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

3L4£

AQRI-

CULTURAL

tIBRARV

iiiiiiii

/ , I .'

ACKNOV/LEDGMENTS

The author wishes to express his sincere appreciation to Or.
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. Murphree, Dr. C. W, Fristce, Dr.
K. C, Gibbs and Dr. L. H. Myers for revievjing the manuscript and
offering assistance. The author also 'wishes to thank Dr. H. L.
Fopenoe, Director of the University of Florida's Center for Tropica]
Agr i cul ture ,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 vjishes to
express thanks to Mr. D. W. Parvin for his aid in interpreting the
computer manuals.

Special tlianks are also due numei-ous people in Costa Rica v;ho
graciously supplied information and insights necessary to the comple-
tion of this v;ork. The Oflcina del Cafe under the direction of Sr.
Alvaro Castro Jimenez supplied transportat i on > office facilities, and
techriical assistance. The author depended heavily on the advice of
Ing. Hugo Castro, inn, Rogel'c Acosta and !nq. Edwin Mann in the
initial phases of the study.

The cooperation of the E/tension Service, of the Costa X\ca
Ministry of Ayricult.ire is also appreciated. AcknovJledgment and
thanks are due the .;vjny people v/ho supplied input-output information.

Additional assistance v.'as given by others including Carlos
Arroyo, Hester Barres, Janes 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 Incenlath for
typing the final manuscript.

I I I

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ii

LIST OF TABLES vi

INTRODUCTION 1

An Economic Background 1

The Importance of Coffee to Costa Rica 3

The Marketing Situation for Coffee 5

The Pros and Cons of Diversification 9

Approaches and Attitudes toward Diversification .... 12

The Problem and Objectives 17

SCOPE AND METHOD OF STUDY 20

Selection of Areas and Farms 20

Description of Farms Studied 22

Pa 1 mares-San Ramon . 22

Alajuela 26

Acosta 28

The Linear Programming Model 30

The Enterprises and Restraints ^I

The Sources of the Budgets ....... ^8

Assumptions of the Model 55

RESULTS 57

Optimal Cropping Plans 57

Farm 1 ' . . . 57

Farm 2 , 58

Farm 3 53

Farm '4 , 59

Farm 5 60

Farm 6 oO

Farm 7 61

Farm 8 , . . , 62

Farm 9 62

Farm 10 63

Farm 11 63

IV

Page

Farm 12 Sk

Farm 13 65

Farm 1^+ 66

i-'arm 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

SUMmRY AND CONCLUSIONS 13^+

Effects of Improved Resource Allocation 13'4

Effects of Taxes or Price Declines I36

Effects of Credit Reduction 137

Effects of Price Differentiation I38

Effects of Increased Credit 139

The Choices of Alternative Crops ]kO

Comparative Costs of Coffee Removal 1 '+3

Potential Effects of Technological Advances

in Coffee Production 1 '4^

The Qualifications of an Acceptable Alternative .... 1 't5

Diversification Versus Production Control ....... 1^6

GLOSSARY 1-^8

APPENDIX , 149

LITERATURE CITED I96

BIOGRAPHICAL SKETCH 202

LIST OF TABLES

Table Page

1. Partial matrix of coffee selling activities ... 37

2. Comparisons between reported and optimal

incomes and coffee output 71

3. The effects of improving coffee production

technology on coffee production and

income Jh

h. Comparisons of optimal incomes and coffee

outputs with traditional versus new enter-
prises on farms in Alajuela and Acosta .... 76

5. Optimal coffee production with specified

price declines per fanega of coffee 77

6. Optimal manzanas in coffee vjith specified price

declines per fanega of coffee 79

7. Optimal net farm income v;i th specified pries

declines per fanega of coffee 8O

8. Alternative crops increased first by declines

in coffee prices 82

9. Coffee production vjith annual payments for

coffee removal 83

10. Net farm income vjith annual payments for

coffee removal 85

11. The alternative crops increased first by

annual paymc^nts for coffee removal 88

12. Comparisons of income and coffee production •^

for differentiated prices versus single

prices ...... 89

13. Optimal coffee production with various

levels of reduced credit 5I

1 ^. Optimal net income with various le^-els of

reduced credit 32

VI

Table
15.

16. ■

17.
18.

19.

20.

21.

22.
23.

25.
26.

27.
28.

29.
30.

Optimal manzanas in coffee with various levels

of reduced credit

Marginal returns to credit with various levels

of reduced credit

Optimal family income with and without off-
farm employment opportunities

Optimal farm income and the reduction of

permanent labor

Effect of additional credit on income and

coffee production

Optimal farm outputs of various crops on
Alajuela farms given base capital and
credit constraints and extra credit
equaling ij^OO and ^2,000 per manzana

Optimal output of various crops on Acosta

farms with high fruit prices given base capita]
and credit and extra credit equaling (;i400 and
i;2,000 per manzana

The effect of a blackberry subsidy on optimal

coffee output

The effect of a strawberry subsidy on optimal

coffee output

Relationships betv;een long-term interest

rates and manzanas planted to limes

Lime prices and lime production

Prices of oranges necessary to Initiate
orange production on farms In Alajuela
and Acosta

Alternative methods of reducing coffee

output on farm 1

Comparative costs of various metliods of

coffee output reduction on farm 2

Alternative methods of reducing coffee

output on farm 3 . , .

Alternative methods of reducing coffee

output on farm h ' .

Page

95
96
97
99

00

102

104

]0k

105
106

109
111
112
113

VI I

Table

31. Comparative costs of various methods of

reducing coffee output on farm 5 • .

32. Comparative costs of various methods of

reducing coffee output on farm 6 . .

33. Comparative costs of various methods of

reducing coffee output on farm 7 . .

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

35. The comparative costs of various methods

of reducing coffee output on farm 9 .

36. The comparative costs of various methods

of reducing coffee output on farm 10

37. The comparative costs of various methods

of reducing coffee output on farm 11

38. The comparative costs of various methods

of reducing coffee output on farm 12

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

^0, The comparative costs of various methods
of reducing coffee output on farm ]U,
given high fruit prices

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

k2. The comparative costs of various methods
of reducing coffee output on farm 16,
given high fruit prices

A-3. The comparative costs of various methods
of reducing coffee output on fGrm 13,
given low Truit prices

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

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

Page

115

116

117
118
120
121
122
123

125

126
127
128

129

130
131

VI I I

Table

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

Page

32

I X

NTRODUCTION

An Economic Background

Costa Rica Is a small country in Central America vn th 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 vjork force in 1363 (45, p. 13). In
addition, agricultural products accounted for approximately 30 per-
cent of the gross national product in I966 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 do.^iinated by small end mldd!e~si 5:ed farms.

In recent years, Costa Rica enjoyed the highest per capita in-
come in Central America. The estimated grcis natioml product per
person in I565 vjas ^'il5 compared to up. 3vBi-3(^'3 cf $303 for all of
Central America (55, p. 3).

In recent years Costa Rica has had a balance oF paymencs iM'oblein.
Foreign debts iiave had to be continually rcnecotl atcd. From 1350 to

2

1967 the value of exports of coffee, bananas, cocao, cotton, beef,
and sugar increased from 532,1 to 702,8 million colones (56, p, 3^+) .
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 2^,3 percent
in 1967 and by 12,2 percent in I968 ( 2^+) .

Production of the basic food crops has exhibited erratic growth
since 1950. Corn imports exceeded exports in 1951, 195^, 1956, 1959,
196^+ and 1966. Per capita consumption of corn in I966 was below the
1950 level (56, p, ^+2) , Rice production has almost tripled since
1950, with a stronger upward trend occurring after I958. V/hile the
change is largely due to an Increased acreage, the average yields
have increased since 1955 (56, p. 5). Per capita consumption of rice
has increased. Bean production has also increased significantly,
doubling the 1350 output in 1967- However, net imports of beans
occurred in 1951, 1956, 1957, 1953, 1961, 1962 and 196^+, Per capita
consumption has Increased 25 percent since 1950 (56, p. 28),

The Costa RIcan population increased from 1,028,175 in 1955 to
i,6U8,8l5 in i960 (15, p. 2), an annual average increase of more than
k percent. While population grov.'th increases the demand for food,
this demand Is also increased by a rise in per capita incomes. If
food output is CO keep pace with the demands of both population and
income grovjth, 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 capita]
goods needed for economic growth (U8, p, 5).

A.TOther problem facing Costa Ricau 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 tiiem. With over 35 percent of the population under
ten years old, education is costly (!9, p. kj) . Rapid population
growth slows occupational changes (1).

Thus while the Costa Rican economy has given the nation the
highest per capita 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 (5^, p. 37). Despite a long history of pro-
duction, coffee output has increased markedly in recent years. From
1955 to 1963 coffee output v.'ss doubled. Yields increased 27 percent
while acreage increased over 58 percent (53, p. 10). Considerable
effort was put into research and extension work which empiiasized tiie
use of modern inputs in coffee production. Higher coffee prices in
the 195Q's encouraged the expansion of coffee onto nevi 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 13 to 233 percent increases in coffee yields in trials running
from 1952 to I957 (69, p. 60), Since I963. production increases in-
dicate an even larger use of off- farm inputs. In I967, coffee output

k

vias more than three times the average output in the 19^8 to 1952
period (29, p. 63). Thus coffee production has led in a change from
traditional tovvard 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 I966, 41.2
percent in I965 and 42.2 percent in 1964 (15, p. I3). Thirty percent
of the economically active population is associated v..'i th 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 v/el i-cared-for coffee planting protects the soil from
driving rain and contour ridges that slow runoff are permanent and
reinforced with v-joody 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 $0,4-5 per
quintal of exported coffee, ^2,00 per quintal of coffee consumed
internally, and i;i0,20 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
advalorum tax is graduated in the folio. (ing manner: 10 percent if
the average price exceeds $42,50 per quintal, ']\ pei-cent if the
average price falls betv/een $40,G0 and $42.50 per quintal, 5 percent
if the average price falls betv/een $37.50 and $40.00 per quintal, and
2-2" percent if Llie .average price fall^ between $35-00 and $37-50 per

5

quintal. No advalorum tax is paid if the price falls below $35-00
per quintal (Sk, 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 ]k
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 I965-66
to 1966-67 to 1967-68, The price decline from I566 to I9S8 brought
about a drop in tax revenue estimated at 2k 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. ^5^+) . 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 follov.'C-d by long periods of sur-
plus and lovj prices (59, p. 8). These drastic price fluctuations
stimulated coffee producers to seek remedial schemes. The first of
these was a Brazilian lav; blccl;ing new plantings in I90?.. Political ly

unpopular, this lav; was repealed and followad by valorization schemes
with which large quantities of coffee were purchased and held off
the market (32, p. kSS) . Brazil borrowed from British banks to make
coffee purchases in I906, 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
19^^ (73, p. ^23).

Coffee production in other Latin American countries expanded in
response to Brazil's price supporting activities. Grov-jth elsewhere
caused Brazil to market a smaller percentage of the world coffee
(73, p. his).

international agreement to control coffee output was first
attempted in Bogata in 1936. Later, the Inter-American Coffee
Agreement was signed by 1^ producing countries and the United States.
Selling quotas vje re established and the agreement lasted from 19^+1
to 19^3 (73, p. ^23). In 1957, coffee producing nations agreed upon
a voluntary system of export regulation. This agreement failed to
check the dovjnv;ard price movement (59, p. 13). Finally, the inter-
national Coffee Agreement received the support of k^ producing and
consuming countries in I9S3. 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. 1^). The
demand for coffee is believed to be inelastic with respect to price.
!f this is true, a free-market solurion would result in lovjer gross
income to producers than would occur if output were limited by some
kind of a cartel arrangement. EconomisLs generally view trade agree-
ments vji th a skeptic eye. Both experience and theory show that, v;i th

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 I965-66 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 (3^). 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 programis to check coffee
output. As mentioned earljer, Brazil first attempted to control out-
put in 1902 vi'\ th a law prohibiting new plantings. More recently pay-
ments have been made fc coffee removal (bO), Participation in the
program, has bean vol untary ,vji th 6'l8 mi II loo coffee trees pulled out
from June, 1S62 to December, I965. Plans had called for the removal

8

of two billion trees and part of the lack of response was blanjed on
inflation which lowered the value of the fixed payments from $0.0A-
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 (7'+). 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 $2'+ 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
Kxportaciones 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 Tui'rialba has initiated a
regional diversification program wi th the Instituto I nterameri cano de
Ciencas Agricolas. Financial support from tlie Oficina del Cafe and

the Agency for International Development (AID), plus technical
assistance from the instituto 1 nterameri cano de Ciencas Agricolas,
the Peace Corps, and the Ministerio de Agr i cul tura .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 thei r own.

In summary, a review of the literature on Latin Am.erican coffee
diversification shovjed more discussion and hypothesizing than prac-
tical resul ts.

The Pros __and Cons of Divers i fi cat ion

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 monoculture include the follov;ing:

1. In some cases the monoculture crop has a clear comparative
advantage both at dom.estic and international levels. The financial
gap between the monoculture crop and next best alternative has been
found to be too v;ide to nrirmit ratic^dl rlianje.

2. It may be easier to raise yields to give higher returns from
on established crop thnr, to press for ":ore complex cropping systems.
The short-run returr.s "o increased cpeci a 1 i zat ion v.'i th eccncnies of

10
scale iTiay be quite high. Th.e new knowledge arid 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.

h. Certain crops have more prestige and social status than
others.

The disadvantages of monoculture bring out accompanying economic
di ff icul 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 monoculture crops are perennials, a
production lag may fo]lov\' 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 tliis lag follows a major change in resource allocation,
rleadjustmsnt 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, snort-term price f 1 uctu-it ions caused by weather and biolocjical
factors. The resulting high prices in the short run may trigger ir-
reversible i nves tmoi-i ts . These investments plus technological advances
can be expected to increase supply '..'hile demand is not likely to
incease faster than population growth. As a result, prices are
exoected to vjeaken over time.

n

k. Trade agreements limit sales to key markets and therefore
increase both price and gross returns for the conimodity 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 econoinic risk {hS,
p. 2k). A number of different sources of i ncomie gives protection
against severe loss caused by insects, disease or bad weather condi-
tions \-;hich 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 v.'ork period. Shifting from an export crop
to food crops can lead to improved -utritional levels, especially if
more fruits and vegetables are introduced Into the diet. Also,
import substitution may save scarce foreign exchar.ge (22, p, 27).

On the other hand diversification faces certain 1 i ini tat i ons.
Research has been focused on a few major expert crops (22, p. 39).
V/ithout much experience or local scr^r'.ific investigation to support
a new enterprise, the innovating producer faces higher uncertainties
with respect t.? the crop respor.'ia to .jn '"avorrb ' e fdctois and condi-
tions ('hi, p. 1), The market for the alternative crop may not be
sufficient to abiorb exparniing pi-oduction at profitable prices (22,
p. k]) . Ev'ir, if a potential demand exists, the marketing facilities
hiay not be adeo^ia te to move the nev^ output to consumers. The econ-
omies of scale may res'jlt in poot^ effici'='ncy as a g-eater number of
crops are produced and volume of som.e crops is reduced. This is

12

particularly a hazard for a nev/ 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 wora complex agricultural
systCirs (^8, p. 2h) . Diversification projects are likely to fail on
farms where administration and managemant inadequacies greatly limit
the returns to coffee because the new enterpi'ises are likely to be
even more difficult Lo manage (27).

Aoproaches and Attitudes toward Diversification

Crop diversification is defined as a movement av/ay from mono-
culture wi tri the gi"ov.'Ing of new or additional crops (22. p. i). 'tore
detailed descriptions may be conflicting ai'd the v-^valuatlon of diver-
sification as a policy measure depends greatly upon just vjhat meaning
is used. A [i;Q3t restrictive definition, anJ o:^e often thought of, Is
the transfer of land from rr.cnocu 1 ture 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
stravv/berr i es 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 swamipy 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
re let i ve 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 un!^uited for
coffee, where mechanization was feasible (63, p. 3). The program in
this case would be to expand output using resources not now used to
grow coffee.

Crop diversification may either expand domestically consumed
crops or promote new export crops. Although import substitution is
reccgnived as beneficial, pi ani-;'"::rs seek to increase earnings of foreign
exchange with new exports. The export deif..^rrd for a product facing a
small country is often highly price elastic (57, p. 1), Thus, gener-
ally the nev^ export crop has an advantaae over do-iestic crops in that
price will remr.in more stable as output is expanded. The small size
of che domestic market is couplc-c with a shortage of capital funds and
modern knovj-how to limit diversi-^ied economic potentii^l (16, p. 33).

14

Furthermore, overproduction of basic food crops rr.ay 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 tlie consuming countries (66, p, ^32). 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. Hov-ever, the
common agricultural export crops are inelastic when the entire world
market is considered since they do not liave close substitutes, do not
have many uses, and do not take a large share of the consumer's in-
come {hk, p. ^+1). After a technological change is widely adopted,
the result of increased output is often lovjer gross revenue. In
this situation, the consumer benefits from lower prices and a higher
real i ncome.

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 lov/or piMces. 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 developm.ent of a new export d'op requires
efficiency IF it is to meet the estabi'shed competition.

Another area of debate centers around the question of who should
diversify. Tvjo nearly opposite views have developed: an efficiency
criterion of m.argi na 1 i ty proposes to rcc/.ive the low profit pioducers.
In opposition, a criterion of welfare seeks the removal of those least
hurt by shifting to alternative 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 (5^+, 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. 1^) has criticized the marginal producer definition
for being concerned with absolute rather than comparative advantages.
Small farms are unsui ted for such alternatives as dairying and fruit
production because ciiey 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 v/ithstand 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 v^hen perennial alternatives are
cons idered.

Welfare considerations cannot be quantified for interpersonal
comparisons. However, value judgments need iiot be made if the level
of alternative output is used to evaluate different policies of diver-
sification. The problem tlien becomes the calculation of the net costs
of removal of a quantity of coffee from production on different farms.

16

If the means of production control is alternative use of land,
this may be stated algebraically as follows:

C =P -C -V -t-C +C
r c c a a s

where

and

C = net cost of removing one fanega of coffee,

P = price of coffee,

c ^

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,

C = cost associated with producing V^,

a "^ -" a '

C = 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

^ r c c

destroyed, the cost is higher as C - P .

A mere general formula can be stated for calculating the unit
cost of coffee removal,

C = I , - l„

Q, - Q2

wtie re

C - net cost of coffee removal
r

i. = net i nccr.ie before change
r

I - net income after ch-inoe
2 ^ - ■

Q = coffee production before c'lange
Q,„ - coffee production after change

Ideally, diversification would seek to reduce coffee vyi Lhout re-
ducing 'ncome. Hcwever, given a price siti'aticn which stimulate:?

17

excess use of resources in coffee production, a more practical policy
would attempt to minimize the costs of controlling output.

The Problem and Objectives

Although coffee is Costa Rica's most important commodity, con-
tinued dependence on that one crop is considered detrimental to
prospects for economic grov;th. This harsh statement Is supported by
a pol I t i ca 1 -economi c 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, !f 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
naximizers? Theodore Schultz has claimed that in traditional agri-
culture farmers are not only profit maximlzers but that they also are
quite efficient profit maxirrlzsrs (62. p. 'ih) . Ibv'ever, most of the
farms studied do not truly fie the derinitlon of traditional agricul-
ture used by Schultz (63, p. 30). Cultural techniques ''or coffee
have not remained unchanged for cenarat i ons and further changes are
occurring (32, p. 432). Costa Rica's emphasis on primary education
already has accomplished much toward the investmenr 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- t radi t i onal 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 (&3, F- 107). Vas t . dl sequi 1 i b-
rium is said to exist with differences in marginal productivity and
overuse or undsruse of factors. The expanding use of fertilizers and
pest control chemicals (15, p. 7) indicates that transitional changes
toward modernization are occurring In Cos'>;a Rica.

Nevertheless evaluating the farm case studies with respect to
expectations given by Schultz's theories may be 1 P; teres 1 1 ng. 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 proyramming 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 methods with some crops while changes occur with others.

Specifically che objectives of this study were to evaluate farm

19
income opportunities frorn 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 vje re taken from a study carried cut under a
University of Florida - AID Contract in Costa Rica, No. la-26l.
Budgeted comparisons between coffee enterprises and leading alter-
natives in 12 areas are given in An Economic Analysis of Coffee Pro-
ducing Areas, Cosca Rica (11),

Palmares-San Ramon, Alajuela and Acosta v.'ere the areas selected
for this intensive study. This selection was based on the following
cri teria:

1. Coffee and alternative crops should be found grovjing under
similar ecological conditions.

2. Enough coffee should be produced to make changes inportant
to national totals.

3. Different areas should I'epresent a wide range of coffee
productivity and alternative choices.

Some consideration was given to including the Turrialba area in
this study, HowR'/er, the d i ver; i f i ca t i on project in that ;^rea was
just beginning to gene'-ate completely new data when this work was
undertaken.

V/ithIn the three areas selected, the local extension agents

20

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 vjas supplied for each enterprise. In
addition, budgets for several crops grown in the areas w'ere 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 Hinisterio de Agri-
cul tura.

It is admitted that the farm case study appi-oach 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, op.e may expect that the "typical farms" of the
extension agents may be aoove average. Nevertheless, this direction
of bias need not be undesirable, since it is this group of farmers
viho 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 genera 1 i zat i ons possible must be limited
to direction and nature of cliange which may i r. turn lead to specula-
tions concerning the response oF the toial coffee industry.

Although quantitative analysis is made at the farm level, extrap-
ol'^ition to larger area'i must be v.'i th descriptive or qualitative anal-

22

ysis. In this manner, associations can be made between poiicy 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.

Fa] ma res- 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, vjhich are not well suited
for coffee, reported coffee on 9^ percent of the farms and 37 percent
of the land in I963 (25).

The soils are f 1 uvi o- 1 acus tr i ne 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 between 900 and 1,200
meters. Practically no rain falls from December to February (6'f).
in the districts of San l^idro and San P\anion of the canton of San
Ramon the Good Hard Eenn typo of coffee is produced at elevations of
1,000 to 1,200 meters. In these districts, coffee utilizes 12 percent
of the land aii<J ii lound en 83 percent of the farms. Tobacco, corn
and beans .-■ire the major crops after coffee in the combined area.
Ac:ording to '-.he I563 census, colTce was gro/jn on 2,733 manzanas.

23

corn on k]7 manzanas, tobacco on 3'^ manzanas, beans on 226 r.anzanas,
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, m.edlum, and large
units grov;ing 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 m.eters on land described as moderately
rough. The farm owner applied modern technology in the forms of
fertilizer, insecticide, herbicides, aiid foliage fertilizer. The
farm contains five manzanas with three manzanas planted to coffee and
two manzanas planted to tobacco and corn. The labor force was made
up of tv.'O hired male employees plus the family labor of a mian, a
boy, and two girls. Fixed expenses, including permanent labor, taxes,
depreciation and rr:ai ntenance , and interest on the land investment
totaled ^6,186. Short-term operating capital Including credit v;as
estimated at i'-iykOO.

Net farm income was calculated as gross returns less reported
annual expenses and estimaled fixed costs including rent, taxes and
hired permanent labor. Net farm Income was estimated at ^22,500.
T!ie farm was chosen as an example of mixed croppinj usinc modern
technology and hand labor.

Farm 2 in San Ramon is a sm:^ll farm scicializing in coffee.
Coffee was grown on all Its seven nianzanas. Labor was supplied by
five male and two female employeos plu:; the family labor of two men
and tv.'O boys. The fixed expenses, including permarent labor, totaled
<;16,690. Operating capital and credit av=5ilab]e ^cr variable costs

Ik

totaled ^^,000. Net farm income was estimated at <i.\S,lSO. This

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

Farm 3 in San Rr?mon is a larger farm producing coffee, corn and
tobacco. The farm covers fifty-six manzanas of vjhich 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 ^60,100 and combined
operating capital and credit totaled i.'J],]QQ. Net farm income was
estimated at <i3^,130. This farm was chosen as an example of a larger
farm in the process of undergoing technological change.

Farm h is located in Palmares on nearly level land at an eleva-
tion of 980 meters. It is a middle-sized farm gro\.'ing 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). r'xed expenses w.^^re calculated at ^!'+,8^0,
Annual operating expenses were i;l6,900. The net farm income vjas
estimated at ^1^,700. This unit vjas selected as an example of a farm
specializing in tobacco v;Ith low yielding coffee grov.n on other land.

Farm 5 is a medium- si zed coffee farm In Palmar-es, The land is
nearly level at an elevation of 1,020 meters. All ten riianr;3nas were
planted to coffee. The farm v.'ork was done by four men; tv.'O were hired
and tv/o vjere membei'f of the ovyning fai^illy. The fixed costs were
estimated at (^10,732 '^'"■'^ tiie sum of ihe operating capital and sliort-
term credit amounted to iG ,U-QCi . The net farm income was estim'^ted
at (^15,5^0. This farm was represen tai i ve of a level of technology
commonly used o'' ■'^■.I'-ms slov/ly adopting changes.

25
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 5il2,'i-80 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 (j6,760. This farm v^as 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 vjas grown on two manzanas. The farm employed one man and
extra work \-;as supplied by two members of the owning family in times
of emerg^^ncy. Fixed expenses vjere calculated at <;6,000 and ^4,300 were
reportedly available for variable expenditures. The estimated net
returns vjere (^3,3^5- 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 tvio manzanas of land of which, one manzana was planted to
coffee and one-half manzana is planted to sun-cured tobacco. One
permanent employee v^as hired and tv;o men and irv;o hoys (part-time) of
the owning family worked on the farm. Fixed '^xnenses were estimated
at (j2,882. Operating capital and short-terrn credit w-'^s limited to
(;^900, The coffee planted v;as not yet in production but anticipated
yields gave expected annual returns of (;i2,653. This Farm was selected
as an ?x^mple of a very small farm using multiple copping.

26

Alaj uela

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 vith 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 19^3 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 vjere 82 manzanas of pineapples, 137 manzanas
of corn, 186 manzanas of beans, 17 manzanas of tomatoes, and ]k
manzanas of cassava reported (25). Coffee v;as reported on 76 percent
of the farms and occupied 26 percent of the land area in I963.

Although rains may occur throughout the year, a dry season ex-
tends from December into April (6'f), The coffee produced is the Hard
Bean type (2). The representative districts selected from the I963
census produced 2.^+3 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 m.eters on gently rolling land in
Alajuela, It is a large family far.ii with kO manzanas, all in coffee.

Political subdivisions do not coincide well with ecological
areas; tl-.erefore, 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 i;i^8,575 and short-run credit arid operating capital totaled (i22,200.
The estimated net returns were (j90,000. 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 tv^o men and a boy of the family. The fixed
expenses were estimated at (^21,875. The operating capital and annual
credit totaled i;il5,750. Estimated net returns were s;17,l60 but young
plants raised expected future returns to (f30,000. This farm was
selected to represent the medium-sized specialized farm with good
a 1 ternat i ves.

Farm 11 covers 10. 5 manzanas of nearly level land at 700 meters
elevation in Alajuela, Coffee v/as planted on five manzanas. Pine-
apple was grown on the remaining land. The farm utilized the labor
of ona hired eriplcyee and one man, tvio boys, one woman, and one girl
of the family la&cr force. The fixed expenses vjere estimated at
si9,670. A sum of ri3,850 v/as available for annual operating expenses.
Farm income vias estimated at i;^2,980. The horticultural crop v-;as
sold into the high-priced fresh fruit market. If the farmer received
processing prices for the pineapple, farm income would fall to C9,330.
Pineapples wou'id be discontinued since returns at processing prices
v;ould fall to about one-half of the cost of production. This farm
was chosen to represent the middie-sized producer of coffee and
frssh market frui t.

28

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

farm contains 5.5 'nanzanas of land of vjhich 2,5 manzanas are planted
to coffee and the remainder is planted to sugarcane. Five men made
up the farm's vjork force; two were hired and three vjere members of
the owning family. The fixed expenses were estimated at i;i7,905 and ,
credit and capital for annual expenses summed to ijl,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
I ntermountai n valley. The coffee of San Ignacio is mostly grovjn at
elevations between 900 and 1,200 meters. In the I963 census, 38 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, 17^ manzanas of corn, 65 manzanas of
sugarcane, and 24 manzanas of cassava (25). Most of the land ^-jas in
pasture or forest.

The dry season is less pronounced in Acosta than in Alajuela,
Only January averaged less than 50 mm of raii\Fall during the period
from 1961 to 1965 (6'f), The heavier and more uniform ;-ainfall brings
about a slight reduction in quality from that which the elevation of
the area would suggest. 0_uality falls into che Hard Bean category
(2). Leaching ond erosion have reduced .:he natural fertility of tlie
area.

Tradic'onal oraccices h5ve persisle:d in the Acosta area. Th<;

29

district coffee yields were 6^ percent of the rational average. San
Ignacio produces only O.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 v^hich fits a definition of
"marginal lard" 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 v.-as supplied by four male family members and
thirteen hired men and one hired v.'oman. Fixed costs were estimated
at i.kl ,hSG and C51,500 were available for annual expenses. The net
farm income was estimated at i;i66,878. This farm was chosen to rep-
resent large farms with poor soils.

Farm ]h is a medium-sized unit in Acosta located on rough land
at 900 meters elevation. The size is 16 manzanas of vjhich 7 manzsnas
were planted to coffee. There \-:ere h manzanas of oranges and 5
manzanas of corn and beans reported. The farm had 3 adult male family
workers. The fixed expenses are ^1,18^ and ij5,9^5 'were available for
annual operating expenses. The estimated net returns were cil 1 ,^:-46
on this middle-sized farm with mixed cropping.

Farm 15 is a small Acosta unit on nearly level ground at 1,100
meters. It had 2 m.anzanas planted to coffee with interplanted orange
trees supplementing farm income. The farm family supplied the labor
of one man and three vjomen. The fixed costs were estimated at ihlh.
Credit and operating capital totaled ^825 for short-run expanses.

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 v/as therefore
adjusted to ^i I ,U03.

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 (;t2,988 and c675 were available for variable costs. The farm
income was estimated at (;il,339. This farm vjas considered typical of
the small, mixed-crop farms of the area.

The Linear Programming Model

It vjould be presumptuous to claim that a study using one year's
data and manv 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
coiHputer. Hovio-ver , the lack of a high degree of accur-^cy o^ 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 technique. However, in this particular study the emphasis is
put upon policy rather than prO'Juv^tion analysis. The input and output
coefficients may or may not represent true possibilities for the farm
groups .-rtudied. Kov^3ver, they do represent the expected possibilities
open to tiiose farmers based upon limited experimental data, reported
experience, and the opinicns of the extension agents v^ho advise themi.
Therefore, the model attempts to predict h.ow it vvould 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 w'as 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 vvere 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-rnatrix was comprised of coefficients of columns representing
labor purchasing, coffee planting, coffee destruction, borrovjlng,
long-term credit and fixed cost transfer.

The matrix included input coefficients for tl'e monthly land and
labor requirements of the various c-i; terpri ses , Additional restraints
Included operating capital, rotational ! i 'ni ta t i ors , and coffee
plantings. Production coefficients were entered In trcnsfer rows
to be sold via selling enterprises. Monthly labor purchasing vjas
limited to a 2 to 1 ratio of adult male labor which allov/ed tvjo
temporary workers to be hired for each permanent employee as a labor

32

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 vjas used during the harvest
months. Excess labor was allowed to be sold during the coffee harvest
but not at other times.

Parametric programming was used to estimate the effect on opti-
mization when changes in policy and in production levels occurred.
Coffee yields vjere programmed downward 50 percent. Coffee price was
reduced (;ii00.00 and printouts were made at <;5.00 intervals. A con-
tinued annual payment for coffee removal vjas considered. Operating
capital v;as reduced 70 percent. A special credit row allowed the
operating capital requirements of expandable alternative crops to be
reduced. Credit was increased. A tv.'o-price system allov.'ed 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 dov^nward. All nev; crops \;ere excluded by down-
ward price manipulation. Family labor and hired labor were permitted
to be shifted off the farm. Lastly, higher yielding coffee alter-
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 taken directly
from enterprise budgets.

Enterprises requiring ncvsf long-term investments were considered
using maintenance costs and return c::Limates for ^n annua! 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 vjas 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 v;as 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 viere entered
into the matrix without calculating invest-nent costs. Additional
plantings were allowed but viere associated vjI th long-term interest
charges on the investment.

Depreciation of the investment was not considered for the per-
mianent crops. if the annual expected returns surpassed returns of
other alternatives after interest costs v;cre paid on the i r,ves tp^ent ,
then it was assumed that the investment was an addition to net worth
which offset the original expenditure.

To further clarify Lhe modil, the mechanics of the less conven-
tional r.-iani pu ] aticns are given in r,-,ore detail. Operating capital was
taken as a short-term credit restraint. All annua! cos's except in-

3^

terest, transportation, and contract harvesting v/sre used to deterir-.Ine

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
follov.'ing 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 allov^ed 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 optima] solution does not include a degree of crop
specialization contrary to required pest control practices.

Coffee plantings were used in three different constraining rows.
fo insure that new coffee land was charged the cost o^ planting
coffee, maximum coffee land vjas 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 eqiial to or greater than reported coffee
land plus the land from vjhich coffee was removed. A third constrain-
ing row set coffee removal at no more than reported cofree land to
block irregular possibilities as payments v/ere i;iacje for coffee
removal in parametric operations.

Coffee harvesting requirements were handled in the following
manner: Coffae sailing enterprises vj£ re given a coffee harvesting
requirement. An option vjas given for ha''vestinq, allovjing either

c

c

35

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

harvest used a cost of ^i'+O.OO 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

hanges s tra i ghtf orvjard. A change row in the matrix contained coef-
ficients of change. Programming cards determined the magnitude of

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

program was continually re-evaluated as additions or subs tractions

v;ere made to coefficients in the objective function. C-' = C. + X.

J J J

(N) where C. ' is the new price, C. is the old price, X. is the change

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 rovsi coefficients was added to the

coefficients of a ncslgnated transfer row. P.' = P. -■■ X. I'N) where

J J J

P.' is the changed yield, P. is the old yield. X. is tnv3 change row
J 'J J

coefficient and N is the parametric multiplie-, P-opor ti onal changes

of several enterprises producing the same prodi'ct was allowed by

setting X. ' s = P. 's.
J J

The effect of special credit fsci; sties to finance the production
of the non- lT3di t ional cash crops was handled by a parametric reduction
of the use of normal operating capitei of '■he 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-
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
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 tc .70. This assumed 30 percent farm supplied operating

capita] as the lower limit of practical credit reduction. K ' = K +

r r

(-X|.) N where K„' is the modified credit constraint, K is the old
credit constraint and -X^ is the coefficient of the exchange column.
In 3 similar manner, an Increase in credit was programoied with avail-
able credit being increased up to c2,000 per manzana.

A programming procedure was devised to evaluate th'^ effect of
differentiating farm prices between sales into the new and into the
cradltlona! market. Since the new market price was roughly 25 per-
cent below the traditional market price and the new market took
■-ounhly 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 oaranetric addi lions. The ( radi t ional market selling

37

enterprise vjas 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 ]. Partial matrix of coffee selling activities

Profit

Coffee transfer row

Limit to traditional market

Change row

Averaged
market

200

1

0

-200

New
market

,60

1

0
0

Trad ! tional

market

0
1

2 1.3 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 v;as cailed v.'heii tlie values
of the change row vvere added to the functional. The price coefficient
for the averaged market was reduced to zero. That of the new market
v;as unchanged and that of the traditional market vjas increased to
106 2/3 percent of the averaged price. in this manner che marginal
return for addl tional coffee on each farni was set equal to ute

38

corresponding marginal returns to the country and each farm v/ould

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 vias programmed with a change row entry in the
coffee tree destruction column. Payments from 0 to (;il,800 per manzana
were covered with printouts on (j200 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 betv;een the 20 percent payment and one of the printed outputs.
When the s trai gh t- 1 i ne interpolation could not be made, the problem
was re-run vjith output demanded where the payment v^as equal to 20
percent of the gross returns to coffee.

Algebraically, C^' = C i-X, (N); where, C . ' is the return for
destroying one manzana of coffee, C. is the cost of destroying one
manzana of coffee, X 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 plantiiig column to block new coffee plar.tirigs if
payments were made for coffee removal.

The effect of outside employment en family income v/as studied
using parametric changes of the objt-ctive function. In an original

39

iratrix, 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 vji th 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, C = X (N); where, C equals outside v;aqe returns per man, X
'Ww vv w

equals the exchange row entry of the legal minimum wage, and N equals
the param.etric 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 vjas smaller, reflecting the part of the
calculated permanent labor coslS 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 v/as restricted to adult male employees.

Parametric changes in the interest rate for long-term investments
were programn.ed with the same procedure as price changes. Tl-.e cost
of borrowing vjas increased from 6 to 20 percent of the investment.
An investment rcvj requires that money be supplied to meet investment
requirements via a borrov.ing activity. An exch'^nge rov; entry is
multiplied by an increasing number so that 1 ' = 1 + X. (N); where,
1' equals the new interest rate. 1 equals the old interest rate, X.
equals the change rovi element in the borrowing colum.n and N equals

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 grcsvth 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 vje re collected with Interviews in a
single year. The effects of one crop on successive crops are not
yet known. Any errors In reporting, I nterpretati ng , 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 or. technology. Will yields
remain constant? Villi they remain proport loni^l if they change? Will
some resources become outdated? If changes occur, when will tliey
occur? Vyi n technological changes be accompanied by changes in factor
prices and product prices? The common ccorioir;Ic practice is to avoid
these questions by assuming constant technology. This excludes a
chief source of^ growth from the growth model.

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 worthv.'hile 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
al ternati ves.

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, che objectives vjere to compare coffee v.'ith alter-
native enterprises. A number of different processes or methods of
growing coffee and different processes for 5CTe of the alternative
enterprises were considered because, v/i th diff>ar;-nt farm resources
and parametric changes, ore particuU^r technology v-'as net obviously
superior to the others. Also, an averaging of inputs and yields is
not particularly meaningful since distinctly diffcrer.t Lcchnologles

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 v;ere taken into account. Activities vjere
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 oe Credito
Agncola de Cartago and the Minlsterio de Agricultura y Ganadena.
These sources supplied data concerning material Inputs, labor hours
and timing of various v-jork operations and expected yields of selected
enterpi'ises in particular ai'eas. In addition, activities were syn-
thesized from foreign Input-output data vjhlch vjsre modified to
anticipate Costa RIcan conditions by adapting iabcr requirements for
particular work operations from currently grovjn crops. Research
studies of the Univcrsidad de Costa Rica were used to supply data
for certain horticultural -jctivities.

Activities based en foreign or small research plot dita were
entered with what v/as believed to be "conservative" yield estimates.
The results of plot trial yields v.'e re estimated by using the lowest
variety yield wliich was not si jni f i cant 1 y different (at a 5 percent
level) from the highest variety yi'^d.

One problem that crcse w.is the fitting of the labor requirements
vjithin the moiitlily labor con;- tr a i n ts . 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 t i ~ie 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, "^his assum.ed
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 overborrovn ng for consumption or non-agricultural
uses are thus avoided. These complications would be difficult to
identify using intervievjs 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 9^ columns. The rov;s included 12 for
monthly labor use, 12 for monthly land use, 12 for temporary monthly
labor use, k for monthly family harvesting labor, 1 for operating
capital and credit use, 3 for tobacco ccntracC; I for i nves tm.ent , 1
for fixed expenses transfer, ]k for product transfer, 3 foi' coffee
land, 1 for coffee harvesting, ' for corn sl-iel!ing, 1 for land
transfer, 1 limiting row for traditional coffee market, sales, 2 crop

rotation requirement rows, 2 labor movement rovjs, 9 exchange rows
for parametric changes, and 1 profit row.

The columns included 10 coffee grovjing activities, 9 tobacco
growing activities, 8 corn grov/ing activities, k joint corn and bean
growing activities, k bean grovjing activities, 2 sesame growing activ-
ities, 2 castorbean growing activities, 5 dairy activities, 2 beef
activities, 1 peanut growing activity, 2 buckwheat growing activities,
1 chickpea grov;ing activity, 1 pigeon pea growing activity, 1 mixed
crop producing activity, 1 annual to monthly land use transfer activ-
ity, 3 coffee selling activities, 3 tobacco selling activities, 3
grain selling activities, 1 calf selling activity, 1 milk selling
activity, k 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 programmied is given in the Appendix,

Only farms 1 and 2 were programmed to allow the use of all the
activities coded for the area. Farms 5, 6, 7 snd 8 were not using
the same high level of technology as that found on farms I 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 v^ould evaluate the acceptability of the change if education
v;ere to permit its occurrence.

Farms 3 ^nd k vje re below -average coffee producers. They wei'e
permitted to use only the l.?ast profilahle cof Fee- g rowi ng activities
of the area. Again a pa r.irr.'^ t r i c procedure allowed higher production.

45

assuming that education and long-term investment could make the higher
yields poss ib le.

In the Alajuela area, the matrix was composed of Sk rows arid 87
columns. The rows included 12 for monthly labor, 12 for monthly land,
12 for temporary labor, k for monthly family labor for coffee harvest-
ing, 1 for investment, 1 for operating capital, 1 for coffee harvest,
19 for product transfers, k for rotation limits, 1 for yearly land,

1 for fixed expenditures, 3 for coffee land, 2 for off-farm labor
movement supply, 1 for limiting sales in the traditional coffee
market, 12 for changes in parametric modifications and 1 for profit.

The colum.ns Included 9 coffee growing activities, 1 lime growing
activity, 1 orsp.v^e growing activity, 3 corn grovjing activities, 2
corn-bean growing activities, 3 bean growing activities, 1 pineapple
growing activity, 1 stra-wberry growing activity, 6 sugarcane growing
activities, 3 cassava gro'-.'Ing activities, 2 cucumber growing activ-
ities, 2 sweetpotato growing activities, 2 tomato grovjing activities,

2 svjeet pepper growing activities, 1 peanut grov.'ing activity, 1
chickpea growing activity, 1 buckwheat grovnng activity, 1 pigeon pea
grov.'ing activity, 1 dairying activity, 3 coffee selling activities,
16 alternative product selling alternatives, 2 coffee harvesting
activities, 2 all-year labor selling activities, k i;iOnthly labor
selling activities; 12 monthly labor niring activities, 1 fixed cost
transfer column, 1 land transfer column, 1 coffee planting activity,

1 coffee destroying activity, and 1 bcrowing activity for long-term
credi t.

With the exception of farm 9, f^'ie two highest-yielding coffee
activities were blocked for tlie original solutions and allowed to

A6

enter with parametric changes. Lime and strav;berry selling activities
reflected prices estimated for processing use. These prices were
(^13. 00 per quintal for limes and ijl.OO per pound for strawberries.
These prices were conservative estimations for the Central American
Common Market and v/ere programmed both upward and downv^ard to fit
conditions of the fresh market and v^orld market, respectively.
Actually, average current prices are much higher in the local fresh
fruit markets; hov»jever, these high prices vjould be unstable in the
face of any sizable change in quantity.

Tomatoes and svveet 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 svjee tpota toes were priced at the reported
market lows of the tv;0 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 experim.ent station of the
Universidad de Costa Rica is located in the zone and specializes in
horticultural crops. It is easier to seil ,-;e\j idias when they have
been tested under local conditions and tli.e results are being applied
by the agronomists on their pi"ivate commercial farms.

!n the study of the Acos ta area, the matrix contained 65 I'ows
and bO columns. The rows included 12 for monthly labor, 12 for
monthly tampor?-ry labor ?L'[:plv, ] <^or i .ives tment , I for operating
capital, 3 coffee l--;nd lirriiting rcv;s , 5 for mcnthly family labor for

hi

harvesting, 1 fixed expenditure transfer row, 1 rev; limiting coffee
sales to the traditional markets, 1 coffee harvesting row, 1 yearly
land supply row, 9 for product transfers, 2 for off-farm labor move-
ment and 1 profit row.

The columns included 7 coffee growing activities, 2 joint coffee-
orange growing activities, 3 joint corn-bean growing activities, 1
blackberry growing activity, 2 orange gro'wing activities, 2 beef
producing activities, 5 dairy activities, 1 lime grov-jing activity,
3 coffee selling enterprises, 8 selling enterprises for other farm
products, 2 coffee harvesting activities, 5 monthly labor sailing
activities, 12 monthly labor hiring activities, 1 fixed cost transfer
column, 2 yearly labor selling activities, 1 coffee planting activity,
1 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 annua! cropping. Corn
and beans were included because they are traditionally grovvn. Other
annuals were excluded from the area's model in order to conform v/ith
conservation !"equ i 3 i t i es.

The coffee yields in Acosta are less than the yields of the other
tv.'O areas. Poorer t-ichnology may have resulted from relative isolation
in past years. Hovjever, lov;er fertility is c'liefly responsible for
lo'wer yields, Tv.'O higher-yielding coffc;e growing activities were
blocked in the initial solution but were allo'wed to enter in a
parametric procedm'e representing technological change.

Two price levels were used for fruit selling in Acosta since

selling opportunities could be greatly affected by the nearness to
market outlets. Risks and transportation costs vyould be reduced if
a processing plant v.'e re 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

!t 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.
Most of the budgets for coffee, corn, beans and sugarcane were
provided by the cooperating exiension ?.gen1.5. Th=se vi^re supplemented
by data provided by the Banco Nacional de Costa Rica (18) and the
Banco de Credito Ag.-icola de Ci'-rtogo (58).

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 ectivities yielding 27.6,
25.7 •^nd 20.0 fanegas per manzana,, !ng. Danilo Zamora collected data
from Palmares farms which reported coffee yields of 1'3.0, 15.0,
snd 5.3 ^anegas per mianzana. The best yields included herbicide use,
three application-: of fertilizers, insecticide use and moderate
pruning and V/'eedirig labor, Coinnif)n piactices incli-'ded the use of

k9

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 vji thout purchased inputs other than sacks and without labor
except harvesting and enough weed cutting to allow the pickers to walk.

The extension agents' fa^m budgets also included corn and bean
activities. Common corn yields ranged from 13.3 to ^0,0 quintales
per manzana. Higher-yielding activities v;ere programmed from data
furnished by Ing, V/alter Villalobcs from k-S Club plots at Santa Ana.
Yields \-;are modified to 70,0 quintales per manzana maximum to corre-
spond with the reportedly poorer grovnng conditions. The m.oderate
use of fertilizer and insecticide, as reported in the v;orkshee ts of
the Banco Nacional, yields 48,0 quintales (13).

Bean activities viere based on budgets from the follov.'ing sources.
Modern technology yielded !8 and 20 quintales par 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 k.2 quiritales per manzana and
traditional methods that yielded 9-6 quintales per manzana (18).

Joint corn and bean production activities were programimed allow-
ing combinations of the average and poorer yielding corn and bean
activities commonly grov.n together.

Tobacco grov/irig activities were progranim.ed from budgets of the
Junta de DePensa del ToL;ico (36) and tl'ie Banco i-lacional (13). Yields

50

ranged from 18 to 20 quintales per manzana but the budgets from the
Banco Nacional used 1 ov^ r levels of inputs.

Sesame v;as 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 ba^ed on synthesized
budgets based upon foreign agronomic data (20, 72). Yields ware
estimated at Jih 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 ^00 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, fluch higher range productivity
was reported In studies made in Puerto Rico (1^). However, the cost
of production v.ould not bo covered by Costa Rican prices.

Pigeon pea producticn activities v;ere based on budgets synthesized
from agronomic data cliiefly from Havya i i (3I, ^O) modified by Costa
Rican recommendat i tii,5 (50). Reported yields reeched 20 quintales per
manzana.

Te

les

51

Buckwheat was Included as a catch crop. Yields viere programmed

at 8 and 20 quintales depending upon the time of planting. Reported
production In Mexico (70) and Ceylon (k) showed that this crop could
be grown in tropical countries. - '

A peanut production activity was included although part of th
soils may not be well adapted. Yield vyas programmed at 20 quintal
per manzana as Banco Naciona] data from Alajuela vjere 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 v;as 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 beneficlos in Palmares in the I966-67 crop year.
Corn and bean prices were reported by the extension agents. Peanut
and sesame prices were included in the credit vjorksheets of the Banco
Nacional (18), Castorbean price was computed from the world dol la-
price. Buckwheat v;as priced arbitrarily low to reflect probably
limited acceptance as a feed grain. The chickpea and pigeon pea
prices reflected estimated v^jholesale prices based upon retail prices
in S.-T'n Jose as coimpared v.'ith beans.

In the study of the Alajuela farms, budget data on coffee produc-
tion were supplied by Ing, Guiilermo Montenegro, Yields rar[ae'J from
25 fanegas per manzana on the best farm to 16 fanegas per manzana on
the poorest farm. The maximum yield pernitted without technological
change inodi f i '. at i ons v^as 20 fanegas per maiizana except on farm 9 which
had already adopted modern production techtii ques. Traditional and

52

semi -abandoned coffee production activities vjere included with the
same coefficients used in the Palmares-San Ramon matrix.

Lime production vjas programmed with input-output data synthesized
from Florida sourcas (kl) . Yields were ^00 quintales per manzana.
This approximates a U. S, yield of kSl 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 (ks) 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 msnzana 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 ^f-S Club budgets
v.'hich reported yields of 90 quintales per manzana and upon extension
agent reports of corn yielding 60 quintales per manzana. Corn produc-
tion was also programmed in joint activities with beans where the
output of 48 faregas and 20 fanegas of corn v.'as produced jointly
with 18 and h fanegas of beans in budgets supplied bv jng. Guillermo
Montenegro,

Bean production was programmed with three distinct levels of
technology. I'lod-srn inputs -/ielded 20 Quintales per manzana (Sl),
broadcast beans yielded 5 quintales per manze-na and traditionally
plante;.! hians yielded 10 q../) '•: ta 1 es per manzara (18).

53

The strawberry producing activity used Florida production data
(12) modified by incomplete data from Alajuela and Heredia farms.
Yields were procrammed at 250 quintales per manzana. This approxi-
mates l'+,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 ICO tons per manzana per
harvest. In a period ranging from 38 to 48 months, three harvests v.'ere
made. Production and inputs v.'ere 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;'.opth grov;ing period (18). Intermediate yields were received
b/ a budgeted production lasting two years (58). Higher yields, 275
quintales per manzana, vere Drograrnmed with more modern inputs and
longer growing period of 26 r^onths (26). Total production per month
increased with age but quality declined.

Cucumber production activities represented different timing of
modern techniauef b-'jsed up3n agronomic data furnished by the Alajuela
experiment farm (52). Yields v.'ere 1 40 cuinteles per manzana.

The svjeetpotato productlcn actlvlLy bes^d on modern inputs (5)
yielded 200 quintales per manzana vjhicii tripled the yield of tradi-
tional methods reported by the Banco Nacional (l8).

A peanut" producing activ'tv utilized a budget reported by the
Cartago bank (5S). Yieiui of 25 oul males per manzana were expected.

Pigeon pea, buckwheat and chickpea production activities were
included, based upon the same sources as used for the Pal mares-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 viere lower than average prices but
subject to considerable error.

In the study of the four farms in Acosta, coffee production
budgets vyere supplied by Ing. Rodrigo Cavallini of the San Ignacio
Extension Agency. The better methods used fertilizer or other
purchased inputs and yielded 10 fanegas par 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 grovjn with oranges and bananas on some farms v^ith
poor yields of both coffie and fruit. Cofiee yitlaed five and six
fanegas and oranges yielded 1,000 to 3.,000 fruit per nanzana on
neglected trees.

An activity used to program the possibilities of tecl-mol ooi ca I
change was supplied by Edwin Mann of the Oficina del Cafe. Me
budgeted production yielding lU fanegas per manzana in an adjoining
district. An activity vjas also included representing semi -abandorrrent
and yielding only two Fanegas per manz-Tna,

A hi'jh yielding orange producing activity vjas included end based
upon data taken from foreign sources (28, h^) . This activity yielded
102,000 orsnces ccr.ipared to 'j3,jG0 crangss per manzana prodijcv^d on
a farm i n ■\co-s ta.

55

Corn and bean production vias reported with low to very low

yields. Joint cropping produced 29, G.k 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 vias included with a yield of 400 quintales per
manzana based upon Florida data (kl) .

The inclusion of a blackberry producing activity also v/as 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 vjithin a set of constraints as
a single criterion for allocating resources. This v:ould deviate
from actual practice especially in those cases vjhere the m.agnitude
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 docs 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 'aM 1 1 actually be interested in fnaximizing profits
only vjithin seme range of acceptable risk.

The model forces all decisions to b3 made at once. Since it is
a static model, grovjth possibilities ai-e not taken Into account. This
is particularly troub]e<;ome In the case of short-term, credit and

56

operating capital restriction. Because of this feature, the model is
conservative insofar as nev; 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 de te rmi ned with the initial
linear programrning 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-vjage labor v.iere generally not constraining. Where horticultural
crops vjere considered, operating capital v;as 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, vjere 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 of 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 coFfce production. A brief suminary of optlm.al resource use will
be given for each of the 16 farns.
Farm I

The optimal solution of this coffee- tobacco farm coincided with
the reported prcducticn. Two manzcn-BS v,/sre planted to thinly spaced

57

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 (ijI9,78l) 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 (;t^,069 and ^215, respectively.
Temporary labor vjas hired in October and January. Permanent labor
was fully utilized in May and October as well as from November through
February v;here the model permitted coffee harvesting to exhaust the
labor supply. A slight excess (?i8l) in operating capital occurred.
Farm 2

The second farm from San Ramon was a small specialized coffee
producer. The optinal solution for the linear programming model
derived for this farm situation also produced only coffee. Net
returns in the model were (pl8,02U ccmparad to (;il5,250 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. V/ith tobacco activities blocked, the
only effective constraint vjas land. Permanent labor was exhausted
only from November through February v^/hen coffee harvesting utilized
all the labor. The farm optimal solution used (^3,157 of (jif.OOO avail-
abl'^ capital and credit. There were 193.2 fanegas of coffee produced.
Farm 3

This farm produced good coffee yields v;i th very high costs.
The model permitted traditional coffee activities on this farm but
tlie five best co rfee-produci ng activities of the region were blocked.
The optimal solution contained 1 ess- i n tens I ve coffee production.

59

more- i ntens i ve 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 ijiS'+JSO to iJi5,5^3. 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,^
quintales of corn, ^9,1 quintales of beans, and 16.3 quintales of
sesame.

The flue-cured tobacco contract row was exhausted as all three
manzands pernntted for tobacco vjere 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 (;45,666 of a (^71,100 operating
capital and credit constraint.
Farm h

The fourth farm studied produced coffee and flue-cured tobacco.
Tobacco was the chief money earner vjI th 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 quintoles of beans, 2,6
quintales of pigeon peas, and 81 pounds of chickpeas. Net returns in
the model situation v;ere ,*31,667 v/hlclT greatly exceeded tii-3 (^1^,700
estimate of income under reported resource use.

60

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 irionths froT November
through February,

Temporary labor was hired in January and August, Family coffee
harvesting vjas limited by the family labor supply in December. Hiring
labor was not constrained by monthly supervisory limitations which
allowed two temporary v^orkers for each family or permanent employee.
The tobacco contract allotment was not exhausted.
Farm 5

The fifth farm specialized in coffee. The rr;3xi mi za t i on of the
linear programming model gave results similar to reported resource
use. The net income for the programmed model was (pl6,876. The in-
crease over the reported income of (j 15-520 v;as explained by spall
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 v/as 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 rs^ported
annual expenditures included som.e long-term investments.

The farm produced 190 fanegas of coffee using the hiiyhest yield-
ing coffee activity permitted in the model.
Farm 6

The sixth farm studied was a small coffee farm with an absentee
owner. The farm ..odel allowed coffe*:: yields slightly above t'nose

61

reported by the farm. This reduced the net loss to i~/SO. 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 vje re not limiting in the model, since reported
preharvest expenditures were (jA-.OOO compared to a (jZjS^^S optima]
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 governiT:ent banks have
used in a policy to subsidize agricultural investment. The nev;
coffee v;as not planted if interest charges rose 0.8 percent. Beans
and sesame were planted instead of coffee. The farm loss increased
from C760 to (;i780.
Farm 7.

The results of programming the seventh farm more than doubled
reported net farm income from C3,3^5 to <;7.l^7. In the optimal
solution, coffee production v;as expanded with 0.2b manzanas planted
to new coffee. Other production activities included v;ere sun-cured
tobacco, corn and beans. Optimal output, before parametric changes,
was maximized with the production of 62,0 fanegas of coffee, ig
quintaies of tobacco, 72.8 quintales of corn, and l8,9 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 nev^ly 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 vn th the
production of 19 fanegas of coffee, 7.8 quintales of sun-cured
tobacco, 2^.0 quintales of corn, 5.^ quintales of beans, and 6.5
quintales of sesame. Optimal net returns were (;;if,005 compared with
a reported i ncoine of (;t2,653- Coffee acreage in the optimum progri
was the same as the farmer's reported acreage.

Credit was severely restricted on this small farm. The shade
price indicated that an additional colon of credit or operating
capital could return (tl.SS 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 teniporary labor v;as hired. The restriction that aliOv.ed
0,5 manzanas of sun-cured tobacco was not used up.
Fa_Qn_^

The nintli fann produced coffee with modern inputs. The model
included activi'c'os for hoi'ti cu 1 tu m I cop prodjct'on. The farm
maximized income vi'i th coffee monoculLure which yielded <;;86,76l net
returns. The former's estiir.ate of net returns v;as s^90,00u, txpendi-

-am

jow

63

tures for temporary labor vje re underestimated vjhich 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 (;i2S,^89 when optimal use of other crops replaced
coffee. However, this farm was reportedly upgrading coffee technol-
ogy in order to receive ij30,000 expected net returns v;i th coffee
monoculture. The optimal cropping pattern included five different
crop-grovyi ng activities producing 213.3 fanegas of coffee, i+8,0 tons
of tomatoes, 1^8,0 tons of sugarcane, 708,0 quintales of s'wee t potatoes
and 5.'^ quintales of chickpeas. Coffee trees were removed from ^,3
manzanas of land. The permanent labor supply v;as Lised up in January,
February, April, August, Sepre;Mber, November and December, Temporary
workers were hired in January, February, Septem.ber and December, The
operating capital c^nd credit con5tr?int v.'as limiting in the model.
Supervis'^ry capacicy did not: limit fiiring temporary workers.
Farm 11

On the eleventh farm, programmed optimal solutions did not
change cofpee acreage from ihe reported land use. Other crops replaced
pineapples. The reported net returns of i-^?.,3'dO exceeded the optima]
income of (j2'i-,536 of the model. This difference occurred because the
model contained fruit prices for industrial use while tha farmer
produced 5-3 manzonas of pineapple foi' the fresh fruit market in

Gk
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 25^+. 5 quintales
of swee tpota toes . Land was fully utilized from November through April.
Permanent labor was fully utilized in all months except October.
Temporary labor vias hired in January, February, April, June, August,
September and December. The capacity to supervise labor v^jas not an
effective restriction in any mionth; however, slack supervisory capac-
ity was reduced to 29. '4 hours in January. The operating capital and
credit row limitation was exhausted. Relational limitations v^ere not
res tr i cti ve.
Farm 12

The twelfth farm v;as programmed v;i th 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 vjhich
yielded nine fanegas per manzana. The activity based on the reported
resource use v\;as dominated by another coffee-producing activity. Net
returns vjere maximized with tlie production of coffee, corn and beans
and limes v;hich netted iJ ,07^ in the model. These returns were
higher than the ^6,122 calcul.ned from the farmer report. Farm out-
put included 22,5 fanegas of coffes, 2^4.1 quintales of corn, ^.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 v/as severely restricted.
Farm 13

The thirteenth farm was programmed using tvjo different sets of
fruit prices representing industrial prices v;i th 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 viere priced at ^ilS.OO per quintal, blackberries at (jl.OO per
pound and oranges at (;i^,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
rrotivation since completely different horticultural alternatives were
placed in the model. Nevertheless, the model shows that if fruit
prices v.'3 re moderately high and stable, considerable changes would
occur and coffee output would be reduced on farm '3.

Profits in the model wer-e maximized with <;89,9^8 netted from
mixed coffee and 1 I m.e production. Coffee tree destruction was
programmed for 32.9^ manzanas. Credit was limited, causing both semi-
abandoricd m.ethods and traditional low-yield methods of coffee to be
used in coffee production. Permanent labor '..'as fully utilized in
January, February, May, July, August, September. October, November
and December. Temporary labor was hired in January, February, May,
July, August and September. The lim.it on superv'ision for temporary
workers was not effective.

66

Farm production was comprised of 195,6 fanegas of coffee and
17,780 quintales of limes. This coffee output v^as reduced from the
present output of 900 fanegas. However, without favorable fruit
prices, the maximum income for farm 13 was <;67,69'^, Therefore, the
reported income of i;i66,878 closely approximated the maximum of the
model when only traditional crops vjere 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; hovjever, temporary labor was hired only in January,
Ma!-ch, July, August, September and October. All but 96 fanegas of
coffee vjere harvested with contract labor.
Fa_rnn_Uf

As with the preceding farm, the fourteenth farm was prograrp,med
using high and 1 ov^ fruit prices. With the higher fruit prices,
opt I ini i^a tion of the model resulted in considerable departure from
reported practices. Although no coffee trees vjcre rem.oved, output
of coffee v.'as reduced from 70.0 to 18. ^^ fanegas v/i th coffee produc-
tion activities using low-yield and sen! -abandoned miethods. Nine

67

manzanas of lines utilized the non-coffee land and 3,600 quintales of
limes were produced. Net returns in the model were ii,lh,121. The
farm reported income was much lower with i.\\ ,kkS 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 iji.Sl per (jl.OO of credit. Per-
manent labor was exhausted except in March and April, The model
resulted in hired labor in January, February, Kay, June, July, August
and September, Labor supervision was not a limiting factor.

The optimal solution in the model for farm 1 '^ v-jas 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 vjere higher than reported
returns, ^16,718 compared to <;! 1,^+46. The increase v/as accomplished
with higher returns to corn and beans vjhich were programmed v^;ith
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 IQ.O fanegas
of coffee, 261,0 quintales of corn and 99-0 quintales of beans.
Operating capital v;5S limiting and the family labor supply was ex-
hausted in all months except March, April and June, Labor v.-as hired
in May, August, September, October, r-'ovember and December.
Farm 1^

The fifteenth farm also was given two different -"^ruit price
situations. V/i tli high fruit prices used first to represent production

68

potentials given a processing plant in the region, the farm model

maximized net returns at iS,'-^59. in comparison, reported production
netted only ii,^f03- Profits were maximized by shifting 0,26 manzanas
of coffee into lime production. Farm output included 17,^ 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 vjas fully utilized and no temporary labor was
hired. Of the C5,^59 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 chat reported because the model included temporary outside
income from coffee harvesting as part of farm income. Of tfie (;i5 , 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 returr.s to land and harvest time
labor. Credit v.'as not a limiting resource vjj-ien low fruit prices were
used in the model. The family labor supply exceeded all rr.onthly
demands for labor and no labor was hired. The excess family labor
was sold during the coffee harvest period ar.d was unused the remainder
of the year. Twenty fanegas of coffee were pror'uced with Che optimal
use of resources.
Farm 16

The sixteenth farm v^as also programmed v.m th liigh 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 ikj^Oy when maximized which surpassed the (;;1,339 cal-
culated as expected income with reported resource use. However,
(j2,273 out of the (;i^,307 represented harvest labor sales.

Credit was severely restricted when high fruit prices reflected
a strong local market. Over (;t5.30 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, i8,2 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 <:3.22 per (;^1.00. Profits were maximized with 0,77 manzana of land
idle. Excess permanent labor occurred in al] months except during
the coffee harvest. No temporary v/orkers were hired. Optim.al sales
included 3.0 fanegas of coffee, 5'-+.0 quintales of corn and 32.2
quintales of beans. Income v;as maximized at (^3,612, of v;hlch C2,1'45
came from labor selling for coffee harvest.

Pol i cy Analysl s

Education for Better Farm Manaqemen t

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
vjork 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 non-econcmlc motivations, that they
are reluctant to change, or that they maintain traditional cropping
patterns out of ignorance. Table 2 shows comparisons betv/een 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 overi ntens i fi cation 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 lovjer programmed coffee output on these
tvjo farms v;as countered by higher output on other farms.

V/hile the sample Is too small to support broad generalizations,
some overproduction of coffee beyond that quantity dictated by strict

Hidden investment in this case nay arise wh.en increased anniial
expenditures occur for a short period before yields are increased to
f ul 1 potent i a1 .

71

Table 2, Comparisons betvyeen reported and optimal incomes and coffee
output

Net

returns

Coffee

output

Farm^

Reported

Optimal

Reported

optimal

(colcne?)

(col ones)

( 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

k

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

20,000

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

]kA .

n,4iuS

24,222

70.0

18,4

14b

]],kk6

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

"The letters A and B are used to designate different fruit pricing

used in Acosta.

are used ro indicate the situations using high

f rui t pr 1 ces
f rui t pr i ces ,

:nd B's are used to indicate the situations usina low

72

profit maximization is indicated. Most of the potential for income
improvement occuiT'^d vji th the land not planted to coffee. Labor
selling activities for the coffee harvest made sizable contributions
to the higher optimal incomes of the smeller farms.

Of the 20 farm s i tuat ions studied, three reported higher incomes
than the optimal permitted in the model. In farm 1 and farm 9 this
difference was due to a lovjer wage cost estimation in the farmer
report than was permitted in the mode). More significantly, the re-
duced optimal income on farm 11 came about because the model only
allowed pineapple sales for export or industrial use vjhile the farm
produced for a much higher domestic fresh-fruit market.

Markedly increased incomes were accompanied by reductions in
coffee output only V'jhen high return fruit or vegetable crops v/ere
considered as alternatives. Thus the higher incom.es 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 G.kS percent on the four farms of the Alajuela area. For Acosta,
optimal resource allocation increased coffee output l,i6 percent
when low fruit prices were used but decreased coffee output 76,26
percent when high fruit prices v;ere 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 vjhich 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 high.er levels of technology in coffee

73

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 vas used to increase coffee transfers until reported yields
of the better farms v;ere equaled. The results shoued that on some
farms modernization Vv'ould 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 vjere obtainable. in Alajuela technological
change permitted yield increases from 20 to 25 fanegas per manzana.
In Palmares-San Ramion the yields were increased from 19 to 27.6
fanegas per manzana. Yields were increased from 10 to 1^ fanegas per
m-inzana 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 v;as increased ll.h 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 I98.8I percent and net income vjas increased
9.38 percent when technological changes for coffee were permitted in
the fo'jr farm situations when high fruit prices were used. Given
low fruit prices, coffee output only increased 15-98 percent but in-
come increased 2'^.,98 percent in the Acosta fari^ siiuatioos.

Higher levels of technology bro'jght si';ali increases In coffee
acreage on a number of the farms. The planting of nev.' coffee was
limited by credit restrictions. The gains i .i programired income were
sufficiently high to motivate change if the im.proved technology can

Ih

Table 3- The effects of improving coffee production technology on
coffee production and income

Coffee

output

1 ncomes

^ a

Farm

Current

I mproved

Current

Improved

technology

technology

technology

technology

(fanegas)

( fanegas)

(colones)

(colones)

3

750.0

1 ,i462.8

35,543

138,168

h

225.0

it25.0

31,667

62,013

5

190.0

276.0

16,877

29,739

6

76.0

110. ^4

760

4,583

7

62.1

9'+.3

7,147

10,979

8

19.0

27.6

4,005

5,720

10

213.3

281.4

29,489

38,460

n

100.0

120.0

24,536

27,989

12

22.5

60.1

7,078

9,440

13A

195.6

656.0

89,948

101 ,272

!3B

900.0

1,032.1

67,694

90,709

l^+A

18. ir

18.4

24,222

24,274

14b

70.0

94.5

16,718

18,361

15A

MA

23.0

5,459

5,707

15B

20.0

23.0

5,209

5.707

I6A

3.0

3.0

4,307

4,307

i6B

9.0

9.0

3,612

3.612

The letters A and B are used to designate different fruit pricing

L'sed 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 pr i ces.

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 k.
On the Alajuela farms, returns from sugarcane approached those of
horticultural crops and optimal coffee output vjas net reduced when
the new crops were included among the alternatives. Hovjever , incomes
v.'ere 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 1^ had sufficient capital and credit
to respond to fruit production opportunities with sizable increases
in income. The addiLional incone potential of fruits and vegetables
v;as limited on the smaller Farms by a shortage of operating capital
and credi t.

Taxation or Price Reduction

The stability of coffee production in farm management plans v;as
further examined vnth price declines parcmetri ca 1 ly prograrnmed into
each fariH situation. Table 5 shov/s the effect of price declines on
coffee output. Reduced output v;ith ftS.OG per fanega price declines
was limited to the blocking of new plantings of coffee trees and the

76

Table k. Comparisons of optimal incomes and coffee outputs with
traditional versus new enterprises on farms in Alajuela
and Acosta

Coffee

output

Net

i ncome

Tradi tiona]

New

Tradi tional

New

Farm

enterprises

enterpri ses

enterpri ses

enterpri ses

(fanegas)

(fanegas)

(colones)

(colones)

9

997.2

997.2

86,761

86,761

10

163.8

213.3

22,586

29.489

11

100.0

100.0

22,239

24,536

12

22.5

22.5

7,003

7,078

13

900,0

196.5

67,694 -

89,948

1^

70.0

18.4

16,718

24,222

15

20.0

17.4

5 , 209

5,459

16

9.0

3-0

3,612

4,307

11

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78

substitution of corn and beans for low-yielding coffee on farm ]k.
Taxes up to $10.00 per fanega would have little effect on coffee out-
put of the farrrs programmed. Except for the newly planted coffee,
coffee production was not responsive to price declines up to .jZO.OO
per fanega. High-yielding coffee and semi -abandoned coffee v.'ere 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 av^^ay 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 J.
Price declines reduced income more rapidly than output and had the
strongest ePFect on those farms highly specialized in coffee produc-
tion. For example, given a ^hO per fanega price decline, income fell
5^ percent on the three largest coffee producing farms and only 22
percent on the farms vji th optimal production less than 100 ganegas.
Met returns on farm 2, farm 5, farm 9 and farm 13B, all of v/hich
specialized in coffee production, v;e re reduced over 50 percent by a
CSO per fanega price decline. 0^ those farms specializing in coffee
production, only on farm 10 did d i ve rs i f i ca !:i on possibilities hold
income above fjO pcixent of initial levels when price v;as 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

79

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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 vjere 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 payirients equal to 20 percent of gross coffee earnings per
manzana viere effective in changing optimal resource allccatioii 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 ccula 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 unch.anged as coffee in
excess of tiie quota is taken out of production.

Then:

PT + (P - DP)N = PT
T + N T + SN

82

Table 8. Alternative crops increased first by declines in coffee
prices

Farm

1
2

3
k

5
6

7

8

9

10

l]

12

13A
13B
]kA
I'fB
I5A
158
16A
16B

Price reduction
per faneqa

(col ones)

100
91

ko

15
57
58
90

15
21
22

^3
60
]k
]k
25
55
29
58
iOO
32

Crop increased

None

Beans, sesame

Beans, corn

Tobacco, beans

Beans, sesame

Beans, sesame

Castorbeans , mi 1 k

Tobacco

Sugarcane, beans

Tomatoes, sugarcane

Limes, peppers

Limes

Limes

Corn, beans

Limes

Corn, beans

L i me s

Corn, beans

None

Corn, beans

83

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1}

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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 k, farm
10, farm 1 3A and 138 with a scheme to pay for coffee removal out of
total sales revenue vji thout 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 sliown in Table 10, either increased or did
not affect farm incomie. One would expect such a program of self-
finaiicing allotment payments to be politically acceptable since it
would not lower the income for any farm. Hov.'ever, 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 this extra 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 ■:.han ij2C pc- r fancga reir.oved, assuming
that payments were spread over iO percent of the ba^e production.

85

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86
This could increase total payments to approximate 1 y i;;60 per fanega.
Therefore, before annual payments may effectively remove coffee, the
alternative use o^^ resources must give returns competitive with coffee
and anple credit must be available to finance such alternative activ-
i ties.

Payment for coffee removal could be more efficacious if made in
a lump sum provided that the marginal interest rate facing the farm
operat 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 v;ere given the
knowledge required to change traditional cropping patterns.

The value of a lump sum paymient is shown in the follov/ing example.
Assume that the government can borrow money at 10 percent annually
for a 10-year period and that a farmer's marginal incerest earning
rate is 30 percent. Then the cost of generating a perpetual psychic
income flow of (jlOO per year is iSO 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 interesi
rates for near subsistence fanners, lump sum al'otnient payments v/ould
cheapen the payments required to motivate change. Installment pay-
men's computed as a share payment for ies croyt-d coffee couid 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 vjould 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
i ncom.e 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 iverage? vjill cend to be lowered. If optimal coffee production
falls belovj historical production, the price averages v;ill 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 shovjs optimal incomes end coffee outputs with single and
differentiated coffee prices. OF those farms vi'\ ^h increased incomes,
coffee output v,'3s unchanged on five farms, increased on two farms.

88

Table 11. The alternative crops Increased first by annual payments
for coffee removal

Farm

Annual payment
per manzana

Crop increased

(colones)

1
2
3

5
6

7

8

9

10
11
12
13A
13B
14A
]kB
i5A
15B
16A
16b

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

None

Mixed crops

Corn, beans

Corn, beans

Beans, sesame

Corn, beans, sesame

Corn, beans, castorbeans

Corn, beans, sesame

Sugarcane

Sugarcane

L i me s

None

Linies

Corn, beans

Limes

Corn, beans

L i me s

Corn, beans

None

None'^

When changes were not initiated by the levels of payment first
programmed, additional runs extended the levels of payment.

Land was idled as credit became more limited due to coffee removal

89

Table 12. Comparisons of income and coffee production for differen-
tiated prices versus single prices

1 ncomes

Co

ffee

Farm

Single price

Jvjo prices

Si nql e pri ce

Tvjo prices

(colones)

(colones)

( fanegas)

(fanegas)

1

19,781

19,781

82.8

82.8

2

18,025

17,484

193.2

193.2

3

35,5^3

45,709

750.0

750.0

4

31,667

26,220

225.0

192.0

5

16,877

16,877

190.0

190.0

6

760

- 1,389

76.0

71.3

7

7,1^^7

6,657

62.0

57.0

8

4,005

3,880

19.0

17.5

9

86,761

86,870

997.2

997.2

10

29,489

32,330

213.3

225.0

11

24,536

24.536

100.0

100.0

12

7.078

7,176

22.5

22.5

13A

89,948

96,670

195.6

650.0

138

67,694

70,792

900.0

650.0

]kA

24,222

24,448

18.4

18.4

l^B

16,718

17,056

70.0

60.1

ISA

5,459

5,975

17.4

. 3.2

iSB .

5,209

4,922

?0.0

20.0

16A

4,307

4,350

3.0

3.0

16b

3,612

3,586

9.0

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 vjere used to separate sales
into nevj and traditional markets, optimal output v-;as reduced or un-
changed on farms where the base used for establishing the traditional
market quota vjas equal to or less than the initial optimal coffee out-
put. However, in those cases where new activities replaced coffee
and optimal output fell vve I 1 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 vjhen prices were differentiated to
reflect different export earnings in new markets and traditional
markets.

Reduction of Credi t

The reduction of credit has been suggested as an effective means
of controlling coffee output. As Table 13 show:, coffee output is
reducible if this measure can be imposed, Hoi-;ever, there are effects
on farm incomes also (Table ]k). In 10 of the 20 farm situations,
income was first reduced by credit restrictions without affecting
coffee production. In four situations where horticultural crop'^. re-
placed coffee in the initial solution a reduction in operating credit
actually brought increased coffee production V\'ithin 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 pi'cduction without encouraging diversification. Coffee land

91

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93
use V'vas reduced only when new plantings occurred in the model. Semi-
abandonment of coffee did not use up restricted credit and the stabil-
ity of land use is shown in Table 15.

The shadow prices of the credit row are given in Table 16. These
showed very high returns to additional credit especially as credit re-
ductions occurred. Before short-term credit effectively '"educed coffee
production, the marginal returns greatly exceeded the usurious rates
of money lenders.

Movement of Labor

The possibility that a higher demand for off-farm labor could
shift resources out of coffee production was considered. Table 17
shows a movement of family workers in 11 of 17 farm situations having
family labor, Hovjever, coffee output was not changed in 15 of the 17
cases. On farm 10, moving workers vjould increase optimal coffee pro-
duction and on farm 13B, the movement brought about a slight decrease
in programmed coffee output. Higher family income from new jobs in-
dicated that family workers rec^iveci less than l3gal vjace rates,

A similar inquiry also Indicated that more permanent labor w3s
hired than needed for maximum net returns. The relaxation of labor
inflexibility showed that 12 of 20 farms could increase profits by
encouraging employee m.obi 1 i ty to other jobs. As slTOvjn in Table 18,
coffee production vias influenced on only two of 17 farms.

The movement oi' labor altered farm output on ?ome farms but the
effects upon coffee output was negligible. Of the farms studied, the
smaller farms have larger percentages of excess labor. This may be
necessary to assure 3:T|jle l^arvesting labor on a nurber of the farms
although motivations other than profit 'laxi mi zat i on appear to be man-
i fes ted.

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98

Extra Credit

Income and coffee output with special credit for crops other than
coffee and expanded total credit are shown in Table I9. The poorer
farms from Palmares shifted resources into coffee production and ex-
panded coffee output with both special and extra credit. With the
special credit, capital formerly used for corn and tobacco vjas released
for coffee.

Programmed extra credit made an additional (^2.000 per manzana
available for annual expenditures. The major difference betv\een the
programmed effects of special credit and extra credit was that more
money was made available in the latter. The incomes for those farm
situations with horticultural crops considered ari.ong the diversifica-
tion alternatives were increased over 10 percent on seven of the eight
farms, over 50 percent on six of eight farms and over 100 percent on
four of eight farms.

Increasing the availability of credit increased coffee production
in tvio of the 20 farm situations studied, decreased production in
seven cases and did not affect coffee production in seven cases. In
the Palmares and San Ramon situations, programmed increased credit did
not cause the alternatives to substitute for coffee. This was also
true in the Acosta situations with low fruit prices. Increased credit
effectively reduced coffee output in the farm models w!iere fruit and
vegetable crops offered a j-iigher net retur- par rp.Bn;:ana than coffee.
Only on farm 11 did labor supervision restrictions prevent the expan-
sion of horticultural production onto coffee land when unlimited credit
vjas made available.

As shown in Table 20 the increase in credit expanded production

99

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101

of strawberries, torratoes and sweetpotatoes on each of the four farms
studied from Alajuela. Intermediate effects of lesser increases of
credit v-jere an expansion of sugarcane and a decrease in limes. Dps
and downs in production occurred with sugarcane and sweetpotatoes as
credit was increased. Sugarcane and tomatoes first replaced coffee
and then higher increases in credit allowed strawberries and svjeet-
potatoes to be expanded as labor supervision became a limiting factor
in certain months. This pattern v\ias not the case on farm 11 because a
lower labor supervision constraint vjas programmed. In that case,
labor supervision blocked expansion of intensive horticultural crops
before coffee output was affected. Those crops giving higher net
returns per manzana than coffee required higher levels of labor as well
as more short-run capital than v-^as required for coffee production.

As shown in Table 21, the Increase of credit availability also
allowed intensive horticultural crops to substitute for coffee. Small
increases in credit Increased coffee output on the two farms which had
the most land not planted to coffee and for which semi -abandoned coffee
was part of the initial optimal solution. Coffee output was reduced to
zero before labor supervisory restraints blocked the production of
blackberri es.

Subsidies for Alternatives

The payment of subsidies for the production of specific alter-
natives is also a possible policy measure, in general, results in-
dicated that this was less efficient than disincentives for coffee
production when the same resuHs \-:ere obtainable. Subsidies for black-
berries or s tra'wberries had no effect on coffee acreage In those situa-
tions where part of the farm v/as not pI-Dnted to coffee. Credit limited

02

Table 21, Optima] output of various crops on Acosta farms v/ith high

fruit price? given base capita] and credit and extra credit
equaling .^i+OO and ^^2,000 per manzana

Farm

Credi t/mz.

Coffee

(faneqas)

Limes
(qui ntales)

Blackberries
(qui ntales)

13A
13A
]3A

Base
t ^+00
^2,000

195.6

162.5

0

17,780
20,644
36,607

0

0

1,402

]k/\

Base
i 400
.;2,00C

18.4
22.8
0

3,600
4,175
5,695

0
0

294

15A
15A
ISA

Base
<i 400
(i2,000

17.4
6.5
0

106
463

0

0

135

16A
i6A
16A

Base
<i 400
^.2,000

3.0
7.7
0

382

2,292
!,350

0

0

500

103

berry production even when high prices v;ere paid. For Alajuela, higher
priced strawberries replaced tomatoes or limes in the model. Black-
berries replaced limes or corn and beans in Acosta as prices were in-
creased. The effect on coffee output was reduced yields as capital
vjas transferred from coffee to berries or increased optimal acreage as
credit limited coffee tree removal. Table 22 shows the effect of a
blackberry subsidy on coffee output, and Table 23 shows the effect of
a strav.berry subsidy on coffee output.

With low fruit prices higher berry prices shifted resources from
coffee but, with high fruit prices, resources were shifted to berry
production from limes and increased coffee output occurred.

Stability of Alternative Crops

Lime production replaced coffee in two of eight farms when the
lime price was programmed at ^13.00 per quintal. Lime production
occurred in the optimal resource use in six of the eight situations.
Limes did not replace coffee when other land was available. The
instaoility of lime production in the model when interest rates were
increased indicates that long-term credit is required at subsidized
rates. As shown in Table 2k, no limes vjouIq be produced with a ]h
percent long-term interest rate. Coffee 'was not removed vjhen an
interest rate of 10 percent v/as used in the program.

The prices of limes necessary to stimulate lime production in
the model are given in Table 25. These prices are lower for Acosta
farms than for Alajuela farms. The price reauired to stimulate coffee
removal is also shovjn. Without extra credit ths poorer farms could
not replace coffee with limes at eny price. A third price column

]0k
Table 22. The effect of a blackberry subsidy on optimal coffee output

Coffee output

Farm

No subsidy

( fanegas)

13A

195.6

13B

900.0

14A

18. if

]kB

70.0

15A

17.^

15B

20.0

16A

3.0

16b

9.0

Subsidy of cSO per
quintal on blackberries

( fanegas)

240.2

900.0

25.7

26.0

11. 1

19.5

3.0

9.0

Table 23. The effect of a strav/berry subsidy on optimal coffee output

Farm

No subsidy

( fanegas)

Coffee output

Subsidy of ^100 per
cuintal of strawberries

( fanegas)

9
10
II
12

997.2

213.3
100.0

22.5

997.2

158. it

89.6

22.5

Table 2k. Relationships betv.'sen long-term interest rates and
manzanas planted to limes

105

In

tei

-es1

: rate in

1 pe

rcent

Farm

6

8

10

12

1^+

planted
0.0

to

, .

9

0.0

0.0

0.0

0.0

10

0.0

0.0

0.0

0.0

0.0

11

1.78

1.52

0.0

0.0

0.0

12

1.79

0.0

0.0

o.c

0.0

13A

kkA5

]GAk

11.50

11.50

0.0

l^+A

9.00 .

9.00

8.11

k.]k

0.0

15A

0.26

0.0

0.0

0.0

0.0

16A

0.96

0.96

0.60

0.60

0.0

106

Table 25. Lime prices and lime production

Farm

Minimum price of limes per quintal

For production
on any land

For production
on coffee land

For a 25/0 increase
in net returns

9
10
11
12
13
]k

15
16

19.37
]k.k5
12.05
12.90
9.73
10.31
10.64
10.68

■colones-

19.37

lif.80
s

11.81
l'+.56
10.6'+

23.^0
16.27
15.3'+
15.3i+
12.70
12.C8
I^+.IS
13. 5^+

Credit restrictions prevent planting coffee land to limes regardless
of price.

107
gives the price necessary to increase farm net returns by 25 percent.
This arbitrary level vias chosen to illustrate that a difference may
exist betv%/een changes that maximize net returns and changes that are
significant enough to motivate action in the face of risk. These
prices may be further modified by calculations to offset changes in
the interest rate. Since the long-term investment is valued at
?;15,000 per manzana and the yield is ^00 quintales of limes per manzana,
each I percent increase in the rate of long-term interest would re-
quire the price of limes to be increased ^;0,375 per quintal. Thus,
if a price of 1^12.00 must be paid to stimulate lime production given
a 6 percent interest rate, the price must be increased to ilS-SO per
quintal if the same production is to occur with a 10 percent interest
rate.

Limes compete well with lower-yielding coffee but the costs of
production would be less on non-coffee land.

in Alajuela swee tpcta toes , peppers and tomatoes were priced at
moderately high levels for processor use under the assumption that
production would be on a contract basis for the Central American
Common Market. The price estimates used were programmed dow^nward to
test the stability of the production of tomatoes and sweetpotatces
in the optimal solutions.

A 25 percent decrease in vegetable prices was accompanied by 3
shift to sugarcane, strawberry and peanut production on farm 10.
Coffee acreage was reduced and sugarcane production was doub'ed. Out-
put was comprised of 179.1 fariegas of coffee, 151. 2 quintales of
strawberries, 16,2 q'lintaies of peiTuts. 362,8 ten-.: of sucarcar.c and
119.5 quintales of sweetpotatces, A 36 percent decline in vegetable

108

prices blocked sweetpotato production. Strav^berry output increased
to 15^.0 quintales and coffee output fell to I76.8 fanegas. Corn
production replaced part of the svjeet potatoes. Sales included 38.3
quintales of corn, 15.1 quintales of peanuts and 369.7 tons of sugar-
cane. These parametric changes showed that strawberries may be a
likely alternative despite their failure to appear in the optimal
solution.

On farm 11, strawberries, peanuts and limes replaced swee tpotatoes
and tomatoes when the prices of vegetables were programmed downvjard.
With a 25 percent price decline for vegetables, the programmed out-
put was 100.0 fanegas of coffee, 1.0 quintal of beans, 95^+.^ quintales
of limes, 77.8 quintales of s travjberr i es , 1.8 quintales of peanuts,
186.8 tons of sugarcane and 100.0 quintales of svjee tpotatoes. With
a further price decline to 50 percent of the vegetable prices, corn
replaced swee tpotatoes. The programmed output vias 100.0 fanegas of
coffee, 25.0 quintales of corn, 887,1 quintales of lim.es, 82.1
quintales of s trawiberr i es , 7.6 quintales of peanuts and I96.9 tons
of sugarcane. Income was reduced from (i2^,536 to (;t23,282 v^hen the
vegetable crops were excluded from the model.

Increases in orange prices resulted in oranges first substituting
for lime'5 and only affecting coffee production on the Alajuela and
Acosta farms after other land was utilized. The orange enterprise
was less competitive than limes, but if prices were stable above ^.o
per hundred^ part of the resoui'ces could be rationally diverted from
coffee to oranges.

Minimumi prices that encourage orange production are shown in
Table 26. The pricr.s for orange prod'jction •v.ere calculated by adding

103

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

Farm

Orange price per hundred

For orange production

"or coffee substitution

9

10

II

12

13A

13B

]kA

]kB

i5A

15B

15A

16b

(colones)

7.2'+
5.82
5.25
^.57
5.10
'+.11
5.02
^+.08
5.17
4.50
^+.38
3.18

(colones)

7.2k
5.82
5.99
6.27
5.63
k.25
5.13
5.07
5.68
4.50

^Credit restriction blocks orange planting on coffee land regardless
op price.

no

the parametric change to the initial price when an iteration brought
oranges into the solution. The prices for coffee removal represent
the price at the first iteration where either tne coffee tree de-
struction activity entered the basis or a coffee production activity
left the basis. Overes 1 1 ma ti on of price needed to initiate coffee
removal could have occurred on those farms where coffee trees were
removed in the initial optimal solution since a print-out vjas not
called at every iteration.

The comparative profitability betvjeen oranges and limies are
close enough to raise doubts as to the superiority of limes over
oranges.

Comparative Costs of Coffee Removal

One means of evaluating the efficiency of various policy measures
is to calculate the costs of removing one fanega of coffee. The base
for corripari sons is the farm price of coffee which estimates the costs
of burning harvested coffee. The comparative costs are derived by
dividing the reduction of income by the number of fanegas reduced.

Tables 27 through 3't show comparative costs for various methods
of reducing coffee output on farms in the Palmares-San Ramon area.
Higher costs for diversification were associated with higher coffee
yields a;id greater reductions in coffee output. Even on farms 1 and
2 Lhe costs of reducing coffee outojt v.'ere reduced if the released
resources were put to alternative use. The cheapest means of reducing
output v;ris blocking nev; plantings but little output was affected.
Credit reduction v.'os not efficient since ncn~coffce crops were affected,
Shifting non-land resources from coffee to alternative crops produced

11

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

Coffee reduction

Method

Cost per faneqa Fanegas Percent

Burning coffee

Credit reduction (70%)

a
Mandatory abandonment

Mandatory semi -abandonment'

Mandatory traditional output
Tree removal and diversification
Tree removal and diversification

^ 203.31

82.8

100.0

2,hl^.k0

3.7

^+.5

158.72

27.6

33.3

155.06

23.6

28.5

15^.37

18.6

22.5

115.30

5.0

6.0

120,63

10,0

12,1

a,~

Costs were calculated v/ith one manzana shifted to less-intensive
production as other production was held constant.

112

Table 28. Cornparatlv/e costs of various methods of coffee output
reduction on farm 2

Method

Coffee reduction

Cost per faneqa Fanegas Percent

Credit reduction (25%)
Credit reduction (30%)
Credit reduction (50%)
Credit reduction (70%)
Burning coffee
Mandatory abandonment

Mandatory semi -abandonment

a
Mandatory traditional output

Tree removal and diversification

Tree removal and diversification

Tree removal and diversification

Calculated by shifting one manzana of coffee to 'ess-productive use
holding other production coristant.

<il35.86

8.2

k.2

135.86

18.7

9.7

135.86

60.5

31.3

137.08

102. i+

53.0

203.31

193.2

100.0

137.22

27.6

14.3

135.86

23.6

12.2

135.95

18.6

9.6

91.02

10.0

5.2

91.02

20.0

]0.k

91.02

40.0

20.7

113

Table 29. Alternative nethoo's of reducing coffee output on farm 3

Coffee reduction

Method Cost per fanega Faneqas Percent

Reduced credit (50%) ^ 236. 22 29.7 ^.0

Reduced credit (60%) I19.2i^ I3O.O I7.3

Burning coffee 203.31 750.0 100.0

Mandatory abandonment 101.62 I5.0 2,0

Mandatory semi -abandonment 92.37 11.0 ] .k

a

Mandatory traditional output 59.80 6.0 0.8

Tree rerr,oval and diversification 36.36 8O.8 10.8

Tree removal and diversification 37.12 86.1 11. 5

Tree removal and diversification kj.kS 287.2 38.3

Tree removal and diversification 51.84 485.8 64.8

Calculated by shifting orie manzana of coffee to a less-productive
use holding other production constant.

11^

Table 30. Alternative methods of reducing coffee output on farm k

Method

Coffee reduction

Cost per faneqa Faneqas Percent

Reduced credi t ( 1 1
Reduced credi t (30%)
Reduced credit (60%)
Reduced credit (90%)
Burning coffee
Mandatory abandonment
Mandatory semi -abandonment
Mandatory traditional output'
Shifting operating capital
Shifting operating capital
Shifting operating capital
Tree removal
Tree removal
Tree removal
Tree removal
Tree removal
Price differentiation

?i220.52

5A

2.k

132.50

31.^

14.0

191.80

50.3

22.4

180. i+2

g'+.o

41.8

203.31

225.0

100.0

109.35

15.0

6.7

103.10

11.0

4.9

112.38

6.0

2.6

16.32

2.9

1.3

27.06

33.0

14.7

37.96

68. i+

30.4

37. 6U

3.6

1.6

ii0.28

4.1

1.8

60.2'+

33.0

14.7

68.23

56.2

25.0

9'k90

225.0

iOO.O

16'4.76

33.0

14.7

Calculated by --.hiflinq on.-^ manzans of coffee to less-productive
use holding other production constant.

115

Table 31. Comparative costs of various methods of reducing coffee
output on farm 5

Method

Coffee reduction

Cost per faneqa Fanegas Percent

Burning coffee
Credit reduction (25%)
Credit reduction (50%)
Credi t reduction (70%)
Credit reduction (90%)
Mandatory abandonment

Mandatory sen! -abandonment

a
Mandatory traditional output

Tree renraval and diversification

Tree removal and diversification

Tree renova] and diversification

Tree removal and diversification

Tree removal and diversification

Tree removal and diversification

Tree removal and diversification

<i203.31

190.0

100.0

112.21

7.3

3.8

112.21

46.6

24.5

112.83

79.2

41.7

115.69

118.9

62.6

127.24

19.0

10.0

120.03

15.0

7.9

112.21

10.0

5.3

57.81

23.5

12.4

60.72

42.4

22.3

61.55

47.3

24.9

66.19

59.6

29.9

72.04

77.6

40.8

75.51

1 10.8

58.3

79.71

142.0

74.7

Calculated by shifting one manzana of coffee to a 1 ess-prcduc ti ve
use while holding other production constant.

116

Table 32. Comparative costs of various methods of reducing coffee
output on farm 6

Coffee reduction

Method

Cost per faneqa

Burning coffee ^203.31

Credit reduction (50%) 7,62

Credit reduction (60%) 73-96

Credit reduction (70%) 93,82

Credit reduction (80%) 99.35

Credit reduction (90%) 102.72

Mandatory abandonment 126,93

Mandatory semi -abandonment 119.'49

a

Mandatory traditional output 112.21

No new planting ^.13

Tree removal and diversification ^+9.9^

Tree removal and diversification 57.19

Faneqas Percent
100.0

76.0

h.l

13.1

23.0

32.7
^2.9
19.0
15.0
10.0

32.^
76.0

6.2
17.2
30.2
U3.0
56.4
25.0

19.7

13.2

6.2

42.6

100,0

Calculated by shifting one manzana of coffee to a lower-yielding
use holding other production constant.

17

Table 33, Cotr.para t i ve costs of various methods of reducing coffee
output en farm 7

Method

Cost per faneqa

Burning coffee . (j203,31

Credit reduction (5%) 15.83

Credit reduction (55%) 399.7^

Credit reduction (60%) 341.10

Credit reduction (70%) 325. OS

Credit reduction (75%) 266.70

Mandatory abandonment 118.13

Mandatory semi -abandonment^ 117,64

Mandatory traditional output 197.73

No nevj planting 0.46

Diversification of non-land resources 63,70

Tree removal and diversification 93.88

Tree removal and diversification 95.47

Tree removal and diversification 106.59

Coffee reduction
Feneqas Percent
62,0 100,0

5.0

5.8

8.7

13.1

18,6

19.0

15.0

10.0

5.0

16.2

38.1

40.2

62.0

8.1
9.4

13.1

21.1

30.0

30.6

24.1

16.1

8.1

2.6.1

61.4

64.8

100.0

Calculated by shifting one manzana of coffee to less-productive
use holding other production constant.

118

Table 3^+. Corr.parati ve costs of various methods of reducing coffee
output on farm 8

Method

Coffee reduction

Cost per faneqa Fanegas Percent

Burning coffee
Credit reduction (35%)
Credit reduction (50%)
Credit reduction (60%)
Credit reduction (75%)
Credit reduction (10Q%)

Tree removal

a
Mandatory abandonment

Mandatory semi -abandonment

Mandatory traditional output

Diversification of capital use

Diversification of capital use

Price differentiation

f203.31

19.0

100.0

1,183.^3

0.8

k.2

339. 'tO

4.1

21.6

23^.15

8.5

kk.7

222.93

9.7

51.1

265.02

15.0

78.9

135.59

19.0

100.0

151.2i+

19.0

100.0

1 43 . ^9

15.0

78.9

136.21

10.0

52.6

15.39

1.5

7.9

60.72

5.0

26.3

80.71

1.5

7.9

Calculated by shifting one manzana of coffee to a less-productive

use holding other production constant.

119
low cost reductions in coffee output. Direct output control was
cheaper than credit reduction on all farms. If coffee trees were
removed, then the cost per fanega of reduced coffee output could be
lessened 25 to 65 percent by shifting resources into alternative
uses. The shifting of ncn-land resources was a cheap way of reducing
coffee output when excess tobacco allotment was available.

In Alajuela the costs of coffee removal were highest for the
smallest farm where the credit restriction bloci<ed the use of higher-
yielding alternative crops. Normally, the cost per fanega of reduced
coffee output was increased as larger quantities of coffee were taken
out of production. However, in the cases v;here subsidy payments for
other crops first shifted resources from the production of a non-
coffee crop to the subsidized crop, the average cost per fanega of
coffee removal was higher when only a small percentage of the coffee
output was affected. Subsidy payments v;ere expensive means of coffee
reduction since non-coffee land was first planted to the subsidized
crop. Credit reduction affected the output of other crops before it
affected coffee on farms 10, il and 12.

Tables 35 through 3? shov; comparative costs of removing one
fanega of coffee through various methods on the Alajuela farms. Since
print-outs were not called for at every iteration of parametric change,
only part of the ie>els of change are given. N'ever the 1 ess , the com-
parative costs indicate that the cheapest i-pethod or controlling coffee
output woiild be carried out with resource re^! 1 ^ocet i on after some
direct control of output. An annual subsidy for coffee tree removal
gives slightly higher removal costs on two of the farms v.'hich indi-
cates that other factors are ri-)ore easily shifted out of coffee

120

Table 35. "he comparative costs of various methods of reducing
coffee output on farm 9

Coffee reduction

Method

Cost per faneqa Fanegas Percent

Burning coffee
Credit reduction (10%)
Credit reduction {207o)
Credit reduction (30%)
Credit reduction (50%)
Credit reduction (70%)
Subsidize oranges (c;3.98/100)
Subsidize limes (^.6.37/qq.)
Subsidize limes (<;b7.95/qq. )
Coffee tree removal
Coffee tree removal
Coffee tree removal
Coffee tree removal
Mandatory abandonment
Mandatory serri -abarioonment
Mandatory traditional output
Resource reallocation
Resource reallocation
Resourc3 reallocation
Resource reallocation

^196.73

997.2

100.0

109.96

62.2

6.2

110.92

126.6

12.7

112.49

195.2

19.6

113.69

332.5

33.3

116.96

512.0

51.3

99.01

709.5

71.2

63.46

259.9

26.1

67.26

666.6

66.8

43.54

171.5

17.2

43.91

180.7

18.1

51.92

309.7

31.1

67.00

454,8

45.6

117.90

25.0

2.5

114.63

21,0

2.1

119.52

16.0

1.6

43.39

176.2

17.7

43.67

183. 9

18.4

49.51

317.2

31.8

54.31

452.0

45.3

Calculated by shifting one manzana from hiohest technology to a
lower level of output holding otner production constant.

121

Table 36. The comparative costs of various methods of reducing
coffee output on farm 10 ■

Coffee reduction

Method Cost per faneqa Faneqas Percent

Burninq coffee

a
Mandatory abandonment

Mandatory semi -abandonment

Mandatory traditional output

Subs

Subs

Subs

Subs

Subs

Subs

Subs

Subs

Subs

dize pineapples (^Il4.91/T.)
dize pineapples (cl56.45/T.)
dize oranges (c'1.96/100)
dize oranges ((jif.98/] 00)
dize strawberries (<J36. 22/qq. )
dize strawberries {cSS.BB/qq.)
dize strawberries (<;i97.7Vqq. )
dize limes (^1.87/qq.)
di ze 1 imcs (cS.SS/qq.)
Coffee tree removal (^572/mz.)
Coffee fee removal (^i789/mz.)
Coffee tree removal (<iS79/mz.)
Coffee tree removal (i;il ,588/mz.)
Resource reallocation
Resource reallocation
Resource reallocation
Resource reallocation
Resource reallocation

<i196.73

213.3

100.0

11'+. 77

20.0

9.4

114.02

16.0

7.5

115.55

11.0

5.2

181.15

17.9

8.4

311.63

58.6

27.5

121.69

141.3

66.2

159.53

213.3

100.0

71.64

26.8

12.6

67.65

32.9

15.^

63.52

55.0

25.8

'46.93

14! ,3

66.2

57.58

213.3

100.0

24.03

26.1

12.2

31.84

7i.3

33. U

36.77

141.3

66.2

38.34

1'49.5

70.1

23.23

15.7

7.'+

24,03

26.1

12.2

36.61

141.3

66.2

40.41

171.0

80.2

46.70

192.1

90.1

^Calculated by shifting one manzana of coffee to a less-productive

use holding other production constant.

22

Table 37. The comparative costs of various methods of reducing
coffee output on farm 11

Coffee reduction

a
a

Method Cost per faneqa Faneqas Percent

Burning coffee
Reduced credit ('40%)
Reduced credit (50%)
Reduced credi t (60%)
Reduced credi t (70%)
Mandatory abandonment
Mandatory semi -abandonment
Mandatory traditional output'
Subsidize oranges (<;i2,92/c ien)
Subsidize pineapples (t296.96/T.)
Subsidize strawberries (f60. 02/qq. )
Subsidize limes (^I.SO/qq.)
Subsidize limes (i;3.7Vqq.)
Coffee tree removal ((;958/mz.)
Coffee tree removal ((jl , 1 24/mz. )
Resource i-eal location
Resource reallocation
Resource reallocation
Resource reallocation

^196.73

100.0

100.0

409.66

7.9

7.9

321.29

16.5

16.5

355.79

24.3

24.3

630.12

19.5

19.5

100.35

20.0

20.0

109.03

16.0

16.0

12^+, 46

11.0

11.0

177.25

100,0

100.0

480.37

36.7

36.7

335.16

10.4

10.4

148.15

5.0

5.0

74.93

98.0

98.0

47.68

20.6

20.6

51.67

86.1

86.1

43.32

0.6

0.6

47.68

20.6

20.6

51.53

83.7

83.7

52.92

100.0

100.0

Calculated by shifting one manzana of coffee to a less-productive
use holding other production constant.

123

Table 38. The comparative costs of various methods of reducing
coffee output on farm 12

M-sthod

Coffee reduction

Cost per faneqa Faneqas Percent

Burning coffee
Credit reduction (70%)
Subsidize oranges (^1. 93/100)
Subsidize limes (i;3.10/qq.)

Coffee tree removal (^i3 , 23 1 .95/mz. )

a

Mandatory abandonment

Mandatory ssmi -abandonment
Resource reallocation

el96.73

22.5

100.0

l,i+78.38

2.k

10.7

l,735.2it

1.3

5.7

62.60

12.5

55.6

359.11

12.2

Sk.2

137.37

9.0

'+Q.0

127. ^+8

5.0

22.2

62.60

12.5

55.6

Calculated by shifting one manzana of coffee to less-productive use
holding other production constant.

]2k
production than is land. As in the Palmares-San Ramon area the larger
reductions in output are generally associated vj\ th higher costs.

On the Acosta farms studied the initial optirr.al coffee production
was much lower vjhen high fruit prices were used than when low fruit
prices were used. As a result, the responses to parametric changes
and comparative costs of reducing coffee output from the initial
optimal production also differed.

V/hen high fruit prices were used, coffee trees were removed in
the two farm situations where other land was not available. V/hen
other land was available, coffee yields vjere reduced as non-land re-
sources were shifted from coffee to limes.

Comparative costs of reducing coffee output on the Acosta farms
ars given in Tables 39 through k2. These costs represent the costs
of reducing the initial programmed optimal coffee output and not the
output reported for the farms under current practices, Lov/er cost
for coffee removal on farm 13 reflected greater ease in shifting t
limes because credit was not as restricted as in the other far
situations. Credit restrictions limited the ability of farm 16 t
respond v.'lth alternative crops when price or otlier incentives en-
couraged the reduction of coffee output.

V/hen low fruit prices were used to represent the alternative
relationships without local processing in the Acosta area, the pro
tion of coffee appi-oxi ma ted the reported production on the farms
studied. V/i th the lower fruit prices, the initial optimal output of
coffee was highei^ than when high fruit prices v^ere used. The compar-
ative costs per fanega of coffee output reduction is giv/en for various
policy me-T^ures in Tpbl^s '•^^ through ^:f . The cheapest method of

:s

to

to

)d'ic-

25

Table 59. The comparative costs of various methods of reducing
coffee output on farm 13, given high fruit prices

Method

Coffee reduction

Cost per faneqa Faneqas Percent

Burning coffee

a

Mandatory abandonment

Mandatory semi -abandonment
Subsidize limes ((;i 1 , 71 /qq. )
Subsidize limes ((;i^. ^8/qq. )
Coffee tree removal (51382. 60/mz, )
Coffee tree removal (^684,61 /mz, )
Coffee tree removal ((j 1 , 260.3'+/mz. )
Resource reallocation
Resource reallocation

^215. 20

195.6

100.0

111.37

6.0

3.1

110.35

k.o

2.0

19.67

66.7

3'4.1

2/4.86

88.8

h5.k

1^.37

62.5

32.0

19.67

66.7

34.1

Ik.SS

88.8

kS.k

l'+.37

62.5

32.0

21.60

87.7

kU.8

Calculated by shifting one manzana of the best coffee to less-
productive use holding other production constant.

126

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

Coffee reduction

Method

Cost per faneqa

Burni ng coffee

Subsidize oranges ((jl.91/qq.)

Subsidize limes ((j2,39/qq.)

Subsidize blackberries ((;20.53/qq. )

Mandatory abandonment^

Mandatory semi -abandonment^

Mandatory abandonment

Coffee tree removal (^625, 02/mz. )

Coffee tree removal (e9'+7.53/mz. )

Coffee tree removal ((j 1 ,389.5 Vmz. )

Resource reallocation

^215. 20

,492.11

^+3.55

50.41

131.97

131.95

145.00

. 26.49

43.55
335.23

26.49

Faneqas

Percent

18.4

100.0

8.6

46.7

5.0

27.2

4.4

23.9

6.0

32.6

4.0

21.7

2.0

10.9

4.8

26.1

5.0

27.2

6.2

33.7

4.8

26.1

"^Calculated by shifting one manzana of the best coffee to less-
productive use holding other production constant.

Calculated by abandonment of one manzana of semi -abandoned coffee
holding ether production constant.

27

Table k] . The comparative costs of various methiods of reducing
coffee output on farm 15, given high fruit prices

Coffee reduction

Method

Cost per fareqa

Burning coffee (;215.20

Reduce credit (50%) 428.89

Reduce credit (6C?/u) 273.83

Reduce credit (70%) 220,70

Subsidize oranges (el. 68/100) 117.53

Subsidize b ^ aclcberri es (ilk. 2S/qq.) 58.76

Subsidize blackberries (.^85. 20./qq. ) 76.^+6

Subsidize limes (il.77/qq.) 28. 7^+

Subsidize lirras ((j7.2Vqq.) ^16. 90

Mandatory abandonment 135.26

Mandatory semi -abandonment 121.15

Coffee tree removal (^711,3Vmz.) 28. 7^

Resource reallocation 28. 7^

Resource reallocation ^.90

Faneqas

Percent

17.4

iOO.O

1.7

9.8

3A

19.5

5.0

28.7

10,2

58.6

6.2

35.6

13.7

78.7

9.2

52.9

}k.S

85.6

10.0

57.5

8.0

^^.0

9.2

52.9

9.2

52.9

14.9

85.6

Calculated by shifting one ir.anzana of coffee t
use holding orher production constant.

o a less-productive

128

Table 42, The comparative costs of various methods of reducing
coffee output on farm 16, given high fruit prices

Method

Coffee reduction

Cost per faneqa Faneqas Percent

Burning coffee

a
Mandatory abandonment

Tree removal (■JS ,6'45 . 77/mz. )

Tree removal (<;4, 200. 65/mz. )

i 215.20

185.70

1 ,822.89

1,927.17

3.0 100.0

2,0 66.7

1.^+ 46.7

2.3 76.7

Calculated by shifting one manzana of coffee into abandonment holding
other production constant.

129

Table k3 . The ccrr.parati ve costs of various methods of reducing coffee
output on farm 13, given low fruit prices

Method

Cost per faneqa

Coffee reduction
Faneqas Percent

Burning Coffee

Credit reduction (10%)

Credit reduction (30%)

Credit reduction (50%)

Credit reduction (70%)

Mandatory abandonment

Mandatory semi -abandonment

Subsidize oranges f <;il ,96/1 00)

Subsidize oranges (.;2, 91/100)

Subsidize limes (i2,8l/qq.)

Subsidize limes (f;5.71/qq.)

Coffee tree removal (ijl 84.00/mz. )

Coffee tree removal ((j577. 06/mz. )

Resource reallocation

Resource reallocation

Resource reallocation

Resource reallocation

Resource reallocation

<i215.20

900.0

75.^1

32.0

77.03

181.3

81.38

323.6

81.21

^93.2

82.22

iO.O

7^.92

8.0

115.11

5^.2

129.33

813.2

li+0.57

^5.7

71.20

771.1

17.72

86.0

3i.9'+

305. 8

]h-.2k

!^.l

17.72

85.0

31.9^

305.8

35.20

k]6.Q

'-^3.15

733.8

00.0

3.6

20.1

36.0

5i+.8

1.1

0.9

6.0

30A

5.1

85.7

9.6

3^.0

i.6

9.6

3^.0

k6.2

81.5

^Calculated by shifting one manzana of coffee land to 1 ess--producti v3
use l-,clding other production constant.

130

Table k'i. The comparative costs of various methods of reducing
coffee output on farm 1^+, given 1 ovj fruit prices

Coffee reduction

Method

Cost per faneqa Faneqas Percent

Burning coffee

Reducing credi t (10%)

Reducing credi r (20%)

Reducing credit (30%)

Reducing credi t (50%)

Reducing credi t (70%)

Subsidize limes (\;2.91/qq.)

Subsidize Mmes ((j5.56/qq.)

Subsidize oranges ((i2. 91/100)

Subsidize b] Bcl;berries (i;i7 1 .60/qq. )

Mandatory obandontTient

Man.:*atory semi -abandonment^

Coffee trr;e removal ((j95S.66/mz. )

Coffee tree removal ((^ 1 , 1 76, 52/mz. )

Coffee tree removal (^1 ,7/-46. 27/mz, )

Resource reallocation

Resource reallocation

Resource reallocation

Resource reallocation

Resource reallocation

Resource reallocation

Reso'^rce reallocation

?i215.20

70.0

100.0

107.16

6.9

9.9

110.31

14.2

20.3

238.28

17.1

24.4

254.97

17.9

25.6

343.40

21.2

30.3

133.73

51.6

73.7

130.35

56.4

80.6

279.20

60.3

86.1

305.80

39.7

56.7

107.71

10.0

14.3

106.29

8.0

11.4

88.34

7.5

10.7

95.41

24.7

35.3

129.54

53.7

76.7

4.78

0.2

0.3

8.32

3.4

4.9

9.00

3.7

5.3

19.78

9.9

14.1

31.42

14.8

21.1

37.81

18.3

26.1

71.49

44.4

63.4

'Calculated by shifting one nanzana of coffee to less-productive use
holding other production constant.

131

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

Coffee reduction

Method Cost per fanega Faneqas Percent

Burning coffee
Reduce credi t (30%)
Reduce credi t (50%)
Reduce credi t (70%)
Mandatory abandonment
Mandatory semi 'abandonm.ent
Subsidize oranges (^1.50/100)
Subsidize oranges (^2.58/100)
Subsidize limes (ijl.GVqq.)
Subsidize limes ('J.78/qq.)
Coffee tree removal (i;i58i . 00/mz. )
Coffee tree removal ((jl ,^^^.93/niz, )
Coffee tree removal ((j I ,748.73/mz. )
Resource reallocation
Resource reallocation

^215.20

20.0

100.0

112.17

1.1

5.5

112.18

k.k

22.0

112,17

7.7

38.5

135.26

10.0

50.0

121.15

8.0

^+0.0

153.32

3.6

18.0

138.07

12.9

Gk.S

65.62

2.6

13.0

87.1^

11.8

59.0

58.10

3.2

16,0

91.35

5.2

26.0

96.36

Ui.2

71.0

58.10

3.2

15.0

80.02

]2.k

62.0

'Calculated by shifting one menzana of coffee to less-productive use
holding other production constant.

132

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

Coffee reduction

Method Cost per faneqa Fanegas Percent

Burning coffee
Reduce credit (50%)
Reduce credi t (60?/o)

Reduce credi t (70%)

a

Mandatory abandonment

a

Mandatory semi -abandonment

Subsidize oranges (?;1. 60/100)
Subsidize limes (ij3.02/qq.)
Subsidize limes (^5,G]/qq.)
Coffee tree removal
Coffee tree removal
Resource real 1 ocatlon
Resource reallocation

Calculated by shifting cne manzana of coffee to less-productive us-j
holding other production constant.

<;215.20

9.0

100.0

^38.77

2.7

30.0

280.88

5.2

57.8

309.17

6.0

66.7

152.32

6.0

66.7

135.63

h.Q

hk.k

557.39

6.0

66.7

139. 0^+

6.0

66.7

321.07

6.0

66.7

251.82

7.^

82.2

^41.31

8.3

92.2

31.57

h.3

^7.8

i<3.3^

6.0

66.7

133
reducing coffee output was to allow resources to be shifted to corn
and beans. Where land v;as initially all in coffee, coffee removal
subsidies were equally effective as direct controls. Subsidizing
orange or lin-;e production v^as over twice as costly as allowing re-
sources to shift to corn and beans. Small quantities of coffee could
be replaced with corn and beans at less than 10 percent of the cost
of destroying harvested coffee on farms 13 and l^f. More restricted
credit made it more costly to replace coffee on the smaller farms.

The following generalizations were drawn from the cost comparisons:

1. Diversified use of resources for the production of alternative
crops vjas a much cheaper means of reducing coffee output than either
destroying harvested coffee or restricting short-term credit without
select! vi ty.

2. Blocking new coffee plantings was the cheapest means of
reducing programmed optimal coffee production.

3. Subsidizing coffee tree removal vjas relatively efficient on
farms v;i th all land planted to coffee but less efficient on previously
diversified farms as a means of coffee production control.

h. Subsidizing alternative crops was efficient as a means of
coffee control only if the best alternative vias chosen and no non-
coffee land was availabl«'i. Distortions in the balance of resources
occurred when s trav;ber r i es or blackberries v.ere subsiaized without
provisions ^or extra credit and in some cases actually increased
programmed coffee out pur.

5. In those form situations where lai-,d was available that was
not planted to coffee, the cheapest reduction in coffee o'jtput ac-
companied a shift of ncn-laid resources from roffee to alternatives.

SUKmRY AND CONCLUSIONS

V/ben this study v^as initiated in 1S67, Costa Rica faced a serious
and imrnediate threat of overproduction of coffee. Although unfavor-
able '/.leather conditions in Brazil reduced the v;orld surplus in the
1368-1963 harvest season, the threat of future overproduction still
exi sts.

Since farm management decisions affecting the quantity of coffee
produced are maae by the farm operators, expansion or curtailment of -
coffee production will depend upon decisions made by individual farm.ers.
With this point in view, the purpose of this study was to evaluate the
farm income opportunities resulting from the production of coffee and
alternative farm products. Comparisons betvieeri programmed optimal
resource use and actual reported resource use were also used to test
the hypothesis that these farriers react to price and cost stimuli in
order to mximize net income. !n addition, the effects of ,:iultiple
pricing of coffee, changes in interest rates and credit, subsidy
programs ar^.d other policy measure; \.'ere e'.aluated v.'ith respect to net
inccr.ic and coffee production. Linear and param:etric proyramming vizrc
th2 princ'pa' analytical -.echniqui's used ' .1 t'-.c t'r.Bivsis. A total of
16 fars'is rc;pre'i3ntl ng different resource sil'jations in three different
areas of Costa Rica V'j.rc lised In the analysis.

E_Ff e c_t s _o r I m p r o v e d P c s o u r c c J\j_ lc;i_c.2 tJ^.CL

Pronri'iyrfliod resou-c". alloc<?.tion to maximize profits ccHised some

13'v

135
shifting of cropping patterns. Nevertheless, given traditional alter-
natives, the stability of coffee acreage was assured by high costs of
planting and of destroying coffee. While some farms did overproduce
coffee, others underproduced in relation to optimum output. Although
the net effect was to reduce coffee slightly on those farms included
in this study, the reduction occurred only on two of the 16 farms.
Therefore, the optir,al allocation of resources would not reduce coffee
production significantly when traditional crops are considered.

This result lends support to Shultz's hypothesis (63, p. 1^) that
producers in underdeveloped countries do attempt to maximize profits
and do allocate given resources efficiently, Mo'-.'ever , the high
returns to additional credit, the entrance of nev; crops into the
■Tiodel , and the apparent mi sa 1 location of resources on certain farms
indicare that the Costa Rican farms studied are rore representative
of tran:;i t lonal than of traditional agriculture.

On the farms studied, farmer allocation of resources used for
other crops vjas, for the most part, less efficiently managed than
those ujec to produce coffee. The only major reallocation of coffee
resources occurred on the farms specializing in flue-cured tobacco.
Thus, comparisons between reported and optimal resource use showed a
concentration of managerial effort in the major cash crops. Although
education arc! extension v.'Ofk m.ay be expected to increase the efficienr.y
of production of alternative c:"ops, the expansion of traditional food
crops does riot offer an incentive for reductions in coffee production.

On the ether hand, horticultural ri-op5 ai^d sugarcane may compete

effectively for some resources gcing tn coffee. Tj^c pro^luction of

limes could pull resources away from coffee if subsidized long-term
credit v.'ere made available and prices v.sre st^abili/.cj with long-term

136

contracts. Considepoble training and farmer education would also be
required. LiiTies vjould compete more favorably with low-yieiding coffee
or corn and baans than with high-yielding coffee, vegetable crops or
sugarcane.

Vegetable crops could potentially receive resources shifted out'
of coffee in the Alajuela area. However, it was noted that vegetable
crops competed more closely vjiih sugarcane and s travjberr i es than with
coffee. When tomatoes and sweetpotatoes replaced sugarcane and beans,
coffee output was not reduced by diversification. Since the risks
and managerial requirements of vecietable production greatly exceed
those of sugarcane production, the program.med higher returns to
vegetable production may not be great enough to motivate change in
that d ! rect Ion.

!n supimary, v/hile tlie maximization of profits may cause some
changes in production patterns, a reduction in coffee output requires
additional action if significant changes are to be made.

Effects of Taxes or Price Declines

The stability o? coffee production under declining prices caused
farm incorT,es to be reduced drastically before output vjas curtailed.
The specialized coffr-e farms Incurred the greatest percentage reduc-
tion of Incoi^e v;hen prices declined. A small tax of vS.QO or ijIO.CO
per fanega vjas Ineffective in reducing coffee production. A price
decline of g^O per foneya reduced net income over 50 percent on many
coffee farms.

The supported price In the trdditional coffee market v;.:iS roughly
2S percent higher than the price in the rest of the world in 19''?.

1^/
Prices for coffee without the coffee agreement could be expected to
fall 30 to 50 percent. If this were to happen, serious losses would
occur on many special iz2d farms and few coffee farms could maintain
net incomes at half of the present level.

When price declines occurred to levels representative of new
coffee market eAport prices, coffee output was reduced on most farms.
This inaicates that from a national viewpoint seme resources are
mi sal located into coffee production. However, the coffee output of
some of the farms, including those with highest yields, was not re-
duced when the coffee price vjas lowered to new market levels. Also
most of the f5rn;s studied produced more than 75 percent of their
initial optima] coffee output at this price. This indicates that
part of the resources going into coffee sold in the new miarket are
not being mi sal located.

Effects of Credit Reduction

A. policy of reducing annual credit would reduce both incomes and
crop oroduction. Incomes of those farms with intensive alternative
crops \;ould be affected most adversely. Returns are high enough to
enable coffee producers to go into unofficial money markets since the
marginal returns to credit exceed 100 percent in tnose cases v.ihere
short-term credit limits coffee output. This policy 'would be effective
only against farmers v;ho are unable to borrow from non-bank sources
because of 1 cvj credit standing, and therefore would be selective
against poorer, sipaller -^arners, incomes en these small diversified
farm.s would be less adversely avfecti^d by policies of taxat'on or
coTfee burninj than by credit restrictions. A general credit reduc-

138
tion would lower coffee production but would not divert resources from
coffee to alternative uses.

Effects of Price Differentiation

Theoretically, a tvjo-price program v^ould equate the marginal
return of the farme'- with the marginal return of the country and v^ould
avoid overa I location of resources to coffee. In practice, difficulties
will arise because the base used to establish the quota for the higher-
priced market may differ from optimal coffee output. It is likely
that those farms with below opiimal historical production would oppose
the policy as it would lower the average price of their optimal produc-
tion. Those farms with a higher historical base than justified by
profit max! mi zation would benefit without necessarily lowering produc-
tion beyond the level that gives niaximum net returns without the policy
measure. Another major difficulty from a political standpoint is that
the gains derived from shifting resources out of coffee are not high.
Also, in situations where technological advances in horticultural crops
may cause a shifting of resources cut of coffee, there might be a
retardation of this shift. Price differentiation would be effective
in reducing the production of coffee only If alternatives surpass new
market coffee in earning "ibility. Major opposition may be expected
from forms \^'■^ere nodemi zat i on of corfee technology is underway. As
wi c'- prodiict'on cor'trol policies in the United States, the most often
heai'd cricici'.^m is that the plan thwarts technological change. This
critici'^m is not justiried since prodi^cticn for the new markets is
al lov/eci,

III suii'imary, this policy cojM r.'duce coffee output and increase

139

farrri income from the levels of the base period. However, It may be
argued that technological change involving new credit for alternative
crops can Increase income and reduce coffee production more. If this
is trL-e, price differentiation could slow diversification in periods
of rapid trans! t ion.

Effects of Increased Credit

increased credit may lead to an increase in coffee production on
some farrriS and to a decrease on other farms, intensive alternative
crops giving higher net returns per manzana than coffee are required
before this measure aids diversification. V/here fruit and vegetable
crops can be marketed, the expansion of credit could botfi increase
income and reduce coffee output. There are situations in vvhich in-
come could be doubled, tripled or quadrupled by increased credit
availability. However, the production of horticultural crops for
industrial utilization requires new skills and technology and close
supervision. !t also involves greater risk in comparison vjith coffee.
The expansion of credit is likely to be effective only if it is
accompanied by education and supervision. it may be that only the
more efficient coffee producers can adopt the more compMcated
managerial techniques needed for fr'ji t and vegetable production, Even
high-yielding coffee can be repla-^ed on land suited for vegecable and
fruit production if prices are stable and extra credit is niade avail-
able. HO'.-.'ever, rinre the m-irkftis fo*" these horticultural commioc I ties
are small, expansion nay be United to non-coffee land.

increased credit brounnt 3'-, expansion or strawberries, sugarcane,
svjeet potatoes anci tomatots 'mo the mode i situations in A jjuela.

1^0
Whether these crops or other crops such as peppers or pickles are
grown vj\]] depend upon the contracts available vor particular crops.
The redi.'ced shadow p'-ices of excluded horticultural production enter-
prises indicated greater ease of subs t i tutabi 1 i ty of one vegetable
for another as more credit becomes available. in the Acosta situa-
tions with high fruit prices, the production of limes and blackberries
was expanded. As In Alajuela, the removal of coffee was profitable
only when the demand for alternatives was sufficient to exhaust ncn-
coffce land.

The Choices of A 1_ t ej; native Crops

Different alternatives were represented In the three zones of
coffee prtrducticn studied. In the Pal marcs-San Ramon ar(:a, income
from tobacco production rivaled that of coffee. However, since
tobacco production was limited by contractual arrangemicn ts , the best
expandable alternatives consisted of beans, sesame, cc-n and beans,
^nd mixed crops. Dairy replaced part of tjie lower-yielding coffee in
the analysis as coffee price was lowered. Generally, th'i first shift,
as pi-ices of i.rffee were reduced, v;as to beans, Pi-oduction patterns
b-'fca-.ie more complex witn diversification. Credit reduction, which
occurred as coffee was removed, shifred prod'jcticn towarcis i-bs i s tone?
ci-c'os. This result caused c'ffrjrenv crops :;o be considere.J as higher
anjourrts of coffee were shifted out of proc:i;c t i on , Firstly, higiTcr-
yielo'ng corn, beans r^nd sesame were considired. However, as prices
of rzY:<}:e foil, credit was liinited. Then castoi-beans and iower-
yiei'dincj corr', bean arid sesame acLiviiies were relatively (rore profit-
able. It m.~y be noted triat, given ample credit, corn, beans and

141

sesama were poor substitutes for tobacco and coffee. However, on the
poorer farms or under reduced credit, ]cv.i-y ieldi ng sesame and broad-
cast beans were among the most profitable alternatives.

The manner in which change is motivated also has an influence on
which crops are produced, A credit reduction favors semi -abandoned
coffee and primitively grown beans and sesame. Taxation or a price
decline would first substitute the highest-yielding corn, bean or
sesame alternatives but greater coffee price declines v;ould be accom-
panied by a shift to traditional food crop activities. An annual
payment for coffee tree removal would tend to shift land ahead of
other resources and encourage less intensive production alternatives.

In Alajuela, vegetable crops and sugarcane compete favorably with
established coffee that yields 20 fanegas per manzana or less. However,
credit restrictions and market limitation? make large-scale changes in
resource allocation uneconomical. Tomatoes and sweetpotatoes showed
the most potential as substitutes for coffee in the model but rela-
tively small price fluctuations could bring peppers, strawberries,
cucumbers and other vegetables into production. Sugarcane also
competed for resources shifted from coffee. As prices were reduced,
sugarcane replaced high-yielding coffee on the largest farm studied.
A difference in resource allocation occurred v/i th price declines VA/ith
specific results depending upon the efficiency of coffee production
and credit limitations. Sugarcane was favored c\'er vegetables wher,
the permanent labor supply was exhausted and limes v^ere favored over
vegetables when short-term credit was r>:o\'Cz limited.

When credit was incredsed, strawoerry, sweetpotato and tomato
production i.'-.creased. When credit vjas re-Juced severely, semi -abandoned

]k2

coffee and traditional crops of corn and beans became the most profit-
able acti vi ties.

The expansion of horticultural crops depends upon stable high
prices for industrial use. Fresh fruit and vegetables for the domestic
market receive much higher prices but also face considerable risk.
These combinations of high average prices and high risks have checked
modernization of technology. Conservative management policy cuts in-
put cost to minimi i-e loss in years of low prices or at least to hold
risks to some acceptable level. This Interplay of risks and profits
in dfcci sion-inaki ng processes cause actual resource use to deviate
from long-run profit maxi mi zaci on. An additional factor of small
market size also afreets decision making. Each of a few producers
may be able to influence price; therefore, the high domestic price
levels were not used in the analysis of fruit and vegetable alternatives.
Opposition to policies favoring the industrialization of horticultural
p.'oducLs may be expected from fruit and vegetable producers currently
serving the fresh produce market.

In Acosta, high fruit prices brought large shifts in resources
from cofF»">e to limes. Blackberries could be encouraged by incr-2?Ged
credit in addition to stable high industrial prices. Additicr.a!
changes to be brought about by other policy .reasures would be slew to
occur and relatively unimportant.

'/hen low fruit prices vjere used in the annlysis,. coffee was
replaced by corn and beans vji ch niode ra ta! y smdll declines in the
coffee price. Poor yielding coffee could be removed by trixarion or
market price declines. But the short-run best allocation of resources
would be disastrous over a long time period. The topoqraphy is rough

143

and unsuited to rev.' crops. Erosion and leaching of soils exposed to
heavy rains would bring e continuing decline of productivity to farms
i n the Acosta area.

Comparative Costs of Coffee Removal

The costs of reducing optimal coffee output using various policy
measures were calculoted for 20 farm situations, 'wide differences
occurred from farm to farm and among methods.

In general, low-yielding coffee was cheaper to remove than high-
yielding coffee. However, the costs of coffee reduction increased as
the quantity reduced on a particular farm increased. The productivity
of the alternatives also entered into the costs of reducing coffee
output. For examp'r-, It was cheaper to remove 25 fanegas per manzana
of coffee from an Alajuela farm with horticultural crops among its
alternatives than to remove 19 fanegas per manzana of coffee from a
farm in Palmares with poor-er alternative possibilities. Credit re-
strictions on poorer farms blocked higher return alternatives and
raised the comparative costs of reducing coffee above the costs of
reducing better yielding coffee en more profitable farms. Small re-
ductions in coffee oij'put were made most cheaply on a^^eraae or lower
yielding farms. However, when larger reductions in coffee output
required destruction of coffee trees, the credit constraint caused
the costs to incre^ise more rapidly on those lower yielding farnis v^hich
also had less operating capital per manzana.

Vihen coffee tree rertioval v/as programmed as the means to reduce
coffee output, highest costs v.erie as --oci a ted wi ch the poorest farms.
V.'ith the exceptions of the poorest farm:;, the costs of removing trees

that produced one fanega of coffee from production vjere below the
costs of burning coffee after it had been produced. Generally, costs
ranged upward from ^1^ per fanega and were clustered below ^llOO. Re-
ducing inputs to coffee, as a means of reducing output, can be expected
to cost C75 to i:\2S per fanega when inputs are not reallocated to
other uses.

Credit reductions sometimes reduced the production of other crops.
\lhen this occurred, credit reductions v;ere an extremely expensive vjay
of reducing coffee output. Costs ranged from ^75 to over (^2,000 per
fanega of reduction in coffee output.

Blocking new plantings reduced optimal coffee output slightly on
two farms. A tax or injunction against new plantifigs would not greatly
reduce potential incomes with present price relationships.

Potential Effects of Technological Advances
in Coffee Production

The iTiajor threat to the acceptance of a program of crop diversi-
fication is tliat technological changes in coffee prcduc*"ion v\il! be
easier to initiate than more complex changes in I'arm munagement vjhich
shift resources out of coffee, Fiirthermoce , given present prices, tho
returns to modernized coffee production surpass leturns to alternatives
that compete with coffee fo-- tlie allocation of crrront resources on
most farms. Many fr'.rms would dojbie net incomes and expand coffee
production if rnoie productive cof fee-gro-.M ng activities could be
criployed. Even with prices or yields lovjei'cd 20 percent, the majority
of I'm farms studied would increase net Incoire, by adopting nev< coffee
technoloqy. The increased coffee output expected from technological
change ■.•/ould orfset reducti or.s expected from d i versi f i ca t I'.)n of curi'ent

1^5

resources. The costs of reallocating resources to reduce coffee out-
put will be increased by advances in coffee production technology.

The programmed incomes on seven out of eight farms with fruit or
vegetable alternatives vjere higher when credit was increased than v,'hen
coff?e technology was changed. Therefore, if production of coffee is
to be held in check by voluntary means, large increases in the supply
of credit for horticultural crops must be made along with investments
for processing plants and extension programs. Involuntary production
controls using farm quotas is another v/ay of checking the potential
expans ion.

The Q^ual i fi cations of an Acceptable Alternative

In a study of this nature, all possible crops cannot be included
because of time and cost restrictions. However, from the crops studied,
certain patterns were indicated. An alternative must net higher returns
per maoiiana than coffee if it is to cause coffee trees to be destroyed
and the land planted to another crop. The diffc'^ence .Tust be high
enough to pay for coffee tree removal.

Smaller downward adjustments in coffee production can occur when
an alternative is superior to other non-coFfec crops and cciipetcs with
coffee for non-land resources. One may expect such changes to be
temporary since over time increased income may al'Cw purchases of
addi t ; onal i npu ts.

Before intensive, horticultural crops replace significant quan-
tities of coffee, add'tionai credit is required.

Or. most coffee farms, labor does not appear to ba a critif;ally
limiting factor o'' production, Fierefoie, an acceptable alternative
may use m.ore hour-wage labor than coffee.

l/f6
Finally, the alternative crop must face a stable price. Profit
maximization is a rational alternative only after risk against loss
is held to an acceptable level. Despite dovjnvjard trends over the past
few years, coffee prices in comparison with alternatives have been
reasonably stable.

Diversification Versus Production Control

Crop diversification and coffee production control are not Iden-
tical. Diversification can be rationally expanded In coffee growing
regions. Diversification Is a relative rather than an absolute concept.
The benefits of risk reduction, income increases and tax-base expansion
occur vjhen the production of other crops is expanded whether or not
coffee output is red;jced. This analysis shovjs that some opportunity
for crop divp.rs I fi cation does exist. However, the projections of
resijl ts of farm case studies to broader generalizations do not j-jstlfy
optimisiT; that crop diversification can effectively control coffee out-
put. Moreover, other factors Influence decision making In addition
to annuoi net Income incentives. The higher lev^i of risk and the
need for closer supervision may block certain activities even when
requirements for new tecnnlcal knowledge are supplied.

Credit reduction can limit coffee production without aiding diver-
si ^I c."i '. i c i and rX. cerate crp,diL e^-.p^juslon csn e \ i dl vers I "^i ca tion vnth-
out limiting coffee output.

Piofitcbie di vers! f Icatl':'--! al rr^rna t i ves icv;er the opportunity cost
associated vjith Involuntary reductl r ns of coffee output. In comparison
with the destruction of h'-'j r vested coffee, the reallocation of resources
to alternative usss is niurh cheaper as a means of controlling national

)k7
coffee sales. Likewise a program of quotas , payment for coffee tree
removal, subsidies to alternative crops, or se'ective credit allocation
probably would rnak3 diversification more acceptable to farmers.

GLOSSARY

] bottle (botelle) = I.5 pounds of milk = 0.6/ liters

1 colon = SO. 150^ (official rate: ^1.00 = ib.SS)

] fanega = ^Lq to 56O pounds of coffee = 400 liters

cherries
or

10^+ pounds of coffee beans-

1 raanzana = 1.727 acres :z: 0,698896 hectares

] metric ton . = 2,2Qi+.6 pounds = \CQC kilograms

1 quintal ^ 100 pounds (Spanish) = kb kilograms

or
101 .'+ pounds (Engl i sh)

1^(8

APPENDIX

150

Introduction to the Appendix

The input and output coefficients used in this analysis are in-
cluded in the Appendix. Changes in technology will undoubtedly alter
yields and both quantity and quality of inputs used. The results and
conclusions reached in this study will hold only as long as the yield
and input coefficients are representative of existing relationships.

The following units of measure apply to the data in the appendix
tables.

All labor, labor supervision and family harvest labor were re-
corded in hours.

The monetary unit Is the colon ((;t6.65 = $1,G0), The er:tries in
the investment rows were made in units of 100 colones. The other
monetary items are in units of one colon.

Land entries are in unil:s of one manzana. Monthly entries were
comibined in the appendix tables with the first and last '-i-onths c'otsig'
nate.d.

Oui:put c.':ef f icients varied from crop to crop. The ,nost corinion
unit, the qji;-it.:;i ({00 Spanish pounds), vjas used for the fol lov,'ing
crops: corn, beans, swee Lpo ta toes , cassava, buckwheat, pigson peas,
chickpeas, limes, cobacco, si rav^'berr i^s and blackberries. Metric ton
v.'as used as the unit oT pj"od>,ic t i on for sugc"~rc3nc, tcinatoes and pp.ppers
Banc-'na output v/as entered in units of 1 JO stems and crsngc output v.v's
enter-cd in units cf 100 fruits., Milk output i-.a;i entered in units of
100 Dottles (!,5 pounds each).

in a number of ths appendix tables monlhs o-' Labor wer^'i cn'il ttod
v,'[-.en no i.^bor was required. Also, in order to conserve space, one or
more digits to the right of the. decimal ^ as used in the proof animi ng
?":a]yscs, v;ere oniit,.rrd in tlie ci;j|Kn(iix t^b'cs.

151

Coefficients for coffee producing activities 1 through 5, Palmares-
San Ramon

Acti vi ty

I tern

1

2

3

k

5

Prof i t

-620.00

-633.55

-6^5.25

-553.05

-1,^86.75

January labor

0

0

1.00

0

0

March labor

2^.00

18.00

50.00

36.00

132.00

Apri 1 labor

50.00

29.00

28.00

0

100.00

May Ubor

103.00

31.00

32.00

12.00

152.00

June labor

23.00

22.00

35.00

66.00

80.00

July labor

15.00

23.00

5.00

^8.00

18.00

August labor

hS . 00

13.00

28.00

30.00

88.00

September labor

20.00

13.00

0

72.00

8^^.00

October labor

6'+. 00

9.00

0

0

128.00

November labor

0

0

1.00

60.00

0

1 rsvestment

13.00^

12.80"^

7.^5*^

0

0

Operati ng capi ta 1

if51.00

A.67.85

509.50

^^38. 00

i ,23'4.00

Rotation, castorbeans

1.00

1.00

1.00

1.00

1.00

Rotation, beans

1,00

1.00

1.00

1.00

1.00

Land, all year

1,00

1.00

1.00

1.00

1 . CO

Coffee land maximum

1.00

1.00

1.00

1.00

1.00

Coffee land minimum.

1.00,
- 27. 60^^

1.00

- 25.70''

1.00

1.00

1.00

Yield transfer

~ 19.00

- 15.00

20.00

Yield change

27.60

25.70

19.00

15.00

20.00

^This activity v.'as blocked on Farms 3 "Snd 'f.

This entry of investment in hundreds of colones vvas not used on Farms
1 and 2 vjhere the investment had already been made.

''Tins investpient entry was used only for Farms 3 ^'^nd 4.

!n this activity, yield v.as placed directly into the coffee transfer
row only in Farms 1 ai-d 2. Parame cr i c prog.-emmi ng procedures a!]o'-.ed
this activity to be considered on Farms 3 through 8.

152

Coefficients for cof
San Ramon

fee p'-oducing activities 6 through 10, Palmares-

Act i vi ty

1 tem

6^

7

8

9

10

Profit

-if82.i+0

-156.25

-770.00

-20.00

-375.00

January labor

0

c

0

0

0

February labor

0

0

0

0

0

March labor

36.00

60.00

132.00

0

60.00

Apri 1 labor

20.00

60.00

15.00

20.00

30.00

May labor

37.00

75.00

0

0

18.00

June labor

12.00

75.00

48.00

0

6.00

July labor

31.00

35.00

36.00

0

0

August labor

12.00

50.00

24.00

45.30

16.00

September labor

0

50.00

36.00

'45.00

30.00

October labor

7.00

0

0

0

0

November labor

0

0

0

0

0

December labor

0

0

0

0

0

! nvestment

0

0

0

0

0

Operati ng capi tal

/405.00

103.00

66ii,00

0

322.70

Rci;atiori, castorbeans

1.00

1.00

1.00

1.00

l.CO

Rotation, becns

1.00

1.00

1.00

1.00

l.GO

Land, all year

1.00

1 ,00

1.00

1.00

1.00

Coffee land maximum

! . 00

1.00

1.00

1.00

1.00

Coffee land minin'um

1.00

1.00

1.00

1.00

1.00

Yield transfer

' 15. CO

~ 5,0c

- 15.00

- i+.OO

- 5.30

Yield change

15.00

5.00

15.00

h.OQ

5.30

a-^

his activity v;es !) locked or- Farms 3 '^nd ^.

153

Coefficients for flue-cured tobacco producing activities, Palmares-
San Ramon

Acti vi ty

1 tern

1

2

3

Prof! t

-2,617.70

-2

,32'+. 80

-2,785.70

Labor:

January

377.00

200.00

125.00

February

59.00

277.00

0

March

0

120.00

0

June

30.00

0

0

July

260,00

0

180.00

August

115.00

2^8.00

69.00

Septenbar

230.00

118.00

153. CO

October

159.00

30.00

93.00

November

315.00

0

22.00

December

510.00

0

137.00

Land:

June-January

1.00

0

0

Jul y-Jenuary

0

0

1.00

August -Jr.nuary

0

1.00

0

1 nvestment

20.00

20.00

20.00

Ope rati ng capi tal

2,if30.95

1,

,231.00

2,703.30

Flue-cure^-l contract

1.00

l.CO

1.00

Tobacco transfer,

flue

20.00

17.60

18.80

15^

Coef f i cients
San Ramrfn

for hurley tobacco producing activities, Palmares-

Act! vi ty

1 ten

1

2

3

Prof i t

-1,4^+7.10

-1 ,105.10

-1,293.70

January labor

675.00

l'i6.00

300,00

February labor

20,00

0

0

July labor

33.00

0

0

August labor

25if.OO

180.00

180,00

Septeinber labor

19^+. 00

127.00

123.00

October labor

iflZ.OO

79.00

183.00

November labor

209.00

192,00

126.00

Oscember labor

462.00

1^6. 00

108.00

Land, August through

December

1.00

1,00

1.00

Opera ti ng capi tal

1 ,289.00

],0i+5.00

1 ,238.00

Burley contract

1.00

1.00

1.00

Tobacco transfer,

bur

ley

18.00

18,00

23.00

155

Coefficients of sun-cured tobacco producing activities, Palmares-

San Ramon

Act i vi ty

tem

.1

Profit
Labor:

January

February

March

July

August

September

October

November

December
Land:

August-December

Sapte~har- January
Cperati ng capi ta]
Sun-cured contract
Tobacco transfer, sun

-1,031.^0

-757.95

-I ,028.80

0

210.00

0

589.0

200.00

210.00

68.00

0

M+.oo

0

17 2,00

0

20^.00

153.00

126.00

59.00

156.00

72.00

137.00

57.00

168.00

98.00

320.00

0

186.00

l.GC

0

1.00

0

1.00

0

905.00

709.10

970.00

1,00

1.00

1.00

13,00

- 19.00

20.00

156

Coefficients of corn producing activities
San Ramon

1 through k. Palmares-

Act!

vi ty

I tern

1

2

3

k

Profit

-^+84.50

-ksk.so

-352.15

-352.15

Labor:

January

90„00

0

66.00

0

April

172.00

172.00

72.00

72.00

May

90.00

90.00

72.00

72.00

June

98.00

98.00

66.00

66.00

July

- 18.00

18.00

^.00

k.QO

August

18.00

18., 00

'+. 00

'4.00

September

0

0

18.00

0

October

0

90,00

0

66.00

Land:

Apri l-January

1.00

0

0

0

Apri 1-Octcber

0

1.00

0

1.00

i>1arch-January

0

0

1.00

0

Oparati ng capi tal

351.^0

351.^0

259.^0

259.40

Corn shel 1 i ng

- 69.09

- 70.00

- ^8.00

~ i+9.00

Corn transfer

- 69.00

- 70.00

- L8.00

- ^9.00

Special credit

-351. '40

-351. i^O

- 259 . '^0

-259.^0

157

Coefficients of corn producing activities 5 through 8, Palmares-
San Ramon

Acti v'

'_ty

1 ten

5

6

7

8

Profit

-156.50

-102.85

-142.95

-142.95 •

Labor:

January

0

0

0

60.00

March

8i+,00

i+8.00

0

0

Apri 1

0

0

19.00

19.00

May

66.00

8.00

24.00

24 . 00

June

5^.00

56.00

41.00

4i.00

July

18.00

0

2.00

2.00

September

0

0

0

10.00

October

66.00

18.00

30.00

0

November

0

18.00

0

0

Land:

March-October

1.00

0

0

0

March- November

0

1.00

0

0

April-July^

0

0

1.00

1.00

Operati ng capi tal

95.00

71.00

n 3 . 40

113.40

Corn she] 1 i ng

- ^0.00

- 2i+.00

- 13.00

- 13.00

Corn transfer

- '40.00

- 2'+,C0

~ 13.00

- 13.00

Special credit

" 95.00

- 71.00

-113.40

- 1 i 3 . 40

ax

rhinly planted corn allows another crop to be planted after July.

158

Coefficients of corn-bean producing activities 1 through 4, Palmares-
San Ramon

Act

i vi ty

1 tern

1

2

3

4

Profit

-4^7.50

-251.95

-163.00

-162.10

Labor:

January

5^.00

0

0

60.00

March

0

8i+.00

0

48.00

Apri 1

72.00

0

19.00

0

May

72.00

102.00

0

56.00

June

66.00

86.00

53.00

68.00

July

i+.OO

18.00

2.00

12.00

August

i+.OO

66.00

72.00

72.00

September

18.00

44.00

66.00

66.00

October

152.00

60,00

20.00

20.00

November

60.00

0

20,00

20.00

December

0

0

0

20.00

Land:

March-January

1.00

0

0

0

March-October

0

1,00

0

March-August^

0

0

1.00

0

Apri 1-August^

0

0

0

i.OO

Operati ng capi tal

353.80

113.70

129.60

89.00

Rotation, beans

1.00

1.00

1.00

1.00

Corn shel 1 ing

- ifS.oo

- 40.00

- 13.00

- 24.00

Corn transfer

- '48.00

- 40.00

- 13.00

- 24. CO

Bean transfer

- 9.60

- 4.00

- 5.40

- 5.40

Special credi t

-353.. 80

"113.70

-129.60

- 89.00

After bean harvest the thinly planted corn allov/ed another crop to be
planted.

159

Coefficients for bean producing activities 1 through k, Palinares-
San Ramon

Act

i vi ty

1 tern

1

2

3

h

Profit

-396.95

-289.85

-116.10

- 62.10

Labor:

January

57.00

0

60.00

60.00

February

0

0

0

28.00

Apr] 1

0

280.00

0

0

May

0

90.00

0

0

June

0

98.00

0

0

July

0

57.00

0

0

August

2^0.00

18.00

0

0

September

92.00

^+7.00

0

0

October

51.00

108.00

80.00

22.00

November

12.00

0

60.00

0

December

'47.00

0

0

0

Land:

September- January

1.00

0

0

0

October-January

0

0

1.00

0

October- February

0

0

0

1,00

Apri 1-Septenber

0

1.00

0

0

Operati ng cap! tal

352.35

261,35

9^4.^0

51.70

Rotation, beans

l.CO

l.GO

l.OG

1.00

Bean transfer

- 20.00

- 18.00

- 9.50

- 'i. 20

Special credit

-352.35

-261.35

- 9^.'^0

- 5i.70

160

Coefficients for sesame and castorbean producing activities, Palmares-
San Raron

Sesame

Castorb

1
lean

I tem

1

2

1

2

Profit

-267.90

-36,25

-i+50.85

-268.ii5

Labor:

January

120.00

80.00

0

0

February

110.00

36.00

0

0

April

0

0

72.00

72.00

May

0

0

22.00

78.00

June

0

0

3.00

60.00

July

0

0

15.00

5^.00

September

0

72.00

0

0

October

272.00

88,00

90.00

90,00

November

^.00

36,00

120,00

100,00

Land:

Sept ember- January

0

1. 00

0

0

October-January

1.00

0

0

0

Apri l~October

0

0

1.00

1,00

Operati ng capi tal

228.75

19.00

370.25

203.70

Rotation, castorbean

0

0

5.00

5.00

Sesame transfer

- 20.00

-11,00

0

0

Castorbean transfer

0

0

- 34.00

- 32.00

Special credit

-228.75

-19.00

-370.25

-203.70

161

Coefficients for dairy activities, Palmares-San Ramon

Actl vl ties

1 tern

1

2

3

k

5

Profit

-476.9

-68.7

-63.0

-467.2

-188.2

Labor:

January

20.0

15.0

15.0

10.0

10.0

February

20.0

15.0

15.0

10.0

10.0

March

20.0

15.0

15.0

10. 0

10.0

Apri ]

20.0

15.0

15.0

10.0

10.0

May

20.0

15.0

15.0

10.0

10.0

June

27.0

15.0

15.0

17.0

17.0

July

2'+,0

29.0

35.0

10.0

10.0

August

2i+.0

23,0

35.0

14.0

14.0

September

2i+.0

15.0

35.0

10.0

10.0

October

24.0

15.0

15.0

10.0

10.0

November

20.0

15.0

15.0

10.0

10.0

December

20.0

15.0

15.0

10.0

10.0

Land ^

2.5

2.5

5.0

1.0

1.0

Opera ti ng cap! tal

2,286.9

608.7

603.0 1

,277.2

977.2

PxOtation, castorbeans

2.5

2.5

5.0

I.O

1.0

Rotation, bears

2.5

2.5

5.0

1.0

1.0

1 r.ves tmcr.t

19.0

6.0

6.0

9.0

9.0

Milk transfer

- 30.0

-10.0

-10.0

- 20.0

- 10.0

Cajf transfer

0

- 0.9

- 0.8

- 0.9

- 0.8

Special credi t

-2.235.9

-603.7

-603.0 -1

,277.2

-977.2

162

Coefficients for beef and pigeon pea and mixed crop producing
activities, Pa!mares-San Ramon

Beef

Pi geon

■ ■ ■

Mixed

1 tem

1

2

pea

crop

Prof i t

-1^+2.00

- 63.00

-308.90

-i+78,20

Labor:

January

5.00

1.00

6.00

5.00

February

5.00

1.00

6.00

86,00

Karch

5.00

1,00

160.00

120, CO

Apri 1

5.00

1.00

2t+0,00

2^+0.00

Kay

5.00

1.00

(70.00

170.00

June

13.00

3^.00

40.00

7^.00

July

13.00

3^.00

0

12.00

August

13.00

3'+.oo

6.00

6.00

Sepcember

5.00

1.00

6.00

118.00

October

5.00

1.00

2i+0.00

140.00

November

6.00

2.00

0

160.00

December

5.00

1.00

0

22.00

Land, all year

1.50

5.00

1.00

1,00

Operati ng cepi ta]

862.00

603.00

272.00

386.20

1 n vestment

8.00

6.00

0

0

Rotation, castorbeans

1.50

5.00

0

0

Rotation, beans

1.50

5.00 -

1.00

1.00

Calf transfer

- 0.75

~ 0.50

0

0

Pigeon pea transfer

0

0

- 20.00

- 13.00

Corn transfer

0

0

0

~ 30.00

Bean transfer

0

0

0

- 4.80

Chickpea transfer

0

0

0

- •'+. 00

Special credi t

-C62.00

"603.00

-272.00

-3S6.00

163

Coefficients fcr peanut, chickpea and buckwheat producing activities,
Palmares-San Ramon

Peanut

Chickpea

Buckv/n

leat

1 tern

1

2

Prof i t

-362.0

-82.6

-105.7

- 56.8

Labor:

January

0

66.0

180.0

12.0

February

0

20.0

0

0

March

0

0

0

120.0

August

kkl.O

0

0

0

September

kS.O

0

0

0

October

12,0

22.0

0

0

Moven'iber

72.0

0

12.0

0

Land:

August-November

1,0

0

0

0

October- February

0

1.0

0

0

November- January

0

0

1.0

0

January-March

0

0

0

1.0

Operati ng caoi ta]

312.0

77.2

72.5

ko.k

Peanut transfer

- 20.0

0

0

0

B'-Jckvjheat transfer

0

0

- 20.0

- 8.0

Chickpea transfer

0

- 5.0

0

0

Special credi t

-312.0

-77.2

- 72.5

-hO.k

]6k

Coffee picking activities, all areas

tem

Profit
Labor:

January

February

November
DeceiTiber

CoT^fee picking activity

January February November December

1.0 1.0 1.0 1.0

1.0

0

0

1.0

0

0

0

0

0

0

1.0

0

0
0

0

1.0

^An additional picking activity for October coffee picking is included
elsewhere for the Acosta area.

165

Coefficients for coffee harvesting and corn shelling operations,
Palmares-San Ramon

Coffee
Hired

harvesting
Fami ly

Corn she

1 1 i nq

I tern

Hand

labor

Contract

Profi t

-i+O.OO

0

0

-1.00

All labor:

January

0.50

5.00

2.

50

0

February

0.50

i+.OO

0

0

November

0.50

4.00

0

0

December

0.50

5.00

0

0

Fami ly labor only:

January

0

5.00

0

0

February

0

i+.OO

0

0

November

0

i+,00

0

0

December

0

5.00

0

0

Coffee harvest

1.00

!.00

0

0

Corn shel 1 i ng

0

0

1.

00

1.00

166

o

a

>

c

13

c

-1

!_

(~

a)

c

•_

3

o

-3

en

<

-— OOOOOOOOOOO OOOCOOOOOOO —

— oooooooooo — o— oooooooooo — o

— ooooooooo — oo

oo

I

ooooooooo — oo

'— oooooooo — ooo— oooooooo — ooo

ooooooo — oooo— ooooooo — oooo

cv^ O ra O

— OOOOOO — OOOOO— OOOOOO — OOOOO

O o-\ O

OOOOO — OOOOOO— OOOOO — OOOOOO

— oooo — OOOOOOO— OOOO — OOOOOOO

O c^

OOO — O OOOOOOO —

o

O C.'J — o o o o o o o o o

O ro

— oooooooooo • -

ooo — oooo oooo

oo — oooo OOOOO

O •- O O O O C OOOOO

i>^ O r~\ O

— -OOOCOOOOOOO— —OOOOOOOOOOO

a

L.

m ••

L.

'_,-^

'jj

l.

*™

(J c

■>^

(U

u

'-

>-

i_

J3

1.

■.:.)

Cj

O

>-

L

Ji

1_

0)

o

l_

f"

J J

E

C*j

J.:

J

CJ) —

1-

m

jj

c:

(U

~

/^

'0

:5

_c

—

i/i

'U

.-\

i=

E

C 1/1

TO

-■3

-IT

- —

U1

Z^

X)

6

"ir

n;

i-

o

.-.

o

>- 3

+-J

"o

■ii

OJ

.— ..-

3

u

O

y

>> 3

.o

c

(U

CJ

-VJ

. •

c

'i

v^

'i_ ">*

i:

— CTl

Q-

^

u

-^-' >

c

-Q

u

L >- c

1 — cr»

D.

-l-J

>

O

.-.-

L

'C

cij

■t)

Q. 03

3

3 -J

c;

t>

o

I'J

.■D '•

Q

TJ

m

a. m 3

3 3

C.)

'J

c

!>

U-

o

->

u.

-4^

< 2:

"")

-? <

O)

o

."SI

o

V- (U

-3

U_

TT

< ?: ->

"5 <

O")

o

:?:

Q

O

o

a; a.

I-

•^

o. n:

Q.

_J

o </>

167

Coefficients for borrowing, planting coffee, destroying coffee, and
fixed costs transfer activities, all areas

Act

i vi ties

Plant

Destroy

Transfer

1 terr,

Borrow

coffee

coffee

fixed costs

Profit

-6.0

- 300.0

- 60.0

-100.0

! n vestment

-1.0

lOi+.O

6.0

0

Interest change

-1.0

0

0

0

Operati ng capi tal

0

2,108.i;

600.0

0

Coffee land maximum

0

1.0

1.0

0

Coffee land minimum

0

0

- 1.0

0

Existing coffee

0

0

1.0

0

Removal payment

0

-9,999.0

1.0

0

Fixed expenditures

0

0

0

1.0

168

Coefficients for moving family v/orkers and permanent employees to
off- farm employment, all areas

Labor movement activities

tern

All labor:

January

February

March

Aori 1

Kay

June

July

August

Saptember

October

November

December
Fami ly I abor:

January

February

Ncvemba r

December
Supe rvi s i on:

January

February

March

Apri i

May

June

July

August

September

Oc tobe r

tsoveir.ber

December
pcimi 1 y rnsn
Men employed
Off- farm pay
Reduced labor'^
Reduced Icbor cost (^costa)

Fami ly

Employees

156.0

156.0

\kk.0

\kk.0

162,0

162.0

150.0

150.0

162.0

162.0

156.0

156.0

16 2.0

162.0

150.0

150.0

150.0

150.0

162,0

162.0

150.0

150.0

120.0

120.0

156.0

0

]kk.O

0

150.0

0

120.0

0

312.0

312.0

288.0

288.0

32i+.0

324.0

300.0

300.0

32'+.0

32'i.O

312.0

312.0

32J+.0

324.0

300.0

300,0

300.0

300.0

32'i.0

3 2'+.0

300.0

300.0

2'; 0.0

240.0

1.0

0

0

1.0

3,116,0

0

0

2,208.0

0

1,520.0

All other areas except Acosta,

169

Coefficients of ri ght-hand-i I de constraints, farms ] through k,
Palmares-San Ramon

Farms

J tern

1

2

3

4

Labor:

January

936.00

1,560.0

1 , 404.0

1,560.0

February

86'+. 0

1 ,440,0

1,296,0

1 ,440.0

March

580.0

1.390,0

1,458,0

1 ,066.0

April

5 -'+0.0

1 ,290.0

1,350.0

1,990.0

May

580.0

1.390.0

1,458.0

1 ,066.0

June

560.0

1,340.0

1 ,404.0

1 ,058.0

July

580.0

1.390.0

1 ,458.0

1 .066.0

August

5^0.0

1,290,0

1,350.0

990.0

September

5^0.0

1 ,290.0

1,350.0

990.0

October

580.0

1,390.0

1,458.0

1 ,066.0

November

735.0

1,390.0

1,350.0

1,360.0

December

720.0

1 ,200.0

1 ,080.0

1 ,200.0

Land, each month

5.0

7.0

56.0

20,0

Ope rati ng capi tal

^, 400.0

4,000.0

71 ,100.0

16,900.0

Burley contract

2,0

0

0

0

Flue-cured contract

0

0

3.0

5.0

Rotation, castorbeans

5.0

7.0

56.0

20,0

Rotation, beans

5.0

7.0

56.0

20.0

Harvest labor:

January

624.0

624.0

0

1.348.0

February

576.0

576.0

0

',252.0

November

435.0

390.0

0

1 ,090,0

December

^80.0

480.0

0

960,0

Supervi si on:

January

624.0

2,184.0

4,680.0

1 ,560,0

February

576.0

2,016.0

4,320,0

1 ,440,0

March

643,0

2,268.0

4,860.0

1 ,620.0

Apri 1

600.0

2,100.0

4,500.0

1 ,500,0

May

648.0

2,268.0

4,850.0

1 ,620.0

June

624.0

2,184.0

4,680,0

i ,560.0

July

548.0

2,268.0

4,860.0

] ,620.0

August

600,0

2, i 00.0

4,500.0

1 ,500,0

September

600.0

2.100,0

4,500.0

i ,500.0

October

648.0

2,268.0

'S 860.0

1 ,62C.O

November

500.0

2,100.0

4,500.0

1 ,500.0

December

480.0

1 ,680.0

3,600.0

i ,200.0

Fixed expenditures

61.8

166.9

601. .0

143.4

Coffee land maximum

3.0

7.0

50.0

15.0

Coffee land minimum

3.0

7.0

50.0

15.0

F.xi sti ng coffee

3.0

7.0

50.0

15.0

Fcmi ]y men

i,0

2.0

0

3.0

Han employed

?.o

5.0

3.0

3.0

Al lot.TV'.nt of quota

62.2

135.0

562.5

16S.7

170

Coefficients of right-hand-side constraints, farms 5 through 8,
Pal ma res- San Ramon

Farms

1 tern

5

6

7

8

Labor:

January

62'+.0

624.0

312.0

624.0

February

576.0

576.0

288.0

576.0

March

6i+8.0

648.0

162.0

418.0

April

600.0

600.0

150.0

390.0

May

648.0

648.0

162,0

413.0

June

624.0

624.0

156.0

404.0

July

6^48.0

648.0

162.0

418.0

August

600.0

600.0

150.0

390.0

September

600.0

600.0

150.0

390.0

October

648,0

648.0

162.0

418.0

November

600.0

600,0

300.0

490.0

December

480.0

480.0

240.0

480.0

Land

10.0

4.0

6.0

2.0

Operati ng capi tal

6,400.0

4,000.0

4,300.0

900.0

Sun-cured contract

0

0

1.0

0.5

Rotation, castcrbeans

10.0

4.0

6.0

2.0

Rotation, beans

10.0

4,0

6.0

2.0

Harvest labor:

January

312.0

0

156.0

624.0

February

288.0

0

144.0

576.0

November

300.0

0

150.0

340.0

December

240.0

0

120.0

360.0

Supervi si on:

January

1,248.0

1 ,248.0

624.0

624.C

February

1,152.0

1,152.0

576.0

576.0

March

1 ,296.0

1 ,296.0

324.0

648.0

April

] ,200.0

1 ,200.0

300.0

6 00.0

Hay

1 ,296.0

1,296.0

324,, 0

648.0

June

1 , 248 . 0

1 ,248.,0

312.0

624.0

July

1 ,296.0

1 ,296.0

324.0

645.0

August

! ,200.0

1 ,200.0

300.0

600. 0

September

1 ,200.0

1 ,200.0

300.0

600.0

October

1 ,296.0

! ,296.0

324.0

648.0

November

1 ,200.0

1 ,200.0

600.0

600. C

Dec''-n:ber

950.0

960,0

480.0

480.0

F i xe ci c X pe n J i t ij n^ '.s

107.3

124.8

60.0

23. S

Co Free land maximum

10.0

3.7

3.0

1.0

Coffee 1 and mi ni mura

10,,0

3.7

^.0

1.0

Existing coffee

10.0

3.7

3.0

1.0

r.jmi 1 y men

2.0

0

G

I.O

I'sen employed

2,0

4.0

1.0

1.0

Al lotm-nt of quota

142.5

53.4

42.7

1 ; .2

71

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172

Coefficients for citrus, stravvberry and pineapple producing activities,
Alaj uela

Acti vi ty

I tern

Profit
Labor:

January

February

March

Apri 1

May

June

July

August

Septerriber

October

November

December
Land t

All year

June-August

September-May
1 nves tment
Ops rati ng capi tal
Lii'e transfer
Orange transfer
Sti'evjberry transfer
pi niajipi e tran^.fer

Rotation ,
Rorat ion

St rav'ber
beans

I es

Potation, cucurbits
Rotation, tomatoes
Special credit

Limes

Granges

Straw-
ber ri es

Pine-

apples

■1,767.6 -2,013.6 -12,817.2 -5,853.0

55.0

54.0

450.0

90.0

10.0

54.0

450.0

90.0

10.0

14.0

450.0

450.0

10.0

14.0

450.0

384.0

55.0

54,0

195.0

439 . 0

55.0

54.0

195.0

234.0

71.0

62.0

195.0

372.0

71.0

54.0

225.0

324.0

58.0

54.0

425.0

2.58.0

10.0

!4.0

395.0

22s. 0

10.0

14.0

. 500.0

0

10.0

14.0

401.0

0

1.0

1.0

0

4.0

0

0

1.0

0

0

0

2.0

0

150.0

162.0

0

0

'60.0

249.0

6,090.0

4,462.5

400.0

0

0

0

0

1 ,020.0

0

0

0

0

- 250.0

Q

0

0

0

- 73.0

1.0

1.0

10.0

0

1.0

i.O

0

0

1.0

l.G

0

0

1.0

1.0

c

0

450.0

~ 249 . 0

-5,090.0

-4,^^2.5

Coefficients for corn producing activities, Alajuela

173

Ac t i vi ty

I tern

Prof] t
Labor:

January

February

March

Apri 1

May

June

July

August

September

October

No/eiTiber

December
Land:

Apri 1-October

Al 1 year
Operati ng capi tal
Corn transfer
Special cred i t
Yield increase

-4^9.5

■715.0

-25K9

0

0

7i^.O

0

0

2^4.0

0

0

36.0

272.0

k.O

0

90.0

90.0

ifl.O

?3.0

98.0

ifl.O

18,0

18.0

58.0

18.0

18.0

72.0

0

0

0

90.0

90.0

0

0

0

0

0

0

0

1.0

1.0

0

0

0

KO

351. if

551. if

^itq.9

80.0

- 90.0

- 60.0

351. if

-551. if

•-li+9.9

1.0

- 1.0

0

Coefficients for bean producing activities, Alajuela

Vk

Acti vi ty

I tern

Profit

Labor:

January
Apr! 1
May

June

July

August

September

October

December
Land:

Apr) 1 -September

September- December
Operating capital
Rotation, beans
B3an transfer
Special credi t

-521.0
0
2.0

■60.8

92.
51.
65.
51.

0

0

1.0
0
^55.2
1.0

■ 20.0
•^55.2

28.

,0

0

0

0

0

0

22.

0

0

60.

0

0

1.

0

51.

7

i.

0

5.

0

51.

-7

■139.1

i+8.0

0

0

0

0

0
72.0

0
72.0

0

1.0
122.0

1.0

10.0

122,0

175

Coefficients for corn and bean, and peanut producing activities,
Aiaj uela

I tem

Corn-bean

Peanut

Profit
Labor:

March

Apri 1

May

June

July

August

September

Oc cober

November

December
Land:

March- December

March-October

Augus t- November
Opera ti ng cspi tal
Corn trans Fer
Bean transfer
Peanut transfer

,0^.\

k.O
28.0
65.0
29,0
48.0

0
9^.0
hS.O
55.0
51.0

1.0

0

0

8/7.0
kS.O
18.0
0

■78.0

72.0

0

2.0
60.0
51.0
40.0
34.0
44.0

0

0

0
42.
•20,
■ 4.

0

-362.0

0
0

c

0

0

i^42.0

48.0

12.0

72.0

0

0
0

1.0

3^2.0

0

0
• 20.0

76

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177

Coefficients for sweetpotato and cassava producing activities, Alajuela

Swsetpo

tato

Cassava

1 tem

1

2

1
1

2

3

Profit

-l,262.'!+5

-3^.25

-669.80

-1,277.75 ■

-1 ,029.00

Labor:

February

0

33^+. 00

0

0

0

Apri 1

192.00

0

120.00

0

0

May

116.00

0

0

80.00

80.00

June

10^.00

0

0

0

198.00 ,

July

28.00

0

102.00

0

0

August

i:38.00

0

0

180.00

56.00

September

0

0

128.00

60.00

0

October

0

384.00

0

0

80.00

November

0

96,00

85.00

85.00

0

December

0

24.00

0

0

0

Land:

All year

0

0

1.00

2.00

2.00

Apr! i -August

1.00

0

0

0

0

October- February

0

1.00

0

0

0

Augijs t-Apri 1

0

0

1.00

0

0

May- June

0

0

0

0

1.00

Operati ng cap; tal

831.15

228.00

200. CO

1,772.00

1 ,300.00

Cassava transfer

0

0

-150.00

- 200.00 ■

- 275.00

Sweetpotato transfer

- 300.00 •

-100.00

0

0

0

Special credi t

- 891.15 ■

-228.00

-200.00

-1,772.00 ■

-1 ,300.00

178

Coefficients for buckv.heat and chickpea producing activities, Alajuela

I tem

Buckv-^heat

Chi ckpea

Profit
Labor:

January

February

March

October
Land:

January-March

October- February
Operati ng capi tal
Buckvvheat transfer
Chickpea transfer
Spec i a 1 cred i t

-56.7

12,0
0
120.0
0

1.0
0

ko.k

- 8.0

0

-ko.k

■82.0

66.

,0

20.

,0

0

22.

,0

0

1.

,0

77.

.2

0

5.

.0

77.

,2

79

Ccef f icltrits for cucumber ard tomato prodijcing activities, Aiajueia

Cucumber

Toiiia to

! tem

Frori t

-] ,601.2

-1 ,602.0

-2,5^^5.0

-2,^6^.0

Labor:

January

0

350.0

592,0

0

Februat y

0

157.0

592.0

0

March

325.0

825.0

0

0

Apri 1

157.0

0

o-'

0

May

825.0

0

0/

102.0

June

0

0

0

522.0

July

0

0

0

2SS.0

August

0

0

0

279.0

September

0

0

168.0

138.0

October

0

0

208.0 ,

378.0

N'oveinLer

0

0

390,0

120,0

December

0

0

37^.0 J

0

Land:

March" May

1.0

0

c

0

January-Mar

ch

0

1.0

0

0

May-October

0

0

0

1.0

Septembcr-Fi

ehruary

0

0

<-0

0

Operating capi

tal

1,^99.2

1,^99.2

1,7VlC

1,655.0

Rotation, toma

tees

0

0

2.0

2.0

Rotation, cucu

rbi C5

if.O

^^.0

0

0

Cucumber trans

rer

140,0

]kO.Q

0

0

Toriiato transfe

r

0

0

- 27.7

(1 ,2

Special credit

-l,i<99.2

-1 ,ifS9.2

-1 ,7^^.0

-1 ,655.C

Coefficients for sv.eet pepper, dairy, and pigeon pea producing
activities, AlajuC. la

180

Pi geon

Feppet

Dai ry

_pea

! tem

1

2

Profits

-1,937.8

-1,243.0

- k67.2

-303.9

Labor:

January

189.0

196,0

10,0

6.0

February

189, 0

156.0

10.0

6.0

March

159.0

156.0

10.0

160.0

April

189.0

156.0

10.0

2'f0,0

May

0

0

10.0

170,0

June

0

0

17.0

i^0,0

July

0

102.0

10.0

0

August

150,0

60.0

li+.O

6,0

September

17^.0

60.0

10.0

6.0

October

168.0

0

10. 0

2if0,0

November

168,0

100.0

!0.0

0

December

92,0

70.0

10,0

0

Land :

All year

0

0

1,0

1.0

July-Apri 1

0

1.0

0

0

August- Apr! !

].0

0

0

0

1 nves In-.ent

0

0

9.0

0

Ope rati ng capi tal

1,298.0

920.0

1,277,2

272,0

Ro ta t i on , s t r a wbe r r i e s

0

0

1,0

0

Rotatior!, beans

0

0

1.0

1.0

Rotation, cucurbits

0

0

1,0

0

Rotation, tomatoes

2.0

2,0

1,0

0

Pepper transfer

9.0

7.5

0

0

fli !k transfer

0

0

20,0

1-1

Calf tr'ons for

0

0

0.9

0

Pigeon pa 3 t r a n s f e r

0

0

0

- 20.0

Special credl £

-1 ,293.0

- 920,0

-1 ,2/7.2

•-272. 0

181

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Coffee harvesting activities, Alajuela

183

I ten-i

Fami 1 y

Hired

Profit

Labor:

January
February

November
December

Harvest labor:
January
February
November
Decen.ber

Coffee harvest

-ifO.O

5.0
k.O
h.Q
5.0

5.0
k.O
k.O
5.0

0.
0,
0.
0,

0

0
0
0

Coefficients of right hand side constraints for Alajuela farms

18^

Fa

rns

! tem

9

10

11

12

Labor:

January

2j8'+.0

1 ,248.0

936.0

1,716.0

February

2,016.0

1,152.0

834.0

1,584.0

March

2,025.0

1,174.0

436.0

1,296.0

Apri I

1,875.0

1,131.0

450.0

1 ,200.0

May

2,025.0

1,174.0

486.0

1,296.0

June

1,950.0

1,131.0

463.0

1,248.0

July

2.025.0

1,174.0

i^86.0

1 ,296.0

August

1,875.0

1,087.0

450.0

1 ,200.0

S'dotCfT.ber

1,375.0

1,087-0

450.0

1 ,2C0.0

October

2,025.0

1,174.0

486.0

1,296.0

November

1 ,950.0

1,125.0

750.0

1,650.0

Decemb3r

1 ,680.0

960.0

720„0

1,320.0

Land

hO.O

15.0

iO.5

5.5

Operating capital

22.200.0

15,750.0

8,850.0

1,115.0

Harvest labor, fan^:ily:

January

62'4,0

468.0

780.0

1 ,404.0

February

576.0

432.0

720.0

1,296.0

liovetr.ber

430.0

375.0

600,0

1,350.0

B;cenber

480.0

360.0

6C0.0

1,080.0

Supervision of labor:

'nrruiiy

3,744.0

2,18^,0

. ' 9 ; n

1,560.0

Fetjr.tery

3.456.0

2,016.0

5 76.0

i,4ua.o

March

3,888.0

2,268.0

648.0

i ,620.0

Apri i

3,600.0

2 J 00.0

600.0

1 ,500.0

May

3,888.0

2,268.0

648.0

1 ,620.0

June

3,744.0

2,184.0

624.0

1 ,560.0

July

3,828.0

2 . 263 , 0

648.0

1 ,620.0

August

3,600.0

2,100.0

600. 0

1 ,500,0

Sep'e.iiiber

3,600.0

2,100.0

600.0

1 .500.0

October

3,388.0

2,268.0

648.0

1 ,620. C

Ncv?.nber

•3,600.0

2.. iCO.O

600.0

1,500.0

Oeceniber

2,880,0

1 .680.0

'iBO.O

1 ,200.0

R o 1 0 1 i 'J .-! , s t r 3 v\' b e i r i o s

40,0

13.0

!0.5

3.5

Rotation, bear.s

40,0

!5.0

10.5

5.5

Rotation. cL'C'jr bi ts

40,0

! - . 0

10.5

5-5

P.o t3 1 ! on , toiia tee s

iiO.O

15.0

10.5

Fix's J expenditures

485.7

56.7

79. 0

Co i' f i" e ( a nd max 1 mum

40.0

■I5..O

5.0

2.5

Coffee land -.•linlmum

^0.0

1:^,0

5-0

E.c'stin^ coffee

4 0.0

:5.0

r- /*,

2.5

!-3n":i ly riion

:'.,o

2.0

1.0

3.0

Man £-.(;iployed

10.0

5.0

1,0

2 . 0

Al Ic-tiTi'int

750.0

225,0

/5.0

18.7

185

Coefficients of coffee producing activities 1 through '+, Acosta

Act

• • 4. a
1 VI ty

1 tem

1

2

3

4

Profit

-677.00

-676.35

-490.00

-489.40

Labor:

January

16,00

3K00

40,00

40.00

February

15.00

68.00

10,00

10.00

March

68.00

71.00

32.00

32.00

April

51.00

18.00

23.00

13. OG

Hay

38.00

18.00

21.00

31.00

June

18.00

18.00

14.00

14.00

July

56.00

73.00

57.00

114.00

A jgust

47. OC

30.00

72.00

72.00

September

32.00

16.00

57.00

0

October

0

16.00

40.00

40.00

i n vestment

12.80

12.80

0

0

Operat: ng ca

pi tal

5^7.75

5^7. '75

315.20

315.20

Land, all ye

;ar

1.00

1.00

1.00

1.00

Coffee land

maximum

l.OC

1.00

1.00

1.00

Coffee land

mini mum

1,00

1.00

1.00

1.00

Coffee trans

fer

0

0

- 10.00

- 10,00

Yield potential

- l''+.00

- 14.00

0

0

Yield change

0

0

10.00

10.00

'^Two activities viere derived from each budget to allov; flexibility in
vo rk scheduling.

186

Coefficients for coffee producing activities 5 through 7, Acosta

Acti vi ty

I ten

Profit
L'^bor :

March

Apri 1

May

July

August

September

October
Opera t i ng cipi tal
Land:

Al I year
Coffee land maxi rtium
Coffee land mini mum
Coffee transfer
Yield change

■155.3

■156,0

■15.0

ifO.O

40. C

0

60,0

80.0

0

6'4.C

kk.O

0

50.0

120.0

0

70.0

0

0

0

0

80.0

100.0

100.0

0

116.0

116.0

0

1.0

1.0

1.0

1.0

1,0

1.0

1,0

1,0

1.0

6.0

- 6,0

- 2,0

6.0

6,0

2.0

187

Coefficients for coffee-fruit producing activities, Acosta

tern

Cof fee-orange-
banana

Acti vi ty

Coffee-orange

Prof i t
Labor:

January

February

March

Apri I

May

June

July

August

October

December
Land

Coffee land maximum
Coffee land rpinimum
Coffee transfer
Orange traiisfer
Banana transfer
Yield change (coffee)

•^59.2

90.0
90.0
90.0
70.0

40.0
10,0
60.0
0
15
120
1

1

0
0
0
0
1.0
5.0
10.0
1.0
5.0

,0
0

•300.0

30.0

30.0

40.0

20.

112.

0

0

120.0

100.0

20.0

1.0

1.0

1.0

• 6.0
■ 30.0

• 0.5
6.0

Coefficients for fruit producing activities, Accsta

188

Act

i vi ty

— =z

B'

lack-

I tem

Orange 1

Orange 2

L i me s

berries

Profit

-2,019.6

-1,^32.1

-1,767.6

-8

,578,7

Labor:

January

5^.0

36.0

55.0

3^0.0

February

5^.0

36.0

55.0

28c. 0

March

I'+.O

0

10.0

280.0

Apr i 1

]k.O

0

10.0

180.0

May

s'+.o

36.0

55.0

lUQ.O

June

5^.0

36.0

55.0

180.0

July

62.0

32.0

71.0

180.0

August

s'+.o

32.0

71.0

l^lO.O

September

5^.0

32.0

58.0

80.0

October

1^.0

0

10.0

30.0

Noveir.ber

M+.o

0

iO.O

i'+0,0

December

14.0

0

10,0

80.0

I nvestrront

162.5

53.2

150.0

0

Ope rati ng capi tal

2^^9 . 0

155.0

^50.0

3

,68'f.O

Land

1.0

KO

1.0

1.0

Orange transfer

-1 ,020.0

- 533.3

0

0

Lime transfer

0

0

- 'iGO.O

0

Blackberry transfer

0

0

0

-

i-so.o

Special credit

- 249 . 0

- 155.0

- 450,0

-3

,6G^.O

CoefficienLs for corn-bean producing activities, Acosta

189

Act i vi ty

1 tern

Profit
Labor:

April

May

June

August

September

October

December
Land

Operati ng capi tal
Corn transfer
Bean transfer
Scecial credi t

-^38.4

■117.7

185.5

12,0

20.0

10.0

36.0

2'+.0

50.0

2i+.0

20.0

0

0

^0.0

0

5^.0

48.0

60,0

21.0

0

0

0

160.0

7^.0

KO

1.0

1,0

318.0

86.0

135.0

29.0

- GA

- 16.0

11,0

- 9.6

- 6.6

313.0

- 86.0

-135.0

Coefficients for dairy producing activities, Acosta

190

Acti vi ty

1 teiTi

1

2

3

k

5

Prof i t

-it76.9

- 68.7

- 63.0

-467.2

-188.2

Lsbor:

January

20,0

15.0

15.0

10.0

10.0

February

20.0

15.0

15.0

10.0

10.0

March

20.0

15.0

15.0

10.0

10.0

Apri 1

20.0

15.0

15.0

10.0

10.0

Hay

20.0

15.0

15.0

10.0

10.0

June

27.0

15.0

15.0

17.0

17.0

July

24.0

29.0

35.0

10.0

10.0

August

2i+.0

29.0

35.0

1 4. 0

14.0

September

24.0

15.0

35.0

10.0

10.0

October

2'4. 0

15.0

15.0

10.0

10.0

Novenber

20.0

15.0

15.0

10.0

10.0

December

20.0

15.0

15.0

10.0

10.0

Land

2.5

2.5

5.C

1.0

1.0

Opcrati ng cap! tai

2,289.9

608.7

603.0

1,277.2

977.2

i nvest.'ienl

19.0

6.0

6.0

9.0

9.0

I'',' 1 k transfer

- 30.0

- IQ.O

- 10.0

- 20.0

- 10.0

Caif transf.-jr

0

0.9

C,8

0.9

- 80.0

Special credit

-2,259.9

-608.7

■■603.0

1 ,2:'7.2

-977.2

Coefficients for beef producing activities, Acosta

191

Acti vi ty

I tern

Profit
Labor:

January

February

March

April

May

June

Jul y

August

September

October

November

December
Land

! nvestment
Operati ng capi tal
Calf transfer
Speci al credi t

-li+2.0

5.0

5.0

5.0

5.0

5.0

13.0

13.0

13.0

5.0

5.0

6.0

5.0

1.5

8.0

862.0

■ 0.7

-862.0

- 63.0

1.0
1.0
1.0
1.0
1.0
1.0
3'^.0
3^.0
1.0
1.0
1.0
1.0
5.0
6.0
603.0

- 0.5
-603.0

0)

1)

.13

192

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Coef f i cie[)ts for coffee harvesting activities, Acosta

19^

i tem

Ha ryes ti nq

Hired

Fami 1 y

October'
picking

Profit
Labor:

January

February

October

Ncverr.be r

December
Family harvest labor;

January

February

October

November

December

•^0.0

1.0

0.5

5.0

0

0.5

k.Q

0

0.5

4.0

1.0

0.5

k.O

0

0.5

5.0

0

0

5.0

0

0

^4.0

0

0

k.O

0

0

k.O

0

0

5.0

0

harvest seascn beg

'The

other two areas

as well as in the p-^ricd from November through February included in
the transfer activiiies cominon to all area matrices.

n one month earlier in Acosta than in the
tiierefore, unused farm labor could be sold in October

195

Coefficients of right hand side constraints for farms !3-l6, Acosta

rarms

I tem

n

]k

M.

16

Labor:

January

2,808.0

468.0

624.0

624.0

February

2,592.0

432.0

576.0

576.0

March

2,835.0

486.0

405 . 0

468.0

Apri 1

2,625.0

450.0

375.0

4c>0.0

May

2,835.0

486.0

405.0

486.0

June

2,730.0

468.0

385.0

468.0

July

2,835.0

486.0

405.0

486.0

August

2,625.0

450.0

375.0

450.0

September

2,625.0

450.0

375.0

450.0

October

2,916.0

486.0

6'i8.0

648.0

November

2,700.0

450.0

600.0

600.0

December

2,100.0

360.0

480.0

480,0

Operating capital

51,^22.7

5,9^5.0

825.0

675.0

Land

101.5

16.0

2.0

6.5

Harvest labor, family:

January

62t|.0

4^8.0

624.0

312.0

February

576.0

437.0

576.0

288.0

October

6^+8.0

486.0

648.0

324.0

November

600.0

450,0

600. 0

300.0

December

'+80. 0

360.0

480.0

240.0

Supervision of labor:

.'anuary

5..30''4.0

936.0

312.0

£24.0

February

^,856.0

864.0

288.0

5 '6.0

Morch

5,508.0

472.0

324.0

648,0

April

5,100.0

900.0

300.0

600,0

May

3,508.0

972.0

324.0

648. 0

.June

5,30'+.0

936.0

312.0

624,0

July

5,508.0

972,0

324.0

648.0

August

5,100=0

900„0

300.0

600.0

5epten:ber

5,100.0

900,0

300.0

600.0

October

5,508.0

97i.O

324.0

643. 0

. November

5,100.0

900.0

300.0

600,0

December

'^oso.o

720.0

240.0

(• i xe d e X pt; nd I L u >" e s

'475 . 0

n ,5

4.2

29.9

Fa mi ly men

4.0

i.O

i.O

Hen employed

9.0

0.0

0.0

1.0

Coffee land maximum

90,0

7.0

2.0

1.5

Coffee lorid miriimum

SCO

7.0

2.0

US

uxi 5t i nu coffee

90.0

7 . 0

2,0

1.5

Al lotment

675.0

52.5

7.5

5.6

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200

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.f;;?ill-j£lil-Lii9_.,._3.j....lii.^i^j^.i\'-l££:£ri-il^ 5:'.'r_Jj- j-cofiOmi a_('.j.."'s ta rr_i ::c_nve„
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201

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B!0CRAPH1C'\L SKETCH

John Lc'.7i5 3iebar vjea born August 13, 193^^'; ai Lafayett-?, Indiana.
He are\; up on d. csne-.Tl pui'poss fann neas Lafayette end graduated fro.r,
Buv'k C: itc-k Hi'jh School in '195'i-- Hf: then attended Pur'due University
v;herc; he received the degree of Baciiolor of Science in Agricuiturs I r.
June, 195", vvith a major in Agronorr.y.

He entered the Graduate School of Kansos State University in
SepteT.her, '35S, ar.d received the degree of Master of Science in June-,
i9£'0, vvi Lh a niojor in Agronomy and a minor in Agricultural LccnO'n.'cs,

I ", r . s I e t

r •ierved in the United States Air Force, He entered

the G'Jiduare School of trie University of Florida in January, 19c-;, He-
left the University in May, 19^5, and farmed cwo years in Indiana. In
September, I966, he returned to the University of Florida and conrin'jed
vjork LOi'jard the degree of Doctor of Philosophy in Agricultural Eco.-,o, ;! ci

In July, 1967, Hr. Bieber began research under the U. 3, A!D/
University of Florida Contract in Costa P.ica. He receiv-^d an interi,-i
staff position in November, I5S8, v;i th the University of Florida in
or<Jer to carry out additional investigations of crop diversification
in Costa Pic-::!. in January, i970, f'*'r. Bieber again entered the
University of Flcrido to ccT.plete the requirements for the Doctor of
Philosophy degr-ee.

■ "■ John Lev.'is Biebar is niar'ried to the foriner Ma'-tha Eugenia Stelle.
He is a niefuhor of Gamna Siqr,ia Delta.

-.■•■^rX". ''K,^'.' .- "•■ I.. -> . ■

This d i sier Lit! on was prepared under the direcuoo oF the chair-
ir.ar. of the carididnte's supervisory corrimittee Bnd hss bc-eri approved by
o]] [^'wrnbers of th-Ji. cornmittea. It v:3'i suiDiiii tttd to the Dean of the
College of Agriculture and to the Gr-iduate Council, and was approved
as partial fulfillment of the requi reinsn ts for th& decree of Doctor
of Pni looophy,

March, 1970

^t>'De5n, College of Agriculture

Dear,, Grades ''c- School

buper Visory t-omni itee;

Jj..Ali.:)lL':x]^h:!±:.

Ihe ! I' nan

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