Title: Diversification opportunities and effects of alternative policies on Costa Rican coffee farms
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 Material Information
Title: Diversification opportunities and effects of alternative policies on Costa Rican coffee farms
Alternate Title: Costa Rican coffee farms
Physical Description: 203 leaves : ill. ; 28 cm.
Language: English
Creator: Bieber, John Lewis, 1936-
Publisher: s.n.
Place of Publication: Gainesville FL
Copyright Date: 1970
 Subjects
Subject: Agriculture -- Economic aspects -- Costa Rica   ( lcsh )
Coffee -- Costa Rica   ( lcsh )
Genre: bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )
 Notes
Statement of Responsibility: by John Lewis Bieber.
Thesis: Thesis (Ph. D.)--University of Florida, 1970.
Bibliography: Includes bibliographical references (leaves 197-201).
General Note: Typescript.
General Note: Vita.
 Record Information
Bibliographic ID: UF00098427
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000401586
notis - ACE7434
oclc - 37747605

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DIVERSIFICATION OPPORTUNITIES

AND EFFECTS OF ALTERNATIVE POLICIES

ON COSTA RICAN COFFEE FARMS














By
JOHN LEWIS BIEBER












A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY














UNIVERSITY OF FLORIDA


1970












ACKNOWLEDGE MENTS


The author wishes to express his sincere appreciation to Dr.

W. W. McPherson, Chairman of the Supervisory Committee, for his

guidance and supervision throughout all phases of this research and

for his valuable suggestions and criticisms in preparing this manu-

script.

Thanks are also due Dr. C. E. Murphrec, Dr. C. W. Fristce, Dr.

K. C. Gibbs and Dr. L. H. Myers for reviewing the manuscript and

offering assistance. The author also wishes to thank Dr. H. L.

Popenoe, Director of the University of Florida's Center for Tropical

Agriculture,for his assistance in obtaining funds for support of the

project. The assistance of the University of Florida's Computing

Center is recognized and appreciated. The author also wishes to

express thanks to Mr. D. W. Parvin for his aid in interpreting the

computer manuals.

Special thanks are also due numerous people in Costa Rica who

graciously supplied information and insights necessary to the comple-

tion of this work. The Oficina del Cafe under the direction of Sr.

Alvaro Castro Jimenez supplied transportation, office facilities, and

technical assistance. The author depended heavily on the advice of

Ing. Hugo Castro, Ing. Rogello Acosta and !ig. Edwin Marin in the

initial phases of the study.

The cooperation of the Extension Service of the Costa itica

Minist-y of Ayricult ,re is also appreciated. Ac!now\Jledgimnt and

thanks are due the ,nny people who supplied input-uutput information.
i i







Additional assistance was given by others including Carlos

Arroyo, Hester Barres, James Ross, Russel Desrosiers, Oscar Benavides,

Robert F. Voertman, J. Robert Hunter, and Ernesto Sanarrusia.

Special thanks are also due Miss Linda DiDuonni and Mrs. Sandi

Davis for typing the first draft and to Mrs. Lillian Ingenlath for

typing the final manuscript.















TABLE OF CONTENTS


Page


ACKNOWLEDGMENTS . . . . . . . . . .

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

INTRODUCTION . . . . . . . . . . . .


An Economic Background . . . . .
The Importance of Coffee to Costa Rica
The Marketing Situation for Coffee . .
The Pros and Cons of Diversification .


Approaches and Attitudes toward
The Problem and Objectives .


Diversifi


SCOPE AND METHOD OF STUDY . . .

Selection of Areas and Farms
Description of Farms Studied

Palmares-San Ramon . .
Alajuela . . . . .
Acosta . . . . .

The Linear Programming Model
The Enterprises and Restraints
The Sources of the Budgets .
Assumptions of the Model . .

RESULTS . . . . . . .


Optimal Cropping Plans


Farm I .
Farm 2.
Farm 3
Farm 4 .
Farm 5
Farm 6
Farm 7
Farm 8 .
Farm 9
Farm 10
Farm 11


. .
. .
. .
. .
cat ion
. .


. . . . . . 20

. . . . . . 20
. . . . . . 22


. . . . 57

. . . . . . 57







Page


Farm 12 . . . . . . . . . .... 64
Farm 13 . . . . . . . . 65
Farm 14 . . . . . . . . . .... 66
Farm 15 . . . . . . . . ... . 67
Farm 16 . . . . . . . . ... . 68

Policy Analysis . . . . . . . .. .. . .69

Education for Better Farm Management ..... .69
Taxation or Price Reduction . . . . . 75
Payments for Coffee Removal . . . . .. 81
Price Differentiation . . . . . ... .87
Reduction of Credit . . . . . . ... .90
Movement of Labor . . . . . . ... .93
Extra Credit . . . . . . . . ... .98
Subsidies for Alternatives . . . . . . 101

Stability of Alternative Crops . . . . . . 103

Comparative Costs of Coffee Removal . . . . .. 110

SUMMARY AND CONCLUSIONS .. . . . . . . . 134

Effects of Improved Resource Allocation . . ... .134
Effects of Taxes or Price Declines . . . ... .136
Effects of Credit Reduction . . . . . ... .137
Effects of Price Differentiation . . . . . 138
Effects of Increased Credit . . . . . . . 139
The Choices of Alternative Crops . . . . .. 140
Comparative Costs of Coffee Removal . . . . .. .43
Potential Effects of Technological Advances
in Coffee Production . . . . . . . .. 144
The Qualifications of an Acceptable Alternative . .. 145
Diversification Versus Production Control . . 146

GLOSSARY . . . . . . . . . . . . 148

APPENDIX . . . . . . . . . . . 149

LITERATURE CITED . . . . . . . . . . . 196

BIOCRAPHI CAL SKETCH . . . . . . . .... . 202












LIST OF TABLES


Table


Partial matrix of coffee selling activities . 37

Comparisons between reported and optimal
incomes and coffee output . . . .. . 71

The effects of improving coffee production
technology on coffee production and
income . . . . . . . . . . 74

Comparisons of optimal incomes and coffee
outputs with traditional versus new enter-
prises on farms in Alajuela and Acosta . . 76

Optimal coffee production with specified
price declines per fanega of coffee . . . 77

Optimal manzanas in coffee with specified price
declines per fanega of coffee . . . . 79

Optimal net farm income with specified price
declines per fanega of coffee . . . .. 80

Alternative crops increased first by declines
in coffee prices . . . . . . . 82

Coffee production with annual payments for
coffee removal . . . . .. . 83

Net farm income with annual payments for
coffee removal . . . . . . . .. 85

The alternative crops increased first by
annual payments for coffee removal . . .. 88

Comparisons of income and coffee production
for differentiated prices versus single
prices . . . . . . . . . . 89

Optimal coffee production with various
levels of reduced credit . . . . . . 91

Optimal net income with various levels of
reduced credit . . . . . . . 92


Page







Table Page

15. Optimal manzanas in coffee with various levels
of reduced credit . . . . . . . 94

16. Marginal returns to credit with various levels
of reduced credit . . . . . . ... .95

17. Optimal family income with and without off-
farm employment opportunities . . . ... .96

18. Optimal farm income and the reduction of
permanent labor . . . . . . ... .97

19. Effect of additional credit on income and
coffee production . . . . . . ... .99

20. Optimal farm outputs of various crops on
Alajuela farms given base capital and
credit constraints and extra credit
equaling (400 and (2,000 per manzana .... 100

21. Optimal output of various crops on Acosta
farms with high fruit prices given base capital
and credit and extra credit equaling (400 and
12,000 per manzana . . . . . . . 102

22. The effect of a blackberry subsidy on optimal
coffee output . . . . . . . ... 1.04

23. The effect of a strawberry subsidy on optimal
coffee output . . . . . . . . 104

24. Relationships between long-term interest
rates and manzanas planted to limes . . .. 105

25. Lime prices and lime production . . . ... .106

26. Prices of oranges necessary to initiate
orange production on farms in Alajuela
and Acosta . . . . . . . ... .109

27. Alternative methods of reducing coffee
output on farm 1 . . . . . . ... .111

28. Comparative costs of various methods of
coffee output reduction on farm 2 . . .. 112

29. Alternative methods of reducing coffee
output on farm 3 .... . . . . . . 113

30. Alternative methods of reducing coffee
output on farm 4 . . . . . . .'. 114






Table

31.


viii


Comparative costs of various methods of
reducing coffee output on farm 5 .

Comparative costs of various methods of
reducing coffee output on farm 6 .

Comparative costs of various methods of
reducing coffee output on farm 7 .

Comparative costs of various methods of
reducing coffee output on farm 8 .

The comparative costs of various methods
of reducing coffee output on farm 9 .

The comparative costs of various methods
of reducing coffee output on farm 10

The comparative costs of various methods
of reducing coffee output on farm 11

The comparative costs of various methods
of reducing coffee output on farm 12

The comparative costs of various methods
of reducing coffee output on farm 13,
given high fruit prices . . . .

The comparative costs of various methods
of reducing coffee output on farm 14,
given high fruit prices . . . .

The comparative costs of various methods
of reducing coffee output on farm 15,
given high fruit prices . . . .

The comparative costs of various methods
of reducing coffee output on farm 16,
given high fruit prices . . .

The comparative costs of various methods
of reducing coffee output on farm 13,
given low fruit prices . . . .

The comparative costs of various methods
of reducing coffee output on farm 14,
given low fruit prices . . . .

The comparative costs of various methods
of reducing coffee output on farm 15,
given low fruit prices . . . .


Page


. . . 116


. . 118


. . . 120


. . . 121


. . . 122


. . . 123



. . . 125



. . 126



. . . 127



. . . 128



. . . 129






Table


Page


146. The comparative costs of various methods
of reducing coffee output on farm 16,
given low fruit prices . . . . . . 132












INTRODUCTION


An Economic Background


Costa Rica is a small country in Central America with a popula-

tion of 1.6 million people living on an area of 19,700 square miles

(46, p. 11). The economy is dominated by agriculture which employed

over 56 percent of the active work force in 1963 (45, p. 13). In

addition, agricultural products accounted for approximately 30 per-

cent of the gross national product in 1966 and about 80 percent of

the value of Costa Rica's exports (17, P. 3). Alleger described

Costa Rica as a nation of small farmers (3, p. 33). This character-

ization does not mean that land is evenly distributed among the pop-

ulation, since farms of over 100 manzanas represent only 10.5 percent

of all Farms and cover 70 percent of the total farmland area (23,

p. 46). Nevertheless, the land distribution figures must be inter-

preted with a realization that many large farms are in remote areas

and that, except for sugarcane and bananas, the intensively grown

crops are dominated by small and middle-sized farms.

In recent years, Costa Rica enjoyed the highest per capital in-

come in Central America. The estimated grc-s national product per

person in 1965 was $415 compared to an average of $303 for all of

Central America (55, p. 3).

In recent years Costa Rica has had a balance of payments problem.

Foreign debts have had to be continually r-mclnocot.ated. From 1950 to






1967 the value of exports of coffee, bananas, cocao, cotton, beef,

and sugar increased from 592.1 to 702.8 million colones (56, p. 34).

While the value of exports has increased despite unfavorable price

trends, the value of imports has increased at a much faster rate.

The value of exports exceeded the value of imports by 24.8 percent

in 1967 and by 12.2 percent in 1968 (24).

Production of the basic food crops has exhibited erratic growth

since 1950. Corn imports exceeded exports in 1951, 1954, 1956, 1959,

1964 and 1966. Per capital consumption of corn in 1966 was below the

1950 level (56, p. 42). Rice production has almost tripled since

1950, with a stronger upward trend occurring after 1958. While the

change is largely due to an increased acreage, the average yields

have increased since 1955 (56, p. 5). Per capital consumption of rice

has increased. Bean production has also increased significantly,

doubling the 1950 output in 1967. However, net imports of beans

occurred in 1951, 1956, 1957, 1959, 1961, 1962 and 1964. Per capital

consumption has increased 25 percent since 1950 (56, p. 28).

The Costa Rican population increased from 1,028,175 in 1955 to

1,648,815 in 1960 (15, p. 2), an annual average increase of more than

4 percent. While population growth increases the demand for food,

this demand is also increased by a rise in per capital incomes. If

food output is to keep pace with the demands of both population and

income growth, further changes in inputs and technology will be re-

quired (13). The allocation of scarce Foreign exchange to food

imports limits the ability of a developing nation to purchase capital

goods needed for economic growth (48, p. 5).

Another problem facing Costa Rican planners is public finances.







The costs of governmental services are increasing. Education is an

example (55, P. 33). Schools are being expanded but more and more

children crowd them. With over 35 percent of the population under

ten years old, education is costly (19, p. 47). Rapid population

growth slows occupational changes (1).

Thus while the Costa Rican economy has given the nation the

highest per capital income in Central America and supports the most

advanced social and educational programs in the region, it faces

demanding requirements if it is to continue to grow.


The Importance of Coffee to Costa Rica


Coffee is Costa Rica's leading agricultural commodity, the chief

export crop, and a major user of agricultural credit and labor.

Coffee has historically accounted for 17 to 26 percent of the value

of agricultural output (54, p. 37). Despite a long history of pro-

duction, coffee output has increased markedly in recent years. From

1955 to 1963 coffee output was doubled. Yields increased 27 percent

while acreage increased over 58 percent (53, p. 10). Considerable

effort was put into research and extension work which emphasized the

use of modern inputs in coffee production. Higher coffee prices in

the 1950's encouraged the expansion of coffee onto new lands. The

modernization of coffee production is indicated by the fact that in

1963, 27 percent of the farms reported fertilizer use on 53 percent

of the land planted to coffee (23, p. 176). Fertilizer responses

gave 18 to 233 percent increases in coffee yields in trials running

from 1952 to 1957 (69, p. 60). Since 1963. production increases in-

dicate an even larger use of off-farm ir--ts. In 1967, coffee output







was more than three times the average output in the 1948 to 1952

period (29, p. 63). Thus coffee production has led in a change from

traditional toward modern farming methods.

Coffee is the major source of foreign exchange for Costa Rica.

It accounted for 38.9 percent of total value of exports in 1966, 41.2

percent in 1965 and 42.2 percent in 1964 (15, p. 13). Thirty percent

of the economically active population is associated with the coffee

industry (54, p. 37). This underestimates coffee's importance as a

source of wage earnings since many children harvest coffee and are

not considered part of the economically active population. The

harvest season usually coincides with school vacations. Also, earn-

ings from coffee picking greatly exceed other farm labor earnings.

Another benefit that comes from coffee production is soil con-

servation. A well-cared-for coffee planting protects the soil from

driving rain and contour ridges that slow runoff are permanent and

reinforced with woody root systems. Much of the land utilized by

coffee is unsuitable for annual cropping unless very elaborate ter-

racing is used (67).

Lastly, coffee is a source of tax revenue. Taxes of $0.45 per

quintal of exported coffee, 2.00 per quintal of coffee consumed

internally, and 0.20 per fanega of coffee fruit processed at the

beneficios support the operations of the Oficina del Cafe. In addi-

tion, an advalorum tax contributes to the national treasury. This

advalorun tax is graduated in the following manner: 10 percent if

the average price exceeds $42.50 per quintal, 72 percent if the

average price falls between $40.00 and $42.50 per qu;ntal, 5 percent

if the average price falls between $37.50 and $40.00 per quintal, and

2-- percent iF Lhe average price falls between $35.00 and $37.50 per







quintal. No advalorum tax is paid if the price falls below $35.00

per quintal (54, p. 56).

Declines in coffee prices are burdensome to governments which

depend upon coffee earnings for foreign exchange and tax revenue

(30, p. 15). This is particularly true in Costa Rica where a 14

percent decline in price resulted in a 57 percent loss in tax revenue

per quintal and a 3 percent decline in price resulted in a 53 percent

loss in tax revenue per quintal in the marketing years from 1965-66

to 1966-67 to 1967-68. The price decline from 1966 to 1968 brought

about a drop in tax revenue estimated at 24 million colones (55,

p. 10).


The Marketing Situation for Coffee


Historically, coffee production has gone through highly cyclical

price periods. Prices fluctuated in a cyclical pattern accentuated

by periodic unplanned changes in supply caused by unfavorable weather

conditions in Brazil (32, p. 454). When drought and frost cut

Brazil's output the price would rise. This high price encouraged

renovation of old coffee orchards and the planting of new ones.

Later, when recovery of damaged groves occurred, output surpassed

the earlier level. Prices then were pushed to new lows causing

abandonment or neglect of coffee farms until unfavorable weather

again stimulated high prices. Over the years, there have been short

periods of shortage and high prices followed by long periods of sur-

plus and low prices (59, p. 8). These drastic price fluctuations

stimulated coffee producers to seek remedial schemes. The first of

these was a Brazilian law blocking new plantings in 1902. Politically





6

unpopular, this law was repealed and followed by valorization schemes

with which large quantities of coffee were purchased and held off

the market (32, p. 456). Brazil borrowed from British banks to make

coffee purchases in 1906, 1917, 1921 and 1927. Larger and larger

crops were encouraged which became more and more difficult to store.

Brazil burned over 78 million bags of surplus coffee from 1931 to

1944 (73, p. 423).

Coffee production in other Latin American countries expanded in

response to Brazil's price supporting activities. Growth elsewhere

caused Brazil to market a smaller percentage of the world coffee

(73, p. 425).

International agreement to control coffee output was first

attempted in Bogata in 1936. Later, the Inter-American Coffee

Agreement was signed by 14 producing countries and the United States.

Selling quotas were established and the agreement lasted from 1941

to 1948 (73, p. 423). In 1957, coffee producing nations agreed upon

a voluntary system of export regulation. This agreement failed to

check the downward price movement (59, p. 13). Finally, the Inter-

national Coffee Agreement received the support of 46 producing and

consuming countries in 1963. By chance, a severe frost cut the

Brazilian output that year and, as a result, quotas were increased

to give help to the coffee consumers as prices rose (59, p. 14). The

demand For coffee is believed to be inelastic with respect to price.

If this is true, a frec-:narket solution would result in lower gross

income to producers than would occur if output were limited by some

kind of a cartel arrangement. Economists generally view trade agree-

ments with a skeptic eye. Both cxperiece and theory show that, with




7

price maintenance at or above a free-trade level, pressures and temp-

tations arise to break or by-pass the agreement since high prices

tend to encourage more production (37, p. 108).

Nevertheless, the International Coffee Agreement has reduced

price fluctuations and is considered to be effective enough to justify

extension for five more years from 1968 (8, p. 188). However, the

reduction in world output from 1965-66 to 1966-67 occurred mostly in

Brazil where weather has historically caused wide production varia-

tion. The 1963 Agreement was concerned with regulating sales and

made no attempt to regulate production.

In 1968, a new article was written into the International Coffee

Agreement setting up the Diversification Fund (34). This change

provides for compulsory payments (U. S. $0.60 per bag in excess of

100,000 bags) into the Fund. This acts as a tax on coffee. Extra

incentive is given to coffee producing nations to devise crop diver-

sification projects. Eighty percent of the compulsory payments can

be used within the producing country on approved projects. If not

used domestically, the unused payment must be paid to the Fund in

freely convertible currency. Thus each country will be motivated to

develop local diversification projects by the desire to conserve

scarce foreign exchange.

Producing countries have used different programs to check coffee

output. As mentioned earlier, Brazil first attempted to control out-

put in 1902 with a law prohibiting new plantings. More recently pay-

ments have been made for coffee removal (bO). Participation in the

program has been voluntary,with 648 million coffee trees pulled out

from June, 1962 to December, 1965. Plans had called for the removal







of two billion trees and part of the lack of response was blamed on

inflation which lowered the value of the fixed payments from $0.04

to $0.01 per tree. The effect on coffee output was small. The

program included no restriction on planting. In Colombia, Inter-

national Development Bank (IDB) loans have been used to further

credit, infrastructure, education, commercialization and industrializa-

tion of alternative products. In Mexico, rubber, citrus and avocado

plantings are being promoted in a program directed at the small

coffee producer (27). In El Salvador, sugar, corn and rice produc-

tion has been expanded. Modern corn and rice production gave returns

reported to range between $3.00 and $7.50 per $1.00 spent on new in-

puts. Nevertheless, the expansion of cereals was accompanied by a

reduction in cotton and beans, rather than coffee (74). In Guatemala,

pilot tests of tea, citrus, dairy and oil palm have been initiated.

Over $1.9 million in foreign money has been invested in the program.

Total costs surpassed $5.8 million invested on 282 farms covering

3,900 hectares (6). Guatemala also is developing a rubber industry.

In 1965, rubber was planted on 26,000 acres. Projections estimate

gross returns of $24 million from 80,000 acres (75).

In Costa Rica, credit for new coffee plantings has been restrict-

ed. In addition, the Universidad de Costa Rica, the U. S. AID Mission,

the University of Florida, and the Centro para la Promocion de

Exportaciones and Inversiones cooperated in a series of observation

trials in six locations scattered throughout the western part of the

coffee growing region. The municipality of Turrialba has initiated a

regional diversification program with the Instituto Interamericano de

Ciencas Agricolas. Financial support from the Oficina del Cafe and






the Agency for International Development (AID), plus technical

assistance from the Instituto Interamericano de Ciencas Agricolas,

the Peace Corps, and the Ministerio de Agricultura,makes this a truly

cooperative venture. The emphasis is primarily on research with a

rapid follow-up of pilot commercial plantings. The project began

with basic studies of fast growing trees, Tilapia fish ponds, and

macadamia nut production. The stated main purpose of the project

was to institutionalize an attitude of dynamic change (9). While it

is still too early to evaluate results, leaders in another municipal-

ity have talked of imitating Turrialba with a diversification project

of their own.

In summary, a review of the literature on Latin American coffee

diversification showed more discussion and hypothesizing than prac-

tical results.


The Pros and Cons of Diversification


There has been much discussion of diversification in recent

years as a method to foster economic development. In a comprehensive

study, Dalrymple (22) has compared monoculture and diversification.

The advantages of rroncculture include the following:

1. In some cases the monoculture crop has a clear comparative

advantage both at domestic and international levels. The financial

gap between the monoculture crop and next best alternative has been

found to be too wide to permit rational change,

2. It may be easier to raise yields to give higher returns from

an established crop than to press for :ore complex cropping systems.

The short--run returns to increased spacializaticn with economies of







scale may be quite high. The new knowledge and skills required to

improve production of an existing crop may be easier to learn than

the technical requirements of a totally new crop.

3. Monoculture is generally focused on export crops which provide

a developing country with needed foreign exchange and an easily

administered tax system.

4. Certain crops have more prestige and social status than

others.

The disadvantages of monoculture bring out accompanying economic

difficult ties:

1. Producers under monoculture are subject to high risk induced

by technical change and insect and disease problems. This is often

labeled "putting a lot of eggs in one basket."

2. Because many of the mcnoculture crops are perennials, a

production lag may follow a decision to increase output. By the

time the crop comes into production considerable investment has

already been made. Excessive reaction to favorable price situations

may occur when this lag follows a major change in resource allocation.

Readjustment of supply to face a lowered price will be sluggish even

in the face of losses since marginal costs may be easily covered.

3. The low price elasticity of demand for coffee results in

sharp, short-term price fluctuations caused by weather and biological

factors. The resulting high prices in the short run may trigger ir-

reversible investments. These investments plus technological advances

can be expected to increase supply while demand is not likely to

increase faster thon population growth. As a result, prices are

exc:< c i:L to we;-i n over time.






4. Trade agreements limit sales to key markets and therefore

increase both price and gross returns for the commodity with an

inelastic demand. Unless each producing country takes actions to

correct the internal distortions of high incentives for the commodity

covered by trade agreements, an imbalance encouraging overproduction

of that commodity will result (53, p. 9).

The advantages and disadvantages of diversification are roughly

the inverse of those of monoculture. Diversification may be advanta-

geous if it more fully utilizes labor and reduces economic risk (48,

p. 24). A number of different sources of income gives protection

against severe loss caused by insects, disease or bad weather condi-

tions which may affect one particular crop but not others. Labor

requirements may be spaced in such a way that one crop uses labor

when another has a slack work period. Shifting from an export crop

to food crops can lead to improved nutritional levels, especially if

more fruits and vegetables are introduced into the diet. Also,

import substitution may save scarce foreign exchange (22, p. 27).

On the other hand diversification Faces certain limitations.

Research has been focused on a few major export crops (22, p. 39).

Without much experience or local scer.tific investigation to support

a new enterprise, the innovating producer faces -igher uncertainties

with respect to the crop response t-o .innvorEbl e factor and condi-

tions (41, p. 1). The market for th.e ~lternat i ve crop may not be

sufficient to absorb expand nr product n at profita :bl prices (22,

p. 41). Even i a potential demand exists the marketing facilities

may not be ade-:uate to rnovi the new output to consumers. The econ-

omies of scale may resul t in poor efficiency as a greater number of

crops are produced and volume of some crops is reduced. This is







particularly a hazard for a new crop introduced without sufficient

volume to utilize efficient processing machinery.

In cases where the established crop is a perennial, a high per-

centage of the costs are fixed. Therefore, replacement by an alter-

native requires that total costs, since all costs are variable, be

considered in comparison with the variable costs in the case of the

established crop. Also, costs associated with removing the old crop

must be added into the cost of establishing the new enterprise.

Another problem arises if a new crop uses either more or less

labor than the established crop (22, p. 38). If the labor require-

ment is much higher, labor scarcity may prevent adequate handling of

the new crop. If much less labor is used, unemployment has social

ramifications that may be prejudicial to the establishment of a new

industry.

The quantity of research, extension work and information services

will have to be expanded if changes require more complex agricultural

systems (48, p. 24). Diversification projects are likely to fail on

farms where administration and management inadequacies greatly limit

the returns to coffee because the new enterprises are likely to be

even more difficult to manage (27).


A!2roachjz and Attitudes toward Diversification


Crop diversification is defined as a movement away from mono-

culture with the growing of new or additional crops (22, p. i). iore

detailed descriptions may be conflicting ad the evaluation of diver-

sification as a policy measure depenJs greatly upon just what meaning

is used. A most restrictive definition, and one often thought of, is

the transFer of land from r:cnoculture to alternative uses. However,




13

other resources besides land may be shifted from one use to another.

Thus, diversification occurs if operating capital or labor is put to

alternative use. For instance if labor or fertilizer is applied to

strawberries rather than to coffee, diversification occurs. Diver-

sification may even occur without a shift in resources or a reduction

in primary crop output. This is possible if unused resources are

associated with monocultural production. Therefore, if a farm were

to begin to grow a crop of dasheen on swampy ground formerly unused,

using surplus family labor and operating capital, this would be an

example of diversification. Thus for the purposes of this study,

diversification is defined as a positive action which reduces the

relative importance of the primary crop.

Crop diversification may occur at either the farm level or the

national level. A recent advisory group proposed that Costa Rica

should concentrate diversification efforts on areas unsuited for

coffee, where mechanization was feasible (68, p. 3). The program in

this case would be to expand output using resources not now used to

arow coffee.

Crop diversification may either expand domestically consumed

crops or promote new export crops. Although import substitution is

reccgnied as beneficial, planners seek to increase earnings of foreign

exchange with new exports. The export dei.rr,nd for a product facing a

small country is often highly price elastic (57, p. 1). Thus, gener-

ally the new export crop has an advantage over domestic crops in that

price will remaiir more stable as outout is expanded. The small size

of the domestic market is coupled with a shortage of capital funds and

modern know-how to limit diversified economic potential (16, p. 33).




14

Furthermore, overproduction of basic food crops may result in govern-

ment losses if high support prices are coupled with export subsidies

(61).

Others suggest that domestic crops offer better diversification

possibilities because benefits of technological change are passed on

to the consuming countries (66, p. 432). Since a particular good from

one country has perfect or close substitutes produced in other coun-

tries, the demand curve facing each country is elastic. However, the

common agricultural export crops are inelastic when the entire world

market is considered since they do not have close substitutes, do not

have many uses, and do not take a large share of the consumer's in-

come (44, p. 41). After a technological change is widely adopted,

the result of increased output is often lower gross revenue. In

this situation, the consumer benefits from lower prices and a higher

real income.

The innovator may initially supply the domestic market when low

early yields are compensated by high prices. Later, costs may be

reduced to permit export or industrial use at much lower prices. In

addition, the scale of operations needed for new export ventures

exceeds the capacity of existing producing units and marketing facil-

ities (17, p. 12). The development of a new export crop requires

efficiency if it is to nmet the estabished comupet Ltion.

Another area of debate rceners around the question of who should

diversify. Two nearly opposite views have developed: an efficiency

criterion of marginality proposes to re;ove the low profit producers.

In opposition, a criterion of welfare seeks the removal of those least

hurt by shifting to alteri.n tive production.






The removal of marginal producers is deemed desirable by both

the Oficina del Cafe in Costa Rica and the Asociacion National del

Cafe in Guatemala (54, p. 32; 27). Fernandez (27) defined marginal

farms as those where costs exceed returns and also small farms where

returns are low. One may note that this approach avoids antagonizing

the politically influential in Guatemala as marginal farms are

"liberated" for other uses. In Costa Rica, selective credit restric-

tions were used to limit the expansion of coffee, particularly into

areas producing low quality-low yield crops. The Oficina del Cafe

favors this policy because it helps to maintain a higher average of

quality as well as to restrain production.

Newman (53, p. 14) has criticized the marginal producer definition

for being concerned with absolute rather than comparative advantages.

Small farms are unsuited for such alternatives as dairying and fruit

production because they cannot take advantage of economies of scale

open to large operators. Small farms also lack reserve capital or

credit availability to enable them to invest in the more productive

alternatives. They are less able to withstand possible loss of a new

venture. The opportunity cost associated with removing coffee includes

interest charges on foregone income, which may be limiting for farms

near the subsistence level of income when perennial alternatives are

considered.

Welfare considerations cannot be quantified for interpersonal

comparisons. However, value judgments need not be made if the level

of alternative output is used to evaluate different policies of diver-

sification. The problem then becomes the calculation of the net costs

of removal of a quantity of coffee from production on different farms.







If the means of production control is alternative use of land,

this may be stated algebraically as follows:

C = P C V + C + C
r c c a a s
where

C = net cost of removing one fanega of coffee,

P = price of coffee,

C = cost of producing one fanega of coffee

V = value of alternative product produced with resources made

available as coffee is reduced by one fanega,

Ca = cost associated with producing V ,

and

Cs = cost of removing trees producing one fanega of coffee.

Thus, the comparative advantage in coffee production may differ from

the absolute advantage where C = P C If coffee is purchased and

destroyed, the cost is higher as C = P .
r c
A more general formula can be stated for calculating the unit

cost of coffee removal.

C = II I

Q, Q,
01 2

whe re

C = net cost of coffee removal
r
I, = net income before change

I = net income after change

Ql = coffee production before change

Q := coffee production after change


IdJealy, diversification would seek to reduce coffee without re--

ducing income However, given a price sitLation which sti rulates




17

excess use of resources in coffee production, a more practical policy

would attempt to minimize the costs of controlling output.


The Problem and Obiectives


Although coffee is Costa Rica's most important commodity, con-

tinued dependence on that one crop is considered detrimental to

prospects for economic growth. This harsh statement is supported by

a political-economic situation in which the sales of coffee to the

high consumption markets is now limited by international agreement.

Thus if substantial growth is to be achieved, it must occur in some

other segment of the national economy.

Technological changes are occurring in coffee production. These

changes enable coffee to be produced at lower unit costs as modern

inputs are added to traditional land and labor. The result is to

increase yields. If resources are not shifted away from coffee, this

increases production. The problem then arises as to which resources

should be shifted to what alternative uses. Also, what policy mea-

sures will facilitate changes which are both efficacious and equitable?

One may also ask, are Costa Rica's farmers functioning as profit

maximizers? Theodore Schultz has claimed that in traditional agri-

culture farmers are not only profit maximizers but that they also are

quite efficient profit maximizers (62. r. 44). llo,ever, most of the

farms studied do not truly fit the definition of traditional agricul-

ture used by Schultz (63, p. 30). Cultural techniques for coffee

have not remained unchanged for generations and further changes are

occurring (32, p. 432). Costa Rica's emphasis on primary education

already has accomplished much to-ward the investment in human resources




18

necessary to change traditional attitudes (63, p. 201). Schultz has

designated the human agent as the key variable in explaining differ-

ences in agricultural productivity (63, p. 17). The existence of

experimental farms, scattered research plots, extension agents and

agricultural schools also takes Costa Rica out of the category of

traditional agriculture.

Costa Rica admittedly does have many traditional farmers. How-

ever, the threat of overproduction of coffee does not come from that

direction. Recent yield increases indicate non-traditional behavior,

while increased coffee acreage has been relatively unimportant (15,

p. 21).

Schultz describes a transitional classification of agriculture

between the traditional and the modern (63, p. 107). Vast.disequilib-

rium is said to exist with differences in marginal productivity and

overuse or underuse of factors. The expanding use of fertilizers and

pest control chemicals (15, p. 7) indicates that transitional changes

toward modernization are occurring in Costa Rica.

Nevertheless evaluating the Farm case studies with respect to

expectations given by Schultz's theories may be interesting. Tradi-

tional farm situations could be expected to give marginal value

products near or at current prices of resources. On the other hand,

transitional farm situations may put extremely high values on certain

resources. Linear programming solutions may be compared with actual

farm operations to judge the efficiency of farm decision makers.

This may be of particular interest in explaining the continuance of

traditional meLhods with some crops while changes occur with others.

Specifically the objectives of this study were to evaluate farm




19

income opportunities from producing coffee and from alternatives and

to determine the effects that selected programs would have on coffee

production, resource use and incomes. At the same time, comparisons

can be made of current resource use and optimal resource use to test

the hypothesis that the farmers are income maximizers and that tradi-

tional economic behavior has economic motivations. In addition, the

comparisons may be used to determine what changes or adjustments in

farm operations would be profitable.












SCOPE AND METHOD OF STUDY


Selection of Areas and Farms


Coffee is grown in Costa Rica under a wide range of climatic

and ecological conditions. Three geographic areas were included in

this analysis. The data were taken from a study carried cut under a

University of Florida AID Contract in Costa Rica, No. la-261.

Budgeted comparisons between coffee enterprises and leading alter-

natives in 12 areas are given in An Economic Analysis of Coffee Pro-

ducing Areas, Costa Rica (11).

Palmares-San Ramon, Alajuela and Acosta were the areas selected

For this intensive study. This selection was based on the following

criteria:

1. Coffee and alternative crops should be found growing under

similar ecological conditions.

2. Enough coffee should be produced to make changes important

to national totals.

3. Different areas should represent a wide range of coffee

productivity and alternative choices.

Some consideration was given to including the Turrialba area in

this study. HnwevPer, the diversification project in that area was

just beginning to generate completely new data when this work was

undertaken.

Within the three areas s'lectcd, the local extension agents




21

selected farms they regarded as typical of size and class categories

common in each particular region. Data were collected by the agents

listing the resources available and the resources used on each farm.

A monthly breakdown of labor was supplied for each enterprise. In

addition, budgets for several crops grown in the areas were made

available from the Banco Nacional de Costa Rica and the Banco de

Credito Agricola de Cartago. The list of enterprises was further

supplemented by crop cost study reports of the Ministerio de Agri-

cultura.

It is admitted that the farm case study approach cannot be sta-

tistically supported. However, by drawing upon the prior knowledge

of the local agricultural scientists, costs could be held to a

fraction of the costs of working with a large random sample. Because

it is likely to be the better farmers who contact and work with the

extension agents, one may expect that the "typical farms" of the

extension agents may be above average. Nevertheless, this direction

of bias need not be undesirable since it is this group of farmers

who are most likely to first respond to economic incentives with

either increased coffee output or diversification to alternative

products.

Furthermore, the farms selected are in no way expected to be

averaged to give a quantitative measure of policy response for more

than each farm itself. The generalizations possible must be limited

to direction and nature of change which may in turn iead to specula-

tions concerning the response of the total coffee industry.

Although quantitative analysis is made at the farm level, extrap-

olation to larger areas must be with descriptive or qualitative anal-






ysis. In this manner, associations can be made between policy re-

sponses and various resource situations or enterprise possibilities.

The uniformity, irregularity or lack of response can be noted for

various policy alternatives. Thus the sample may indicate the kind

of farm likely to support or oppose a particular political measure.


Description of Farms Studied


The farms selected for analysis are all located on the Pacific

side of the Continental Divide. Farms were selected to represent the

most common size-type categories found in the three areas.


Palmares-San Ramon

This area is made up of the intensive coffee-growing districts

of the cantons of the same names. The Canton of Palmares, except for

the districts of Candelaria and Esquipulas, which are not well suited

for coffee, reported coffee on 94 percent of the farms and 37 percent

of the land in 1963 (25).

The soils are fluvio-lacustrine groups containing diatomite

(iO, p. 3). Internal drainage may be a problem on level areas. A

distinct dry season extends from December into April and Good Hard

Bean (2) type of coffee is grown at elevations beLween 900 and 1,200

meters. Practically no rain falls from December to February (64).

In the districts of San Isidro and San Ramor of the canton of San

Ramon the Good Hard Bean type of coffee is produced at elevations of

1,000 to 1,200 meters. In these districts, coffee utilizes 12 percent

of the land and it found on 83 percent of the farms. Tobacco, corn

and beans are the major crops after coffee in the combined area.

According to .:ih. 1563) census, coffee was gro.n on 2,783 manzanas,





23

corn on 417 manzanas, tobacco on 314 manzanas, beans on 226 manzanas,

and sugarcane on 106 manzanas. Coffee output of the districts within

the area made up over 3.6 percent of the national total and yields

were roughly 37 percent above the national average in 1963 (25).

Eight farms were used to cover a range of small, medium, and large

units growing coffee alone and in combinations of coffee and tobacco.

Farm 1 is a small coffee-tobacco unit in San Ramon. It is situ-

ated at an elevation of 1,080 meters on land described as moderately

rough. The farm owner applied modern technology in the forms of

fertilizer, insecticide, herbicides, and foliage fertilizer. The

farm contains five manzanas with three manzanas planted to coffee and

two manzaras planted to tobacco and corn. The labor force was made

up of two hired male employees plus the family labor of a man, a

boy, and two girls. Fixed expenses, including permanent labor, taxes,

depreciation and maintenance, and interest on the land investment

totaled (6,186. Short-term operating capital including credit was

estimated at (4,400.

Net farm income was calculated as gross returns less reported

annual expenses and estimated fixed costs including rent, taxes and

hired permanent labor. Net farm income was estimated at (22,500.

The farm was chosen as an example of mixed croppingr usinc modern

technology and hand iabor.

Farm 2 in San Ramon is a small farm specializing in coffee.

Coffee was grown on all its seven :manzanas, Labor was supplied by

five male and two female employees plus the family labor of two men

and two boys. The fixed expenses, including permarent labor, totaled

(16,690. Operating capital and credit available for variable costs







totaled 14,000. Net farm income was estimated at <15,250. This

farm was chosen as an example of a specialized modern coffee producer.

Farm 3 in San Ramon is a larger farm producing coffee, corn and

tobacco. The farm covers fifty-six manzanas of which fifty manzanas

were planted to coffee. The farm has a flue-cured tobacco contract

for three manzanas. There were nine permanent employees and an

administrator. The fixed expenses totaled t60,100 and combined

operating capital and credit totaled 071,100. Net farm income was

estimated at 134,130. This farm was chosen as an example of a larger

farm in the process of undergoing technological change.

Farm 4 is located in Palmares on nearly level land at an eleva-

tion of 980 meters. It is a middle-sized farm growing both coffee

and tobacco. The farm reported fifteen manzanas planted to coffee

and five manzanas in flue-cured tobacco. The farm hired two permanent

employees and family labor consisted of three men, three girls, and

two boys (part-time). Fixed expenses were calculated at 1!4,840.

Annual operating expenses were (16,900. The net farm income was

estimated at t14,700. This unit was selected as an example of a farm

specializing in tobacco with low yielding coffee grovn on other land.

Farm 5 is a medium-sized coffee farm in Palmares. The land is

nearly level at an elevation of 1,020 meters. All ten manzanas were

planted to coffee. The farm work was done by four men; two were hired

and two were membe-rs of the owning family. The fixed costs were

estimated at 010,732 and the sum of the operating capital and short-

term credit amounted to ,6,400. The net farm income was estimated

at (15,520. This farm was representative of a level of technology

commonly used o:i iQ.ms slowly adopting changes.







Farm 6, also located in Palmares, is found on gently sloping

land at 1,030 meters' elevation. It is a small farm with four manzanas

of land of which 3.75 manzanas were planted to coffee. The farm hired

four permanent employees. It had no administrator nor family workers.

The (12,480 estimate of fixed expenses included very high permanent

labor costs. There were (4,000 available for annual expenses, in-

cluding credit and operating capital. The farm operated at a loss

estimated at 6,760. This farm was chosen as an example of a small

property owned by an absentee owner.

Farm 7 is located on moderately rugged land at 1,025 meters'

elevation in Palmares. It is a small farm of six manzanas with three

manzanas of coffee and one manzana of sun-cured tobacco reported.

Pasture was grown on two manzanas. The farm employed one man and

extra work was supplied by two members of the owning family in times

of emergency. Fixed expenses were calculated at (6,000 and (4,300 were

reportedly available for variable expenditures. The estimated net

returns were (3,345. This unit was selected to represent a small

farm with mixed production.

Farm 8 is also located on moderately rugged land in Palmares.

It covers two manzanas of land of which one manzana was planted to

coffee and one-half manzana is planted to sun-cured tobacco. One

permanent employee was hired and two mer. and t:.o boys (part.-time) of

the owning fami ly worked on the farm. Fixed e.ixpenses wiere estimated

at (2,882. Operating capital and short-term credit was limited to

(900. The coffee planted was not yet in production but anticipated

yields gave expected annual returns of (2,653. This Farm was selected

as an example of a very small farm using multiple cropping.







Alajuela

The Alajuela area is located around the town of the same name.

The land is made up of rolling hills with occasional areas of nearly

level topography. In general, the soil is rich, being influenced

by reoccurring ash fall, which results in andosols with high organic

matter content, although considerable variation is found in both top-

soils and subsoils (71, p. 26). The elevation ranges between 700

and 1,100 meters. The districts of Alajuela, San Jose and Desamparados

were used to represent the region in the 1963 census data. In 1963,

average coffee yields for these three Alajuela districts were 27 per-

cent above the national average and coffee occupied 1,980 manzanas.

In the same year, there were 82 manzanas of pineapples, 137 manzanas

of corn, 186 manzanas of beans, 17 manzanas of tomatoes, and 14

manzanas of cassava reported (25). Coffee was reported on 76 percent

of the farms and occupied 26 percent of the land area in 1963.

Although rains may occur throughout the year, a dry season ex-

tends from December into April (64). The coffee produced is the Hard

Bean type (2). The representative districts selected from the 1963

census produced 2.43 percent of the national output of coffee. How-

ever, the ecological conditions of these districts extend into parts

of adjacent districts.

Four farms were selected for study from this region. Farm 9 is

located near an elevation of 1,100 meters on gently rolling land in

Alajuela, It is a large family farii with 40 m nanzanas, all in coffee.




IPolitical subdivisions do not coincide well with ecological
areas; th;'lrefo"re, the census data must be interpreted cautiously.




27

Labor was supplied with ten permanent employees and the family labor

of two men and two boys (part-time). The fixed expenses were estimated

at 48,575 and short-run credit arid operating capital totaled 22,200.

The estimated net returns were 90,000. This unit was chosen to

represent large coffee farms with both good coffee and horticultural

possibilities.

Farm 10 is located on rolling land above 1,000 meters. All of

its 15 manzanas were planted to coffee. Labor was supplied by five

permanent employees and two men and a boy of the family. The fixed

expenses were estimated at 21,875. The operating capital and annual

credit totaled 15,750. Estimated net returns were 17,160 but young

plants raised expected future returns to 30,000. This farm was

selected to represent the medium-sized specialized farm with good

alternatives.

Farm 11 covers 10.5 manzanas of nearly level land at 700 meters

elevation in Alajuela. Coffee was planted on five manzanas. Pine-

apple was r-own on the remaining land. The farm utilized the labor

of one hired employee and one man, two boys, one woman, and one girl

of the family labor force. The fixed expenses were estimated at

9,670. A sum of 8,850 was available for annual operating expenses.

Farm income was estimated at 42,980. The horticultural crop was

sold into the high-priced fresh fruit market. If the farmer received

processing prices for the pineapple, farm income would fall to 9,330.

Pineapples would be discontinued since returns at processing prices

would fall to about one-half of the cost of production. This farm

was chosen to represent the middle-sized producer of coffee and

fresh market fruit.




28

Farm 12 lies on rolling land at 1,100 meters near Alajuela. The

farm contains 5.5 manzanas of land of which 2.5 manzanas are planted

to coffee and the remainder is planted to sugarcane. Five men made

up the farm's work force; two were hired and three were members of

the owning family. The fixed expenses were estimated at 7,905 and

credit and capital for annual expenses summed to 1,115. The annual

net return to the reported sugar and coffee enterprises was estimated

to be 6,122. This farm was chosen to represent small mixed-crop

farms.


Acosta

This area is around San Ignacio de Acosta. This zone lies to

the south of the Central Valley on rugged, eroded latosols of an

intermountain valley. The coffee of San Ignacio is mostly grown at

elevations between 900 and 1,200 meters. In the 1963 census, 98 per-

cent of the farms reported growing coffee on 15 percent of the area.

In 1963, coffee was reported on 1,098 manzanas which greatly sur-

passed 317 manzanas of beans, 174 manzanas of corn, 65 manzanas of

sugarcane, and 24 manzanas of cassava (25). Most of the land was in

pasture or forest.

The dry season is less pronounced in Acosta than in Alajuela.

Only January averaged less than 50 mm of rainfall during the period

from 1961 to 1965 (64). The heavier and more uniform rainfall brings

about a slight reduction in quality from that which the elevation of

the area would suggest. Quality Falls into the Hard 3ean category

(2). Leaching and erosion have reduced The natural fertility of the

area.

Traditional practices h\/e persisted in the Acosta area. The





29

district coffee yields were 64 percent of the national average. San

Ignacio produces only 0.67 percent of the Costa Rican coffee; however,

conditions are similar in other districts along and beyond the southern

rim of the Central Valley. The area was included in the study to

represent a poorer coffee-growing region which fits a definition of

"marginal land" based on low output per unit of land and labor (27).

Farm 13 is a large coffee producer in Acosta. The farm covers

101.5 manzanas of rugged land averaging 1,100 meters elevation.

Coffee occupied 90 manzanas and 1.5 manzanas were planted to oranges.

The remaining ten manzanas were used to produce a joint crop of corn

and beans. Labor was supplied by four male family members and

thirteen hired men and one hired woman. Fixed costs were estimated

at 47,496 and 51,500 were available for annual expenses. The net

farm income was estimated at 66,878. This farm was chosen to rep-

resent large farms with poor soils.

Farm 14 is a medium-sized unit in Acosta located on rouah land

at 900 meters elevation. The size is 16 manzanas of which 7 manzanas

were planted to coffee. There were 4 manzanas of oranges and 5

manzanas of corn and beans reported. The farm had 3 adult male family

workers. The fixed expenses are 01,184 and 5,945 were available for

annual operating expenses. The estimated net returns were l l,4L46

on this middle-sized farm with mixed cropping.

-Fa-m 15 is a small Acosta unit on nearly level ground at 1,100

meters. It had 2 manzanas planted to coffee with interplanted orange

trees supplementing farm income. The farm family supplied the labor

of one man and three women. The fixed costs were estimated at 424.

Credit and operating capital totaled 825 for short-run expenses.




30

The farm reported a net income of (1,003. However, the costs included

an abnormally high number of new plants which suggested that an

investment was being made. The net income estimate was therefore

adjusted to l1,403.

Farm 16 is a second small farm in Acosta. The farm contains

6.5 manzanas of which 1.5 are planted to coffee with scattered orange

trees. The labor was supplied by one man and one woman of the owning

family and also a hired man and a hired woman. The fixed expenses

totaled 2,988 and 675 were available for variable costs. The farm

income was estimated at 0l,339. This farm was considered typical of

the small, mixed-crop farms of the area.


The Linear Programming Model


It would be presumptuous to claim that a study using one year's

data and many imported or estimated production coefficients would be

sufficiently accurate to permit the calculation of optimal management

plans to the five decimal places provided by the University of Florida

computer. However, the lack of a high degree of accuracy of data

should in no way be a deterrent to the use of the computer to solve

problems of a practical nature.

Linear programming is now widely used as a farm management anal-

ysis techniqjqe. However, in this particular study the emphasis is

put upon policy rather than production analysis. The input and output

coefficients may or may not represent true possibilities for the farm

groups studied. However, they do represent the expected possibilities

open to th-ose farmers based upon limited experimental data, reported

experience. and the opinions of the extension agents who advise them.

Therefore, the model attempts to predict how it would pay the farm




31

decision makers to react to a series of policy measures given certain

expected cost-output relationships for coffee and various alterna-

tives. The results were expected to provide implications with respect

to actions that farmers would be likely to take in response to program

changes.

The linear programming model is a computational method used to

minimize or maximize a linear function given a series of linear in-

equalities as restraints (33, p. 7). Net returns or profits were

maximized for each farm studied. The matrix in each application of

the model was comprised of the following sub-matrices: A production

sub-matrix was made up of input-output coefficients for the monthly

land and labor requirements, capital requirement, rotation require-

ments and coffee land requirements for the various crop production

activities. Costs were included as negative entries into the profit

row and yields were included as negative entries in transfer rows.

A selling sub-matrix consisted of price coefficients in the profit

row and unitary entries in the product transfer rows. A transfer

sub-matrix was comprised of coefficients of columns representing

labor purchasing, coffee planting, coffee destruction, borrowing,

long-term credit and fixed cost transfer.

The matrix included input coefficients for the monthly land and

labor requirements of the various enterprises. Additional restraints

included operating capital, rotational limitations, and coffee

plantings. Production coefficients were entered in transfer rows

to be sold via selling enterprises. Mornthly labor purchasing was

limited to a 2 to 1 ratio of adult male labor which allowed two

temporary workers to be hired for each permanent employee a a labor






supervision restraint. Planting and destroying coffee trees were

entered with cost and investment coefficients. New investments were

not limited but were given a cost through a borrowing column. Coffee

harvesting was handled by using both contract and family labor harvest-

ing. A special family labor restriction was used during the harvest

months. Excess labor was allowed to be sold during the coffee harvest

but not at other times.

?arametric programming was used to estimate the effect on opti-

mization when changes in policy and in production levels occurred.

Coffee yields were programmed downward 50 percent. Coffee price was

reduced (100.00 and printouts were made at 5.00 intervals. A con-

tinued annual payment for coffee removal was considered. Operating

capital was reduced 70 percent. A special credit row allowed the

operating capital requirements of expandable alternative crops to be

reduced. Credit was increased. A two-price system allowed differ-

entiated coffee prices to reflect returns to coffee sold in tradi-

tional and in new markets. Prices and yields of certain alternatives

were moved upward and downward. All new crops ,were excluded by down-

ward price manipulation. Family labor and hired labor were permitted

to be shifted off the farm. Lastly, higher yielding coffee aiter-

natives were allowed to enter with additional investments.

The farms selected were analyzed using linear programming to

maximize profits for a one-year period. Annual crops were considered

using land, labor, and operating capital coefficients taker directly

from enterprise budgets.

Enterprises requiring new long-term investments were considered

using mnir ntenance costs and return estimates for an annual period




33

after commercial production would become stabilized. Establishment

costs of permanent crops were calculated with opportunity costs

included as part of the investment to be considered using interest

charges. Thus, loss of income during the establishment period was

estimated and added to material and labor costs making up the in-

vestment calculation. The cost of making this investment was computed

using a low interest rate of 6 percent which assumed special long-

term credit subsidies for permanent crops.

The coefficients for intermediate length enterprises were calcu-

lated by summing the budget entries for the years of duration of the

crop. Thus the unit used was a "planting unit" or that area planted

each year and assumed an averaging of resource use over time. This

admittedly limited the strategy of cropping plans to be considered,

but this simplification greatly reduced costs of analysis while

coinciding with the generally followed procedure of "evening out"

inputs over time.

Presently established long-term coffee enterprises were entered

into the matrix without calculating investment costs. Additional

plantings were allowed but were associated with long-term interest

charges on the investment.

Depreciation of the investment was not considered for the per-

manent crops. If the annual expected returns surpassed returns of

other alternatives after interest costs were paid on the investment,

then it was assumed that the investment was an addition to net worth

which offset the original expenditure.

To further clarify the model, the mechanics of the less conven-

tional manipulations are given in more detail. Operating capital was

taken as a short-term credit restraint. All annual costs except in-




34

terest, transportation, and contract harvesting were used to determine

the requirement for operating capital of each enterprise. Reported

expenditures were taken as the row constraint for each farm.

Rotational restraints for annual crops were handled in the

following manner: Total farm land was used as the constraint on the

right-hand side. Each manzana of permanent crop used one unit of the

rotational limitation. Then the crops requiring rotation received

a coefficient equal to the minimum number of years during which only

one crop would be permitted. A coefficient of one allowed an average

of no more than one crop of beans per year. A coefficient of five

allowed only one crop of cucurbits in five years. This restraining

row cannot be used to set up the sequence of a rotation but does

insure that the optimal solution does not include a degree of crop

specialization contrary to required pest control practices.

Coffee plantings were used in three different constraining rows.

To insure that new coffee land was charged the cost of planting

coffee, maximum coffee land was set equal to or less than reported

coffee land plus land newly planted to coffee. To account for tree

removal costs of shifting land from coffee to other uses, another

row set optimal coffee land equal to or greater than reported coffee

land plus the land from which coffee was removed. A third constrain-

ing row set coffee removal at no more than reported coffee land to

block irregular possibilities as payments were iiade for coffee

removal in parametric operations.

Coffee harvesting requirements were handled in the following

manner: Coffee selling enterprises were given a coffee harvesting

requirement. An option was given for ha i esti n, allowing either






contracted labor or family labor to be used. The first means of

harvest used a cost of i40.00 per fanega. The utilization of family

labor used monthly labor resources and family labor resources at a

level of four or five hours per fanega each month during the harvest

period.

Parametric programming was used to anticipate the effect of

various policy manipulations and to test the stability of the optimal

plan in the face of certain price and yield changes.

The use of selling enterprises made the programming of price

changes straightforward. A change row in the matrix contained coef-

ficients of change. Programming cards determined the magnitude of

change and the frequency of printout. Thus, the solution of the

program was continually re-evaluated as additions or substractions

were made to coefficients in the objective function. Cj' = C. + X.

(N) where C.' is the new price, C. is the old price, X. is the change
J J J
row entry and N Is the parametric multiplier ranging from 0 to a

given maximum. Prime decline used a negative X. value.
J
Yield changes were programmed using "PARAROW" parametric addi-

tions or subtractions. The solution of the program was re-evaluated

as a changing multiple of change row coefficients was added to the

coefficients of a designated transfer row. P.' = P. X. (N) where
J J J
P.' is the changed yield, P. is the old yield, X. is the chance row
J J J
coefficient and N is the parametric multiple e. Proportional changes

of several enterprises producing the same product was allowed by

setting X.'s = P.'s.
J J
The effect of special credit facilities to finance the production

of the non-t-rditional cash crops was handled by a parametric reduction

of the use of normal operating capital of the favored enterprises.





36
This assumes a selective policy of credit expansion for crop diver-

sification. The "PARAROW" operation was mechanically like the yield

change procedure. K.' = K. + X. (N) where K.' is the new credit co-
J J J J
efficient, K. is the old credit coefficient, X. is the exchange row

coefficient and N is the parametric multiplier. With (X.) equal to
J
K. and N equal to one, K.' equals zero and the credit needs of
J J
favored crops are all supplied by the new unrestricted source of

credit.

The effects of a reduction of credit were analyzed by using

"PARARHS" procedures. The parametric programming of the credit

constraint, a right-hand side value, used an exchange column coeffi-

cient set equal to the credit constraint. The parametric multiplier

ranged from 0 to .70. This assumed 30 percent farm supplied operating

capital as the lower limit of practical credit reduction. K = K +
r r
(-Xr) N where Kr' is the modified credit constraint, Kr is the old

credit constraint and -Xr is the coefficient of the exchange column.

In a similar manner, an increase in credit was programmed with avail-

able credit being increased up to c2,000 per manzana.

A programming procedure was devised to evaluate the effect of

differentiating farm prices between sales into the new and into the

tradition! market. Since the new market price was roughly 25 per-

cent below the traditional market price and the new market took

-oughly 25 percent of total output, the differentiated new market

price was set at 80 percent of the current farm price and the tradi-

tional market price set at 106 2/3 percent of the current farm price.

The coefficient of the new market coffee selling enterprise was not

changed by the paral etric additions. The traditional market selling




37

enterprise was given a functional coefficient of zero. The exchange

row included coefficients for both the traditional market selling

enterprise and the current coffee selling enterprise. The former was

the estimated price that coffee would receive were it sold only in

the traditional market. The latter was a negative price coefficient

devised to exclude the average or current coffee selling activity as

the sale via the higher priced selling enterprise was permitted.



Table 1. Partial matrix of coffee selling activities



Averaged New Traditional
S market market market

Profit 200 1.60 0

Coffee transfer row I 1 1

Limit to traditional market 0 0 1

Change row -200 0 213 1/3




By referring to Table 1, it may be seen that parametric changes

will first block selling in the averaged market and then permit

selling in the traditional market. Sales in the traditional coffee

market were limited to 75 percent of the reported output of each

farm. The single printout of the solution was called when the values

of the change row were added to the functional. The price coefficient

for the averaged market was reduced to zero. That of the new market

was unchanged and that of the traditional market was increased to

106 2/3 percent of the averaged price. In this manner -he marginal

return for additional coffee on each farm was set equal to the






corresponding marginal returns to the country and each farm would

receive a price based upon new market returns for production in

excess of the quota. In this manner, the benefits of higher prices

in the traditional markets could be passed on to the producers with-

out increasing incentives for overproduction.

The payment for the removal of coffee trees was programmed as

a continuing payment made after the removal of coffee trees. An

activity for coffee destruction was given an annual cost to force

the payoff for coffee removal to be made in five years. Subsidizing

coffee tree removal was programmed with a change row entry in the

coffee tree destruction column. Payments from 0 to 1,800 per manzana

were covered with printouts on 200 intervals. The calculation of

payments equal to 20 percent of coffee's gross returns per manzana

was made using interpolation where there was no change in resource

use between the 20 percent payment and one of the printed outputs.

When the straight-line interpolation could not be made, the problem

was re-run with output demanded where the payment was equal to 20

percent of the gross returns to coffee.

Algebraically, Cd' = Cd +Xd (N); where, Cd' is the return for

destroying one manzana of coffee, Cd is the cost of destroying one

manzana of coffee, Xd is the unitary exchange row entry and N is the

parametric multiplier which represents varying levels of subsidy

payment. In addition, the exchange row contained a large negative

entry in the coffee planting column to block ne;w (offee planitings if

payments were made for coffee removal.

The effect of outside employr'ent on family incomee was studied

using param'-tric changes of the objective function. In an original




39

matrix, a column represented the reduction in monthly labor supply as

a man left the farm for other employment. Additional entries rep-

resented the effect on the monthly hiring of temporary labor and

monthly family labor for coffee harvesting. Also, a constrained row

restricted movement to adult male family members. Since this use of

resources received no returns it would not come into the initial

optimal solution. Then returns were entered with parametric changes

to record the response of income and output as outside opportunities

increased to the level established by minimum legal wage laws. There-

fore, CW = X (N); where, C equals outside wage returns per man, X

equals the exchange row entry of the legal minimum wage, and N equals

the parametric multiplier ranging from zero to one.

The effect of moving permanent employees to other jobs was

handled in a similar manner. The only differences were that the

exchange row coefficient was smaller, reflecting the part of the

calculated permanent labor costs that are not cash expenditures,

and entries for family coffee harvesting were not applicable since

permanent employees are paid by the fanega for harvesting coffee.

Movement was restricted to adult male employees.

Parametric changes in the interest rate for long-term investments

were programmed with the same procedure as price changes. The cost

of borrowing was increased from 6 to 20 percent of the investment.

An investment rcw requires that money be supplied to meet investment

requirements via a borrowing activity. An exchange row entry is

multiplied by an increasing number so that 1' = 1 + Xi (N); where,

1' equals the new interest rate, 1 equals the old interest rate, X.

equals the change row element in the borrowing column and N equals





40

the parametric multiplier. This procedure was used to evaluate the

effect of interest rate changes on the stability of resource use.

The fact that economic development is a long-term phenomenon

may stimulate objections to the use of a model maximizing returns to

a single year. With more work and more computer expenditures, it

would have been possible to build a growth model maximizing returns

over a period of five, ten, or even twenty years. Such a study could

quite dramatically illustrate the gains to be derived from more in-

vestment capital, cheaper interest rates, longer term loans and the

cumulative effects of modern inputs.

However, there is a danger in trying to extract too much informa-

tion from limited data. The data were collected with interviews in a

single year. The effects of one crop on successive crops are not

yet known. Any errors in reporting, interpretating, and evaluating

the data will be multiplied not only by the simple coefficient of

time but also by complex coefficients reflecting the fact that each

year's output becomes part of the next year's inputs.

To be accurate, the long-term growth model also requires correct

predictions of the interest rates and future credit availability.

An even more difficult prediction centers on technology. '.ill yields

remain constant? Will they remain proportional if they change? Will

some resources become outdated? If changes occur, when will they

occur? Will technological changes be accompanied by changes in factor

prices and product prices? The common economic practice is to avoid

these questions by assuming constant technology. This excludes a

chief source of growth from the growth model.




41

Those crops of greater economic importance are likely to receive

the greater agronomic research. This means that specialization or

monoculture is likely to be encouraged by technological change.

Also, larger commercial farms can be expected to change faster than

small, near subsistence units. Thus, while it is possible to predict

the direction or tendencies of certain differences in crop technology,

the magnitude of those differences will be difficult to estimate.

These statements should not be taken as a general argument

against long-term planning or programming. Such an attack on problems

is worthwhile despite the inherent difficulties. However, with the

data and prior knowledge available, it was decided that more practical

information could be gleaned from a single-year model having more

alternatives.


The Enterprises and Restraints


One of the basic assumptions of the linear programming model is

that a finite number of alternatives and resource restrictions exist.

The number of combinations of factors must be limited but the degree

of limitation is arbitrary and depends upon the use that is to be

made of the model.

In this study, the objectives were to compare coffee with alter-

native enterprises. A number of different processes or methods of

growing coffee and different processes for scrTe of the alternative

enterprises were considered because, with differ-nt farm resources

and parametric changes, one particular technology was not obviously

superior to the others. Also, an averaging of inputs and yields is

not particularly meaningful since distinctly different technologies




42

related to differences in yields are known to exist. In the decision

as to the number of processes for an enterprise the availability and

the accuracy of the data were taken into account. Activities were

chosen to represent different intensities of the use of labor and

operating capital as well as different levels of modernization.

Activities found on the poorer farms were included in the better

farms' alternatives. However, the activities requiring high levels

of technical skills were blocked for the poorer farms in the initial

solution and were considered only in special parametric procedures.

The activities were coded from budget data supplied by the ex-

tension agents and also from the budgets of the agricultural credit

reports of the Banco Nacional de Costa Rica and the Banco de Credito

Agricola de Cartago and the Ministerio de Agricultura y Ganaderia.

These sources supplied data concerning material inputs, labor hours

and timing of various work operations and expected yields of selected

enterprises in particular areas. In addition, activities were syn-

thesized from foreign input-output data which were modified to

anticipate Costa Rican conditions by adapting labor requirements for

particular work operations from currently grown crops. Research

studies of the Universidad de Costa Rica were used to supply data

for certain horticultural activities.

Activities based on foreign or small research plot d-ta were

entered with what was believed to be "conservative" yield estimates.

The results of plot trial yields were estimated by using the lowest

variety yield which was not significantly different (at a 5 percent

ievel) from the highest variety yield.

One problem that crcse was the fitting of the labor requirements

within the monthly labor con:i:r a ints. In most cases the farm enter-





prise reports placed the labor for each work operation within a

given month; however, in a few instances the reports spread work

over a two- or three-month period. Overlapping time periods would

have made the computations much more costly. Instead, two processes

were sometimes used for timing the input use of labor, and the labor

uses in other cases were arbitrarily placed in months so as not to

compete with coffee harvesting.

The use of operating capital and short-term credit was handled

together to avoid antagonizing the cooperating farmers. The extension

agents felt that loan information was personal and requested that

this section be deleted from the original forms. Therefore, total

short-term expenditures were used as the right-hand-side constraint

for the operating capital and credit row for each farm. This assumed

that the farmers were using as much bank credit as they could get.

As a result, the credit situation is oversimplified in the model, but

the complications of overborrowing for consumption or non-agricultural

uses are thus avoided. These complications would be difficult to

identify using interviews since some common practices are of question-

able legality.

For the Palmares and San Ramon areas, the programming matrix

was made up of 81 rows and 94 columns. The rows included 12 for

monthly labor use, 12 for monthly land use, 12 for temporary monthly

labor use, 4 for monthly family harvesting labor, I for operating

capital and credit use, 3 for tobacco contract; I for investment, I

for fixed expenses transfer, 14 for product transfer, 3 for coffee

land, I for coffee harvesting, 1 for corn shelling, I for land

transfer, I limiting row for traditional coffee market sales, 2 crop







rotation requirement rows, 2 labor movement rows, 9 exchange rows

for parametric changes, and I profit row.

The columns included 10 coffee growing activities, 9 tobacco

growing activities, 8 corn growing activities, 4 joint corn and bean

growing activities, 4 bean growing activities, 2 sesame growing activ-

ities, 2 castorbean growing activities, 5 dairy activities, 2 beef

activities, 1 peanut growing activity, 2 buckwheat growing activities,

I chickpea growing activity, 1 pigeon pea growing activity, I mixed

crop producing activity, 1 annual to monthly land use transfer activ-

ity, 3 coffee selling activities, 3 tobacco selling activities, 8

grain selling activities, I calf selling activity, 1 milk selling

activity, 4 monthly labor selling activities, 2 yearly labor selling

activities, 12 temporary labor hiring activities, 1 borrowing activ-

ity, 1 coffee planting activity, 1 coffee-destroying activity and one

fixed cost transfer column. A listing of the row entries of each of

the activities programmed is given in the Appendix.

Only farms I and 2 were programmed to allow the use of all the

activities coded for the area. Farms 5, 6, 7 and 8 were not using

the same high level of technology as that found on farms 1 and 2.

The two most productive coffee-growing activities were blocked by

removing the coffee production transfer card. A change row entry was

substituted and an investment entry added so that a parametric pro-

cedure would evaluate the acceptability of the change if education

were to permit its occurrence.

Farms 3 and 4 were below-average coffee producers. They were

permitted to use only the least profit table coffee-growing activities

of the area. Again a parametric procedure allowed higher production,




45

assuming that education and long-term investment could make the higher

yields possible.

In the Alajuela area, the matrix was composed of 94 rows and 87

columns. The rows included 12 for monthly labor, 12 for monthly land,

12 for temporary labor, 4 for monthly family labor for coffee harvest-

ing, I for investment, 1 for operating capital, 1 for coffee harvest,

19 for product transfers, 4 for rotation limits, 1 for yearly land,

1 for fixed expenditures, 3 for coffee land, 2 for off-farm labor

movement supply, I for limiting sales in the traditional coffee

market, 12 for changes in parametric modifications and 1 for profit.

The columns included 9 coffee growing activities, I lime growing

activity, 1 orange growing activity, 3 corn growing activities, 2

corn-bean growing activities, 3 bean growing activities, 1 pineapple

growing activity, 1 strawberry growing activity, 6 sugarcane growing

activities, 3 cassava growing activities, 2 cucumber growing activ-

ities, 2 sweetpotato growing activities, 2 tomato growing activities,

2 sweet pepper growing activities, 1 peanut growing activity, I

chickpea growing activity, 1 buckwheat growing activity, 1 pigeon pea

growing activity, 1 dairying activity, 3 coffee selling activities,

16 alternative product selling alternatives, 2 cofFee harvesting

activities, 2 all-year labor selling activities, 4 monthly labor

selling activities, i2 monthly labor hiring activities, 1 fixed cost

transfer column, 1 land transfer column, I coffee planting activity,

I coffee destroying activity, and 1 borrowing activity For long-term

credit.

With the exception of farm 9, the two highest-yielding coffee

activities were blocked for the original solutions and allowed to





46

enter with parametric changes. Lime and strawberry selling activities

reflected prices estimated for processing use. These prices were

013.00 per quintal for limes and (l.00 per pound for strawberries.

These prices were conservative estimations for the Central American

Common Market and were programmed both upward and downward to fit

conditions of the fresh market and world market, respectively.

Actually, average current prices are much higher in the local fresh

fruit markets; however, these high prices would be unstable in the

face of any sizable change in quantity.

Tomatoes and sweet peppers were also priced for processing use.

The price used for sweet peppers was lower than the quoted contracting

price because pepper contracts were tied to tomato contracts.

Cassava, cucumbers, and sweetpotatoes were priced at the reported

market lows of the two years prior to the survey. Parametric changes

lowered vegetable prices to levels competitive in the world market.

While substantial changes in technology would be required if

the fruit and vegetable activities replaced coffee, these changes

were permitted in the model because the extension and research facil-

ities seem capable in the zone. The experiment station of the

Universidad de Costa Rica is located in the zone and specializes in

horticultural crops. It is easier to sell new ideas when they have

been tested under local conditions and the results are being applied

by the agronomists on their private commercial farms.

In the study of the Acosta area, the matrix contained 65 rows

and 60 columns. The rows included 12 for monthly labor, 12 for

monthly temporary labor SL'p!)v, 1 for investment, 1 for operating

capital, 3 coffee 1nd limiting rcws, 5 for monthly family labor for




47

harvesting, I fixed expenditure transfer row, 1 row limiting coffee

sales to the traditional markets, I coffee harvesting row, I yearly

land supply row, 9 for product transfers, 2 for off-farm labor move-

ment and I profit row.

The columns included 7 coffee growing activities, 2 joint coffee-

orange growing activities, 3 joint corn-bean growing activities, I

blackberry growing activity, 2 orange growing activities, 2 beef

producing activities, 5 dairy activities, I lime growing activity,

3 coffee selling enterprises, 8 selling enterprises for other farm

products, 2 coffee harvesting activities, 5 monthly labor selling

activities, 12 monthly labor hiring activities, I fixed cost transfer

column, 2 yearly labor selling activities, 1 coffee planting activity,

I coffee destroying activity, and I borrowing activity for long-term

credit.

Since the growing periods for all the crop activities programmed

for Acosta overlapped, land was programmed as a single resource

instead of being divided into monthly intervals of use.

Ecologically, Acosta is poorly suited to annual cropping. Corn

and beans were included because they are traditionally grown. Other

annuals were excluded from the area's model in order to conform with

conservation requisities.

The coffee yields in Acosta are less than the yields of the other

two areas. Poorer technology may have resulted from relative isolation

in past years. However, lower fertility is chiefly responsible for

lower yields. Two higher-yielding coffee growing activities were

blocked in the initial solution but were allowed to enter in a

parametric procedure representing technological change.

Two price leve!s were used for fruit selling in Acosta since







selling opportunities could be greatly affected by the nearness to

market outlets. Risks and transportation costs would be reduced if

a processing plant were built in the area. Prices were discounted

30 percent for limes, 25 percent for oranges and 50 percent for black-

berries when local outlets were not anticipated.

In calculating production costs for the production activities,

short-run interest charges of 8 percent were added to the costs of

materials.


The Sources of the Budgets


It was necessary to use agronomic data from several different

sources to construct the matrix of input and output data used in the

study. Farm resource information and input-output data were provided

by the extension agents in each area analyzed. The extension agents

collected data from the farms they considered typical of the various

farm size and type classifications found in their particular region.

Host of the budgets for coffee, corn, beans and sugarcane were

provided by the cooperating extension agents. These were supplemented

by data provided by the Banco Nacional de Costa Rica (18) and the
/ /
Banco de Credito Agricola de Cartago (53).

San Ramon and Palmares farms were grouped together in the

Analysis. !ng. Efrain Abarca collected data from San Ramon including

budgets used in the most productive coffee activities yielding 27.6,

25.7 and 20.0 fanegas per manzana. Ing. Danilo Zamora collected data

from Palmares farms which reported coffee yields of 19.0, 15.0,

and 5.3 fanegas per manzana. The best yields included herbicide use,

three applications oF fertilizers, insecticide use and moderate

pruning and weeding labor. Common practices included the uLe of




49

fertilizer and insecticides and gave yields above the national average.

Coffee activities using traditional methods were programmed from data

of the Banco Central de Costa Rica (7). Moderately heavy labor with

few purchased inputs produced a yield of 9.0 fanegas per manzana.

An activity of semi-abandoned coffee was based upon conversations

with Ing. Hugo Castro. Yields up to 4.0 fanegas per manzana were

obtainable without purchased inputs other than sacks ard without labor

except harvesting and enough weed cutting to allow the pickers to walk.

The extension agents' farm budgets also included corn and bean

activities. Common corn yields ranged from 13.3 to 40.0 quintales

per manzana. Higher-yielding activities were programmed from data

furnished by Ing. Walter Villalobos from 4-S Club plots at Santa Ana.

Yields were modified to 70.0 quintales per manzana maximum to corre-

spond with the reportedly poorer growing conditions. The moderate

use of fertilizer and insecticide, as reported in the worksheets of

the Banco Nacional, yields 48.0 quintales (18).

Bean activities were based on budgets from the following sources.

Modern technology yielded !8 and 20 quintales per manzana according

to budgets derived from a ministry of agriculture publication (51).

The Banco Nacional supplied budgets of low-yielding bean crops from

broadcast planting that yielded only 4.2 quintales per manzana and

tradition! methods that yielded 9.6 quintales per manzana (18).

Joint corn and bean production activities were programmed allow-

ing combinations of the average and poorer yielding corn and bean

activities commonly grown together.

Tobacco growing activities were programmed from budgets of the

Junta de DeFensa del Tobaco (36) and the Banco Wacional (18). Yields




50

ranged from 18 to 20 quintales per manzana but the budgets from the

Banco Nacional used lower levels of inputs.

Sesame was programmed using a budget of traditional methods

supplied in Ospino's work (58). A budget of modern practices for

growing sesame were synthesized using data from the United States

(21, 38, 39). An estimate of yield expectations was placed at 20

quintales per manzana despite reported yields up to 35 quintales per

manzana.

The castorbean production activities were based on synthesized

budgets based upon foreign agronomic data (20, 72). Yields were

estimated at 34 quintales per manzana.

The traditional activities for producing dairy were based upon

reported budgets from Atenas by Ing. Adrian Prado. More intensive

dairy production activities were based on budgets from Heredia

supplied by Ing. Carlos Norza. Production ranged from 400 to 1,200

bottles of milk per manzana with extensive land use and from 1,000

to 2,000 bottles with more intensive operations.

An extensive beef calf producing activity and a moderately inten-

sive beef producing activity were budgeted by Ing. Ramon Castro in

San Carlos with one cow per 5.0 manzanas in the first case and one

cow per 1.5 manzanas in the second. Much higher range productivity

was reported in studies made in Puerto Rico (14). However, the cost

of production w-ould not be covered by Costa Rican prices.

Pigeon pea production activities were based on budgets synthesized

from agronomnic data chiefly from Hawaii (31, 40) modified by Costa

Rican recommendatiur;s (50). Reported yields reached 20 quintales per

manzana.






Buckwheat was included as a catch crop. Yields were programmed

at 8 and 20 quintales depending upon the time of planting. Reported

production in Mexico (70) and Ceylon (4) showed that this crop could

be grown in tropical countries.

A peanut production activity was included although part of the

soils may not be well adapted. Yield was programmed at 20 quintales

per manzana as Banco Nacional data from Alajuela were used to synthe-

size a budget.

Chickpeas were included as a dry season catch crop. Cultivation

is similar to beans (47) and the yield expectations are 5 quintales

per manzana.

A mixed crop enterprise was synthesized from other budgets

combining corn and legumes with high labor inputs.

Coffee price was determined by an unweighted average of the

prices paid by the beneficios in Palmares in the 1966-67 crop year.

Corn and bean prices were reported by the extension agents. Peanut

and sesame prices were included in the credit worksheets of the Banco

Nacional (18). Castorbean price was computed from the world dollar

price. Buckwheat was priced arbitrarily low to reflect probably

limited acceptance as a feed grain. The chickpea and pigeon pea

prices reflected estimated wholesale prices based upon retail prices

in Scn Jose as compared with beans.

In the study of the Alajuela farms,budget data on coffee produc-

tion were supplied by Ing. Guillermo lontenegro. Yields ranged from

25 fanegas per manzana on the best farm to 16 fanegas per manzana on

the poorest farm. The maximum yield permitted without technological

change modifications was 20 fanegas per ,:aiizana except on farm 9 which

had already adopted modern production techniques. Traditional and






semi-abandoned coffee production activities were included with the

same coefficients used in the Palmares-San Ramon matrix.

Lime production was programmed with input-output data synthesized

from Florida sources (42). Yields were 400 quintales per manzana.

This approximates a U. S. yield of 462 bushels per acre. Costs of

establishment used U. S. costs but annual labor costs were modified

by data from Costa Rican orange production budgets.

The orange production activity was programmed from data of

modern orange production in Guatemala (49) and Florida (28). Yields

vary with the age of the trees but an estimated yield of a mature

grove was taken at 1,020 cien (hundred fruit). This approximates

300 boxes of fruit per acre.

Tomato production activities covered common and modern producing

techniques. The common yield of 11.25 tons per manzana was reported

in a budget worksheet of the Banco Nacional (18). Experimental

results of agronomic trials show yields that surpass 20 tons per

manzana (26).

Corn production activities were based upon 4-S Club budgets

which reported yields of 90 quintales per manzana and upon extension

agent reports of corn yielding 60 quintales per nanzana. Corn produc-

tion was also programmed in joint activities with beans where the

output of 48 fanegas and 20 fanegas of corn was produced jointly

with 18 and 4 fanegas of beans in budgets supplied by Ing. Guillermo

Montenegro.

Bean production was programmed with three distinct levels of

technology. Modern inputs fielded 20 cuintales per manzana (51),

broadcast beans yiclded 5 qui;tales per ilanzana and traditionally

planted hl ans yielded 10 qi.-itta es per narlnzra (18).




53

The strawberry producing activity used Florida production data

(12) modified by incomplete data from Alajuela and Heredia farms.

Yields were programmed at 250 quintales per manzana. This approxi-

mates 14,600 pounds per acre. Good California yields, for comparison,

ranged from 48,000 to 60,000 pounds per acre (43) and Israel increased

its average yields from 3,000 to 10,350 pounds per acre in six years

(35).

Sugarcane production activities were programmed from budgets

supplied by the Banco Nacional (18) and the extension agency. Produc-

tion ranged from 60 tons per manzana to 100 tons per manzana per

harvest. In a period ranging from 38 to 48 months,three harvests were

made. Production and inputs were totaled for the entire periods.

Three cassava producing activities were programmed. Traditional

methods were represented by a budget which yielded 150 quintales in

a 21-i:month growing period (18). Intermediate yields were received

by a budgeted production lasting two years (58). Higher yields, 275

quintales per manzana, were programmed with more modern inputs and

longer growing period of 26 months (26). Total production per month

increased with age but quality declined.

Cucumber production activities represented different timing of

modern techniques based upon agronomirc Jta furnished by the Alajuela

experiment farm (52). Yields were 140 quinteles per manzana.

The sweetpotato production activity bas-d on modern inputs (5)

yielded 300 quintales per manzana which tripled the yield of tradi-

tional methods reported by the Banco Nlacional (18).

A peanut producing activity utilized a budget reported by the

Cartago bank (jS). Yieids of 25 'quintales per manzana -were expected.





54

Pigeon pea, buckwheat and chickpea production activities were

included, based upon the same sources as used for the Palmares-San

Ramon study.

Prices were based on an unweighted average for coffee, reported

lows of vegetable prices, and estimates of potential industrial

prices for tomatoes, peppers, strawberries and citrus. Since the

projected horticultural marketing assumed much greater volume than

current sales, price predictions were lower than average prices but

subject to considerable error.

In the study of the four farms in Acosta, coffee production

budgets were supplied by Ing. Rodrigo Cavallini of the San Ignacio

Extension Agency. The better methods used fertilizer or other

purchased inputs and yielded 10 fanegas per manzana which was con-

sidered high for this region. Traditional methods yielded six

fanegas and used small quantities of fertilizer and heavy labor in-

puts. Coffee was grown with oranges and bananas on some farms with

poor yields of both coffee and fruit. Coffee yielded five and six

fanegas and oranges yielded 1,000 to 3,000 fruit per manzana on

neglected trees.

An activity used to program the possibilities of technological

change was supplied by Edwin Marin of the Oficina del Cafe. He

budgeted production yielding 14 fanegas per manzana in an adjoining

district. An activity was also included representing semi-abandonment

and yielding only ,two fanegas per manza.ra,

A high yielding orange producing activity was included and based

upon data taken From foreign sources (28, 19). This activity yielded

102,000 oranges ccnpared to 53,300 oranges per manzana produced on

a farm in Acosta.







Corn and bean production was reported with low to very low

yields. Joint cropping produced 29, 6.4 and 16 fanegas of corn com-

bined with 11, 9.6 and 6.6 fanegas of beans.

The same beef and dairy production coefficients used in the

Palmares study were included in the matrix.

Lime production was included with a yield of 400 quintales per

manzana based upon Florida data (42).

The inclusion of a blackberry producing activity also was based

upon a composite of information from Florida (65) and Costa Rica.

Yields of 16,000 pounds per manzana were anticipated.


Assumptions of the Model


There are certain assumptions and limitations of the model which

should be clarified before the results are interpreted. Linear

programming uses profit maximization within a set of constraints as

a single criterion for allocating resources. This would deviate

from actual practice especially in those cases where the magnitude

of gain is so slight as to not make a more complicated program worth

the trouble when compared to a simpler, more easily managed plan of

operations. Also, the model does not take uncertainties and risks

into account. Risks, that may be either real or imagined, enter into

farm decision making. Yields and prices vary from year to year.

The farm operator will actually be interested in maximizing profits

only within soine range of acceptable risk.

The model forces all decisions to be made at once. Since it is

a static model, growth possibilities are not taken into account. This

is particularly troubiecome in the case of short-term credit and





56

operating capital restriction. Because of this feature, the model is

conservative insofar as new resources are not permitted to be

generated over time.

All units are considered divisible. This does not cause a

problem except in the case of cattle and labor movement. Theoreti-

cally, part-time employment could explain fractional units of labor

movement.












RESULTS


Optimal Cropping Plans


In general, cropping patterns determined with the initial

linear programming model did not greatly differ from the reported

practices. With one exception, land was fully utilized during at

least part of the year. Unused permanent labor was often available

except during coffee harvest. The restrictions on hiring temporary

day-wage labor were generally not constraining. Where horticultural

crops were considered, operating capital was restrictive. This

restriction occurred also on the smaller, poorer coffee farms.

Marginal returns to short-term credit, calculated by using reported

expenditures as a base, were either zero or well over the established

interest rate. Parametric changes found coffee production to be

stable in the face of moderate coffee price and yield decreases but

responsive to technological improvements and credit manipulations.

Changes in the availability cf credit, the interest rate, the labor

supply, and product prices had a much greater effect on the alloca-

tion of resources among the various alternative crops than they had

on coffee production. A brief summary of optimal resource use will

be giver for each of the 16 farns.

Farm I

The optimal solution of this coffee-tobacco farm coincided with

the reported production. Two manzOn.ss were planted to thinly spaced





58

corn and tobacco and three manzanas were used to produce coffee. In

addition, two manzanas of buckwheat were planted in the dry season.

The net returns (W19,781) fell slightly below the farmer-estimated

returns (22,500) because the model included extra labor costs and

lower corn prices. Land and burley tobacco contracts were limiting

rows with marginal values estimated at 4,069 and 215, respectively.

Temporary labor was hired in October and January. Permanent labor

was fully utilized in May and October as well as from November through

February where the model permitted coffee harvesting to exhaust the

labor supply. A slight excess (81) in operating capital occurred.

Farm 2

The second farm from San Ramon was a small specialized coffee

producer. The optional solution for the linear programming model

derived for this farm situation also produced only coffee. Net

returns in the model were ;18,024 compared to 15,250 calculated

from the farmer report. The model permitted the use of a higher-

yielding coffee activity which proved slightly more profitable than

the technology actually used. With tobacco activities blocked, the

only effective constraint was land. Permanent labor was exhausted

only from November through February when coffee harvesting utilized

all the labor. The farm optimal solution used 3,157 of 4,000 avail-

able capital and credit. There were 193.2 fanegas of coffee produced.

Farm 3

This farm produced good coffee yields with very high costs.

The model permitted traditional coffee activities on this farm but

the five best coffee-producing activities of the region were blocked.

The optimal solution contained less-intensive coffee production,





59

more-intensive use of the non-coffee land, and no reduction in coffee

acreage. Coffee output was reduced 25 percent from the reported

output. This change was accompanied by an increase in net income

from (34,130 to 35,543. The optimal plan made heavier demands on

management with seven producing activities instead of three. Sales

included 750 fanegas of coffee, 52.8 quintales of tobacco, 108.4

quintales of corn, 49.1 quintales of beans, and 16.3 quintales of

sesame.

The flue-cured tobacco contract row was exhausted as all three

maiizanas permitted for tobacco were planted in the farm model.

Permanent labor resources were exhausted in all months except May

and October. Coffee harvesting exhausted the labor supply during

November, December, January and February. Temporary labor was hired

in March, June, July, August and September.

The optimal plan used less than i45,666 of a 71,100 operating

capital and credit constraint.

Farm 4

The fourth farm studied produced coffee and flue-cured tobacco.

Tobacco was the chief money earner with coffee grown to supplement

income with very low labor inputs. The linear programming model of

this farm situation resulted in considerable changes in resource use.

Coffee output was increased from 80 to 225 fanegas and tobacco

production was reduced from 90 to 46.8 quintales. In addition, corn,

beans, and mixed crops were substituted for tobacco. Sales also

included 36.7 quintales of corn, 59.9 quintales of beans, 2.6

quintales of pigeon peas, and 81 pounds of chickpeas. Net returns in

the model situation were 331,667 which greatly exceeded thZ 14,700

estimate of income under reported resource use.







The model used all available land from July through January,

all available operating capital and credit, and all permanent labor

in April, June, July, August and the harvest months from November

through February.

Temporary labor was hired in January and August. Family coffee

harvesting was limited by the family labor supply in December. Hiring

labor was not constrained by monthly supervisory limitations which

allowed two temporary workers for each family or permanent employee.

The tobacco contract allotment was not exhausted.

Farm 5

The fifth farm specialized in coffee. The maximization of the

linear programming model gave results similar to reported resource

use. The net income for the programmed model was 16,876. The in-

crease over the reported income of 15,520 was explained by small

savings in the accounting of harvesting costs and the fact that some

of the reported expenditures were long-term investments.

In the model, permanent labor was fully employed during the

harvest period from November through February. All 10 manzanas of

land were used and family coffee harvesting was limited by the avail-

ability of family labor in December. An excess in the operating

capital and credit row occurred in the mode! because the reported

annual expenditures included some long-term investments.

The Farm produced 190 fanegas of coffee using the highest yield-

ing coffee activity permitted in the model.

Farm 6

The sixth farm studied was a small coffee farm wi th an absentee

owner. The farm .-odel allowed coffee yields slightly above those







reported by the farm. This reduced the net loss to 760. This

loss occurred with an increase in both yield and coffee acreage

above the reported numbers.

In the model, all four manzanas of land were planted to coffee

for profit maximization. This occurred with 0.25 manzanas of new

coffee planted.

The optimal solution maximized returns to land. Credit and

operating capital were not limiting in the model, since reported

preharvest expenditures were 4,000 compared to a 2,565 optimal

preharvest expenditure. A large surplus of permanent labor occurred

in all months except during the coffee harvest period.

The new coffee planting was not stable in the face of interest

changes on the required investment. The rate initially used was 6

percent representing a minimum charge which government banks have

used in a policy to subsidize agricultural investment. The new

coffee was not planted if interest charges rose 0.8 percent. Beans

and sesame were planted instead of coffee. The farm loss increased

from 760 to 780.

Farm 7

The results of programming the seventh farm more than doubled

reported net farm income from 3,345 to 7.147. In the optimal

solution, coffee production was expanded with 0.26 manzanas planted

to new coffee. Other production activities included were sun-cured

tobacco, corn and beans. Optimal output, before parametric changes,

was maximized with the production of 62.0 fanegas of coffee, 19

quintaies of tobacco, 72.8 quintales of corn, and 18.9 quintales of

beans.





62

The optimal production exhausted the credit and operating capital

row, land rows from September through January, and the sun-cured

tobacco contract. Permanent labor was exhausted in all months except

July. Temporary labor was hired in all months except July and August.

Labor supervision did not limit hiring temporary labor in any month.

The newly planted coffee was not stable when interest charges

on investments were increased. Interest charges would block new

plantings when the rate was increased 0.1 percent.

Farm 8

The eighth farm model results optimized resource use with the

production of 19 fanegas of coffee, 7.8 quintales of sun-cured

tobacco, 24.0 quintales of corn, 5.4 quintales of beans, and 6.5

quintales of sesame. Optimal net returns were 4,005 compared with

a reported income of 2,653. Coffee acreage in the optimum program

was the same as the farmer's reported acreage.

Credit was severely restricted on this small farm. The shadow

price indicated that an additional colon of credit or operating

capital could return d2.96 additional net income.

Land was fully utilized except in the month of February. Extra

labor was available in all months except during the coffee harvest

season. No temporary labor was hired. The restriction that allowed

0,5 manzanas of sun-cured tobacco w'as not used up.

Fa rm _

The ninth farm produced coffee with modern inputs. The model

included activit!os for horticultural crop prodjct-on. The farm

maximized income with coffee monoculture which yielded 86,761 net

returns. The Farmer's estimate of net returns was 90,00. Expendi-







tures for temporary labor were underestimated which limited the

operating capital and credit row. The monthly labor supplies were

fully used in January, February, June, July, September, November and

December. Temporary labor was hired in June, July and September.

Supervision limitations did not restrict temporary labor hiring.

Farm 10

Analysis of the tenth farm showed that an increase in farm in-

come could accompany crop diversification. Net returns were increased

from 17,160 to (29,489 when optimal use of other crops replaced

coffee. However, this farm was reportedly upgrading coffee technol-

ogy in order to receive (30,000 expected net returns with coffee

monoculture. The optimal cropping pattern included five different

crop-growing activities producing 213.3 fanegas of coffee, 48.0 tons

of tomatoes, 148.0 tons of sugarcane, 708.0 quintales of sweetpotatoes

and 3.14 quintales of chickpeas. Coffee trees were removed from 4.3

manzanas of land. The permanent labor supply was used up in January,

February, April, August, September, November and December. Temporary

workers were hired in January, February, September and December. The

operating capital and credit constraint was limiting in the model.

Supervisory capacity did not limit hiring temporary workers.

Farm 11

On the eleventh farm, programmed optimal solutions did not

change coffee acreage from the reported land use. Other crops replaced

pineapples. The reported net returns of Cl2,980 exceeded the optimal

income of (24,536 of the model. This difference occurred because the

model contained fruit prices for industrial use while tha Farmer

produced 5.5 manzanas of pir.eapple for the fresh fruit market in






64

San Jose. The high domestic market prices of fruits and vegetables

were not used in the model, since these prices could not be received

if any significant portion of coffee resources were shifted into

horticultural production.

Sales to maximize net revenue included 100.0 fanegas of coffee,

2.0 quintales of beans, 710.9 quintales of limes, 13.0 tons of tomatoes,

0.8 tons of green peppers, 236.0 tons of sugarcane and 254.5 quintales

of sweetpotatoes. Land was fully utilized from November through April.

Permanent labor was fully utilized in all months except October.

Temporary labor was hired in January, February, April, June, August,

September and December. The capacity to supervise labor was not an

effective restriction in any month; however, slack supervisory capac-

ity was reduced to 29.4 hours in January. The operating capital and

credit row limitation was exhausted. Roeational limitations were not

restrictive.

Farm 12

The twelfth farm was programmed with only the poorer coffee-

producing activities of the Alajuela region. The farm reported poor

coffee yields averaging 10 fanegas per manzana. The programmed

optimal solution contained the traditional coffee activity which

yielded nine fanegas per manzana. The activity based on the reported

resource use was dominated by another coffee-producing activity. Net

returns were maximized with the production of coffee, corn and beans

ard limes which netted 07,078 in the model. These returns were

higher than the 6,122 calculated from the Farmer report. Farm out-

put included 22.5 fanegas of coffee, 24.1 quintales of corn, 4.8

quintales of beans and 717.2 quintales of limes.





65

Excess permanent labor occurred in all months except the coffee

harvest period. No temporary labor was hired. All coffee was

harvested with Family labor. Credit was severely restricted.

Farm 13

The thirteenth farm was programmed using two different sets of

fruit prices representing industrial prices with and without a

processing plant located in the Acosta region. Two completely dif-

ferent diversification pictures are presented since it is questionable

whether or not the area could support a processing plant. First,

higher fruit prices were used to evaluate diversification alternatives.

Limes were priced at 0i3.00 per quintal, blackberries at (1.00 per

pound and oranges at (4.00 per hundred. The results of profit

maximization in the model showed a sizable departure from the reported

resource use. This does not dispute a theory of farmer profit

motivation since completely different horticultural alternatives were

placed in the model. Nevertheless, the model shows that if fruit

prices were moderately high and stable, considerable changes would

occur and coffee output would be reduced on farm 13.

Profits in the model were maximized with 89,948 netted from

mixed coffee and lime production. Coffee tree destruction was

programmed for 32.94 manzanas. Credit was limited, causing both semi-

abandoned methods and traditional low-yield methods of coffee to be

used in coffee production. Permanent labor was fully utilized in

January, February, May, July, August, September, October, November

and December. Temporary labor was hired in January, February, flay,

July, August and September. The limit on supervision for temporary

workers was not effective.








Farm production was comprised of 195.6 fanegas of coffee and

17,780 quintales of limes. This coffee output was reduced from the

present output of 900 fanegas. However, without favorable fruit

prices, the maximum income for farm 13 was 67,694. Therefore, the

reported income of 66,878 closely approximated the maximum of the

model when only traditional crops were grown and coffee output was

900 fanegas. Other sales included 333.5 quintales of corn and 126.5

quintales of beans. In the model, a higher-yielding corn and bean

activity substituted for the corn and bean activity actually reported

in use.

In the second analysis, low fruit prices were programmed at

9.00 per quintal of limes, 0.50 per pound of blackberries and 3.00

per hundred oranges. With the lower fruit prices, the available

credit and operating capital row was not a limiting factor. All

coffee land remained in coffee but no new coffee was planted. The

most advanced technology permitted by the model was used to produce

corn and beans. Permanent labor was exhausted in all months except

April and June; however, temporary labor was hired only in January,

March, July, August, September and October. All but 96 fanegas of

coffee were harvested with contract labor.

Farm 14

As with the preceding farm, the fourteenth farm was programmed

using high and low fruit prices. With the higher fruit prices,

optimization of the model resulted in considerable departure from

reported practices. Although no coffee trees were removed, output

of coffee was reduced from 70.0 to !8.4 fanegas with coffee produc-

tion activities using low-yield and semi -abandoned methods. Nine




67

manzanas of limes utilized the non-coffee land and 3,600 quintales of

limes were produced. Net returns in the model were (24,222. The

farm reported income was much lower with l 1,446 netted from tradi-

tional grain crops and common coffee practices.

The operating capital and credit row were severely limited in

the model with a marginal return of i.51 per 1.00 of credit. Per-

manent labor was exhausted except in March and April. The model

resulted in hired labor in January, February, May, June, July, August

and September. Labor supervision was not a limiting factor.

The optimal solution in the model for farm 14 was completely

different when lower fruit prices were used to represent alternatives

without a nearby processing plant. The maximization of net returns

with traditional crop alternatives resulted in 70.0 fanegas of coffee

output. Maximum net returns in the model were higher than reported

returns, 16,718 compared to ll ,446. The increase was accomplished

with higher returns to corn and beans which were programmed with

moderately poor yields that greatly exceeded reported farm production.

Nevertheless, insofar as coffee production was concerned, the model

results coincided with the actual reported production.

Maximum net returns occurred with the production of 70.0 fanegas

of coffee, 261.0 quintales of corn and 99.0 quintales of beans.

Operating capital was limiting and the family labor supply was ex-

hausted in all months except March, April and June. Labor was hired

in May, August, September, October, NIovember and December.

Farm 15

The fifteenth farm also was given two different fruit price

situations. With high fruit prices used first to represent production







potentials given a processing plant in the region, the farm model

maximized net returns at i5,459. In comparison, reported production

netted only 1,403. Profits were maximized by shifting 0.26 manzanas

of coffee into lime production. Farm output included 17.4 fanegas of

coffee and 105.9 quintales of limes.

The farm model used all credit and operating capital available.

Labor was fully used only from October through February during the

coffee harvest. Land was fully utilized and no temporary labor was

hired. Of the 5,459 netted on the farm, 2,353 were received from

off-farm coffee harvesting.

When lower fruit prices were used in the linear programming

analysis of farm 15, all land was planted to coffee. Farm production

in the model slightly exceeded reported coffee output. Income was

above that reported because the model included temporary outside

income from coffee harvesting as part of farm income. Of the 5,209

net returns, 2,308 was income from coffee harvesting work on other

farms. Commonly grown coffee replaced the reported poorer yielding

coffee-orange activity.

Coffee production maximized the returns to land and harvest time

labor. Credit w:as not a limiting resource when low fruit prices were

used in the model. The family labor supply exceeded all monthly

demands for labor and no labor was hired. The excess family labor

was sold during the coffee harvest period ard was unused the remainder

of the year. Twenty fanegas of coffee were produced with the optimal

use of resources.

Farm 16

The sixteenth farm was also programnied with high and low fruit




69

prices. The farm had reported 7.5 fanegas of coffee production from

1.5 manzanas of coffee. When high fruit prices were used, the

maximization of profits shifted resources into lime production and

coffee land was utilized in semi-abandoned coffee production. Net

returns reached 4,307 when maximized which surpassed the 01,339 cal-

culated as expected income with reported resource use. However,

t2,273 out of the 4,307 represented harvest labor sales.

Credit was severely restricted when high fruit prices reflected

a strong local market. Over 5.30 marginal returns were estimated

per colon of additional credit. Almost 1.2 manzanas of land were idle

in the optimal solution. Output included 3.0 fanegas of coffee, 382.4

quintales of limes, 18.2 quintales of corn and 27.3 quintales of beans.

Permanent labor was fully employed only during the coffee harvest and

no temporary workers were hired.

When low fruit prices were used in the programming model, limes

were excluded and resources were allocated to traditionally grown

coffee and low-yielding corn and bean activities. Credit was severely

restricted with a marginal return for operating expenditures estimated

at A3.22 per 1.00. Profits were maximized with 0.77 manzana of land

idle. Excess permanent labor occurred in all months except during

the coffee harvest. No temporary workers were hired. Optimal sales

included 9.0 fanegas of coffee, 54.0 quintales of corn and 32.2

quintales of beans. Income was maximized at 3,612, of which 2,145

cane from labor selling for coffee harvest.


Po i y Ana 1 ysi s


Education for Better Farm Manacement

The first section of this chapter compared reported and optimal




70

programmed resource use given the price and credit relationships faced

by the farmers without a positive policy to foster crop diversifica-

tion. Nevertheless, the changes in resource use may be attributed to

a general policy of better farmer education and expanded extension

work that would be required before part of the alternatives programmed

could be put into practice.

Therefore, the first policy consideration to be considered repre-

sents education for better farm management. It is often heard that

farmers produce coffee because of nor-econcmic motivations, that they

are reluctant to change, or that they maintain traditional cropping

patterns out of ignorance. Table 2 shows comparisons between the

reported and optimal values for net returns and for coffee outputs.

The results do not indicate that irrational overproduction of coffee

was prevalent. Of the 16 farms studied, only two farms demonstrated

overproduction of coffee. Overproduction on farm 3 could be attrib-

uted to overintensification where returns could be increased by

reducing inputs and yields. The huge quantities of fertilizer re-

ported represented either mismanagement or hidden investment.

Overproduction on farm 10 occurred because either sugarcane or horti-

culture gave returns high enough to replace part of the moderately

high-yielding coffee. The lower programmed coffee output on these

two farms was countered by higher output on other farms.

While the sample is too small to support broad generalizations,

some overproduction of coffee beyond that quantity dictated by strict




1Hidden investment in this case may arise when increased annual
expenditures occur for a short period before yields are increased to
full potential.









Table 2. Comparisons between reported and optimal incomes and coffee
output




Net returns Coffee output

Farma Reported Optimal Reported Optimal
coloness) coloness) (fanegas) (fanegas)

1 22,500 19,781 82.8 82.8
2 15,250 18,025 179.9 193.2
3 34,130 35,543 1,000.0 750.0
4 14,700 31,667 80.0 225.0
5 15,520 16,877 190.0 190.0
6 6,760 760 56.3 76.0
7 3,345 7,147 45.0 62.0
8 2,653 4,005 15.0 19.0
9 90,000 86,761 1,000.0 997.2
10 17,160 29,489 300.0 213.3
11 42,980 24,536 100.0 100.0
12 6,122 7,078 25.0 22.5
13A 66,878 89,948 900.0 195.6
13B 66,878 67,694 900.0 900.0
14A 11,446 24,222 70.0 18.4
14B 11,446 16,718 70.0 70.0
15A 1,403 5,459 10.0 17.4
15B 1,403 5,209 10.0 20.0
16A 1,339 4,307 7.5 3.0
16B 1,339 3,612 7.5 9.0



aThe letters A and B are used to designate different fruit pricing
used in Acosta. A's are used to indicate the situations using high
fruit prices and B's are used to indicate the situations using low
fruit prices.





72

profit maximization is indicated. Most of the potential for income

improvement occurred with the land not planted to coffee. Labor

selling activities for the coffee harvest made sizable contributions

to the higher optimal incomes of the smaller farms.

Of the 20 farm'situations studied, three reported higher incomes

than the optimal permitted in the model. In farm I and farm 9 this

difference was due to a lower wage cost estimation in the farmer

report than was permitted in the model. More significantly, the re-

duced optimal income on farm 11 came about because the model only

allowed pineapple sales for export or industrial use while the farm

produced for a much higher domestic fresh-fruit market.

Markedly increased incomes were accompanied by reductions in

coffee output only when high return fruit or vegetable crops were

considered as alternatives. Thus the higher incomes are associated

with higher risks. Optimal allocation of resources reduced coffee

output 3.06 percent on the eight farms of the Palmares-San Ramon area,

and 6.46 percent on the four farms of the Alajuela area. For Acosta,

optimal resource allocation increased coffee output 1.16 percent

when low fruit prices were used but decreased coffee output 76.26

percent when high fruit prices were used, A sizable reduction occurred

in the Acosta output only when high yielding alternatives were compared

with low yielding coffee.

Changes which reduced coffee output brought activities into

production which utilized more modern inputs than the alternative

crops usually receive in Costa Rica. This gives rise to a question

of whether or not the same relationships would exist if new coffee

growing activities represented higher levels of technology in coffee







production. In order to keep the model representative of current

technological proficiency, certain high-yielding coffee activities were

flagged from use in 17 of the 20 farm situations. Then parametric

programming was used to increase coffee transfers until reported yields

of the better farms were equaled. The results showed that on some

farms modernization would be justified if over 60 percent of antici-

pated yields were obtainable. In the Alajuela and Palmares-San Ramon

areas, it would pay most farms to adopt modern inputs if over 80 per-

cent of the recorded yields were obtainable. In Alajuela technological

change permitted yield increases from 20 to 25 fanegas per manzana.

In Palmares-San Ramon the yields were increased from 19 to 27.6

fanegas per manzana. Yields were increased from 10 to 14 fanegas per

manzana in Acosta.

The results of the programmed technological advances are given

in Table 3. In the Alajuela and Palmares-San Ramon farm situations,

optimal coffee output was increased 72.4 percent and farm income was

increased 110.23 percent when higher-yielding coffee activities

modified the output of nine farms. In the Acosta programs, coffee

output was increased 198.81 percent and net income was increased

9.38 percent when technological changes for coffee were permitted in

the four farm situations when high fruit prices were used. Given

low fruit prices, coffee output only increased 15.93 percent but in-

come increased 26.98 percent in the Acosta farm situations.

Higher levels of technology brought snmll increases in coffee

acreage on a number of the farms. The planting of new coffee was

limited by credit restrictions. The gains iI programrrd income were

sufficiently high to motivate change if the improved technology can









Table 3.


The effects of improving coffee production technology on
coffee production and income


Coffee output Incomes
Current Improved Current Improved
Farm technology technology technology technology


(fanegas)


750.0
225.0
190.0
76.0
62.1
19.0
213.3
100.0
22.5
195.6
900.0
18.4
70.0
17.4
20.0
3.0
9.0


(fanegas)


1 ,462.8
425.0
276.0
110.4
94.3
27.6
281.4
120.0
60. 1
656.0
1 ,032. 1
18.4
94.5
23.0
23.0
3.0
9.0


coloness)

35,543
31,667
16,877
760
7,147
4,005
29,489
24,536
7,078
89,948
67,694
24,222
16,718
5,459
5,209
4,307
3,612


coloness)

138,168
62,013
29,739
4,583
10,979
5,720
38,460
27,989
9,440
101,272
90,709
24,274
18,361
5,707
5,707
4,307
3.612


aThe letters A and B are used to designate different fruit pricing
used in Acosta. A's are used to indicate the situations using high
fruit prices and B's are used to indicate the situations using low
fruit prices.


- ---~------------





75

increase production to the levels reported on the better farms. The

higher yields obtainable in Alajuela and Acosta resulted in greater

increases in income in comparison with the Acosta farms. Coffee

production, therefore, is expected to increase unless positive aids to

diversification or production controls are put into effect.

Comparisons between optimal coffee production and maximum net

returns for traditional versus new enterprises are shown in Table 4.

On the Alajuela farms, returns from sugarcane approached those of

horticultural crops and optimal coffee output was not reduced when

the new crops were included among the alternatives. However, incomes

were increased where coffee yields were limited to 20 fanegas per

manzana. Actually, the higher credit requirements of the horticultural

alternatives caused the optimal coffee tree destruction to be lower

when horticultural crops were included on Farm 10.

In the Acosta farm situations the exclusion of fruit production

alternatives lowered optimal income and caused a sizable increase in

coffee output. Farm 13 and farm 14 had sufficient capital and credit

to respond to fruit production opportunities with sizable increases

in income. The additional income potential of fruits and vegetables

was limited on the smaller Farms by a shortage of operating capital

and credit.


Taxation or Price Reduction

The stability of coffee production in farm management plans was

further examined with price declines parometrically programmed into

each fari situation. Table 5 shows the effect of price declines on

coffee output. Reduced output with ,5.00 per fanega price declines

was limited to the blocking of new plantings of coffee trees and the









Table 4. Comparisons of optimal incomes and coffee outputs with
traditional versus new enterprises on farms in Alajuela
and Acosta


Coffee output
Traditional New
enterprises enterprises
(fanegas) (fanegas)

997.2 997.2

163.8 213.3

100.0 100.0

22.5 22.5

900.0 196.5

70.0 18.4

20.0 17.4

9.0 3.0


Net
Traditional
enterprises
coloness)

86,761

22,586

22,239

7,003

67,694

16,718

5,209

3,612


income
New
enterprises
coloness)

86,761

29,489

24,536

7,078

89,948

24,222

5,459

4,307


Farm


I _
_II


__I~ ~ __ I_ __ __













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U-NC


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-:1-


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N c- c\, O 0 N N r-- 0 m c 0 C- AZ










co a~o) \,o o) m Ln a r o-:1- 0 c -o N)-
\ 1 - a) o- -- u-
--\o-o- NN NN-m n


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00r\0 L 0 ND






OOO^MOOCO










L~ NC-M Cr- ir 0
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Lr\cM cN~ni-r-(- o
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x0N 00 -N 0 0
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cNl rm 0- ) \o CM







LrN 00-t N 0 0

N-- O-f0--
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N LM 0 CO CN r 0 c Q
N- a0-'- N


C 0 0 0 0 0 0 NC c. 0 tr\ k 0 0 C 0-- 0

oo -- CM ru 0 nN or. C0 N C-\ o o -- r cM









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substitution of corn and beans for low-yielding coffee on farm 14.

Taxes up to $10.00 per fanega would have little effect on coffee out-

put of the farms programmed. Except for the newly planted coffee,

coffee production was not responsive to price declines up to 20.00

per fanega. High-yielding coffee and semi-abandoned coffee were the

most stable. With high coffee yields, the alternatives are poorly

competitive and with very low-yields, stability is assured by severe

credit limitations. Resources were shifted away from coffee when the

coffee price fell to new market price levels.

Table 6 shows the effect of price declines on coffee acreage.

Coffee acreage was more stable than coffee output. Coffee land was

notably more stable on the smaller farms as price declines were pro-

grammed.

The effect of price declines on farm incomes is shown in Table 7.

Price declines reduced income more rapidly than output and had the

strongest effect on those farms highly specialized in coffee produc-

tion. For example, given a (40 per fanega price decline, income fell

54 percent on the three largest coffee producing farms and only 22

percent on the farms with optimal production less than 100 ganegas.

Net returns on farm 2, farm 5, farm 9 and farm 13B, all of which

specialized in coffee production, were reduced over 50 percent by a

50 per fanega price decline. Of those farms specializing in coffee

production, only on Farm 10 did diversification possibilities hold

income above 50 percent of initial levels when price was reduced 25

percent. In the Acosta farm situations, high fruit prices resulted

in both higher incomes and greater income stability in the face of

coffee price declines. The effect of a price decline on income was













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--N N U- r~~ I-- o I-r N u c-D CoL, cN r- 0 o C4

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CL I o 01\ CO L ,% i r oo r>Lroo r O O '3 cM O cM Lr\
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81

greater on the farms from the Palmares-San Ramon area where expandable

alternatives fell further behind coffee in terms of net returns.

The alternative crops that increased first as coffee prices were

programmed downward are shown in Table 8. In the Palmares-San Ramon

area the farms first responding to a price decline were those with

unused tobacco allotment. Among the Alajuela farms studied the larger

farms responded first to a price decline. This also held true among

the farms from Acosta although irregularities occurred with respect

to which set of fruit prices were used.


Payments for Coffee Removal

The programmed effect upon coffee output of annual payments for

coffee tree removal is shown in Table 9. Farm 1, farm 2 and farm 9

were not responsive to removal subsidies because of high returns to

coffee. Farm 16 was not responsive because of severe credit limita-

tions.

Annual payments equal to 20 percent of gross coffee earnings per

manzana were effective in changing optimal resource allocation in five

of the 20 farm situations studied. Given a 25 percent discounted

price for 25 percent of the output of coffee, the payment of 20 per-

cent of base gross returns could be made for the withdrawal of coffee

production without extra taxes or loss to coffee producers. The

following formula can be used for calculating a self-paying subsidy

for coffee removal. Let the coffee price be unchanged as coffee in

excess of the quota is taken out of production.

Then:

PT + (P DP)N PT
T+N T + SN









Table 8. Alternative crops increased first by declines in coffee
prices




Price reduction
Farm per faneqa Crop increased
coloness)

1 100 None
2 91 Beans, sesame
3 40 Beans, corn
4 15 Tobacco, beans
5 57 Beans, sesame
6 58 Beans, sesame
7 90 Castorbeans, milk
8 15 Tobacco
9 21 Sugarcane, beans
10 22 Tomatoes, sugarcane
11 43 Limes, peppers
12 60 Limes
13A 14 Limes
13B 14 Corn, beans
14A 25 Limes
1IB 55 Corn, beans
15A 29 Limes
15B 58 Corn, beans
16A 100 None
16B 32 Corn, beans












00 NOC4 rr- CD 0 0 00 00C 0 CD0

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CCo N N0 (Y CQ N N 0. C\ N N 4 4 CO 00
CN Or ) G Lr\ 0 N. M N. N- 0 "n -t Co 0 N-'\0 cn 7\



Co N ~00 ~0 c~~0 00 Nc f ~0- 0
0N C7) \0o C\ -D-\ 0L\ M 0 Cl M Co 0 r-C 0 -










CoO' u~ Ln or N- U\ L- 0-' -- 0 c.- .-- -- N- Nr
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Co -C 0 00 cn C) 0 N, cn 0 Lr\' D -D 0 0 00
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N C-4 0 [f0 0 C 0 N 0 N L\ \ Co 0T CN 0 C. C>
00 () U\ 04(J"r-,Ln-C)--- 0 C:C710 r--N-.-N
-NN-- r, '4N4- I-Q-m










<~ m n M 'r, < O
- N1 m -r Lr \. 0 N-Co0 cD- CN m~ crZi in. 'ikn '







and

S = TD
T + N ND

where:

P = price in traditional market,

D = discount for sale in new market,

T = traditional market quota,

N = surplus over quota,

S = share of payment for exiting firms.

This indicated that coffee could be removed from farm 3, farm 4, farm

10, farm 13A and 13B with a scheme to pay for coffee removal out of

total sales revenue without lowering the average price to producers.

However, such a scheme would reduce coffee output over 10 percent

only on farm 3, farm 10 and farm 13A. A shortage of operating capital

blocked coffee removal since available credit was reduced by the cost

of coffee tree removal.

Annual payments, as shown in Table 10, either increased or did

not affect Farm income. One would expect such a program of self-

financing allotment payments to be politically acceptable since it

would not lower the income for any farm. However, the effect upon

coffee production appears to be relatively low with respect to the

quantities of coffee produced above traditional market quotas. Addi-

tional money could be made available from the Diversification Fund of

the International Coffee Agreement but even with Lhis extr ; subsidy

the annual payments are unlikely to motivate much change. IF $0.30

per quintal of sales were made available for this subsidy, annual

payments could be increased less than 20 per Fancga removed, assuming

that payments were spread over iO percent of the ba:e production.













- Ln 0 cnLrL--\ -- co CO INr--\ -t r-o "M
co" 0 ,O, Lt- N-I _r-- 0 \.o --d- r- o r o "--. o" 0 -
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or D0 0 0 r, (n -.. 0 CD -- f Lt.\ (D -3- n \.,
.CmN N-0jC'0N-N-'.L
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NO- Ltl- \N N-- '. O- o- -, N u\ -:
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- Ln (r r- o Lr\L \ N -\. CO Cm c- CO m\ oN- s N
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O~on LCr '.0 N-~- '.0 -^ N- i r N-dZ '.0 L\ Lr\-3t r'
- ^- c' CO NM N CO M. N -









< on < on -l < C a: <
- N rCn -t Ln \' CO c Cmo N cM (M -T -d- L Lr\ 0 \'.


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This could increase total payments to"approximately (60 per fanega.

Therefore, before annual payments may effectively remove coffee, the

alternative use of resources must give returns competitive with coffee

and ample credit must be available to finance such alternative activ-

ities.

Payment for coffee removal could be more efficacious if made in

a lump sum provided that the marginal interest rate facing the farm

operate or is well above the rate at which money is available from

bank sources. A lump sum payment equal to the earning differential

between coffee and its next best alternative divided by the marginal

interest rate for the farmer would be necessary to motivate rational

change. Poorer farmers with scarce capital and poor credit standings

would be most responsive to such payments if they were given the

knowledge required to change traditional cropping patterns.

The value of a lump sum payment is shown in the following example.

Assume that the government can borrow money at 10 percent annually

for a 10-year period and that a farmer's marginal interest earning

rate is 30 percent. Then the cost of generating a perpetual psychic

income flow of (100 per year is 50 for each of 10 years. Higher

farmer interest rates or extended pay-off periods would further reduce

the annual cost of generating a given psychic income flow.

Because of the difference between bank rates and marginal interest

rates for near subsistence farmers, lump sum allotment payments would

cheapen the payments required to motivate change. installment pay-

ments computed as a share payment for destroyed coffee could be paid

to a government fund out of coffee export sales without lowering the

average price paid to remaining producers. The government fund then




87

could borrow to pay lump sum allotments for coffee removal. If the

above example of 30 percent farmer marginal interest holds true, then

the cost of generating an income flow is halved and changes in output

can be motivated through a program of allotment payments in 10-year

installments from coffee sales without lowering average price. Limi-

tations of credit affecting the outputs and incomes would be removed

as the subsidy payments would be a source of operating capital.

The cropping pattern encouraged by subsidy payments for coffee

removal is similar to the changed output programmed with coffee price

declines. Favored crops are listed in Table 11.


Price Differentiation

Price differentiation is theoretically efficient in that it

permits the national marginal returns for coffee to be passed back to

the farmer. Output of coffee should be reduced or held constant while

income is either increased or unchanged. One difficulty in applying

this measure is the establishment of the quota for the traditional,

higher-priced market. If optimal coffee production is above the his-

torical base used to calculate the traditional market quota, coffee

price averages will tend to be lowered. If optimal coffee production

falls below historical production, the price averages will tend to be

increased. If new crops shift resources away from coffee in the

initial optimal solution, higher marginal returns to coffee may cause

an increase in coffee production as some resources are shifted back

into coffee production.

Table 12 shows optimal incomes and coffee outputs with single and

differentiated coffee prices. Of those farms with increased incomes,

coffee output was unchanged on five farms, increased on two farms,









The alternative crops increased first by annual payments
for coffee removal


Annual payment
per manzana
coloness)
a
3,000
2,900
600
600
1,100
1,100
2,200
2,600
1,700
500
900
3,000
400
200
700
900
800
600
3,000
3,000


Crop increased


None
Mixed crops
Corn, beans
Corn, beans
Beans, sesame
Corn, beans,
Corn, beans,
Corn, beans,
Sugarcane
Sugarcane
Li mes
None
Limes
Corn, beans
Limes
Corn, beans
Limes
Corn, beans
None
Noneb


sesame
castorbeans
sesame


aWhen changes were not initiated by the levels of payment first
programmed, additional runs extended the levels of payment.

bLand was idled as credit became more limited due to coffee removal.


Table 1 .


Farm


1
2
3
4
5
6
7
8
9
10
11
12
13A
13B
14A
14B
15A
15B
16A
16B


~~_I~T_
_L__




89

Table 12. Comparisons of income and coffee production for differen-
tiated prices versus single prices


Farm


Incomes

Single price Two prices
coloness) coloness)

19,781 19,781

18,025 17,484

35,543 45,709

31,667 26,220

16,877 16,877

760 1,389

7,147 6,657

4,005 3,880

86,761 86,870

29,489 32,330

24,536 24,536

7,078 7,176

89,948 96,670

67,694 70,792

24,222 24,448

16,718 17,056

5,459 5,975

5,209 4,922

4,307 4,350

3,612 3,586


-- ---


Sinqle pri
(fanegas)

82.8

193.2

750.0

225.0

190.0

76.0

62.0

19.0

997.2

213.3

100.0

22.5

195.6

900.0

18.4

70.0

17.4

20.0

3.0

9.0


Coffee

ce Two prices
(fanegas)

82.8

193.2

750.0

192.0

190.0

71.3

57.0

17.5

997.2

225.0

100.0

22.5

650.0

650.0

18.4

60.1

8.2

20.0

3.0

5.6


-------------- -- --





90

and reduced on three farms. Of the eight farms with reduced coffee

output, five had income reduced below the single price optimum.

When differentiated coffee prices were used to separate sales

into new and traditional markets, optimal output was reduced or un-

changed on farms where the base used for establishing the traditional

market quota was equal to or less than the initial optimal coffee out-

put. However, in those cases where new activities replaced coffee

and optimal output fell well below the historical base, then the

optimal coffee output increased or was unchanged when prices were

differentiated. Income increases were associated with above optimal

production in the base period. Increased shifts toward lime or

vegetable production did not occur when prices were differentiated to

reflect different export earnings in new markets and traditional

markets.


Reduction of Credit

The reduction of credit has been suggested as an effective means

of controlling coffee output. As Table 13 shows, coffee output is

reducible if this measure can be imposed. However, there are effects

on farm incomes also (Table 14). In 10 of the 20 farm situations,

income was first reduced by credit restrictions without affecting

coffee production. In four situations where horticultural crops re-

placed coffee in the initial solution a reduction in operating credit

actually brought increased coffee production within certain levels of

constraint. The magnitude of the income reduction is greater where

the optimal solution contained alternative crops. Since credit avail-

ability was tied to coffee land in the model, a credit restriction

cut coffee production without encouraging diversification. Coffee land




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