.63. .190 3 032.9316.
Appendix 27
‘ Sflflfl .' ’
counnurosrn
lanrrxx*nnnur
10 â€NEW!
'ï¬ï¬‚l usrunmm
cnwnnnrorumxrn
nun: HIUEHEH
NDNUMEI
1
Cm)
Subroutine to convert detached fruit in weight to
number.
Appendix 2 8
PEEK!
ifllfln “UNIT!
Subroutine tc calculate percent detached fruit and
indicate bunch code.
mm "A
mm: DB4!
Appendix 29
IS FRUIT BRUISED
1!: ADD .1“
SzhnD .6$
7: ADD .8$
3172
X:ADD 1.0$
STIR
r
Cm D
Subroutine to calculate fatty free acid.
192
Appendix 30 . .
‘ snmn
Subroutine to calculate standard premium.
Appendix 31
- PORIxO‘NZ
NRJaomï¬
‘ 8 QUANTITY flow»
Subroutine to calculate quantitypremium.
Appendix 32
xnrnunzr
VIRLMII:
a
Subroutine (or calculation of loss and harvest
composition.
SUI! RIPE mm
II 3! FROM
0001!?!
PORJ35‘1'06
SUI! OVER RIP!
mm II V!
m QUANTITY
m PWAGES
m
30
n e
unmvn'
axiom
ARE WITHIN 5‘ OP
,0
n4.
1
IF
GRAND
TOTAL<
V
IPVR’D'R
V
1m...
8)..
0mm
mum
1N1
a):
mum
mum
I
c
V
197
’ PRINT
â€WIPE E35562â€
AID ESTIMATE L033
emu: mean
um: um: a mm
-on. "w! mm om
«mm. mm
[ J
[w 7“]
{no 1'0 PALHROYAL ’
Appendix 33
PRNHPEMDM
HISNWI
'Palmkey' subroutine to aid in adjustment of
harvesting system.
199
CW)
I
mar:
nun-mu muss
mm. m mu-
. m: or mum
I CALCULATIOI
mnuzr
rman mu: m w n ma
you me
mummomn: ’ men or was
. to a
rumour: m ' ‘ ‘ v
mu: ton ‘ mu m
momma. run 4»
on mm 11' so: am an:
Appendix 34 3 3 0
F
PRINT INTERNAL
ms or mun»
m IOU y f
um 5001mm mm mm: ma
cam stow [
n l
CALCULATE
m annum cnsa A
new
I
pann-
5.
'RALMROYAL' subroutine for calculation of internal
rate of return, and equivalent annual cash flow.
Appendix 3 5
m
. mi
73‘
77‘
4 F- '
19 46 - L950 DURA . F :965 TENERA
J —
Average: 26 '46" kilos 4 Avenage .- 19922 kilos
F? -
H { in
F'- - -
- . r
‘ I'
_ 4 - '
H . 'r- F
102039405050 020203015
MUMWWINMMMMDMM
MAW 1m
.202
Appendix 36
. all content /
i a
C O
2 ' 5
é :3
~1- I’ a.
q Q)
. E
e' ‘F .\e
0 10 20 30 40
‘/o detached frmt to rota! frwr
PROBABLE RELATIONSHIP BETWEEN OIL CONTENT AND OIL OUA’. ITY
AND PERCENTAGE DETACHED FRUIT.
(AFTER A. SOUTHWORTH 1973)
203
Appendix 37
Oil Palm Quality Control
â€'15? Program
10 DIN TB(2.6.3)
II DIN PL(500.6)
12 DIN CI(2.6)
IS DIN OT(2.6)
16 DIN NIBS).PV(35)
IB ‘REN DICITONARY OF VARIABLES
- I9 REN A - VALUE OF OIL
20 REN AG - AGE OF PALN TREE
II REN AR - AREA HARVESTED
23 REN B -VALUE OF KERNAL
24 REN BC! - BUNCH CODE NANE
ZS REN BU - INDIVIDUAL BUNCH UT.
30 REN C FLAG VARIABLE
40 REN C3 ARRAY OF COLOURS
AI REN D - BUNCHES PER ACRE
42 REN DF! DETACHED FRUIT (U OR N)
15 REN CI - PRINT FLAG (UNRIPE)
A6 REN C2 - PRINT FLAG (OVERRIPE)
A0 REN F! - FARNER’S NANE
50 REN FCS - FRUIT CONDITION
52 REN FF - ‘ FFA CONTENT
SJ REN FV - FRUIT VALUE
60 REN I - INDEI VARIABLE
63 REN IR - INCORRECT ITEN NUN
67 REN IR - INTERNAL RATE OF RETURN
70 REN J - INDEB VARIABLE
71 REN KP - KERNAL PRICE (NITON)
70 REN RR - ANT RERNAL I FRUIT
75 REN R - INDEX VARIABLE
76 REN LF - LOOSE FRUIT‘
77 REN LB - LOVER BOUND (IRR CALC)
70 REN N - NARGIN
7’ REN N - TENPORARY STORAGE VAR
00 REN NV - EQUIVALENT CASH FLOU
BI REN O - ANT OF OILIFRUIT
02 REN OR! - FLAG VARIABLE
03 REN OP - OIL PRICE (NITON)
BS REN P - PRICE(SEOO2¢PRICE(TABLE)
90 REN PC‘ BUNCH CODE
93 REN PL - PRINT LISTING
’7 REN PN - PRENIUN AWARD BABED ON 0 DETACHED FRUIT
I00 REN PNI PLANTATION NANE
IIO REN PR - PRENIUN RATE
III REN PT‘ - POINTER INTO PL
III REN PV - PRESENT VALUE
IIS REN Q - QUANTITYISUN OF BUNCH UT.IN KG.)
II? REN OT QUANTITY TOTALS
I20 REN OZ QUANTITY PRENIUN
I23 REN 03 OUANTITY PRENIUN CALC
125 REN R PERCENT DETACHED
127 REN RF TOTAL VT RIPE FRUIT
130 REN SN! STATE NANE
I33 REN ST SUBTOTAL
I40 REN S2 - STANDARD PRENIUN
I43 REN T - TOTAL FOR FARNER
I50 REN TB - TABLE OF VALUES
ISS REN TD - TINE DELAY (DAYS)
156
137
I50
159
160
161
163
165
166
167
I59
I70
I7I
I72
174
I76
I70
100
102
100
I06
100
I90
I91
I93
I94
I96
THE
I90
200
202
203
204
205
206
230
240
230
260
205
270
200
290
300
310
320
330
340
330
353
357
350
360
362
303
370
300
390
400
405
REN
tum
nan
REN
REN
REN
an:
an:
REN
am
am
nan
nan
non:
PR IN?
mm
mm
PRINT
PRINT
PRINT
mu?
PRINT ;
INPUT
HONE
PRINT
PRINT
PRINT
TOTAL
PRINT
PRINT :
PRINT
PRINT
PRINT
PRINT
PRINT
REN
PRINT :
INPUT
GOSUB
PRINT :
PRINT
INPUT
IF F!
INPUT
INPUT
TF
0)
9L3
TOTAL FRUIT VT
UPPER BOUND (IRR
TOTAL VT UNRIPE
PRESENT VALUE
BUNCH VEIGHT
VARIETY NANE
VARIETY NUNBER
VARIETY NANE TABLE
TOTAL VT OVERRIPE FRUIT
VT OR NUN DETACHED FRUIT
FRUIT PER BUNCH
YEAR (IRR CALC)
CORRECT? VARIABLE
UB -
UR
v -
V0
V0
V0 -
VN! -
VR -
u -
y -
YR -
23
CALC)
FRUIT
(LB)
TAB( 10);"OIL PALN QUALITY CONTROL"
TABI 10)."BY"
PRINT
TAB( 10);"ERNEST NESHACK-HART"
PRINT
PRINT
â€HIT RETURN TO BECIN.".KI
PRINT "VELCONEITHIS PROGRAN PREDICTS THE N FFA"
"AND OTHER FIELD FACTORS SUCH AS DEGREE OF BRUISING AND TINE DELAY'
"THE PERCENT DETACHED FRUIT IS CALCULATED BY RELATING THE VEIGHT OF DETACH
FRUIT VEIGHT."
PRINT
PRINT
"THIS PROGRAN ALSO PERFORNS HARVEST CONPOSITION ANALYSISâ€
"THE HARVEST CONPOSITION IS CATEGORIZED INTO UNRIPE.RIPE.OVER-RIPE'
PRINT “YOU VILL ALSO BE ASKED TO EVALUATE THE CONDITION OF THE FRUITLETâ€
“IF BRUISED.VHAT IS THE DEGREE OF BRUISING"
"VHENEVER IN DOUBT REFER TO THE ’HELP'NENU“
“'3 BEGIN PROG 0"
PRINT : PRINT .
"ENTER PRENIUN:
1000
PRINT : PRINT : PRINT
“ENTER FARNER'S NANE '
"'QUIT' TO FINISH DAY: '
“QUIT" THEN GOTO 9000
"ENTER STATE NANE:
“ENTER PLANTATION NANE:
PRINT
â€zPR
:F!
".SNS
â€:PN!
LET PT‘ I 0
LET ST a 0.0
LET T
PRINT .
PRINT
INPUT
IF V!
PRINT
INPUT
INPUT
INPUT
INPUT
PRINT
INPUT
IF PC
I 0
PRINT “ENTER VARIETY“
â€(DURA. PISIFERA. TENERA)â€
"'END' TO END CUSTONER: "
"END" GOTO 600
"ENTER NO. DETACHED FRUITS"
â€OR VEIGHT DETACHED FRUIT: ";
"NUNBER OR VEIGHT (N.V)? ":DFI
â€ENTER BUNCH VEIGHT (RG): â€:Q
â€ENTER TINE DELAY (DAYS): ":TD
"ENTER FRUIT CONDITION“
â€(U. N. S. V. 1. OR HELP):
0 e "HELP" THEN GOSUB I500
:V9
â€$FCO
205
410 INPUT "ENTER AGE OF PALN (YEARS) ".AG
4I3 REN GOTO ACCURACY CHECK
417 GOSUB 7000
420 IF DFS I "V" THEN GOSUB 5000
133 GOSUB 1000
034 IF C I - I THEN GOTO 350
433 REN GOTO FFA CALC
43‘ GOSUB 4000
439 REN GOTD STANDARD PREN CALC
440 GOSUB 3000
060 LET PT‘ I PT‘ 0 I
465 IF PT‘ I 300 THEN GOTO 600
470 LET P I TB(V%.PC‘.0) o 52 + 02
400 LET ST I O S P
002 IF PCS ( ) "V" GOTO 405
403 IF FCC ( ) "X" GOTO 490
009 REN STORE INFORNATION FOR PRINTING LATER
490 LET PLIPT‘.0) a V‘
300 LET PL(PT‘.I) - PC‘
510 LET PLIPT‘.2) - PF
520 LET PL(PT‘.3) a 52
$30 LET PLIPT‘.4) e ST
540 LET PLIPT‘.5) s 0
330 PRINT
560 GOTO 350
600 REN PRINT RESULTS
610 PRINT . PRINT : PRINT PRINT
620 PRINT "PARNER'S NANE: ":F0
630 PRINT â€ESTATE; ";PNI
640 PRINT "STATE: “.SNS
650 PRINT
653 IF PT‘ I 0 GOTO 999
660 FOR I a I TO PT‘
670 PRINT "PRESS (SPACE) TO CONTINUE": GET 2!
600 PRINT "VARIETY; ".VNI(PL(I.0))
690 PRINT â€DEGREE OF RIPENESS. ";BCI(PL(I.I))
700 PRINT "NFFA: ".PL(I.2)
7I0 PRINT “PROBABLE COLOR: ".CS(PL(I.0).PL(I.I))
720 PRINT "STANDARD PRENIUN: ".PL(I.3)
730 PRINT "SUBTOTAL: ".PL(I.4)
740 LET T I T o PL(I.4)
750 PRINT
735 IF PL(I.2) ( 2 THEN GOTO 700
760 PRINT "GOOD FARNERS SUPPLY FRESH FRUIT VITH"
770 PRINT “FFA LESS THAN 2‘.â€
700 PRINT
790 PRINT “EXTRA ANOUNT DUE TO PRENIUN IS: ";PL(1.3) ' PLII.5)
000 PRINT
010 REN TOTAL QUANTITIES FOR PRENIUN CALC
020 LET QT(PL(I.0).PL(I.I)) I OT(PL(I.0).PL(I.I)) + PL(I.5)
030 NEXT I
060 PRINT
070 REN GOSUB QUANTITY PRENIUN CALC
000 GOSUB 6000
090 PRINT "QUANTITY PRENIUN: ".03
900 PRINT "TOTAL DUE FARNER: ":03 o T
930 INPUT "DO YOU NEED AN ECONONIC ANALYSIS OF YOUR HARVEST CONPOSITION (YIN)? ";2
960 IF 2! I "Y“ OR 28 I “YES†THEN GOSUB 0000
963 PRINT
970 INPUT "DO YOU VANT ADVICE ON YOUR HARVESTING SYSTEN (YIN)? ":20
975
900
999
1000
1001
1005
1010
1020
1030
1040
1045
1050
1060
1070
1000
1090
1100
1110
1115
1120
1130
1140
1150
1160
1170
1100
1190
1200
1210
1500
1510
1520
1530
1540
1550
1560
1570
1500
1590
1600
1605
I610
1620
1630
1640
1650
1655
1660
1670
1600
1690
1900
2000
2010
2020
2030
2040
2050
2060
2070
206
IF 21 a "Y" OR 25 I "YES†THEN GOSUB 3500
PRINT
GOTO 265
REN SUDROUTINE TO LOAD TABLES
FOR I I 0 TO 2
READ VNS(I)
FOR J a 0 TO 6
FOR K I 0 TO 2
READ TB(I.J.K)
NEXT K
READ C5(I.J)
NEXT J
NEET I
FOR I I 0 TO 6
READ BC5(I)
NEST I
FOR I a 0 TO 2
FOR J a 0 TO 6
REN CONVERT PRICE PER TON TO PRICE PER KILOGRAN
LET TB(I.J.0) I TE(I.J.0) I 1000
NEXT J
NEST I
FOR A I I TO 11
READ N(E)
NEXT E
FOR A I 12 TO 35
LET N(E) I 260.52
NEET E
RETURN
REN HELP SUBROUTINE
PRINT
PRINT "NRNR'N'RRRN'NRNRNRNNu
PRINT
PRINT "FRUIT CONDITION SHOULD BE CATEGORIEED“
PRINT "ALONG THE FOLLOVING LINES."
PRINT .
PRINT "1 UNBRUISED (NO BRUISIND)"
PRINT '2. NODERATELY BRUISED (LESS THAN 20‘ DRUISED)"
PRINT "3. SEVERELY DRUISED (20-50‘ BRUISED)"
PRINT "0. VERY SEVERELY DRUISED (SD-75‘ DRUISED)
PRINT "5. EETRENELY SEVERELY BRUISED ()75‘ BRUISED)
PRINT
PRINT "ENTER 'U' FOR UNBRUISED"
PRINT " ‘N' FOR NODERATELY BRUISED'
PRINT " '5' FOR SEVERELY BRUISED"
PRINT †'V' FOR VERY SEVERELY BRUISED.'
PRINT " OR '2' FOR EETRENELY BRUISED."
PRINT
pat"? "eaeeetseeeeaeeteeeltn
PRINT
INPUT "ENTER FRUIT CONDITION (V. N. S. 2. OR V): ".FCI
RETURN
REN SUBROUTINE TO CALCULATE i DETACHED FRUIT.
REN AND ALSO INDICATE VHAT CODE THE BUNCH BELONGS.
IF V6 ( ) "DURA" THEN GOTO 2100
REN VARIETY IS DURA
REN 65‘ OF BUNCH VT.IS FRUIT AND
REN AVE.VT. OF EACH FRUIT IS .017LB(C.V.S HARTLEY)
LET Y I .65 0 Q
LET LF I (Y 0 2.2) I .017
2000
2090
2095
2100
2110
2120
2130
2140
2150
2160
2170
2100
2200
2210
2220
2230
2240
2250
2260
2270
2200
2300
2310
2320
2330
2340
2350
2400
2410
2420
2430
2440
2450
2460
2470
2400
2600
3000
3010
3020
3050
3060
3070
3000
3090
3100
3400
3500
3530
3540
3550
3560
3570
3575
3500
3590
3600
3610
3615
3620
207
LET R I (V I LF) 3 100
LET V‘ I 0
GOTO 2400
IF V5 I ) "PISIFERA" OOTO 2200
RE" VARIETY IS PISIFERA -
REN 62‘ OF BUNCH VT I3 FRUIT AND
REN AVE VT.OF EACN FRUIT I3 .015LD(C.V S HARTLEY)
LET Y I 62 5 O
LET LF I (Y ' 2.2) I 013
LET R I (V I LP) 0 100
LET V‘ I I
COTO 2400
IF V5 ( ) "TENERA" THEN OOTO 2300
REN VARIETY I3 TENERA
REN 60‘ OF BUNCH VT.IS FRUIT AND
REN AVE.VT OF EACH FRUIT I3 013LEIC v.3 HARTLEY)
LET Y I 6 P O
LET LF I (Y P 2.2) I ,013
LET R I (U I LF) 0 100
LET V‘ I 2
COTO 2400
REN
REN VARIETY IS NOT "DURA" ."PISIFERA" .OR "TENERA"
PRINT "INVALID FRUIT VARIETYâ€
PRINT "NUST DE EITHER 'DURA'. 'PISIFERA'. OR 'TENERA'"
LET C I - I
GOTO 2600
REN INDICATE DUNCN CODE
REN DASED ON ‘ DETACHED FRUIT
LET PC‘ I 0
IF R ) 0 THEN PC‘ I 1
IF R ) 10 THEN PC‘ I 2
IF R ) 20 THEN PC% I 3
IF R ) 40 THEN PCS I 4
IF R ) 60 THEN PC‘ I 5
IF R ) 00 THEN PC‘ I 6
RETURN
REN SUDROUTINE TO CALCULATE STANDARD PRENIUN
REN
LET 52 I 0
REN FFA CONTENT IS GOOD
REN PRENIUH IS .104 OF THE CURRENT PRICE FOR EACH PERCENTAGE POINT BELOW 2‘
REN
REN
LET S2 I (2 — FF) ! (TB(V$.PC$.0) ' .104)
IF TI(V$.PC$.1) I 1 AND 82 ) 0 THEN 32 I 0
RETURN
REN SUIROUTINE FOR HARVESTING SYSTEN ANALYSIS
PRINT "ENTER VARIETY YOU HARVESTâ€
INPUT “(DURA. PISIFERA. OR TENERA): ":VO
INPUT “ENTER RDETACHED FRUIT OF YOUR HARVEST CONPOSITION: ":R
IF V9 ( ) "DURA" COTO 3600
LET EU I .1779 § 1.233 0 AC
LET LF I - .553 + .2110 3 R
LET HS I LF I EU
COTO 3700
IF V5 ( ) “TENERA†COTO 3640
LET IV I - .0257 o 1.291 0 AC
LET LF I - .124 o .4003 4 R
LET NS I LF I RU
208
3621 LET PH I 0 0013 I (0 0065006 + (0 00004704 4 R))
3624 INPUT "PRICE OF OIL: â€;OP
3626 LET N I OP 4 (1 o 1.306 4 PH - 0.0104 4 PR 4 R) 4 (0.0456 0 0.0013 4 R)
3630 GOTO 3700
3640 IF V4 ( ) “PISIFERA†GOTO 3600
3650 LET IV I .7326 I 1.192 4 AG
3655 LET LF I - 415 o 2965 4 R
3660 LET NS I LF I BU
3670 GOTO 3700
3600 PRINT “TYPE SHOULD BE DURA. PISIFERA. OR TENERA "
3690 GOTD 3530
3700 PRINT PRINT “LIKELY AVERAGE BUNCH VEIGHT. “;BV
3710 PRINT "NIN HARVESTING STANDARD: "LNS
3715 PRINT "THE EQUIVALENT LOOSE FRUIT"
3716 PRINT "LOOSE FRUIT BEFORE CUTTING THE BUNCH IS: “.LF
3717 PRINT
3720 PRINT "IF HARVESTING INTERVAL IS SEVEN DAYS."
3721 PRINT "THE HA2 PRENIUH IS: ".PH
3724 PRINT "THE REVENUE DUE FARHER 15' “:N
3900 RETURN '
4000 REN SUBROUTINE FOR RATE OF
4010 REN ACIDIFICATION CALCUATION
4100 IF TD I 0 THEN GOTO 4400
4110 REN CALCULATE FFA BASED ON TINE DELAY
4120 IF VS ( ) "TENERA" THEN COTO 4200
4130 LET FF I .724 + (.663 4 TD)
4135 LET FF I (FF 0 ( 614 o (.0104 4 R))) I 2
4140 GOTO 4000
4200 IF VI ( ) "DURA“ THEN GOTO 4300
4210 LET FF I .726 + ( 654 4 TD)
4215 LET FF I (FF 9 ( 601 o ( 0195 4 R))) I 2
4220 GOTO 4000
4300 IF Vt ( ) "PISIFERA" THEN GOTO 4350
4310 LET FF I .704 o ( 521 4 TD)
4315 LET FF I (FF + ( 601 + (.0173 4 R))) I 2
4320 GOTO 4000
4350 REN VARIETY TYPE IS NOT
4360 REN "DURA“.“PISIFERAâ€. OR "TENERA"
4370 PRINT "UNRECOGNIZED TYPE"
4300 GOTO 4990
4400 REN TINE DELAY IS ZERO
4410 REN CALCULATE FFA BASED ON
4420 REN PERCENTAGE DETACHED FRUIT
4430 IF V0 ( ) "DURA†THEN GOTO 4500
4440 LET FF I .601 c ( 0195 4 R)
4450 GOTO 4000
4500 IF V1 ( ) “TENERA†THEN GOTO 4600
4510 LET FF I .614 o (.0104 4 R)
4520 GOTO 4000
4600 IF VS ( ) "PISIFERA" THEN GOTO 4350
4610 LET FF I .601 o (.0173 4 R)
4620 GOTO 4000
4000 REN IF FRUIT IS DRUIBED.
4010 REN ADD .4‘ TO THE FFA VALUE
4020 IF PCS ( ) TN†GOTO 4040
4030 LET FF I FF + .4
4040 IF FC6 ( ) "S" GOTO 4060
4050 LET FF I FF 0 .6
4060 IF PC! ( ) â€V“ GOTO 4000
4070 LET FF I FF 0 .0
4000
4090
4990
5000
5010
5020
5030
5040
5050
S100
5110
5120
5200
5210
5220
3300
5400
5410
5500
5510
5520
5530
5540
5550
5560
5570
5500
5590
5600
5610
5620
5630
5640
5653
5660
5670
5600
5690
5710
5720
5730
5740
5750
5760
5770
5700
5790
5000
5010
5020
5030
5040
5050
5070
5000
5900
6000
6010
6020
6030
209
1? EC: < > "X" COTO 4790
LET T? 3 FE o 1 0
RETURN
REN SUBROUTXNE TO CALCULATE
REN THE NUNBER DETACHED FRUITS
REN IRON VEIGHT OT LOOSE FRUITS
1? vs ( ) "DURA" THEN COTO $100
LETU-(U'ZUI 017
COTO 5400
1? Vs c ) "PISIFERA" THEN COTO 5200
LE? U , (V I 2 2) I 01:
COTO 3400
I? vs < > "TENERA" THEN OOTO 5300
LET U s (U I 2 2) I 013
COTO 5400
RETURN
RETURN
RETURN
REN PALH KEY
REH ROOM FOR PRINT STATEMENTS
REN
PRINT
PRINT "CALCULATION OF OIL PER HESOCARP. EPA. DETACHED FRUIT "
PRINT
INPUT â€ENTER TYPE (DURA. PISIFERA. TENERA)’ ".03
PRINT
INPUT "U UNRIPE FRUIT ".UR
INPUT "N RIPE FRUIT' ".RF
INPUT "5 OVERRIPE FRUIT ".VR
PRINT
REN CALC OF OPH AND FFA
LET OF I 0
LET DF a 1 4 UR
LET OF I DF 9 f 3 4 RF)
LET DF : DF 0 I 45 4 VR:
IF VS ( ) "TENERA" GOTO 5720
LET PT I 613 o 0104 4 DP
LET ON a 45 59 o 13 4 DP
COTO 5020
IF U: 4 > "DURA" GOTO 5760
LET FF I 601 o 0194 4 DP
LET OH I 45 59 o 13 4 DP
COTO 5020
IF VS ( ) "PISIFERA" GOTO 5000
LET FF 3 601 o 0173 4 DP
LET ON a 45 59 c 13 4 DP
COTO 5020
PRINT "TYPE HUST 0E DURA. PISIFERA. OR TENERA "
GOTO 5550
PRINT
PRINT "OPN"."FFA"."DF"
PRINT OH.FF.DF
PRINT
INPUT "WOULD YOU LIKE TO TRY ANOTHER CONEINATION".24
IF 28 I "YES†OR 24 I "Y" GOTO 5550
RETURN
REN SUBROUTINE TO CACULATE
REN QUANTITY PRENIUH
LET 03 I 0
FOR I a 0 TD 2
6040
6050
6060
6070
6072
6000
6090
6120
6330
6310
6320
6330
6340
6350
6360
6370
6380
6390
6400
6110
6420
6430
6440
6450
6460
6470
6480
6490
6500
6510
6520
6530
6540
6550
6360
6570
6500
6590
6600
6610
6620
6630
6640
6650
6660
6670
6600
6690
6700
6710
6720
6730
6740
6750
6760
6770
6700
6790
6000
6010
EOR J a 0 TO 6
IE TOII.J.I) s 1 THEN COTO 6000
IF TB(I.J 2) ) OT(I.J) THEN GOTO 6000
LET OZ 6 (PR 4 TBdI.J.0)I
LET O3 - OT(1.J1 4 OZ 0 O3
NEXT J
NEXT I
RETURN
REN SUBRCUTINE PALN ROYAL
PRINT
REN ROOH TOR PRINT STATEMENTS
REN
INPUT "ENTER RATE OF INTEREST â€FIR
INPUT “ENTER NUHBER 3? YEARS '.YR
PRINT
LET N : 1R
LET PVIYR) = 0
FOR I . (YR - I) TO 0 STEP - I
LET Pv<X) : ;Per . 1; . H<XI1 / c; 6 IR;
NEXT X
PRINT
FOR 1 a 0 TO ~YR - :3
PRINT "PRESENT VALUE FOR YEAR ".x." ".PV(X)
NEXT x
PRINT
INPUT "PRESS (RETURN) TO CONTINUE".Z$
PRINT
REN CAPITAL PROFILE
LET C = 0
POR X a 1 TO YR
LET C s - MIX) + C 4 (1 9 IR)
PRINT "CAPITAL FOR YEAR ".X." “.C
NEXT I
INPUT "PRESS (RETURN) TO CONTINUE".ZS
PRINT
LET IR 3 15
LET LR : - 3
LET U3 6 3
LET V a 0
FOR X 2 (YR - I) TO 0 STEP - 1
LET V a (V + HIXI) I (1 9 IR)
NEXT X
IE A85 (V) ( 01 COTO 6740
IP V I 0 COTO 6700
RE" V ) 0. INCREASE IR
LET LR : IR
LET IR I ((UR - LE) I 2) 6 LB
GOTO 6600
RE" V ( 0. DECREASE IR
LET UR : IR
LET IR I ((UE - LB) I 2) 6 LB
GOTO 6600
PRINT "INTERNAL RATE OF RETURN ".IR
PRINT
PRINT "DO YOU VANT THE EQUIVALENT CASH FLOW"
PRINT "0E THE NET PRESENT VALUE".
INPUT 20
LET IR 4 N
IE 20 I ) “YES" AND I: ( ) "Y" GOTO 6900
INPUT "POR VHAT YEAR’â€.YR
6020
6030
6040
6050
6060
6300
“300
7310
'020
7040
7050
7060
7070
7000
7305
7393
7130
7110
â€120
7130
7140
7150
7300
7205
7210
7220
7230
7240
7250
7260
727
7200
7205
7290
7295
7300
7310
7320
7350
7360
7370
7400
7410
7420
7425
7430
7450
7160
7470
7500
7513
7700
7705
7710
7720
7730
7740
7745
7750
7760
LET NV 3 PVIYR> ' IR ' ((1 . IR) A ya)
LET Nv . NV I (((1 . IR) A YR) - I)
PRINT
PRINT "ANNUAL CASH PLOU " Nv
CSTO 6760
RETURN
REN SUBROUTINE TO CHECK
REN INPUT ERRORS
REN ECHO PRINT
.PRINT PRINT
PRINT â€IS THIS INFORMATION CORRECT’"
PRINT
PRINT â€1 VARIETY †V:
PRINT â€2 DETACHED FRUIT â€,U
PRINT â€3 VEIGHT OR NUMBER “.DPS
PRINT â€4 BUNCH WEIGHT ".0
PRINT â€5 TINE DELAY †TD
PRINT “6 FRUIT CONDITION †PCS
PRIIT "7 PALH TREE AGE â€.AC
PRINT
PRINT "CORRECT 7YIN†" 5 T OK!
IE OK: : â€Y†OR OK? 3 “YES†THEN GOTO 7700
PRINT "WHICH ITEH IS INCORRECT (1«7)4 †GET IS
LET IN a VAL ‘15)
IE IN ( ) 1 THEN CCTO 7250
PRINT "ENTER VARIETY"
INPUT "(DURA. PISIPERA. TENERA) ".VS
CCTO 7000
IE I‘ ( > 2 THEN GOTO 7255
PRINT â€ENTER NUHBER OE DETACHED FRUITSâ€
INPUT "OR VEIGHT DETACHED ".U
SOTO 7000
IE 1‘ ( > 3 THEN COTO 7300
INPUT "VEIGHT OR NUHBER (U.N) â€.DPS
GOTO 7000
IE 1‘ < 3 4 THEN GOTO 7350
INPUT â€ENTER QUANTITY (KG) " 0
GOTO 7000 .
IP 1‘ < I 5 THEN GOTO 7400
INPUT "TINE DELAY (DAYS) ".TD
CCTO 7000
IE IN I ) 6 THEN GOTO 7450
PRINT "ENTER FRUIT CONDITIONâ€
INPUT "(U. H. S. V. X. OR HELP) ".ECI
IE PC: 3 "HELP†THEN GOSUI 1500
GOTO 7000
IP 1‘ ( > 7 THEN COTO 7500
INPUT "ENTER AGE OE PALN (YEARSI' ".AG
COTO 7000
PRINT "ITEM NUHBER HUST BE A NUMBER EROH 1 TO 7 "
GOTO 7200
IE V: : "DURA“ OR VS : "PISIEERA" OR V! - "TENERA" GOTO 7740
PRINT
PRINT "VARIETY HUST 0E 'DURA'. ‘PISIEERA'. OR 'TENERA‘
PRINT "PLEASE CHECK VARIETY "
GOTO 7000
IP DE: - "U“ OR DE! 1 "N" GOTO 7700
PRINT
PRINT "ITEN NUHBER THREE (VEIGHT OR NUHBER)“
PRINT â€RUST 0E EITHER 'U' OR ‘N' "
‘770
7775
7700
7705
7790
7000
‘010
7015
'020
7030
7930
5000
0010
00:5
9020
0030
0040
0045
0050
0060
0070
0000
0090
0093
0097
0100
0120
0130
0140
0150
0160
0170
0190
0200
0210
0230
0240
0250
0260
0263
0265
0267
0260
0270
0275
0279
0200
0202
0204
0206
0207
0200
0290
0293
0296
0299
0300
0304
0306
21
PRINT â€PLEASE CHECK UEI
SOTO 7000
IF PC: a
PRINT
PRINT
PRINT
PRINT
PRINT
PRINT
"U†OR PC! a â€H" OR FC!
â€FRUIT CONDITION NUST
"‘H' FOR
â€SEVERELY BRUISED ‘U‘
"BRUISED. OR 'X'
'PLEASE CHECK FRUIT CONDITI
BE 'U
FOR
U INE FOR COST ANALYSIS
3T ADDITIONAL INF
ER VARIETY YOU HARVEST
F AREA HARVESTED
QUANTITY HARVESTED
BUNCH VEIGHT (LBS)
FRUIT HEIGHT (5)
CURRENT OIL PRICE (N
CURRENT KERNAL PRICE
INPUT
INPUT
INPUT
INPUT
INPUT '
PRINT
INPUT
PRINT
LET
LET
3n 3
RF 2
VR 5
FOR
FOR '
REN TOTAL UNRIPE
LET UR ; UR 0
NEXT J
FOR J a 3 TO 4
REN TOTAL RIPE FRUIT
LET RF : RP . OTII J)
NEXT J
FOR J a 5 TO 6
REN TOTAL OVERRIPE FRUIT
LET VR 6 UR o OTII J)
NEXT J
NEXT I
LET TF 3
LET UR =
LET RF :
LET VR 3
PRINT
PRINT
PRINT
PRINT
PRINT
GOSUB
REN
IF
â€EIITER
"ENTER
â€ENTER
'ENTER
ENTER
â€ARE YOU A CROUCR. FROCESSO
3
-= 0
B) O“
4 0
C
C
3
0
0
I
J a 0
TO 2
on
Cu
N
FRUIT
OT(1.JI
UR 6 RF 6 UR
iUR I TE) 6
(RE I TF) 4
(7R I TE) '
100
100
100
"PERCENTAGE
â€PERCENTAGE
â€PERCENTAGE
UNRIPE FR
RIPE FRUIT
OVERRIPE FRUIT
F50°
OECIEE WHICH HESSAGE
(UR 6 VR) ( 30 THEN SOTO 0330
IE A53 (UR - VRI ( 5 THEN GOTO
I? UR ) VR TNEN COTO 0310
RE" OVERRIPE I UNOERRIPE
LET C2 3 I
IF UR I 30 THEN
COTO 033°
LET C1 8 1
NODERATELY BRUISED.
VERY
FOR EXTREHELY BR
\ACRES) "
(TONS) “ O
CHT OR NUHBER "
3 "5†OR FC5 3 â€V" OR PC} a â€X" GOTO 7900
' FOR UNBRUISED â€
‘3' FOR"
SEVERELYâ€
ISED "
ON
CRNATION
".VS
AR
FU
.OP
.KP
I'FCN) u
leT 3“) "
R. OR 00TH’ ".2!
6! .UR
.0 'RF
00 ‘VR
I3 APPROPRIATE
0320
P)
}.J
CO
0310 REM UNDERRIPE ) OVERRIPE
0312 LET C1 = 1
0315 IF UR ) 30 THEN LET C2 : I
0317 GOTO 0330
0320 REM PRINT ROTH MESSAGES
8324 LET CI 4 1
0327 LET C2 = I
0329 REM PRINT MESSAGES
8330 IF C1 ( > 1 THEN GOTO 0360
8333 PRINT PRINT "MUCH OIL IS LOST BY HARVESTING UNRIPE BUNCHES "
0340 PRINT â€THE PREMIUM OBTAINED FOR LOVER FFA‘"
0345 PRINT "CANNOT OFFSET THIS LOSS "
0350 PRINT PRINT "ANNUAL LOSS PER HECTARE FOR UNRIPE BUNCHES ".2 5 0 (( 010 l O) ' OP) I AR.
NIHECTARE“
0360 IF C2 ( > I THEN 3000
8365 PRINT
8370 PRINT "EXCESSIVE LOOSE FRUITS AND BUNCHES ARE MORE PRONE"
0300 PRINT “TO DAMAGE. LEADING TO HIGH FFA$ OIL CONTENT.â€
5390 PRINT "WHICH YIELDS POOR OIL QUALITY “
500 REM CALCULATE LOSS PER ACRE
0510 REM CALCULATE BUNCHES LOST PER ACRE
8520 LET L = 0 / ((VB 0 05) I 1000)
0550 IF 2! ( ) "PROCESSOR†AND 25 ( ) "P" AND 20 ( ) "B†AND 20 ( ) "BOTH" GOTO 0650
8560 REM CUSTOMER IS A PROCESSOR
570 O 2 EU 0 367
0580 LET A 3 OP ' (O I 1000000)
0590 RR a EU 0 075
8600 LET B = KP ' (KR I 1000000)
0610 LET EU a A 6 B
0650 IF ES ( ) "GROUER" AND 25 ( ) "G" GOTO 0700
8660 REM CUSTOMER IS A GROVER
06?0 INPUT “ENTER FRUIT PRICE (TON) ".FP
8600 LET D a FP * (EU I 1000000)
0690 LET TV a D
0700 PRINT PRINT â€ANNUAL LOSS PER HECTARE FOR OVERRIPE BUNCHES' ";2 5 ' (FV ' L)." N/HECTAREâ€
0000 PRINT ' PRINT
0005 GOSUB 6300
0810 INPUT "DO YOU WANT AN AID IN ADJUSTING YOUR HARVESTING SYSTEM (YIN)’ ".25
0020 IF 2: a "Y" OR 25 : "YES" THEN GOSUB 5500
0990 FOR I a 0 TO 2
0991 FOR J a 0 TO 6
0904 OT(I.J) = 0
8996 NEXT J
0997 NEXT I
0999 RETURN
9000 PRINT PRINT
9010 PRINT "PROGRAM ENDED "
0020 STOP
500 REM CALCULATE OIL PER MESOCARP AND FFA
9520 LET DP 3 0
9530 REM 10‘ OF UNRIPE IS DETACHED
9540 LET DF 3 .1 0 UR
9550 REM 30% OF RIPE IS DETACHED
9560 LET DF 3 DP + ( 3 0 RF)
9570 REM 40$ BUT NOT MORE THAN 50‘ IS DETACHED
7500 LET DP 3 DP + ( 05 ' VR)
9590 IF V5 ( ) "TENERA" GOTO 9630
9600 LET FF 3 .613 + 0134 0 DE
9610 LET OM ; 45 59 o 13 ' DF
9620 GOTO 9750
9630
9640
9650
9660
9670
9680
9690
9700
9710
77:0
9750
9760
9780
9003
10000
10010
10020
10030
10046
10050
10060
10070
10080
10C90
10100
10130
10140
10150
10160
10170
10180
10190
10100
18230
10240
10250
10260
10270
10280
10290
10300
10310
10320
10330
10340
10350
10360
10370
10400
10410
10420
10430
10440
10450
10460
10470
13480
10490
10500
214
IF VS ( ) “DURA" GOTO 9670
LET FF a .601 + 10194 ' DF
LET OM x 45 59 6 13 0 DE
GOTO 9750
IF VS 1 ) â€PISIFERA" GOTO 9710
LET FF 3 601 + 0173 ' DF
LET OM : 45 59 o 13 ' DF
GOTO 9750
PRINT â€VARIETY MUST BE DURA. PISIFERA. GR TENERA "
RETURN
PRINT â€OIL PER MESOCARP"."FFA"
PRINT OM. FF
PRINT
RETURN
REM DATA FOR TABLE
REM T8(2.6.22
DATA "DURA"
DATA 0.1.0."BLACK"
DATA 10.1.0.â€DLACK"
DATA 40.1.0.“BLACK"
DATA 75.0.20."SHINY BLACK"
DATA 7510 20,"SNINY BLACK"
DATA 40.1.0."SHINY BLACK"
DATA 10.1.0.“SHINY BLACK"
DATA "PISIFERA"
DATA 0.1.0."GREEN"
DATA 10.1.0."YELLOU GREEN OR 35‘ RIPE COLOUR"
DATA 40.1 0.â€YELLOU ORANGE OR 50% RIPE COLOUR“
DATA 75.0.20."0RANGE OR 70% RIPE COLOUR"
DATA 75.0,20."RED GRANGE OR 90% RIPE COLOUR"
DATA 40.1.0 "RED OR 95‘ RIPE COLOUR"
DATA 10.1.0."RED OR 100% RIPE COLOUR"
DATA "TENERA"
DATA 0 1.0 â€GREEN“
DATA 10.1.0.â€YELLOV GREEN OR 40$ RIPE COLOUR"
DATA 40.1.0.â€YELLOU ORANGE OR 50% RIPE COLOUR"
DATA 75.0.20."ORANGE OR 70‘ RIPE COLOUR"
DATA 75.0.20."RED ORANGE OR 00% RIPE COLOUR"
DATA 40 1.0.â€RED OR 90‘ RIPE COLOUR"
DATA 10.1.0."RED OR 100$ RIPE COLORâ€
DATA â€VERY UNRIPEâ€
DATA "UNRIPE"
DATA "UNDER RIPE"
DATA "JUST RIPE"
DATA "RIPE"
DATA "OVER RIPE"
DATA "VERY OVER RIPE"
REM TABLE FOR IRR CALC
DATA -1067 05
DATA -220 99
DATA -120 09
DATA ~36 47
DATA 25 02
DATA 109 02
DATA I34 52
DATA 210 52
DATA 260 52
DATA 260 52
DATA 260 52
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