This dissertation hasbeen microfilmed exactly as received ... · feeding study by Schrader and King...

This dissertation has been

microfilmed exactly as received66-13,705

HOGG, Howard Carl, 1935-AN ITERATIVE LINEAR PROGRAMMING PROCEDUREFOR ESTIMATING PATTERNS OF LAND USE.

University of Hawaii, Ph.D., 1966Economics, agricultural

Univemity Microfilms, Inc., Ann Arbor, Michigan

AN ITERATIVE LINE~~ PROGRAMHING PROCEDURE

FOR ESTIMATING PATTERNS OF LAND USE

A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF THE

UNIVERSITY OF HAWAII IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN AGRICULTURAL ECONQ~ICS

JAi'lUARY 1966

By

Howard Carl Hogg

Thesis Committee:

Arnold B. Larson, ChairmanLudwig Auel'"Edmund R. BarmettlerKyohei SasakiFrank S. Scott, Jr.

ACKNOWLEDGMENTS

The method of analysis employed in this investigation is based

upon suggestions made by Arnold Larson. I am very g~ateful for the

assistance and for his continued encouragement throughout the study. I

also want to thank the other members of my thesis committee:

Frank Scott, Jr.; Edmund Barmettler; Kyohei Sasaki; and Lud~g Auer.

In addition, I would like to acknowledge the invaluable contributions

of Tamotsu Sahara, Iwao Kuwahara, Mrs. Faith Fujimura, and Mrs. Barbara

King.

Financial support for part of this work was provided by the

Department of Land and Natural Resources, State of Hawaii. In this

connection, I am especially grateful to Paul Tajima for his efforts in

making funds available. The Statistical and Computing Center of the

University of Hawaii generously allocated several hours of computer time

for program testing and data analysis.

TABLE OF CONTENTSPage

THEORETICAL BASIS OF LAND USE PATTERNS

• • • • • 0 • 0 • 0• 0 • • • • • 0CHAPTER

CHAPTER

10

II.

INTRODUCTION •

Objectives

Procedure •

• • • • 0

• • •

• • • • • •

. . . . . .• • • • • • •

• • • • • • •

• • • • 0 •

1

1

2

4

Linear Programming and Production Allocation

Model Required for This Study

Problems • • • • 0 • • • • • · . . . . . . . .· . . . . . . . .

5

8

Basic Assumptions •••••••• • • • •• 9

Economic Model and Estimating Procedure •• 10

• • • • • • • • • • • •

• • • •

• • • • •

14

22

24

· . . . .· . . . .. .• •• •

• •

Mathematical Model •

Limitations of Model

BASIC DATA •••••III.CHAPTER

Land Productivity Classification •• • • • •• 24

Production Costs and Yields by Land

Productivity Class. • • • • • • • • • • • •• 28

Pineapple • • • • • • • • • • • • • • • •• 28

Supply • • • • • • • • • • • • 0 0 • • • • •

Crop Group I • • • • • • • • • • • • • • • • •

Crop Group II • • • • • • • • • • • • • • • • •

Dernand • 0 • • • • • • • • • • • • • • • 0 •

• • • • • • • • • • • • • • • • • •

ESTllo1ATED MARKET SUPPLY AND DEMAND FUNCTIONS.

29

32

50

64

64

65

66

68

• •

• • • • •

• • • • •. . . . . . .• • • • • • • • • •

Vegetable Crops

Orchard Crops

Pasture

IV.CHAPTER

ii

Page

CHAPTER V. ESTIMATED LAND USE PATTERNS • · • • • • • • • • • 78

Land Use Patterns for the Entire Area • • • • • 78

Land Use Pattern IA • • • • • · • • • · • • 82

Land Use Pattern IB • • • • · • • · • • · · 86

Land Use Pattern IC • • • • • • • • • • · • 88

Land Use Pattern ID • • · · • • · · • · · • 90

Land Use Pattern IE • · • • • • · • · • • • 92

Land Use Patterns for Hoolehua • • · · • • • • 95

Land Use Pattern IIA. · · · • • • • • • · • 95

Land Use Pattern lIB • • • • • • • • • • • · 96

Land Use Pattern IIC. • • • • • • • • • • • 97

Land Use Pattern IID. • • • · • • • • • 98

Stability of the Land Use Patterns • • • • • • 99

Derived Labor Demand Curves for Hoolehua • • · 100

CHAPTER VI. SOME ECONOMIC EFFECTS OF EXPANDED PRODUCTION ON

EXISTING STATE PRODUCERS, THE STATE AS A WHOLE,

AND THE CONSUMER · • . • • · • • • • • • • • • • 104

Changes in Producer and State Output • • • • • 104

Changes in Producer and State Income • • · • • 107

Changes in the Wholesale Price Level ~ • • • · 109

CHAPTER VII. SOME ADDITIONAL CROPS THAT OFFER POTENTIAL FOR

• • • • • • •

Vegetable Crops •

• • •· . . III

III

113

• •• •

• • • •

• • • •• •

· .· .· .· .• •• • •

· . . .

· . .Orchard Crops •

ACREAGE EXPANSION

iii

Page

CHAPTER VIII. S~~1ARY AND CON:LUSIONS ••••••• • • • • •• 116

Surmnary • • • 0 • • • • • • 0 • • • • • • • •• 116

Implications • • • • • • • • • • • • • • • •• 117

Validity of Findings •••••••• • • • 119

Suggestions for Further Research • • • • • •• 121

APPENDIX A.. DESCRIPTION OF LAND TYPES • • • • • • • • • • • 123

APPENDIX B. IP~IGATION REQUIREMENTS • • • • • • • • • • • • • • , "0

.L "'0

Waimanalo. . . • • • • . . . . • • . . . . . • • 128

Waianae Kai and Hoolehua • • • • • • • • • • • • 130

APPENDIX C.

APPENDL'{ D.

DEMAND CURVE TABLES

SUPPLY CURVE TABLES

. . . . . . . . . . . . . . . .• • • • • • • • • • • • • • • •

132

136

APPENDIX E. COMPUTING FACILITIES REQUIRED FOR DATA ANALYSIS •• 140

Computing Time by Phase • • • • • • • • • • • •• 140

Computing Time Required for Estimating Land

Use Patterns • • • • • • • • • • • • • • • • 140

REFERENCES • • • • • • • • • • • 0 • • • • • • • • • • • • • • • 143

CHAPTER I

INTRODUCTION

Development of land resources poses many problems for the planning

agency. From the viewpoint of the economist perhaps the most important

problems are those relating to the desirability of a project in terms

of some income or efficiency objective. Typically, a project objective

is stated as the maximizat!on of individual, regional, or national

income depending upon the jurisdiction of the developing organization.

To effectively evaluate a land development, in terms of its objectives,

it is necessary to anticipate the pattern of land use that will likely

result in the project area. This study presents a quantitative approach

for estimati~g land use patterns when use of the project land is

restricted to agricultur~~ Planning problems concerned with agricul

tural as compared to non-agricultural uses, project financing, condi

tions of repayment, and form of tenure are ignored. The empirical

application of the estimating procedure consists of estimating land use

patterns for a project currently being undertaken by the State of

Hawaii.

Objectives

The primary objective is to develop a procedure for estimating the

perfect competition eqUilibrium production of several crops, on lands

of varying quality, in spatially separated producing areas. Market

supply and demand equations are include~ in the model so that the

patterns of land use ~ll be established by market forces. The model

indicates, by physical land productivity class and proje~t area, the

acreage used to produce each crop. It also gives the equilibrium

2

market price and quantity supplied for each included commodity.

The obj~ceive of the empirical applications of the model was to

estimate patterns of land use based on different wage rate, labor

availability, and unit size assumptions for several tracts of state

owned land that are included in a current development project. These

lands, which are located at Waimanalo and Waianae Kai on the Island of

Oahu and at Hoolehua on the Island of Molokai, make up the three

project areas considered in this study.

Procedure

The first step was to construct cost of production and yield

budgets, by land productivity class, for a group of crops that repre

sent most of the production alternatives faced by prospective farmers

in the project areas. These budgets are based upon existing cost of

production studies supplemented by interviews with crop specialists.

Because of limited resources, only those crops for Which cost studies

are presently available are included in the analysis. This group of

crops includes pineapple, pasture, papaya, apple banana, tomatoes, snap

beans, cantaloupe and Manoa lettuce.

Market supply and demand functions were developed for each crop

and included in the estimating model. This was done in an effort to

duplicate the market forces that actually contribute to the establish

ment of a land use pattern~ For the purposes of this analysis, it is

assumed that all vegetable crop production will be sold on the Honolulu

market 0 This market restriction is imposed because producers will not

export until the Honolulu market price falls to a point Where export

offers the most profitable outlet. This price would probably be some-

what lower than the West Coast price less transportation charges.

Available information suggests that under prevailing management prac

tices, which are assumed, this price would not be sufficient to cover

production costs (39; 49, p. 11). The estimating model is an iterative

linear programming system that allows for adjustments in supply from

existing producers as price changes and does not permit production to

exceed quantity demanded at any given price. This model constructs a

static equilibrium lur a specified number of crops while allowing them

to compete ~th one another for scarce resources as they would in the

market. The scarce resources are allocated to their most profitable

use.

3

4

CHAPTER II

THEORETICAL BASIS OF LAND USE PATTERNS

For the purposes of this study, economic rent is defined as the

difference between production costs, with opportunity costs added, and

total revenue. Opportunity costs in this case equal the level of rent

that could be earned in the next best use. The difference between

production costs and total revenue is defined as net returns. This

procedure varies from the conventional treatment of economic rent which

equates it to net returns (4, p. 159).

Ricardian rent theory relates levels of economic rent (net returns

by the above definition) to land quality. In the Ricardo model, price

is determined by the per unit production cost on the lowest quality

land needed to supply a single market. If this model is generalized to

include several crops, and the land qualities are defined as producing

regions, it is identical to the standard interregional programming model

formulated for multiple producing regions and a single market.

This chapter presents a procedure for estimating the regional

production of several crops to be sold in a single market, that

incorporates certain aspects of both the Ricardo and standard inter

regional models. In this study, unless otherwise specified, each

separate quality of land is considered a distinct producing region.

However, the identity of each land quality found in a project area is

retained so that patterns of land use can be constructed. These use

patterns indicate the acreage, by land quality, devoted to the produc

tion of a particular crop in each of the three project areas described

earlier. A project area may contain more than one quality of land but

must have uniform transportation costs. These use patterns are

5

identical to those implied in Ricardian rent theory if the latter is

generalized to the multiple crop case.

Linear Programming and Production Allocation Problems

Linear programming models relevant to the present investigation

can be classified into three groups. The first of these is the stand

ard interregional competition model of the Egbert-Heady t)ye (18 j

p. 218). This model features spatially separated producing and con

suming regions and fixed regional quantities demanded for each included

commodity. This model can be formulated to maximize net returns or

minimize production and transportation costs while meeting the specified

regional quantities demanded. The cost minimization problem is of

primary interest in the present case. Whittlesey and Skold (66) have

shown that the dual of this model constructs a stepped supply function

for each crop exchanged in a given market. A function of this type is

shown in Figure 1. The solid horizontal segments PIa, P2b, and P3c

represent production and transportation costs for three producing

regions. If a regional quantity demanded of Q3 is specified, market

price would be P3 which is the production cost in the least efficient

producing region supplying this market. With price set at P3 regions

one and two earn a positive net return. Region one, for example, earns

a net return equal to [(P3-Pl)(Ql-QO)].

The formulation of Yaron and Heady (67) represents the second type

of linear programming model relevant to the present study. Their model

accommodates a single producing and consuming region and declining

demand curves for several commodities by approximating the marginal net

revenue function with a step function as shown in Figure 2.

6

Figure 1. F-.coduct Supply Curve for .2 Consulling RegiQn

f~egionsContainin~ T~rce ?j:od~cin~

Po S'I

~_________ ______~ _;,l cIIIIIIIIIIIII

P2 ------ - -~i""'--------'! bIIiI•

Price,cost

Qo Q3Quantity

Figure 2. Step Approximation of rlarginal Net Revenue Function

MNR ::t MR-l'-IG-

$HNR

MNR2 1-----..lJ=====~

QoQuantity

7

Solution is achieved by considering each step of the function as a

separate activity (a sub-activity of the activity in question). Net

revenue equals the final MNR multiplied by the final quantity.

The third type of regional programming model features multiple

producing and consuming regions and a declining demand curve for a

single commodity. An example of this type of model is the beef cattle

feeding study by Schrader and King (55). Their model constructs

regional supply functions which allow market equilibria and correspond

ing regional production and supply patterns to be computed within the

program. The regional supply of carcass beef is a function of feeder

cattle, feed concentrates, and hay or other roughages, all of which can

be shipped between regions. Demand curves are incorporated by varying

the quantity restrictions and the values of the objective function in

successive approximations. These variations are made to be consistent

with estimated market demand curves.

Certain assumptions inherent to the above models limit their

usefulness in the present case. The Egbert-Heady model does not

incorporate downward-sloping demand functions. Inclusion of such func

tions would allow determination of the optimum quantity supplied to each

market, the corresponding market price, and the optimum level of produc

tion for each region rather than merely market price and the optimum

regional production corresponding to a predetermined quantity demanded

(or supplied, as supply equals demand) for each consuming market. The

Yaron-Heady approach fails here because it is not applicable to

multiple producing regions, and the Schrader-King formulation considers

only a single commodity and does not allow alternative uses to compete

for scarce resources as required by the present problem.

Model Required for This Study

A model incorporating multiple producing regions, a single con

suming region, and a downward-sloping demand curve for each of several

commodities is needed for the present investigation. There are two

possible approaches for constructing a model of this type. The first

would be to construct regional supply and demand functions for each

commodity, and determine the equilibrium prices and commodity flows by

quadratic programming. Quadratic programming occurs When the objective

function is non-linear resulting from a downward-sloping linear demand

curve~ A spatial price equilibrium model that could be adapted to this

problem has been formulated by Takeyama and Judge (62). As a second

approach the standard interregional model can be modified so that it

will accommodate a demand function.

The budget data used in this analysis assume constant production

costs on each quality of land. Regional supply curves based on these

budgets would be step functions, with each step representing a land

quality found within a particular project area (Which would correspond

to a producing region in the Takeyama-Judge model). These functions

could then be smoothed for adaptation to the Takeyama-Judge model.

Treating each land quality as Q r~oducing region results in a step

function identical to that shown in Figure 1. Because the product

supply function can be directly related to the step-function of the

standard interr~gionaimodel and because only one consuming region is

included there appears to be no real advantage in using the Takeyama

Judge model. Also, at present no computer program is available for

this purpose at the University of Hawaii and therefore the second

approach, modification of the standard interregional model, is used

8

9

here.

Basic Assumptions

Most interregional programming studies have certain assumptions in

common. These assumptions are listed below.

(a) The basic assumptions of all linear programming problems

apply, viz. (1) additivity of activities; (2) divisibility

of factors and commodities; (3) a finite number of activi

ties; (4) constant input-output coefficients; and (5) one

or more restrictions.

(b) The supply of land within each producing region is of a

uniform quality.

(c) The level of management is uniform, for each crop produced,

within each producing region. That is, all producers in a

given region have the same input-output coefficients.

(d) All inputs except land, and in a single case labor, are

assumed to be available in unrestrictive quantities at

tbeir current market prices.

(e) The system is static and refers to market supply and demand

o£ a single year.

(f) Producers seek to maximize net returns in a competitive

market.

In addition to those listed above, the present model makes the

following assumptions.

(g) The selected crops included in the model represent the

entire range of production choices faced by prospective

farmers in the project areas.

(h) The market demand curves of the model are independent.

10

Economic Model and Estimating Procedure

In this study the programming problem is solved by varying price

in successive program solutions. This procedure requires that prices

be increased just enough in each solution to allow the introduction of

the next best land as, for example, in Figure 1 from Pl to P2 to P3 and

Ql to Q2 to Q3 until mark~t demand is satisfied. The objective function

for this solution consists of the net returns earned by the respective

activities. A solution is obtained in this manner that is identical to

that of the cost minimization problem if the objective function values

are kept current as price is varied.

With multiple crops the following four conditions must hold for an

optimum solution:

(1) Total market supply equals demand simultaneously for all

crops.

(2) Market price is determined by production cost, including

opportunity cost, of the least productive region.

(3) Each producing region earns a net return consistent with a

single product price for each commodity exchanged in the

market.

(4) A single region producing more than one commodity earns the

same net return in each use.

These conditions are identical to those for the cost minimization formu

lation of the standard interregional model. Egbert and Heady (9, p. 4

of supplement) construct, from these conditions, a proof of static

short-run competitive equilibrium. This proof is summarized, and re

lated to the present model, in the next section of this chapter.

Bringing new agricultural land into production can be viewed as

11

moving a market from an existing equilibrium to a new one. The

availability of new land tends to shift the existing supply function,

causing the new equilibrium to be established. This process is illus

trated in Figure 3. The intersection of the existing market supply

curve (5) and market demand curve (D) represents ~he initial ~quilib

rium. When the new land is made available the market supply function

shifts to 51, establishing a new equilibrium. A smooth supply curve

(S) is used to represent the relationship between market price and the

quantity supplied by existing producers of this commodity because the

relationship is estimated by a regression model that results in a con

tinuous function. The new supply curve (51) is approximated by a step

function because production costs are assumed constant for each region.

For example, at a per unit cost of Cll the quantity Qll-QOl could be

gro~ in region one. The total market supply at thi& price is Ql1'

which equals the quantity from the newly developed area (Q11-QOI) plus

the original production (Q01-0) before development.

To establish an optimum solution for multiple crops, the program

first enters crop one wdth price set at Cl1 Which is the production

cost in the most productive producing region. Output at this price

equals Q11' which is less than the quantity demanded at this price,

therefore price is increased to C2l which allows the next best pro

ducing region to enter production. This process continues until supply

equals demand for this crop. Crop one now meets three of the require

ments for an optimum solution: (1) market supply equals demand at C31'

Q31; (2) market price equals the production cost in the least produc

tive region; and (3) net return equals [(C21-Cll)(Qll-POl)] in region

one and [(C3l-C2l)'Q2l-Qll)] in region two.

12

Figure 3. Hypot.hetical Supply and DCiil.:J.nd Functions for Tvlo. Crops

Price,cost

C21

Cll -- --/ir-j;,....----IIIIII

o

1

~sI DIIIIIIII

s

Quantity

Price,cost

Crop T,·;o

s

QuantityQ02

--------~-l SlI~D

------ I

/I II II II II II II I

o

13

The second step in the solution process is to enter crop two. The

net returns earned per acre of crop one are added to the per acre

production costs of crop t~ in regions where crop one was introduced.

This is equivalent to charging an opportunity cost to crop two con

sidering crop one the best alternative use at this point. Crop two is

now entered in the same manner as crop one until supply equals demand.

At this point crop two meets the first three requirements of an optimum

solution. The fourth and final requirement, that each region producing

more than one crop earn the same return in all uses, holds for both

crops. Region one now produces crop two only and earns [(C22-Cll)

(Q12-Q02)] + [(C3l+Cll)(Qll-QOl)] net returns. Region two is used in

the production of both crops earning a net return of [(C3l-C2l) (Q2l-Qu)]

in each case.

Because region one and part of region two have been bid away from

crop one supply is less than demand for this commodity which violates

the first requirement for an optimum solution. Adding the total net

return earned by both crops to the production costs of crop one for

each region in which they were introduced, as an opportunity cost,

allows it to be re-entered as before. The solution process is repeated

entering each crop on land classes of diminishing quality until supply

equals demand. The crops are re-entered until this condition (supply

equals demand) holds simultaneously for both crops. Only at this point

are all of the requirements met.

The objective function consists of the net returns earned by the

respective crops. As each crop is introduced on a particular land

class the objective function is modified so that the accumulated net

return is added to the objective function term of the appropriate

14

activityo The objective function must be constantly updated as the

solution proceeds from one stage of the solution to the next if the

competitive strength of the crop is to be accurately specified.

Under certain circumstances convergence of the system to a final

solution may be slow. The production costs of single crops in each of

the regions differ from one stage of the solution to the next. These

differences, for each region, equal the sum of the net returns earned

in the previous stage by the crop in question, and all crops subse

quently entered before the initial crop is re-entered. If it is

necessary to enter all of the crops a number of times before achieving

a solution, the competitive advantage of the better lands is reduced.

This results from adding subsequent layers of opportunity cost to the

production costs of each crop, which reduces the cost differences

between lands of different qualities. Reducing the cost differences

slows the rate of convergence.

The program includes a counter that is incremented each time the

final crop, of the group being considered, is entered. The solution

can be interrupted and intermediate results obtained by specifying the

number of entries that are to be allowed. This counter acts as a check

that can be used to prevent long machine runs resulting from slow

convergence. A discussion of several methods of forcing solutions in

these cases is discussed in Appendix E.

Mathematical Model

The formal linear programming model used in this study is

summarized below.

Xij • Level of j-th activity in i-th regiono

nrij = Net returns per acre of j-th activity in i-th region.

lij m Land resource required by j-th activity in i-th region.

Wij Q Quarterly labor requirement per acre of j-th activity in

i-th region.

~ij • Per acre yield of j-th activity in i-th region.

LCi • Land ~onstraint in i-th region.

Wi D Labor constraint in i-th region (Pattern IE only).

Qj D Quantity constraint of j-th activity for Honolulu market.

Pj • Market price of j-th activity.

eij ... Production cost, including opportunity rent, of j-th acti

vity in i-th region.

~e objective is to maximize

f(nr) • f1Xij • nrij

with each of j activities subject to the following constraints:

(a) Land constraint

~ li j • Xi j < LCiJ

(b) . Quantity constraint

The non-negativity assumption is:

Xij ~ 0

An iterative procedure was employed for estimating each optimum

land use pattern. This procedure systematically varies Qj and the

associated values of nrij until a final solution is achieved. The

requirements for an optimum solution are:

(a) t Xij • qij ... Qj

15

16

(b) Pj m elj , Where i a the least productive region in which

crop j is produced

... 0, otherwise

(d) Each region producing more than one crop must earn equal

net returns from each crop.

The above conditions must hold simultaneously for all activities.

The conditions given above are nearly identical to those offered

by Egbert and Heady (9, p. 2 of supplement) as proof of a static short-

run competitive equilibrium. Their proof applies to the cost minimiza-

tion formulstion of the standard interregional model. The conditions

for an equilibrium solution to a two product (A and B), single market,

problem are summarized below. Industry supplies for n regions are

given by:i~

(a) Qa" ~ qiaial

wi th industry demand equal to:

The equilibrium conditions are:

i:Sn(b) Qb - ~ qib

i-l

The cost of qia and qib is always less than or equal to the cost of

qi+l,a or qi+l,b Which allows regional returns for A and B to be

specified by:

Solution of the Egbert-Heady model results in the following revenue

conditions: (1) product A will be supplied by a producing region only

if it earns a net return larger than B; (2) only B will be supplied if

17

it earns the largest return; and (3) both A and B ~11 be supplied only

if they earn the same net return. The symbols are defined as follows:

Qa ... Equilibrium quantity of A

QJ,- Equilibrium quantity of B

qia .. Quantity of A supplied by i-th region

qib .. Quantity of B supplied by i-th region

Da .. Demand for A

~. Demand for B

Ka • Per capita consumption of A

Kb • Per capita consumption of B

p. Population

ita .. Net return of A in i-th region

itb .. Net return of B in i-th region

Yia • Yield of A in i -th region

Yib • Yield of B in i-th region

Pa • Market price of A

1b • Market prl ce of B

cia" Cost of producing A in. i-th region

cib ,.. Cost of producing B in i-th region

The major difference between the two models is that in the present

case Qa and Qb' as well as Pa , Ph, qia, qib, Ria, and Rib are deter

mined within the model. Also, in the Egbert-Heady model land rents or

regional returns (Ria and Rib) and product prices are determined in the

dual solution. Solving a two product problem with the present model

would result in a set of values for Qa' Qb' Pa , Pb' qia' qib' Ria' and

Rib- If the resulting Qa and Qb is then used in solving the Egbert

Heady model an identical set of Pa , Pb' qia, qib, Ria' and Rib would be

18

obtained. The following illustration shows the equivalence between the

two methods. This example utilizes two products, tomatoes (A) and

snapbeans (B), that could be grown in two newly developed areas

(Region 1 and Region 2) and are to be sold on a single market. The

symbols used are identical to those defined earlie~ except as indicated.

Given the market supply and demand functions:

Tomatoes Snapbeans

Demand;

Supply;

P m 25.64 - .0008 Qo P • 45.01 - .0142 QD

Qs • 3709.32 + 41.9292 P Qs. 945.12 + 12.7332 P

Where; p. Market price (cents per pound)

Qs • Quantity supplied by existing producers (1000 pounds)

Qo • Quantity demanded (1000 pounds)

Per acre production costs:

Tomatoes

Region 1; $4,243

Region 2; 4,284

Per acre yield (l000 pounds):

Tomatoes

Region 1; 31.2

Region 2; 34.0

Snapbeans

$6,336

3,750

Snapbeans

36.0

30.0

Available land in each region:

Region 1;

Region 2;

680 acres

56 acres

The following results were obtained by the procedure outlined in this

study.

Qa D 10,783,000 pounds

Qb· 1,091,000 pounds

19

Pa a 13.59 cents/pound

Pb = 13.63 cents/pound

qla a 324.3 acres

qlb a 0 acres

q2a - 19.6 acres

q2b· 36.4 acres

RIa. 0 dollars

RIb· 0 dollars

R2a • 340 dollars

R2b a 340 dollars

Total cost • 1,607,796 dollars

The Egbert-Heady version of this model was then solved. The objective

in this case is to minimize production costs while meeting the level of

demand specified by Qa and Qb. The primal solution gave the results

listed below.

Qa a 10,783,000 pounds (given)

Qb· 1,091,000 pounds (given)

q1a· 324.3 acres

o acres

q2a· 19.6 acres

q2b a 36.4 acres

Total cost • 1,607,796 dollars

The dual of this solution was not computed because the sources of value

that determine the imputed rents and product prices can be easily

traced. Product prices must be high enough to allow production costs

to be covered in all producing regions. The minimum per unit prices

that would accomplish this, in the present example, are:

20

Pa c 13.59 cents

Pb = 13.63 cents

The respective product demand curves indiuate that the amount taken off

the market at these prices is just equal to the amount produced (plus

the marketings of existing producers). The imputed rent for tomatoes

(Region 2) is equal to the difference between the per unit production

cost and market price multiplied by the tomato yield. Snapbeans must

match this return or be forced out of Region 2. In other word~, the

rent imputed to tomatoes becomes an opportunity cost to snapbeans.

Therefore, regional per acre net returns become:

Rla = 0 dollars

RIb = 0 dollars

RZa = 340 dollars

R2b = 340 dollars

The only difference in the results of the t~ methods is the inability

of the Egbert-Heady model to determine Qa and Qb.

Table 1 is provided to s~~~ how this model ~uld appear in a con

ventional simplex tableau. It will be noted that the C and Qj entries

have been left blank in Table 1. This was done because these values

are computed within the program and change with each stage of the

solution. The subscripts of the real activities (A) represent region

and crop, respectively. The coefficients corresponding to these activi

ties are the amounts of the respective factors required to produce one

unit of the activity. The other terms have already been defined.

The necessary data for estimating a land use pattern with this

model are listed below.

TABLE 1. BASIC SIHPLEX TABLEAU FOR LAND USE PATTERN IIA

C-4 0 0 0 0 0 0Resource or Activi ty Real Acti vi ties Di S lOsa1 Activities

C Activitv Level .Kj I AL2 AI.3 11.1 4 A2.1 A?? A2 :3 A?!.J. LC, , LC, ? Q1 Q? Q~ Q'~

0 Le1,1 680 1 1 1 1 1

0 LC1,2 219 1 1 1 1 1

0 Q1 31.2 26.6 1

0 Q2 18.3 15.8 I

0 Q3 52.0 47.0 1

0 Q4 36.0 32.0 1

N.....

22

(a) Production cost and yield data, by producing region, for

each crop to be included in the analysis.

(b) Demand functions for all of the included crops with

downward-sloping demand curves. Crops ~nth perfectly

elastic demand are included by entering the appropriate

return in the objective function for th~ activity.

(c) Supply functions for all crops to be included that do

not have perfectly elastic demand.

The program output includes the total market supply, price, and acreage

of newly developed land used for producing each crop.

Limitations of Model

The model used in this study accommodates downward-sloping demand

curves in a particular type of regional programming problem. This

represents some refinement over previous studies of this nature but is

still subject to a number of limitations. The more important limita

tions are listed below:

(a) Not all fanners within a region have the same input-output

coefficients.

(b) Farmers within a region may use different production

methods.

(c) Constant returns to scale may not exist over the entire

production range.

(d) The speed of convergence to a final solution may be slow

under certain conditions

(e) The supply curves of existing producers are assumed to be

independent.

(f) The market demand curves are assumed to be independent.

Thls may not be too important in the present case because

quantities of competing products were included as inde

pendent variables in several preliminary demand equations,

and were found to have no significant influence on price.

23

24

CHAPTER III

BASIC DATA

Most of the data used in this report exist in published form;

others are derived from unpublished manuscripts. The land classifica

tion material and production cost and yield budgets are based upon

existing studies. When these cost of production studies do not

distinguish between lands of different quality, cost and yield adjust

ments were made by utilizing published materials dealing with the

required cultural practices for the several crops. Information obtained

from interviews with Experiment Station, Extension Service, and

industry specialists was also helpful in making these adjustments.

Land Productivity Classification

The land productivity classification used in this study is that

developed by the Land Study Bureau, University of Hawaii. The basic

classification used by the Bureau is land type. "A given land type

includes a group of lands having equivalent physical productivities as

a consequence of similar chemical and physical soil features" (44,

p. 117). In the Bureau classification, land productivity ratings are

the common denominator for suitability classes on a state-wide basis.

These ratings "••• interpret the interacting complex influences of

climate, surface relief, drainage, wind velocities, soil characteris

tics, and cultural practices that are associated with each land type••• "

and relate the physical land quality to levels of output assuming

prevailing management practices (44, p. 127).

Master productivity ratings were first developed by the Bureau to

indicate the over-all suitability of a land type for agricultural

25

production. These ratings were established by a productivity index

that considers the character of the soil profile, texture of the sur

face soil, slope of the land, climate, and miscellaneous factors. This

procedure, which is a modification of the Storie Index Method, can be

summarized by the following formula (44, p. 128):

Land productivity index m A • B • C • X • Y

where:

A Q decimal equivalent of percentage rating for general character

of the soil profile.

B = decimal equivalent of percentage rating for texture of the

surface horizon.

C - decimal equivalent of percentage rating for slope of the

land.

X m decimal equivalent of percentage rating for site conditions

other than those covered by factors A, B, and C (salinity,

soil reaction, ~nd, etc.).

Y - decimal equivalenc of percentage rating for rainfall.

The actual percentage rating for each factor associated ~th a particu

lar land type is based on a state-~de rating scheme developed for

local conditions. Low quality lands would have a low index rating. As

the computed index approaches 100 per cent, land quality increases.

A single land type would not be equally suitable for all crops.

Because of this, single use ratings were developed by the Bureau to

indicate the SUitability of a land type for alternative single uses.

In general, the single use ratings are based on the same factors con

sidered in establishing the master productivity ratings. It was

necessary, however, to consider subjectively the added reqUirements

26

peculiar to individual uses. While the single use ratings are not as

objective as the master ratings, they do allow some control over a

major weakness of the index method. If a single factor has a lo~

percentage rating it could substantially reduce the level of the master

rating. If this over-all rating is then used for single uses it is

likely that the limiting factor will not be as restrictive in some uses

as in others. Slope, for example, is far more important When classify

ing vegetable land than when classifying pasture land.

In this study yields are associated with each individual land

productivity class by assuming levels of inputs that approximate the

modal state-wide level for each land class. The productivity ratings

are used as an indicator of physical quality and as a basis for mapping

areas of uniform quality from parcels containing several productivity

classes.

The Land Study Bureau uses lower case letters to indicate the

productivity of a land type in individual uses with land type and use

designated numerically. In Table 2, for example, land type 23i (i

designates irrigated land) is rated ~ for pineapple, ~ for vegetables,

~ for pasture, and 2 for orchard crops. The possible ratings range

from class a lands with the highest productivity to class ~ lands which

are unsuited for intensive agricultural uses. For the purposes of this

study the productivity ratings for individual uses are of primary

importance; however, the individual land type descriptions are provided

in Appendix A. The characteristics of the land areas utilized in this

analysis are given in Table 2.

27

TABLE 2. LAND TYPES, PRODUCTIVITY RATINGS,

AND ACREAGE OF LANDS UNDER STUDY

Land TypeY Productivity Rating by use~ At;reagetdLocation of Lands

Hoolehua 17i 1a 2a 6a 7a 2

11 1a 2b 6a 7a 678

3i ld 2c 6a 7b 219

7 le 2e 6e 7e 152

Waianae Kai 23i ld 2c 6a 7b 22

56i le 2e 6d 7e 1,008

57 le 2e 6e 7e 56

58 le 2e 6e 7e 410

Waimanalo 3i la 2a 6a 7a 5

4i 1a 2a 6a 7a 4

51 la 2a 6a 7a 47

8i lc 2b 6a 7b 31

9i 1b 2a 6a 7b 62

19i lb 2b 6a 7b 35

321 ld 2d 6b 7c 19

35i ld 2b 6a 7b 20

371 ld 2c 6b 7b 9

4li ld 2d 6b 7c 3

Waimanalo 421 ld 2c 6b 7b 60

47i 1d 2d 6c 7d 6

561 Ie 2e 6d 7e 104

28

TABLE 2. (Continued) LAND TYPES, PRODUCTIVITY RATINGS,

AND ACREAGE OF LANDS UNDER STUDY

Location of Lands Land Type~ Productivity Rating by Use§{ Acreage£!

57

58

le

le

2e

2e

6e

6e

7e

7e

9

8

!V Those lands designated suitable for agricultural production areassumed to be irrigated. Irrigated lands are designated by (i).

B{ Uses 1, 2, 6, and 7 represent pineapple, vegetables, pasture, andorchard crops, respectively. The lower case letters indicate theland productivity class for each use. Class e lands are unsuitedfor intensive agricultural production.

£! Acreages were measured by planimeter from U. S. Geological Surveymaps of a 1:25,000 scale.

Production Costs and Yields by Land Productivity Class

The crop budgets developed in this report are based on existing

cost of production studies. In some cases the data have been modified

to insure comparability between crops and to allow estimation of costs

and yields over a range of land qualities. The budgets are believed to

represent levels of inputs and yields that are now being realized in

the State of Hawaii.

Pineapple

In this study, pineapple is considered a potential crop on only

the Hoolehua lands. Cost of production data are not required as the

State would receive a uniform rental rate per acre for all lands con-

sidered suitable for pineapple production and leased to a plantation.

The rental rate used in this report is 30 dollars per acre per year

which is believed to represent a reliable estimate of the actual rental

should the land be leased.

29

Pasture

In Hawaii, ranches vary from small part-time operations to units

that rank among the nation's largest. Management practices within any

given size group vary substantially between firms.

The practices assumed for this report apply to a hypothetical 250

animal unit. Basic data utilized in constructing this unit were taken

from 11 questionnaires from a large state-wide survey conducted by the

Land Study Bureau in 1963 (20). Only records of respondents with

150-500 animals and Whose ranches had been classified by the Bureau were

used. Selection of the 150-500 animal size group corresponds to average

sized full-time beef operations in Hawaii (21, 22, 23, 24, 2S).

The basic plant and set of equipment for a 2S0-animal Hawaiian

ranch is described in the footnotes of Table 3. Production costs are

presented in Table 4. Because the production costs of the sample

ranches appear to vary with the number of animals, such a relationship

is assumed for the purposes of this study and results in identical per

unit production costs for ranches on all land classes. This assumption

is substantiated, in part, by the evidence presented in Table 4.

The pasture improvement and supplemental feeding expenditures given

in Table 4 should not be viewed as per acre or per animal costs. In

Hawaii, many ranchers practice pasture improvement on selected areas

wi thin their unit (so-called "emergency" or "fattening" pastures) lolhile

others improve limited areas annually as part of a long range program

of development. Supplemental feeci is usually fed for finishing steers

and heifers or to the breeding herd. Seldom, except in emergencies, is

it fed to the entire herd.

30

TABLE 3. ANNUAL COSTS OF PLANT AND EQUUMENT

FOR A 2s0-ANIMAL RANCH - 1963

Item Present Value!! DepreciationlY Interest£! Total

Equipment2./ $ 1,500 $250 $ 90 $ 340

Improvements~ 9,700 850 582 1,432

HerdY 42,100 0 2,525 2,525

Total $4,297

!I The present value is that reported by sample producers and is assumedto equal one-half of their original investment in each categoryexcept herd where it represents the total value.

~ Depreciation values are those reported by sample ranchers.

£! Interest is calculated at 6 per cent of present value.

21 It is assumed that ranch equipment consists of a truck purchased for$2,000, a military jeep purchased for $600, and miscellaneous equipment valued originally at $400. The useful life of these items isassumed to be 12 years which corresponds to producer reports.

~ Improvements consist of a single storage shed which originally cost$920 (present value $460) and fencing valued initially at $18,500(present value $9,250). The useful life of improvements is assumedto be 23 years which corresponds to producer reports. With a fixednumber of animals, ranches operating on poor quality lands ~ll besubstantially larger than those operating on good land. This sizedifference would require more boundary fencing but less crossfencing. For the purposes of this study the single fencing investment is used for all ranches.

£I The herd consists of 81 cows ($16,038), 36 heifers ($5,148), 63steers ($13,680), 66 calves ($5,808) and 4 bulls ($1,232) whichrepresents the sample ranch average as of January 1, 1963 (see 22,p. 12 for the calculation of animal values). Replacement heifersand sale of discarded cows is assumed to offset herd depreciation(2, p. 22).

31

TABLE 4. ANNUAL BEEF PRODUCTION COSTS AND PER ACRE YIELD

BY LAND PRODUCTIVITY CLASS FOR A 250-ANIMAL RANCH - 1963

Cost by Land Productivity ClassItem a b c d

LaborY $ 5,130 $ 5,130 $ 5,130 $ 5,130

Maintenance of Improvements£! 450 450 450 450

Cost of Equipment Operation£! 500 500 500 500

Supplemental FeedE! 1,325 1,325 1,325 1,325

Land Clearing~ 500 500 500 500

Ferti li zationY 200 200 200 200

Weed Control.al 120 120 120 120

Plant, Equipment, and Management!Y 6,697 6,697 6,697 6,697

Real Property Ta~!I 553 513 778 1,767

Gross Income Taxi! 95 49 43 38

Total Cost 15,570 15,484 15,743 16,727

Grazing krestsJ 556 815 1,440 3,840

Cost Per kre 28 19 11 4

Yield Per AcreY 160 53 27 10

Gross Per AcreID! 32 11 5 2

!I Labor is calculated on the basis of 16.4 hours per animal @$1.25 perhour or $20.50 per animal per year (24, p. 12).

B{ Includes materials and contract fence repair. Cost is that reportedby sample producers.

£! Gas, oil, and repairs as reported by sample producers.

E! Figure is that reported by sample producers. At $5.30 per animal itagrees closely with the average for the Land Study Bureau's statewide survey (20).

For the$100 to $150

32

~ Average annual expenditure reported by sample producers.type of clearing usually done in Hawaii, costs vary fromper acre (24, 25, p. 6).

£I Average annual expenditure reported by sample producers. This outlay represents about 2 tons of a complete commercial fertilizer.For example, 10-10-10 fertilizer costs $4.85/cwt.

sf Average annual expenditure reported by sample produpers.

hi A management fee of $2,400 is charged to each unit. Operator incomeis the sum of this fee and the operator's labor income.

!I Based on producer and tax office reports.

jJ Based on ~ per cent of gross sales computed with a five-year averageprice.

~ Average reported acreage for sample ranches operating on landclasses a and b. Acreages for classes c and d were estimated bymultiplying the class carrying capacity-mid-pOint by total animalunits for the ranch (192 animal units). According to the Land StudyBureau classification, the five sample ranches operating on theseland classes are overstocked.

!! Estimated live beef gains per acre per year are based on an assumed200-pound required gain per year per animal and the mid-point of thecarrying capacity range for each land class. The resulting valuesagree closely with Land Study Bureau estimates.

!I Based on the 1963 average price of 20 cents per pound.

The ranch budgets indicate that pasture enjoys a margin of 2.5

cents per pound on class ~ pasture land. However, these budgets do not

allow for breeding herd maintenance, which makes it unlikely that beef

could be grown under any circumstances. If even a minimal adjustment is

made (10 per cent of the gain assigned to herd maintenance) beef could

not be profitably produced on even the class ~ pasture lands. Beef

production is therefore omitted from further consideration.

Vegetable Crops

Detailed cost and yield budgets were prepared for snapbeans,

tomatoes, Manoa lettuce, and cantaloupe. These basic budgets are for

units of four-acres in size, which represent the average size of

33

vegetable farm found in Hawaii. Per acre production costs for a 2S-acre

unit were derived from these budgets by spreading the costs of plant and

equipment and return to management over the larger acreage. This proce-

dure assumes that the level of yields and inputs; e.g., fertilizer,

insecticides, harvesting labor, etc., used for the four-acre units will

prevail on the 2S-acre units.

The set of equipment described in this section is similar to that

found on four- to 10-acre vegetable farms in Hawaii (39, p. 12).

Table 5 gives annual costs of plant, equipment, and management for a

"typical" unit. A suitable irrigation system for an irrigated four-

acre vegetable farm would include the following items (39, p. 13):

Item .Amount ~-6" Aluminum Pipe 726 Feet $982

4" Aluminum Pipe 1200 Feet 818

Sprinklers, Misc. Hardware 385

7~ hp Pump (1800 REM) 473

The other equipment items needed to make up a set of equipment con-

sistent with prevailing management practices in Hawaii are as follows:

Ford 2000 Tractor

2-Bottom Plow

Disc and Spike-Tooth Harrows

6-hp Garden Tiller

Power Sprayer

Light Truck

A single building of 1200 square feet constructed at a cost of $2.20 per

square foot is needed for storage purposes and is included.

An attempt has been made to specify accurately existing production

costs and yields for each of the vegetable crops assuming production on

the designated land classes. Tables 5 through 9 give production costs

and marketable outputs for the crops being considered. The difference

TABLE 5. ANNUAL COSTS OF PLANT AND EQUIPMENT FOR A FOUR-ACRE VEGETABLE FARM - 1963

Item Initial Value!l Life Salvage _Depreciation Interest Insurance Total Costl Acre

Buildings $2,640 20 $264 $118.80 $ 79.20 $13.00 $ 211 53

Irrigation 2,660 20 133 126.35 79.80 - 206 52

Tractor 3,500 12 100 283.33 105.00 - 388 97

Garden TillerlY 715 10 35 68.00 21.45 - 89 22

2-Bottom Plow 530 12 15 42.92 15.90 - 59 15

Disc-HarrOl07 460 12 20 36.67 13.80 - 50 12

Spike-Harro\07 40 12 - 3.33 1.20 - 5 1

Spray 310 10 16 29.40 9.30 - 39 10

Farm Truck (3/4 Ton) 1,000 7 100 128.00 30.00 16.50 332~ 83

Totals $1,379 345

!I All items are new except the spike-harrow and truck. Price data were obtained from local distributorsfor all new items except the bUilding. It is assumed that a suitable building could still be constructed for $2.20 per square foot.

~ Gravely Custom (six hp).

~ Includes 70 per cent of annual operating costs based on 10,000 miles annually. w+'

35

between yield and marketed output, spoilage, is influenced by several

factors; e.g., weather and rate of movement through the market. This

makes it difficult to define a single spoilage rate that would apply to

a given crop produced under diverse conditions. In addition, existing

data are not sufficient to define rates that would apply to a specific

area or season. It is possible with existing information, however, to

estimate an average percentage loss that could be expected throughout

the year (39, pp. 29 and 55; 50, pp. 10 and 17). The portion of total,

harvested output, for which the producer receives payment is the

marketed output.

A procedure developed by the Soil Conservation Service is used to

estimate irrigation requirements and is outlined in Appendix B.

Basically, this procedure consists of first using pan evaporation data

(47) and consumptive use requirements to estimate gross water needs.

Secondly, rainfall is adjusted by considering the effects of consumptive

use needs, irrigation in inches, and gross rainfall to estimate effec

tive rainfall. The gross consumptive use less effective rainfall gives

irrigation requirements. An irrigation efficiency of 60% is assumed.

At the present time, the State of Hawaii supplies irrigation water to

Waimanalo farmers at a cost of eight cents per 1000 gallons plus a

monthly assessment of $2.50 per acre. Distribution facilities are being

prepared to deliver water from the Molokai Irrigation Project to the

Hoolehua Lands. The cost of this water will be eight cents per 1000

gallons plus a monthly assessment of $1.10 per acre (Honolulu Advertiser,

Septenber 30, 1964). There is presently no irrigation project in the

Waianae area. As water rates are not available for Waianae, it was

decided to use the Waimanalo costs for these lands.

36

Real property tax rates were supplied by the First and Second

Taxation Divisions of the State Department of Taxation. Hoolehua Farm

Lands are currently assessed at a uniform rate, While the study lands at

Waimanalo are valued at two distinct levels with the higher assessment

corresl~nding to the lands considered suitable for intensive cultiva

tion. In general, the Waianae Kai lands are assessed at a low rate.

This low valuation results from two factors: 1. the lands are, in the

most part, not suited for intensive agricultural uses, and 2. large

parcels are assessed at a uniform rate (small areas of better quality

land are not delineated). Approximately 22 acres of the study lands at

Waianae Kai are classified as £ productivity (land type 23i) Which are

well suited to intensive uses. According to the State Department of

Land and Natural Resources, the study lands, with improvements, will be

assessed at about $700 per acre ($1000 market value) after development.

For the purposes of this study this rate is assumed to apply uniformly

to all study lands except those used for grazing and pineapple. A

management fee of $2,400 per unit ($600 per acre) is charged to each

vegetable farm. This fee plus the operator's labor income equals

operator income.

The State Department of Land and Natural Resources will establish a

system of permanent windbreaks on the Molokai Farm Tract before disposi

tion of the units. It is assumed that wind will be effectively con

trolled for vegetable production before farming begins and that the

established windbreaks will be available l~thout direct cost to

individual farmers (51).

TABLE 6. SNAPBEAN PRODUCTION COSTS AND YIELDS PER ACRE BY LAND CLASS - 1963~

Expenditure by Land ClassItem Amount Price Per Uni t a b c d

Yield (Pounds) 11 ,100 10,000 8,900 7,800

Spoilage (10 per cent) 1,100 1,000 900 800

Marketed (Pounds) 10,000 9,000 8,000 7,000

Per Crop Production Costs Applicable to All Areas:

Seeds 50 Pounds $ .60 Pound $ 30 $ 30 $ 30 $ 30

Fertilizer (5-10-10) 850 Pounds 4.20 100 Pounds 36 36 36 36

Insecticide:

DDT 8 Pounds 1.70 4 Pounds 3 3 3 3

Toxaphene 20 Pounds 3.10 4 Po\lInds 16 16 16 16

Weedicide 20 Gallons 1.90 Gallon 38 38 38 38

Crates .25 Each 56 50 45 39

TractorW 10 Hours .80 Hour 9 9 9 9

Labor:£!

Growing 1.25 Hour 319 319 319 319

Harvesting 1.25 Hour 656 621 580 507 w......

TABLE 6. (Continued) SNAPBEAN PRODUCTION COSTS AND YIELDS PER ACRE BY LAND CLASS - 1963!1

Item Amount

Mi sce11aneaus

Gross Income Tax21

Cornmission~

Total Per Crop Production Costs

Production Costs Peculiar to Area:!!

Water - Waimanalo

Water - Waianae Kai

Water - Hoolehua

Freight from Molokai

Real Property Tax - Waimanalo

Real Property Tax - Waianae Kai

Real Property Tax - Hoolehua

Management Fee

Expenditure by Land ClassPrice Per Unit a b c d

$ 8 $ 8 $ 8 $ 8

12 11 10 9

367 330 293 257

1,550 1,471 1,387 1,271

$140 $140 $140 $140

164 164 164 164

147 147 147 147

$7.00 Ton 155 140 125 109

10 10 10 10

10 10 10 10

10 10 10 10

600 600 600 600

wCIO

TABLE 6. (Continued) SNAPBEAN PRODUCTION COSTS AND YIELDS PER ACRE BY LAND CLASS - 1963!1

Item Itnount Pri ce Per Uni tEXpenditure by Land Classabc d

Total Annual Costs - Waimanalo

Total Annual Costs - \~ai anae Kai

Total Annual Costs - Hoolehua

$5,745

7,319

7,457

$5,508

7,004

7,126

$5,253

6,668

6,775

$4,908

6, 20l~

6,295

!I The cost figures presented in this table are based on studies by Douglas J. McConnell (39).

B1 Tractor operating costs are estimated at $.80 per hour. Fixed costs of ownership are given inTable 3.

£! Harvesting and marketing labor costs are estimated from data presented in McConnell's report (39)and interviews with Experiment Station specialists.

~ Gross income tax is computed as ~ of gross income based on a five-year average price.

!I Wholesale commission is computed as 15% of gross receipts based on a five-year average price.

£! Computation of annual water and freight costs assumes three crops at Waimanalo and four crops atWaianae Kai and Molokai.

VJ\0

TABLE 7. TOMATO PRODUCTION COSTS AND YIELDS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963~

Land Productivity ClassItem /mount Pri ce Per Uni t a b c d

Yield (Pounds) 20,000 18,300 15,600 13,900

Spoilage (15 per cent) 3,000 2,700 2,300 2,100

Marketed (Ibunds) 17,000 15,600 13,300 11,800


Seeds .25 Ounces $30.00 Ounce $ 8 $ 8 $ 8 $ 8

Fertilizer (5-10-10) 1500 Pounds 4.20 100 Ibunds 63 63 63 63

Insecti cides 56 Pounds .85 Pound 48 48 48 48

Fungicides 48 Ibunds 1.30 Ibund 62 62 62 62

Herbicides 15 Gallons 1.90 Gallon 28 28 28 28

Fumigants 8 Gallons 3.35 Gallon 27 27 27 27

Manure 1500 Pounds 3.50 100 Pounds 52 52 52 52

Flats and Crates .30 Each 180 165 140 125

String 45 Pounds 1.00 Pound 45 45 45 45

~o

TABLE 7. (Continued) TCMATO PRODUCTION COSTS AND YIELDS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963!1

Land Productivity ClassItem Pmount Pri ce Per Uni t a b c d

Labor:'2!

Growing 246 Hours $1.25 Hour $ 308 $ 308 $ 308 $ 308

Harvesting 1.25 Hour 569 563 488 438

Tractorf2! 20 Hours .80 Hour 16 16 16 16

Power-Spray2! 6 Hours .25 Hour 2 2 2 2

Power Cultivator£! 10 Hours .25 Hour 2 2 2 2

Stakes 25 25 25 25

Miscellaneous 36 36 36 36

Gross Income TaxY 16 15 13 11

Wholesale CommissionB! 516 473 401 358

Total Per Crop Production Costs 2.003 1,938 1,764 1,654

Production Costs Peculiar to Area:h!

Water - Waimanalo $ 99 $ 99 $ 99 $ 99

Water - Waianae Kai 109 109 109 109

Water - Hoolehua 92 92 92 92 ./:'t-'

TABLE 7. (Continued) TOMATO PRODUCTION COSTS AND YIELDS PER ACRE BY LAND PRODUCTIVITY CLASS· 1963!1

LandEToductrvlty ClassItem /mount Price Per Unit a b c d

Freight from Molokai $ 140 $ 128 $ 109 $ 97

Real Property Tax - Waimanalo 10 10 10 10

Real Property Tax - Waianae Kai 10 10 10 10

Real Property Tax - Hoolehua 10 10 10 10

Management Fee 600 600 600 600

Total Annual Costs - Waimanalo $5,060 $4,930 $4,582 $4,362

Total Annual Costs - Waianae Kai 5,070 4,940 4,592 4,372

Total Annual Costs - Hoolehua 5,192 5,051 4,684 4,107

!I The cost figures presented in the table are based on a study by J. A. Mollett (41).

E1 Harvesting and marketing labor costs are estimated from data presented in Mollett's report (41) andinterviews ~nth Experiment Station specialists.

£! Tractor operating costs are estimated at $.80 per hour. Fixed costs of ownership are given inTable 3.

gj Power spray operating costs are estimated at $.25 per hour. Fixed costs of ownership are given inTable 3.

.l:N

TABLE 7. (Continued) TCMATO PRODUCTION COSTS AND YIELDS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963

!I Power cultivator operating costs are estimated at $.25 per hour. Fixed costs of ownership are givenin Table 3.

!! Gross income tax is computed as ~ of gross income based on a five-year average price.

s! Wholesale commission is computed as 157. of gross income based on a five-year average price.

hi Computation of annual wateI' and freight costs assumes two crops in all producing areas.

~w

TABLE 8. MANOA LETTUCE PRODUCTION COSTS AND YIELDS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963!1

Land Productivity ClassItem hnount Price Per Uni t a b c d

Yield 13,300 12,200 11,100 8,900

Spoilage (15 per cent) 2,000 1,800 1,700 1,300

Marketed (POunds) 11,300 10,400 9,400 7,600


Seeds 1 Pound $4.75 Pound $ 5 $ 5 $ 5 $ 5

Fertilizer (10-10-5) 1600 Pounds 4.75 100 Pounds 76 76 76 76

Manure 4 Cubic Yards 6.00 Cubic Yard 24 24 24 24

Fungicides 6 POunds 1.30 Pound 8 8 8 8

Insecticides 6 Pounds .85 Pound 5 5 5 5

Crates .25 Each 92 84 77 61

Labor:!Y

Growing 170 170 170 170

Harvesting 189 185 168 134

Power Cultivator£! 6 Hours .25 Hour 2 2 2 2

Power spray'Y 3 Hours .25 Hour 1 1 1 1~~

TABLE 8. (Continued) MANOA LETTUCE PRODUCTION COSTS AND YIELDS PER ACRE

BY LAND PRODUCTIVITY CLASS - 1963!1

Land Productivity ClassItem !mount Price Per Uni t a b c d

Mi see11aneous $ 18 $ 18 $ 18 $ 18

Gross Income Tax!! 8 8 7 6

Wholesale Commission£! 275 252 229 183

Total Per CrOp Production Costs 873 838 790 693

Production Costs Peculiar to Area:~

Water - Waims~alo $ 132 $ 132 $ 132 $ 132


Water - Hoolehua 139 139 139 139

Freight from Molokai 233 214 194 156





~V1

TABLE 8. (Continued) MANOA LETTUCE PRODUCTION COSTS AND YIELDS PER ACRE

BY LAND PRODUCTIVITY CLASS - 1963!1

Land Productivity ClassItem Amount Pri ce Per Uni t a b c d


Total Annual Costs - Wai anae Kai 5,476 5,301 5,061 4,576


!I The cost figures presented in this table are based on a study by J. A. Mollett (40).

~ Harvesting and marketing labor costs are estimated from data presented in Mollett's report (40) andinterviews with Experiment Station specialists.

£! POlver cultivator operating costs are estimated at $.25 per hour. Fixed costs of ownership are givenin Table 3.

~ Power spray operating costs are estimated at $.25 per hour. Fixed costs of ownership are given inTable 3.

!I Gross income tax is computed as ~ of gross income based on a five-year average price.

£! Wholesale commission is computed as 15% of gross income based on a five-year average price.

at Computation of annual water and freight costs assumes four crops at Waimanalo and five crops atWaianae Kai and Molokai.

~0"

TABLE 9. CANTALOUPE PRODUCTION COSTS AND YIELDS PER ACRE

BY LAND PRODUCTIVITY CLASS - 196)!!

Item /mount Price Per Un! tLand Productivity Classabc d

Yield (Pounds)

Spoilage (15 per cent)

Marketed (Pounds)


8,800

1,300

7,500

7,200

1,100

6,100

6,200

900

5,300

5,800

900

4,900

Seeds 2 Pounds $2.85 Pound $ 6 $ 6 $ 6 $ 6

20 Gallons 1.90 Gallon

Fertilizer (5-10-10)

Insecticide: Malathion

vleedicide

Crates

Tractor.JY

Labor:£!

Growing

Harvesting

1000 Pounds

24 Pounds

9 Hours

4.20 100 Pound

3.25 4 Pound

.25 Each

.80 Hour

42

13

38

44

7

290

182

42

13

38

36

7

290

159

42

13

38

31

7

290

137

42

13

38

29

7

290

126

~

"

TABLE 9. (Continued) CANTALOUPE PRODUCTION COSTS AND YIELDS PER ACRE

BY LAND PRODUCTIVITY CLASS - 196J!/

Land Productivity ClassItem Amount Price Per Unit a b c d

Miscellaneous $ 8 $ 8 $ 8 $ 8

Gross Income Tax9! 7 5 5 4

Wholesale CommisSio~ 175 145 125 115

Total Per Crop Production Costs 806 745 698 675

~oduction Costs Peculiar to Area:£!

Water - Waimanalo $ 138 $ 138 $ 138 $ 138



Freight from Molokai 93 75 66 60





.j:'-(Xl

TABLE 9. (Continued) CANTALOUPE PRODUCTION COSTS AND YIELDS PER ACRE

BY LAND PkODUCTIVITY CLASS - 1963!1

Land Productivity ClassItem hnount Pri ce Per Uni t a b c d


Total Annual Costs - Waianae Kai 3,555 3,370 3,229 3,159


!I The cost figures presented in this table are based on studies by Douglas J. McCOnnell (39).

£! Tractor operating costs are estimated at $.80 per hour. Fi:ced costs of ownership are given inTable 3.

£I Harvesting and marketing labor costs are estimated from data presented in McCOnnell's report (39)and interviews with EXperiment Station specialists.

2! Gross income tax is computed as ~ of gross income based on a five-year average price.

~ Wholesale commission is computed as 15% of gross income based on a five-year average price.

f! Computation of annual water and freight costs assumes three crops in all producing areas. Whencantaloupe was being produced in Hawaii the prevailing practice was to grow a single crop each yearin rotation with some other crop such as snapbeans or broccoli.

~\0

50

TABLE 10. ANNUAL PER ACRE COST OF PRODUCTION (25-ACRE UNITS)

FOR SELECTED VEGETABLE CROPS BY LAND PRODUCTIVITY CLASS - 1963

Land Productivity ClassItem a b c d

Snapbeans

Waimanalo $4,951 $4,715 $4,459 $4,114

Waianae Kai 6,525 6,209 5,873 5,409

Hoolehua 6,663 6,332 5,981 5,501

Tomato

Waimanalo $4,226 $4,136 $3,788 $3,568

Waianae Kai 4,276 4,146 3,798 3,578

Hoolehua 4,398 4,257 3,890 3,313

Manoa Lettuce

Waimanalo $3,785 $3,645 $3,453 $3,065

Waianae Kai 4,682 4,507 4,267 3,782

Hoolehua 4,898 4,704 4,444 3,921

Cantaloupe

Waimanalo $2,723 $2,538 $2,397 $2,327

Waianae Kai 2,761 2,576 2,435 2,365

Hoolehua 2,838 2,635 2,485 2,409

51

Table 10 gives the per acre cost of production by land class for

the 25-acre units. Yields for these units are the same as those given

earlier for the four-acre farms.

Orchard Crops

Detailed cost and yield budgets were prepared for apple banana and

papaya. The unit sizes represented by these budgets are four and five

acres. respectively. which corresponds to the state average. As in the

case of vegetables. the 25-acre budgets are basically the same as those

of the smaller units ~th plant. equipment. and management costs spread

over more acres. For bananas, however. a used tractor and trailer is

added ~th the larger unit.

The sets of equipment described in Tables 11 and 15 are similar to

those found on four-acre banana farms and five-acre papaya farms in the

State of Hawaii (30. 32). The irrigation plant is the same as that

previously described for vegetable farms.

Cost figures given in Tables 12 and 16 are explai~ed. when

necessary. to ease interpretation. In Table 12. the planting is depre

ciated over a 25-year period and interest is charged at a rate of six

per cent on one-half the original investment. For papayas land clearing

is depreciated over a nine-year period as papayas are usually not grown

more than three crop cycles (of three years each) on a given site. This

results from a substance given off by the papaya root which poisons the

soil. The costs in Table 11 are depreciated over three years and

interest is charged at a rate of six per cent on one-half the original

investment.

The production costs that apply to all producing areas are

explained in detail in the body of Tables 13 and 17. Water requirements

52

were estimated w1th the same procedure outlined for vegetables (see

Appendix B). Irrigation labor cost is determined by using the rate of

one man-hour per 27,000 gallons of water delivered ($1.25 per 27,000

gallons). This rate is based upon a 1958 papaya study for the Waimanalo

area (34, p. 14). The real property tax rates used in the vegetable

budgets are also used for orchard crops. These rates are discussed in

the previous section. Ammlagement fee of $2,400 per unit is charged to

each orchard unit. This rate is equal to $600 per acre for bananas and

$480 per acre for papaya.

Sufficient data to estimate fruit spoilage by area and season are

not available, but it is possible to estimate a gross percentage of

fruit loss that would apply to all producing areas for the entire annual

production. This figure relies heavily on discussions ~th EXperiment

Station specialists.

Table 19 gives the per acre production costs for the 25-acre units.

Yields levels are assumed to be identical to those specified for the

smaller fanns.

TABLE 11. ANNUAL COSTS OF PLANT AND EQUIIMENT FOR A FOUR-ACRE BANANA FARM - 1963

Item Value Years Life Depreciation Interest Total Cost/Acre

Quonset (20 x 50) $ 600 20 $ 30 $ 18 $ 48 $ 12

Truck (l~ ton) 2,000 12 160 60 377Y 94

Irrigation 2,660 20 133 80 213 53

Jeep (Mi 11 tary) 600 6 100 18 118 30

Power Sprayer 600 10 60 18 78 20

l<napsack Sprayers (4) 140 5 28 4 32 8

Tractor and Irai 1erP.! 1,000 25 40 30 70 18

Miscellaneous 200 4 50 6 56 14

~ Includes 70 per cent of operating costs based on 10,000 miles annually.

E! This item to be used for 25-acre unit only and is not to be included in the four-acre budget.

VIu.>

TABLE 12. COST PER ACRE OF ESTABLISHING A BANANA PLANTING - 1963

Operation Cost/Acre

Land clearing (Contract) $100

Pile and burn brush (20 man-hours @$1.25)

Preplanting weed control: Straight aromatic oil, 100 gallons @18 cents • $18;Dalapon, 10 pounds @$1.05 = $10.50; 6 man-hours @$1.25 • $7.50; jeep andpower sprayer, 3 hours operating costs • $3.00

Prepare planting basins: Spacing 15' by 15' or 194 basins per acre, contract @$30;plus 10 man-hours for layout @$1.25 = $12.50

Suckers for planting (194 plants @$.50 each)

Planting: 40 man-hours @$1.25 = $50

Total Per Acre

Cost Per Acre Per Year (25-year life and 6 per cent interest)

25

39

42

97

50

$353

$ 25

VI~

TABLE 13. ANNUAL APPLE BANANA COSTS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963

_~ ~_ _ _Ope~ation


Land ProductiYity Classabc d

Weed Control: A contact herbicide concentrate containing50 pounds of pentachlorophenal dissolved in50 gallons of aromatic oil will be made at thefarm. Ten gallons of concentrate will beemulsified in 100 gallons of water. Cost pergallon is 6 cents. (55 gallons weedicide •$3,30; 17 man-hours @$1.25 • $21.25; jeep andsprayer 5 hours @$1.00 m $5.00; knapsacksprayer 12 hours, fixed costs only).

$ 30 $ 30 $ 30 $ 30

Ferti 11 zing:

Harvesting:

A balanced fertilizer (10-10-10) or equivalentapplied at the rate of 12 pounds per mat inthree applications. (194 mats x 12 pounds =2,328 pounds @$4.85 per 100 pounds = $112.91;3 applications x 2 man-hours per application@$1.25 • $7.50; jeep 6 hours @$1.00 = $6.00).

Includes time used in pruning suckers, locatingmature fruits, picking, severing old stalks,loading on truck, and packing in tubs. (Class a:360 bunches @30 pounds, 63 man-hours @$1.25 •$78.75, 23 truck hours @$1.00 • $23.00; Class b:250 bunches @30 pounds, 57 man-hours @$1.25 •$71.25, 29 truck hours @$1.00 • $21.00; Class c:183 bunches @30 pounds, 42 man-hours @$1.25 •$52.50, 15 truck hours @$1.00 a $15.00; Class d:150 bunches @30 pounds, 34 man-hours @$1.25 •$42.50; 13 truck hours @$1.00 • $13.00).

$ 126

$ 102

$ 126

$ 92

$ 126

$ -68

$ 126

$ 56

VIVI

TABLE 13. (Continued) ANNUAL A]?PLE BANANA COSTS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963

Lmld Productivity ClassOperation a b c d

Indi rect Labor: Includes bookkeeping, going after supplies, $ 6 $ 6 $ 6 $ 6repairing equipment, etc. (5 man-hours@$1.25 • $6.25).

Gross Income Tax: Computed as ~ per cent of gross income. $ 4 $ 3 $ 2 $ 2(Gross based on five-year average price).

Wholesale Cammission: Computed as 18 per cent of gross income. $ 151 $ 108 $ 79 $ 65(Gross based on a five-year average price).

Production Costs Peculiar to Area:

Water - Waimanalo $ 222 $ 222 $ 222 $ 222



Freight from Molokai ($7.00 per ton) 37 26 19 16




Irrigation Labor Cost - Waimanalo 111 111 111 111V10-.

TABLE 13. (Continued) ANNUAL APPLE BANANA COSTS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963

Land Productivity ClassOperation a b c d

Irrigation Labor Cost - Waianae Kai $ 119 $ 119 $ 119 $ 119

Irrigation Labor Cost - Hoolehua 119 119 119 119



Total Annual Costs - Wai anae Kai 1,639 1,585 1,531 1,505

Total Annual Costs - Hoolehua 1,659 1,594 1)533 1,504

VI......

TABLE 14. ANNUAL PER ACRE APPLE BANANA YIELDS AND SPOILAGE RATES BY LAND PRODUCTIVITY CLASS

Land Productivity ClassItem a b c d

Yield (Pounds) 10,500 7,500 5,500 4,500

Spoilage (15 per cent) 1,600 1,100 800 700

Marketed Output (Pounds) 8 ,'~OO 6,400 4,700 3,800

TABLE 15. ANNUAL COSTS OF PLANT AND EQUUMENT FOR A FIVE-ACRE I:'.APAYA FARM - 1963

Item Initial Value Years Life Depreciation Interest Total Cost/Acre-Building (rough wood) $ 600 20 $ 30 $ 18 $ 48 $ 10

Truck (l.!z ton) 2,000 12 167 60 384!1 77

Truck (old used) 1,000 10 100 30 130 26

Irrigation Plant 2,660 20 133 80 213 43

Miscellaneous Equipment (Crates, 975 3 325 29 354 714 knapsack sprayers, hand tools,etc.)

Totals $1,129 $227

!I Includes 70 per cent of operating costs based on 10,000 miles annually.VI<Xl

TABLE 16. ANNUAL COSTS PER ACRE OF ESTABLISHING A PAPAYA PLANTING - 1963

Operation

Land Clearing: $150 for bulldozing, $30 for rolling land after dozing,site has nine-year life (3-3 year cycles)

Layout and dig holes: 25 man-hours @$1.25 = $3l.25!crop (3 years)

Plant and Mulch: 20 man-hours @$1.25 - $25.00!crop (3 years)

Thin Seedlings: 16 man-hours @$1.25 • $20.00!crop (3 years)

Plant Thinning: 8 man-hours @$1.25 • $lO.OO!crop (3 years)

Preplanting weed control: 6 man-hours @$1.25 n $7.50; 50 gallonsaromatic oil @$.19 per gallon a $9.50

Total

Cost!Acre

$180

31

25

20

10

17

Cost!Acre! Year

25

37

$62

VI\0

TABLE 17. ANNUAL PAPAYA COSTS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963

___ __ Operation


COsfTAiire- by Land PioducfivTIY Classabc d

Weed COntrol: A contact herbicide consisting of 1 pound ofpentachlorophenate (@ $.36 per pound), 8 gallonsof aromatic oil (@ $.19 per gallon) and 1 poundof emulsifier (@ $.40 per pound) in 50 gallonsof water is mixed on the farm. 300 gallons willbe applied annually spraying every 2 months at arate of 50 gallons per acre (300 gallons spray@$4.56 per 100 gallons m $13.68; 37 man-hours@$1.25 a $46.25)

Fertilizing: A complete (10-10-10) fertilizer is appliedmonthly for 33 months. Application is at a rateof 2,900 pounds per year. (49 man-hours @$1.25 m

$61.25; 2,900 pounds fertilizer @$4.85 per100 pounds Q $140.65)

Pest Control: During the three-year life of the planting2,500 gallons of spray are applied per acre.(50 pounds of wettable sulphur @$.15 per poundand 17 pounds of "captan" @ $1.30 per pound ...$29.60; 83 man-hours @$1.25 ... $103.75

Harvesting and Packing Labor: Picking frequency varies from2 to 3 times per week during the year. Sortingand packing is done immediately after picking.(Class a, 205 man-hours @$1.25 ... $256.25;Class b, 176 hours @$1.25 a $220.00; Class c,114 man-hours @$1.25 a $142.00; Class d,66 man-hours @$1.25 a $82.00)

$ 60

202

133

256

$ 60

202

133

220

$ 60

202

133

142

$ 60

202

133

82

~o

TABLE 17. (Continued) ANNUAL PAPAYA COSTS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963

Cost/Acre by Land Pioductivity ClassOperation a b c d

Other Costs: Minor repairs, maintenance of vehicles, $ 8 $ 8 $ 8 $ 8bookkeeping, etc. (A flat charge of $25per crop cycle)

Gross Income Tax: Computed as ~ per cent of gross income 8 6 4 2(Gross based on five-year average price)

Wholesale Commission: Computed as 18 per cent of gross 273 220 144 84income (Gross based on five-year averageprice)

Production Costs Peculiar to Area:

Water - Waimanalo $ 154 $ 154 $ 154 $ 154

Water - Waianae Kat 165 165 165 165


Freight from Molokai ($7.00 per ton) 63 51 33 19




0'\I-'

TABLE 17. (Continued) ANNUAL PAPAYA COSTS PER ACRE BY LAND PRODUCTIVITY CLASS - 1963

CostlAcre by Land Productivity Cl~

Operation a b c d

Irrigation Labor Cost - Waimanalo $ 71 $ 71 $ 71 $ 71

Irrigation Labor Cost - Waianae Kai 78 78 78 78

Irrigation Labor Cost - Hoolehua 78 78 78 78



Total Annual Costs - Waianae Kaf 1,962 1,871 1,715 1,693


0'\N

TABLE 18. PER ACRE PAPAYA YIEIDS AND SPOILAGE RATES BY LAND PRODUCTIVITY CLASS - ANNUAL

ItemLand Productivity Classabc d

Yield (Pounds)

Spoilage (15 per cent)

Marketed Output (Pounds)

18,000 14,500

2,700 2,200

15,300 12,300

9,500

1,400

8,100

5,500

800

4,700

TABLE 19. ANNUAL PER ACRE COST OF PRODUCTION (25-ACRE UNITS)

FOR APPLE BANANA AND PAPAYA BY LAND PRODUCTIVITY CLASS - 1963

Item

Apple Banana

Waimanalo

Waianae Kai

Hoolehua

Papaya

Waimanalo

Waianae Kai

Land Productivity Classabc d

$ 923 $ 869 $ 815 $ 789

944 890 836 810

964 899 838 809

$1~258 $1,167 $1,011 $ 889

1,276 1,185 1,029 1,007

Hoolehua 1,322 1,219 1,045 909 0'-W

64

CHAPTER IV

ESTIMATED MARKET SUPPLY AND DEMAND FUNCTIONS

To obtain estimates of market revenue for different output levels,

it was necessary to derive a market demand function for each of the

included crops. As this study deals with bringing new land into produc

tion, it is also necessary to know how existing producers of the various

crops will react to changes in product price. In order to qu~tify this

relationship supply curves were estimated for several of the crops under

study.

Crop Group I

Three crops (pineapple, pasture, and papaya), which either have

perfectly elastic demand or would earn a uniform net return regardless

of output, are included in this group. Pineapple is included because

its rent has been fixed at $30 per acre per year. This rental rate can

be viewed as a constant net return regardless of the acreage devoted to

this use. In the case of beef it was not felt that the additional

production resulting from the relatively small acreages (about 200 acres

on Oahu) considered here would have any appreciable effect on beef

prices. Perfectly elastic demand is assumed over the range of quanti

ties considered in this analysis. For this commodity the 1963 average

annual price is used. The final crop in this group, papaya, presents a

somewhat more problematical situation. During the past four or five

years the papaya industry has developed a substantial export market

which is now believed to determine local price. Although only limited

evidence is available to substantiate this position, it seems evident

from the follo~nng observations:

65

(a) Production is now shifting to the Island of Hawaii

(currently about 80 per cent of the State total).

(b) About 40 per cent of the production on Hawaii is now

being exported to the Mainland.

(c) The Mainland market being much larger than the Hawaii

market could probably absorb much larger quantities

(than current export levels) with little effect on price.

~iven the above observations (and assumptions), an increase in the

Honolulu market price should divert papaya from the export channels to

the Honolulu market, reducing prices. The opposite, increased exports,

would result from a price reduction. This stabilizing action should

cause the Honolulu price to reflect both the local and export price

(the West Coast price would be the local price plus transportation

costs). The 1962-63 average market price is used for this crop.

If a supply increase will not reduce price, as is the case for

these crops, a supply analysis is not needed. In other words, produc

tion from existing producing areas would not be affected by the

increased output.

Crop Group II

The crops included in this group (snapbeans, tomatoes, Manoa

lettuce, cantaloupe, and apple banana) have downward-sloping demand

curves, which means that increased production will reduce price. Demand

and supply equations were computed for each of these crops with the

exception of cantaloupe for which a supply curve was not estimated.

Local cantaloupe production has fallen off to an insignificant amount

(10,000 pounds in 1963), consequently, no local production is assumed.

66

Demand

A graphic multiple regression analysis was conducted for each of

the vegetable and orchard crops. The purpose of this investigation was

to discover what factors had influenced the price of the several

commodities in the past and the general form of these relationships.

This analysis employed quantity, month, population, and time as inde

pendent variables in each case. The results of this analysis indicated

that the best fits were obtained when quantity, month, and time were

included as independent variables. The relationship between the

dependent variable and each independent variable was found to be linear

or nearly linear in every case except for the seasonal pattern.

The second phase of the demand analysis eonsisted of statistically

fitting multiple regression models using a number of different inde

pendent variables. An effort was made at this point to determine which

factors were responsible for the observed seasonal shifts. Foytik (15)

cites studies dealing with over 20 commodities for which seasonally

shifting demand has been observed. These shifts are much more pro

nounced for perishables than for staples, and appear to result from a

combination of influences that are difficult if not impossible to

quantify. In several cases seasonal changes in supplies of competing

products were found to influence commodity prices but in none of these

cases were the B coefficients significant at the five per cent level.

In this study the convention of using month as a "proxy" for the

combined influences of supplies of competing products, intra-year

prefe~ence changes, and other relevant factors is followed. In the

final model the year was divided into t\~ six-month periods (corres

ponding to the seasonal pattern from the graphic analysis) one with

67

higher and one ~nth lower than average prices. Zero-one variables were

then introduced and assigned to each period. The resulting general

model is given by (1).

(1) p m A + B1Q + B2Sl + B3S2 + B4Y

where:

P = Honolulu wholesal~ pri~e (cents/pound)

A = Intercept term

Bi = Regression coefficients

Q = Honolulu market supply (1000 pounds)

51 - High price season (variable equals 1 for months with

higher than average prices and zero for other months)

52 = Low price season (variable equals 1 for months with

lower than average prices and zero for other months)

Y = Year

Direct solution of this model will always result in an indeterminate

solution or a singular matrix (a matrix with no inverse). To avoid

this problem the model can be fitted by simply deleting one of the

seasonal shift variables (64).

The computed demand functions (for 1963), converted to an annual

basis, are given in Table 20. The complete demand equations including

the coefficients for season and year are given in Appendix C.

If the equations given in Table 20 are to represent average annual

prices, 50 per cent of annual production must be sold during each

season. Average marketings of snapbeans, tomatoes, Manoa lettuce, and

apple banana during the high price season accounted for 51, 51, 48 and

44 per cent of the annual average, respectively, for the 1959-63 period

(see Appendix C for the average monthly sales of each crop). Figure 4

68

TABLE 20. ANNUAL DEM~~D EQUATIONS FOR SELECTED CROPS - 1963

Crop A Intercept

Snapbeans 45.01

Tomatoes 25.64

Manoa Lettuce 31034

Cantaloupe£! 25.72

Apple Banana 10.56

B Coefficient(Quantity)

-~(j142

(.00105)

-.0008(.00035)

-.0086(000103)

-.0048(.00057)

-.0002(.00005)

t

.48**

8 **• 2

d~

2.50N•S•

!I Durbin-Watson statistic for serial correlation in the residuals.

** Significant at one per cent level.

£! Test inconclusive.

£! This function was fitted ~thout the seasonal and time variables.

21 Not computed because banana eventually drops out of the analysis(see text).

presents the demand equations for the four vegetable crops in graphic

form. Apple banana eventually drops out of the analysis and is not

included in the final land use patterns so a graph for this crop is not

presented. Apple banana was deleted because the estimated 1963 market

price ~as lower than per unit production costs on all land classes.

Supply

This analysis is confined to the response of local producers to

price change, excluding imports, for the follovnng reasons:

(a) The State of Hawaii is self-sufficient in the production of

all of the crops in question except tomatoes and cantaloupe.

69

Figure 4. Graphic Presentation of Demand and Supply Equations

S

Supply:Xl ~ -77.66 .0785X2R2 =.: .27

Demand:Xl ~ 45.01 - .0142X2R2

=: .70

---------------~f_____. I D

rIIJ

II

~ 40.....l-l

(:l.<

~ 30CI1!/JQ)

....-I

~ 20

:J....-I

~ 10go::r:

70Snapbeans

........"Cl

§ 60o0.-!/J~ 50Q)c:>'-'

o 200 400 600 800 1000 1200 1400 00 1800 2 00Honolulu l'iarket Supply (1000 pounds)

D

SUfll)ly:Xl ~ -644072+ .3791X2R2= .47

D\2m<lnd::·~1 =31034 - .0086X2.,1.\.4= 048

Hanoa Lettuce

-------------------------~

IiIII

3

Q)

~ 15!/JQ).-lo$10:J

....-I:J

'6 5r::S

........]g 30.!/JoWr::~ 25'-'

o 200 400 600 800 1000 1200 1400 1600 1800 2000Honolulu Market Supply (1000 pounds)

Figure 4 (Continued)

70

35

OJ 20u....~0..

OJ 15.-IcoC/)

OJ.-Io 10~::l.-I

.:; 5os:::2

CantaloupeDcr:1c.nd:

Xl 25.72 - .0048X2

R20= .82

D

o 200 400 600 800 1000 1200 1400 1600 1800 2000Honolulu H3".r!<et Supply (1000 pounds)

35Tomatoes

-] 30::l00-......~ 25s::OJu'-"

OJ 20u....~0-

OJ 15o-lcoC/)

OJ

'6 10~-::l

r-l 5::lr-I0s::0::r::

0 1

De;nand::{l = 25.64 - .0008X4R2 = .38

Supply:Xl -88.47 + .0238X2

2R =.25

D

2 3 4 5 6 7 8 9 10Honolulu i'1ari<;et Supply (1,000,000 pounds)

71

(b) The market structure of tomatoes and cantaloupe is of a

form that allows local production to be expanded, without

depressing local price, until all imports are replaced.

This market structure is indicated in Figure 5.

Where:

1. Hawaii is a deficit region importing Q2-Ql from

the West Coast.

2. The West Coast is a surplus region exporting Q2-Ql

to Hawaii. The figures given are approximate 1963

values for tomatoes in Hawaii and California (note

that the quantity axis for the West Coast is of a

much larger scale than that of Hawaii).

3. TIle equilibrium prices are Pw for the West Coast

and Pw + T (transportation cost) for Hawaii. The

West Coast supply curve has shifted from So to SA

because of the exporting activity.

4. Expansion of local supply (SH) could take place with

out significantly depressing price until local supply

equals total market supply (ST).

(c) Given the above assumptions it follows that expansion of

tomato and cantaloupe production would not reduce price until

imports are displaced. Beyond this point (Q2' PH in Figure 2)

price will move down the local demand curve as quantity

i~creases and existing producers will react in accordance

with their respective supply schedules.

Because the effect of price ch~nge on quantity supplied was of

primary interest a relatively simple supply model was selected. Perhaps

Figure 5. Theoretical Market Structure ~

of Tomatoes and Cantaloupe

72

Haviai i Hest Coast

~So

P'l = Pr,; -:- TIP'"r.. t, f ~':

I~ /1 I

I II I I

I I I DI

I I I II I I I

I I II II I I I

I I II I I II I I II I I III I

IT

I II I 54(Ql) 56(Q2)

I II I

6(Q2) 4(Ql)

Quantity (mi llion pounds)

73

the simplest possible supply function is that represented by Equation

(2).

(2) Qs = A + BPS-l

where:

Qs • quantity supplied

Pt-l = lagged market price

Beginning with this model, a graphic multiple regression analysis was

conducted to detennine the most appropriate price lag for each crop and

to test certain other variables. This analysis indicated a seasonally

shifting supply curve and the presence of a trend factor for all of the

crops except apple banana, for which the time variable was not signifi

cant. The most significant price lags for nearly all of the crops were

those corresponding to their respective growing periods. The model was

fitted by employing zero-one variables for the seasonal (quarterly)

shifts. The resulting model is of the form (3).

(3) Qs - A + BlPt-l + B2Ql + B3Q2 + B4Q3 BSQ4 + B6Y

where:

Qs = quantity supplied to Honolulu market by local producers

(1000 pounds)

A • intercept tenn

Bi - regression coefficients

Pt - l = Honolulu wholesale price lagged one time period

Qi ~ zero-one variables for quarters (each variable takes the

value of 1 for the months in that quarter and zero for

the other months)

If all four quarters are treated as zero-one variables a singular matrix

will result. Solution in this case was accomplished by deleting the

74

fourth quarter, ~, then estimating the coefficients with conventional

methods (64).

Supply functions were estimated with the model described earlier,

for all crops except cantaloupe. After estimation, the functions were

simplified to a form compatible with the demand equations. These

functions are given in Table 21.

TABLE 21. ANNUAL SUPPLY EQUATIONS FOR SELECTED CROPS - 1963

B CoefficientdYCrop A Intercept (Price) t R2

12,7332 2.38** .27** *Snapbeans 945.12 1.54( 5.3400)

3,709.32 41.9292 1.46W ** 1.16**Tomatoes .25(28.7664)

1,700.52 2.6376 .40N•S• ** 1.64~Manoa Lettuce .47(6.6480)

.26N•S• ** tYApple Banana 6,205.92 23.7840 .38(90.5112)

~ Durbin-Watson statistic for serial correlation in the residuals.

** Significant at one per cent level.

* Significant at five per cent level.

W Significant at 10 per cent level.

~ Test inconclusive.

tY Not computed because banana eventually drops out of the analysis(see text)¥

Deletion of the seasonal variables is based on the assumption of uniform

marketings by quarter for all of the crops in question, and that this

marketing pattern will continue in the future for any new land brought

into production. Table 22 indicates the quantity marketed in each

quarter for the several crops.

75

TABLE 22. PERCENTAGE MARKETED BY QUARTER FOR SELECTED CROPS 1959-63

Per Cent by QuarterCrop Ql Q2 Q3 ~

Snapbeans 22 27 26 ~

Tomatoes 26 31 22 21

Manoa Lettuce 25 W ~ ~

Apple ~nana 22 20 27 30

The price lags used in estimating these equations differ among the

crops considered. For snapbeans and tomatoes the most suitable lag was

found to equal their respective growing periods, or three and four

months. Manoa lettuce price is lagged one month but it takes two months

to produce a crop. This may result from the relatively high cost of

harvesting labor which could limit harvesting in periods of low price.

For apple banana a lag of six months is used and appears justifiable

from the standpoint of production methods. During low price periods,

fertilizer applications may be reduced and the planting allowed to

deteriorate, in which case about six months would lapse before the

effects are evident in reduced yields. About the same length of time

may be needed to rehabilitate neglected stands.

Figure 4 presents the computed supply equations in graphic form for

all crops in Group II with the exception of cantaloupe and apple banana.

The designated functions for these crops are omitted for reasons

previously given.

Estimated 1963 prices for snapbeans and Manoa lettuce were deter-

mined by simultaneous solution of the supply and demand equations.

Estimates of the 1963 market supply of these crops ~rere made by solving

76

the supply equations with the equilibrium prices. For cantaloupe and

tomatoes, total market supply functions were not computed. In the case

of these crops, the 1963 market price is estimated by first fitting a

simple linear regression to sales over time to estimate the 1963 supply

then solving the demand equations with this quantity. The resulting

functions (sales over time) are given below as equations (4) and (5).

(4) Xl = - 67,806.5 + 35.08X2 (cantaloupe)

(5) Xl = -160,897.2 + 85.37X2 (tomatoes)

where:

Xl :a Honolulu aOO'la1 supply (1000 pounds)

X2 ... Year

The regression coefficients for these equations are significant at t,ro

and one per cent while the R2,s are significant at five and one per

cent, respectively. Table 23 compares the estimated 1963 prices and

quantities with those actually recorded. The material presented in

Figure 4 and Table 23 should, for the most part, be self-explanatory.

However, the graph of tomato supply and demand may require further

clarification. This graph is actually the left-hand side of Figure 5

with the total market supply function missing. Market price, estimated

as outlined above, is indicated by point (a) in Figure 4. The local

supply function (SL) then indicates the quantity supplied by local

producers at this price (4,560,000 pounds as contrasted to the actual

local supply of 4,429,000 pounds). The difference between total market

supply and local supply equals estimated imports (2,130,000 pounds as

compared to 2,338,000 pounds actually recorded).

77

TABLE 23. ACTUAL AND ESTIMATED 1963 WHOLESALE PRICES

AND MARKET SUPPLIES FOR SELECTED CROPS

Crop

Snapbeans 27.2 1,202 26.8 1,286

Tomatoes 20.0 6,767 20.3 6,690

Manoa LettuceY 18.1 1,455 16.3 1,744

Cantaloupe£! 15.0 1,467 20.6 1,059

Apple Banana£! 9.7 5,363 9.3 6,437

~ The 1963 production of Manoa lettuce was abnormally low resulting inan unusually high price. 1962 actual and estimated prices are 15.8and 16.6 cents, respectively.

E{ The actual 1963 price recorded here is that received by localproducers for the 10,000 pounds marketed in 1963. It has littlemeaning for comparative purposes. Cantaloupe price was 17.5 cents,20.4 cents, and 17.3 cents for the years 1960, 1961, and 1962. The1961 and 1962 market supply of cantaloupe was 768 and 880 thousandpounds, respectively.

£! The market supply figures recorded here are for all bananas.

78

CHAPTER V

ESTLMATED LAND USE PATTERNS

In this chapter the estimating procedure developed earlier is used

to estimate patterns of land use for the three project areas being con

sidered. Each of the estimated land use patterns represents a competi

tive equilibrium under a particular set of assumptions and subject to

the limitations of the estimating model.

79

en class a orchard la~d.

Because of the above qualifications, only the vegetable crops need

to be considered within the program for most of the land use patterns.

The other crops that can be profitably grown are pineapple at Hoolehua

and papaya on class ~ orchard land. Both of these crops are assumed to

have perfectly elastic demand which allows them to be dealt with

indirectly by charging an opportunity cost, corresponding to their fixed

net return, to vegetable crops grown on the appropriate land classes.

If the vegetable crops are not at least as profitable the lands would be

allocated to either papaya or pineapple. Vegetable lands ~th the same

productivity rating can now be grouped, ~th identity of the different

lands being retained only when differences in opportunity costs occur.

In view of these considerations, the land class designations indicated

in Table 24 were established. The basic difference between Tables 2 and

24 is that in Table 24 all land types, in a given project area, that

have identical production costs for the uses being considered, have been

combined into a single land class designated Lei. In the case of area

one, additionally, land types one and 17 are combined because of the

small acreage of land type 17 (two acres).

To estimate realistically the land use patterns in the areas under

study, the possibility of encountering other restrictive factors (in

addition to land area and quantity) had to be considered. Preliminary

investigation indicated four factors that could potentially be restric

tive. They were: labor availability at Hoolehua, capital limitations,

freight service from Molokai, and water supply during seasonal peaks at

Hoolehua.

80

TABLE 24. LAND CLASSES USED IN THIS STUDY AND THEIR CHARACTERISTICS

L.S.B.Land Projec, L.S.B. Productivity MeasuredClass Area.! Land Type!Y Ratings£! Acreage

LCl 1 1, 17 la 2b 7a 680

LC2 1 3 ld 2c 219

LC3 2 23 2c 22

LCq. 3 3, 4, 5 2a 7a 56

LC5 3 9 2a 62

LC6 3 8, 19, 35 2b 86

LC7 3 37, 42 2c 69

Lea 3 32, 41, 47 2d 28

!I Pro j ect areasrespectively.

W See Table 2.

£! See Table 2.Hoolehua.

1, 2, and 3 are Hoolehua, Waianae Kai, and Waimanalo,

Pineapple is omitted from all project areas except

The budgets used in this analysis are based upon labor intensive

production methods that would require a large amount of hired labor for

a 25-acre unit. It is assumed that the necessary labor would be avail-

able on Oahu at the $1.25 wage rate. Molokai, however, does not have

the population of Oahu and it is conceivable that a labor shortage

could arise if a sizeable development of 25-acre units were attempted at

Hoolehua. For this reason several alternative use patterns were

estimated that incorporate different labor assumptions. These patterns

utilize wage rates, for Hoolehua, of $1.25 per hour (a common agricul-

tural wage), $1.50 per hour (the wage rate--including fring benefits--

earned by seasonal plantation employees on Molokai), and $2.00 per hour

81

(a wage rate that approximately equals that of permanent plantation

employees on Molokai). An additional pattern (IE) was estimated that

assumes 12 - 25-acre units at Hoolehua and imposes a quarterly labor

restriction on these operations.

Capital, the second potential restriction, was found on closer

investigation to pose no problems. State development programs for

agricultural land have typically included provision for adequate credit

at modest interest rates (48, p. 21).

This analysis assumes that Molokai production will be shipped to

Oahu by barge. Barge service is currently available and the existing

capacity is sufficient to handle the additional output. Air freight is

available but at a substantially higher cost. The problems encountered

with barge service are scheduling and transit time which appear to be

non-optimal for diversified crop production. Analysis of these problems

is beyond the scope of this study, therefore, no transportation restric

tions are imposed in this analysis. Tentative plans for a vacuum

cooling plant at Kaunakakai, Molokai, and increased barge service were

discussed at the Molokai Farm Conference on May 22, 1965. It was con

cluded at this time that if these facilities were needed they would be

provided.

The manager of the Water and Land Development Division, Department

of Land and Natural Resources, assured the Conference participants that

sufficient water would be available throughout the year. He also

indicated that as the need increases additional facilities will be

added. Plans are currently being prepared for a storage reservoir and

feeder lines to tap additional sources of supply.

82

One of the estimated patterns (IA) indicates that a substantial

acreage of papaya could be brought into production at Hoolehua. In

fact, it suggests an increase of nearly 50 per cent in total state

production. It has been assumed in this study that papaya production

could be expanded without depressing its price. While this assumption

is probably true for limited additional quantities it may not hold for

an increase of this magnitude. It was not considered necessary to

develop this possibility any further, however, because of the marginal

nature of this crop at Hoolehua where the cost-return margin on LCI is

.4 cents per pound. It is also doubtful that sufficient labor could be

provided at a wage of $1.25 per hour which, for all practical purposes,

excludes pattern LA from consideration.

Operator income, for all of the patterns discussed in this report,

is the sum of the return to management and labor income. In dollar

terms this would mean a return of $8,650, $9,900, and $12,400 for

patterns IA-IIA ($1.25 wage rate), IB-IIB ($1.50 wage rate), and IC-IIC

($2.00 wage rate), respectively. These income estimates assume 5,000

hours of family labor and a $2,400 return to management in each case.

The difference between the residual (net returns), associated ~th the

more productive land classes in some of the patterns, and contract land

rent would also accrue to the operator.

Land Use Pattern IA

This land use pattern assumes that management practices prevailing

in other producing areas will be used in the areas to be developed,

except in the following cases:

(a) Cantaloupe is treated as a multiple crop rather than being

gro,vn once each year in rotation with some other crop.

83

(b) A 25-acre production unit is assumed.

For the purposes of this pattern it is assumed that all of the required

labor would be available at a wage rate of $1.25 per hour. Under the

25 acre assumption and with a labor cost of $1.25 per hour, papaya can

be profitably grown on LCl end LC4' Because of the earlier assumption

that papaya faces a perfectly elastic demand curve it follows that it

could utilize the entire areas of these lands without reducing price.

The initial per acre opportunity rents for this pattern are Lel = $61

(papaya net return), LC2 = $30 (pineapple net return), and LC4 m $153

(papaya net return). In other words, if the vegetable crop in question

could not earn at least as much on a particular land class the land

would be allocated to the production of papaya or pineapple. Utiliza

tion of the remaining land classes begins with no rental charge.

Tables 25 and 26 summarize this use pattern.

Three acres of LC3 and all of LC7 and LC8 remain idle in this land

use pattern. The net returns figures represent returns over and above

all production costs (except land rent or interest on investment in

land). Economic rent, as defined in this report, occurs on LC2t LC5'

and LC6. TIlis interpretation of economic rent is a little uncomfortable

because it assigns greater rent values to certain lands that are physi

cally poorer than other lands. LC6' for example, enjoys an economic

rent of $164 per acre (the L.S.B. productivity rating is b) while LC4

earns no economic rent (L.S.B. productivity rating is a). This is true

because the $420 economic rent earned by tomatoes on LC4 becomes a

production cost for cantaloupe and subsequently becomes a production

cost for tomatoes because cantaloupe earns a like amount. The net

returns values computed for this pattern follow the traditional

84

TABLE 25. LAND USE PATTERN IA ASSUMING A 25-ACRE UNIT

AND A $1.25 \4AGE RATE2I

LC6Crop 1 2 3 4 5 7 8

Tomatoes

Acres 242 8 86

Net return per acre $61 $420 ($420) $164

Cantaloupe

Acres 48 62

Net return per acre $420 $423

Manoa lettuce

Acres 19

Net return per acre $0

Snapbeans

Acres 21


Papaya

Acres 417


Pineapple

Acres 219


TOTAL ACREAGE 680 219 22 56 62 86 69 28

!I Bracketed values are net returns that would have been earned if thecrop had been produced on that land class.

85

TABLE 26. CHARACTERISTICS OF THE COMPUTED EQUILIBRIUM

CropTomatoes Cantaloupe Manoa Lettuce SnapbeansItem

Equilibrium price(cents/pound) 13.80 13.85 9.02 17.72

Total Market Supply(thousand pounds) 14,800 2,483 2,595 1,922

Production fromnew land(thousand pounds) 10,515 2,483 871 751

(Ricardian) view of economic rent for each single use. Tomatoes, for

example, earn $420 on land of productivity!. (LC4 and LC5) and $164 on

land of productivity ~ (LC6). LCI also has a physical productivity of ~

but has higher costs of production due primarily to location. Net

return on LCI equals the return of papaya ($61). Economic rent for

tomatoes is $267 (LC4)' $164 (LC6), and $0 (LCI) which is the marginal

land class or extensive margin. When multiple crops are considered

each LCi earns the same net return in each use in which it is engaged.

If there is no alternative use, and only one LCi is used; e.g. Manoa

lettuce on LC3 net return and economic rent is $0. Where one crop earns

slightly more than another, on a given LCi , the use earning the highest

return prevails; e.g., tomatoes and cantaloupe on LCS. As discussed

earlier, the conventional treatment of economic rent ignores opportunity

rent when computing production costs in the case of multiple crops. If

the reader prefers the conventional treatment, he can simply interpret

the net returns as economic rent.

86

Land Use Pattern IB

The assumptions of land use pattern IB are identical to those of

IA except that labor is charged $1.50 per hour at Hoolehua. The basis

for using this wage rate was developed earlier. Papaya cannot be grown

at Hoolehua under this wage assumption so an initial opportunity rent

exists only for LCl and LC2 and is determined by pineapple ($30 per acre

per year in each case). Tables 27 and 28 summarize this use pattern.

In this land use pattern only LC8 remains idle. The indicated net

returns values for pattern IB are now influenced by a factor other than

land quality. This is the higher labor cost imposed at Hoolehua which

makes the Oahu lands appear more productive (they are more productive in

an economic sense).

87

TABLE 27. LAND USE PATTERN IB ASSUMING A 25-ACRE UNIT

AND A $1.50 WAGE RATE FOR HOOLEHUA~

LC1 2 3 4 5 6 7 8Crop

Tomatoes

Acres 152 3 86 69

Net return per acre $30 ($137) $722 ($722) $441 $113

Cantaloupe

Acres 35 62


Manoa lettuce

Acres 18


Snapbeans

Acres 4 17


Pineapple

Acres 528 219


TOTAL ACREAGE 680 219 22 56 62 86 69 28

!Y Bracketed values are net returns that would have been earned if thecrop had been produced on that land class.

88


ItemCrop

Tomatoes Cantaloupe Manoa Lettuce Snapbeans

Equilibrium price(cents/pound) 14.69 15.19 9.39 18.82

Total market supply(thousand pounds) 13,688 2,194 2,552 1,844

Production on new land(thousand pounds) 9,368 2,194 826 660

Land Use Pattern IC

This pattern is based upon the same assumptions as patterns IA and

IB except that labor at Hoolehua is now charged a wage rate of $2.00

per hour. The reasons for selecting this wage rate were discussed

earlier. The opportunity rent for LCI and LC2 is again determined by

pineapple ($30 per acre per year). This pattern is summarized in

Tables 29 and 30.

In this use pattern none of the lands remain idle. The main

difference between this and previous solutions is that diversified crop

production, on the lands included in this analysis, has shifted entirely

to Oahu. The beginning of this shift was evident with pattern IB but was

not complete until IC was estimated.

89

TABLE 29. LAND USE PATTERN IC ASSUMING A 25-ACRE UNIT

AND A $2.00 WAGE RATE FOR HOOLEHUA!!

Crop

Tomatoes

Acres

LC1 2 3 4

56

5

7

6

86

7

59

8

Net return per acre

Cantaloupe

Acres

Net return per acre

Manoa lettuce

Acres

Net returns per acre

Snapbeans

Acres

Net returns per acre

Pineapple

Acres 680 219

Net returns per acre $30 $30

7

$807

16

$807

$1,164 $1,317 $986 $578

58

($1,164)$1,320

10

$612

28

$319

TOTAL ACREAGE 680 219 22 56 62 86 69 28

~ Bracketed values are net returns that would have been earned if thecrop had been produced on that land class. The acreage and returnsfigures given in this table actually leave approximately 450 thousandpounds of tomatoes (production of 15 acres) in disposal. Thisapproximation was accepted because of the computing time involved inbringing this system to equilibrium.

90


CropTomatoes Cantaloupe Manoa lettuce SnapbeansItem

Equilibrium price 16.43 17.82 10.74 20.79(cents!pound)

Total market supply 11,512 1,646 2,395 1,706(thousand pounds)

Production from new land 7,109 1,646 667 496(thousand pounds)

Land Use Pattern ID

This pattern assumes prevailing management practices (except in the

case of cantaloupe which is treated as a multiple crop) and a production

unit of four acres (the prevailing unit size). It is further assumed

that family labor is sufficient to meet the needs of this unit, there-

fore, no restrictions other than land area and quantity are imposed.

The four vegetable crops are treated within the program with pineapple

included, as before, by adding an opportunity rent to the production

costs of the various vegetables On the relevant land classes. Tables 31

and 32 summarize this use pattern.

As indicated in Table 31, the entire areas of LC3, LC7, and LCaremain idle in this pattern. The net returns figures can be interpreted

as before.

91

TABLE 31. LAND USE PATTERN 10 ASSUMING A FOUR-ACRE UNIT

AND A $1025 WAGE RAT"#!

LC 1 2 3 4 5 6 7 8Crop

Tomatoes

Acres 104 42 86

Net return per acre ~~n $450 ($450) $130....oJ"

Cantaloupe

Acres 14 62


Manoa lettuce

Acres 13


Snapbeans

Acres 16


Pineapple

Acres 547 219


TOTAL ACREAGE 680 219 22 56 62 86 69 28

Y Bracketed values are net returns that would have been earned if thecrop had been produced on that land classo

92



Equilibrium price 16.23 17.53 10.61 19.84(cents/pound)



Land Use Pattern IE

The assumptions of use pattern IE are similar to those of earlier

patterns except that a quarterly labor restriction is imposed on

diversified crop production at Hoolehua. This pattern assumes a wage

rate of $1.25 per hour.

Quarterly labor requirements for each crop were estimated from the

per acre growing and harvesting requirements and the cropping patterns

presented in Appendix B. Table 33 contains the quarterly labor require-

ments, for the several diversified crops, used in this pattern.

TABLE 33. QUARTERLY PER ACRE LABOR REQUIREMENT FOR DIVERSIFIED CROPS

Cro

Tomatoes 246 451 246 451

Cantaloupe 359 359 359

Manoa lettuce 284 426 426 284

Snapbeans 752 752 752 752

Papaya 108 108 108 108

93

The quantity of labor available during each quarter was estimated

as follows:

(a) The pattern assumes that 12 units ~ll be developed on 300

acres of LCI. This corresponds to the ~tate development

plan announced on May 22, 1965.

(b) It is assumed that each family ~ll be able to supply 5000

hours of labor per year. The basis for this estimate ~11

be discussed in a later section.

(c) The State of Hawaii reported 91 persons unemployed on

Molokai as of April 1, 1965. It is assumed that all of

these workers would b~ willicg to work on the newly

developed lands at $1.25 per hour.

On the basis of the above assumptions, available labor is estimated at

247,200 hours per year or 61,800 hours per quarter. Tables 34 and 35

summarize the pattern of land use resulting.

The net return values given in Table 34 are substantially larger

than those of Table 25. As indicated earlier the only difference

between these patterns is the labor restriction imposed at Hoolehua.

The difference can be attributed to an economic rent accruing to labor

as a factor of production. No effort is made to distinguish between

these land and labor rents because for the purposes of this study these

values can be related to land alone.

Of the 300 acres of LC1 available for diversified crop production,

166 acres shifted to pineapple in this solution. In practice this means

that under the assumptions of this use pattern only 45 per cent or 11

acres of each 25-acre unit would be utilized.

94

TABLE 34. LAND USE PATTERN IE ASSUMING A 25-ACRE UNIT,

A $1.25 WAGE RATE, AND A QUARTERLY LABOR RESTRICTION AT HOOLEHUA!!

LC 1 2 3 4 5 6 7 8Crop

Tomatoes

Acres 130 4 23 86 69

Net returns per acre $310 $113 $691 ($691) $413 $89

Cantaloupe

Acres 33 62

Net returns per acre $787 $790 ($89)

Manoa lettuce

Acres 18

Net returns per acre $154 ($89)

Snapbeans

Acres 4 25


Pineapple

Acres 546 219


TOTAL ACREAGE 680 219 22 56 62 86 69 28

Y Bracketed values are net returns that would have been earned if thecrop had been produced on that land class.

95



Equilibrium price 14.60 15.48 9.35 18.62(cents/pound)



Land Use Patterns for Hoolehua

All of the land use patterns discussed in this section were esti-

mated by simply determining how much additional production ~uld be

reqUired to force price to the production cost level. This procedure

can be used because the diversified crops considered in this study fail

to use the entire acreage of the best land at Hoolehua. Because of this

simplification it was possible to estimate four land use patterns

wdthout using the program. These use patterns are identical to patterns

LA, IB, IC, and ID discussed earlier except that they apply only to

Hoolehua.

Land Use Pattern IIA

This land use pattern assumes the same per acre input levels as lA,

multiple cropping of cantaloupe, and a 2S-acre production unit. Land

area and quantity demanded are the only restrictions imposed on this

pattern. The assumed wage rate is $1.25 per hour. Table 36 summarizes

use pattern IIA. Table 36 indicates that the entire area of LCl could

be brought into production of the five crops considered.

No economic rent accrues to LCI or LC2 in this pattern. Papaya

could utilize the entire area of Lel at a rental rate of $61 per acre

96

per year and pineapple could utilize LC2 at $30 per acre per year.

These amounts were added to the production costs of the other crops.

This means that at the indicated prices a $61 net return for LCl and a

$30 net return for LC2 exists for the designated uses.

TABLE 36. LAND USE PATTERN IIA ASSUMING A 25-ACRE UNIT

AND A $1.25 WAGE RATE

Production fromCro New Land

(thousand pounds

Tomatoes 13.8 14,800 10,512 337

Cantaloupe 14.6 2,317 2,317 127

Manoa lettuce 9.1 2,585 861 17

Snapbeans 17.7 1,923 753 20

Papaya 9.0 10,525 2,739 179

Pineapple 219

TOTAL 899

~ This price is equal to per unit production costs including a $61 peracre opportunity rent for papaya on LeI.

~ Papaya is grown on the 179 remaining acres of LCl and pineapple onthe 219 acres of LC2•

Land Use Pattern lIB

Use pattern lIB is identical to pattern IIA except that labor is

charged at the rate of $1.50 per hour. The basis for this rate was

discussed earlier. Table 37 summarizes pattern IIB. The interpretation

of the acreage estimates given in Table 37 is identical to that of

pattern IIAc

97

TABLE 37. LAND USE PATTERN IrB ASSUMING A 25-ACRE UNIT


Equilibr}Uffi Total Market Production fromCrop Price!. Supply New Land AcreageP!

Tomatoes 14.8 13,550 9,220 296

Cantaloupe 15.9 2,046 2,046 112

Manoa lettuce 10.0 2,482 754 14

Snapbeans 19.7 1,782 586 16

Pineapple 461

TOTAL 899

~ This price is equal to per unit production costs including a $30opportunity rent for pineapple on LC1• Papaya cannot be grown iflabor is $1.50 per hour.

E/ Pineapple is grown on the remaining 242 acres of LCl and on the 219acres of LC 2.

Land Use Pattern IIC

Use pattern IIC is identical to both patterns IIA and IIB except

that labor is now charged $2.00 per hour. The basis for using this wage

rate was discussed earlier. Table 38 summarizes pattern IIC.

98

TABLE 38. LAND USE PATTERN rIC ASSUMING A 25-ACRE UNIT


Equilibr;um Total Market Production fromAcreageE!Crop Price! Supply New Land

Tomatoes 17.1 10,675 6,249 200

Cantaloupe 18.8 1,442 1,442 79

Manoa lettuce 11.1 2,353 623 12

Snapbeans 23.9 1,487 238 7

Pineapple 601

TOTAL 899

!I This price equals per unit production costs including a $30 opportunity rent for pineapple on LC1.

~ Pineapple is grown on 382 acres of LCl and 219 acres of LC2.

Land Use Pattern lID

This land use pattern assumes prevailing management practices

(except in the case of cantaloupe which is again treated as a multiple

crop) and a four-acre unit. It is further assumed that family labor is

sufficient to meet the needs of this unit, therefore, no restrictions

other than land area and quantity demanded are imposed. The assumed

wage rate is $1.25 per hour. Table 39 summarizes pattern lID. Inter-

pretation of the data given in Table 39 is identical to that of earlier

use patterns. The only difference between the assumptions of this

pattern and IIA is that unit size has been changed (from 25 to four

acres).

99

TABLE 39. LAND USE PATTERN IID ASSUMING A FOUR-ACRE UNIT


Equi 11 br}um Total Market Production fromAcreageP.!Crop Pricea Supply New Land

Tomatoes 16.2 11,800 7,411 238

Cantaloupe 18.7 1,462 1,462 80

Manoa lettuce 10.6 2,412 684 13

Snapbeans 19.8 1,775 578 16

Pineapple 552

TOTAL 899

!I This price equals per unit production costs including a $30 opportunity rent for pineapple.

~ Pineapple acreage includes 333 acres of LeI and 219 acres of LC2•

Stability of the Land Use Patterns

It would have been possible to re-run each final land use pattern

with a linear programming system that ranges the restrictions and

objective function. This is usually done to indicate the range over

which these values could vary without changing the basis or solution.

It appears that this procedure would give some idea of the tolerance or

allowable error that could occur in the cost budgets or demand and

supply functions. Such an interpretation must be approached with some

caution. The quantity restrictions at the equilibrium position are

established by the intersection of the market demand function and the

newly generated supply function for each crop. A change in quantity

must be accompanied by a shift of either the supply or demand curve,

which would also change price. A change in price would, in turn, change

the level of net return. Because these values are inseparably related,

100

ranging them individually ~uld have little meaning. The objective

function supplies a certain amount of information about the computed

equilibrium. Referring to Table 27 it is obvious that if cantaloupe net

returns fell (due to a cost or demand shift) from $725 to $721 per acre,

a basis change would occur. Similarly, a cantaloupe price rise would

cause tomato to be displaced from LC40 The objective function, because

of the way it is computed, is in effect already ranged. The land areas,

in a particular application, are fixed, which eliminates the need for

ranging these restrictions.

Solutions would need to be tested for stability whenever additional

restrictions have been imposed (other than land area and quantity). In

this study only pattern IE, with quarterly labor restrictions, is in

this category. It was decided not to rearun this pattern because of the

time involved and the limited value of the additional information.

Derived Labor Demand Curves for Hoolehua

An interesting by-product of land use patterns A, B, and C are the

labor demand curves that can be constructed from them. Using per acre

per year labor requirements data for the five diversified crops, a

total requirement can be estimated for each pattern. For the purposes

of thib study the relationship of primary interest is the quantity of

labor that can be hired at each wage rate. Estimated available family

labor is deducted from the total requirement to obtain the quantity of

labor that could be hired at each wage.

Available family labor can be estimated directly from the cost of

production budgets. It is generally assumed that family labor is

sufficient to operate a four-acre diversified crop unit. If these

101

family operated farms produced single commodities, the following

quantities of family labor \~uld be required:

Tomatoes 5,576 hours per year per farm

Cantaloupe 4,308 hours per ~~ per farm

Manoa lettuce 5,680 hours per year per farm

Snapbeans 12,032 hours per year per farm

Papaya 2,500 hours per year per farm

Tomatoes and cantaloupe are the largest land users, among the diversi

fied crops, in most of the patterns considered in this study. For this

reason and due to a lack of data family labor is assumed to be 5,000

hours per family per year. With this amount of labor available, a

family operated unit producing only snapbeans would not be possible.

It is necessary to establish the number of diversified crop units

that could be developed under each use pattern. This must be done for

patterns A, B, and C under both I and II development plans. Table 40

gives these values.

Figure 6 shows the relationship between the hourly wage rate and

the difference between required labor and available family labor

(converted to annual worker units). The resulting functional relations

can be interpreted as derived labor demand CU4ves for Hoolehua under the

assumptions of patterns rA, IB, IC and lIA, lIB, IIC, respectively. The

function representing these relationships is of the form (6).

(6) Xl = a - bX2

where: Xl = hourly wage rate

X2 = number of hired workers

As would be expected, the curves shown in FigurE~ 6 are quite different.

102

If only the Hoolehua lands are developed (II) labor commands a much

higher wage than if the entire area being studied (I) is developed.

There are currently 91 unemployed persons on Molokai that could be

given full-time work at wages in excess of $1.50 per hour under either

plan.

TABLE 40. LABOR REQUIREMENT FOR SELECTED LAND USE PATTERNS

Entire Area Developed

PatternRequired

Units Labor(hours/year)

FamilyLabor(hoursyear)

Worker a/uivalent-

IA ($1.25 wage)

IB ($1.50Hoolehua wage)

IC ($2.00Hoolehua wage)

Hoolehua Only

27

6

o

581,494

211,888

o

135,000

30,000

o

446,494

181,888

o

215

87

o

IIA ($1.25 wage) 27

IIB ($1.50 wage) 18

IIC ($2.00 wage) 12

768,543

601,256

401,979

135,000

90,000

60,000

633,543

511,256

341,979

305

246

164

~ Represents hired labor divided by 2,080 hours which is taken to beone full-time employee.

-. 3.001Ill-lC\l 2.75.-l

.-l0"0

2050'-'

(lJuell 2.25""'(!)00C\l 2.00~

>..-l 1.75""'::J,Q""~.I

....~ 1.50~-<

1.25

1.00

.75

.50

.25

Figure 6. Derived Demana Curves for Hired Labor at Hoolehua

(I) Xl C 1092 - .003X2

(II) Xl = x.77 - .005X2

on1" (II)...... Hoolehua __ .'.1

---------------- . "' d8velop,d (I)-------- En ti 2.'2 en: cd

I

o 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400

X2 Hi red ','Orkers

.....ow

104

CHAPTER VI

SOME ECON~~IC EFFECTS OF EXPANDED PRODUCTION

ON EKISTING STATE PRODUCERS, THE STATE AS A WHOLE, AND THE CONSUMER

It is obvious that development of new agricultural land under any

of the patterns discussed in this report would work to the disadvantage

of existing state producers. What is not so obvious is that the

consumer and the state as a whole may benefit, perhaps greatly, from

such a development. The purpose of this chapter is to examine some of

the measurable changes that would be expected to occur under selected

land use patterns. The land use patterns estimated for the entire study

area, that assume 25-acre units and Hoolehua wage rates of $1.25 and

$1.50 per hour, and that pattern assuming a four-acre unit with a $1.25

wage rate will be analyzed in this context. Similar results would be

obtained for the corresponding Hoolehua patterns because the final

product prices are nearly identical in each case. Pattern ID (25-acre

unit and a $2.00 wage rate) is omitted because it was not considered

likely to result in the study area.

Changes in Producer and State Output

Table 41 gives the expected change in output for existing state

producers and the expected change in total state production, for

pattern ~ The indicated changes occurring in existing producer out

put are very small. Total state output on the other hand has increased

sUbstantially in every case.

Table 42 indicates the expected change in output for existing state

producers and the state as a whole, for pattern lB.

In general, the results of this development plan are nea~ly

identical to those presented in Table 41. It will be recalled that the

105

TABLE 41. CHANGES IN OUTPUT RESULTING FR~i L~ID USE PATTERN LA

THAT ASSUMES A 25-ACRE UNIT p~D A $1.25 WAGE RATE

Change in OutputExisting State Projected State For Existing Producers

Cro Production Production Amount Acres!!thousand pounds thousand pounds (thousand pounds

Tomatoes 4,560 14,800 -275 9

Cantaloupe 0 2,483 0 0

Manoa lettuce 1,744 2,595 - 20 .5

Snapbeans 1,286 1,922 -115 4

Papaya 10,525 15,654 0 0

~ The acreage estimates were made by dividing pounds by the product ofclass b yield multiplied by the number of crops that can be grown atWaimanalo.

TABLE 42. CHANGES IN OUTPUT RESULTING FROM LAND USE PATTERN IB

THAT ASSUMES A 25-ACRE UNIT AND A $1.50 WAGE RATE FOR HOOLEHUA

CroExisting State Projected State

Production Production(thousand pounds (thousand pounds

Change in OutputFor Existing ProducersAmount Acres!!

thousand pounds

Tomatoes 4,560 13,688 -240 8


Manoa lettuce 1,744 2,552 - 18 .4

Snapbeans 1,286 1,844 -102 4

~ See footnote a, Table 41.

only assumption difference between these patterns is in the wage rate at

Hoolehua. The effect of this difference was to increase all of the

prices slightly, which iu turn reduced total market supply. Existing

106

state producers would find that they are slightly better off under IB

a~d their production would decline less than in the case of IA.

Table 43 presents the output changes associated with land use

pattern ID. This pattern is based upon the prevailing (diversified

crop) unit size of four acres. Because of this it would be expected to

have a smaller effect on existing producers than either of the

previously discussed patterns.

TABLE 43. CHANGES IN OUTPUT RESULTING FROM LAND USE PATTERN ID

THAT ASSUMES A FOUR-ACRE UNIT AND A $1.25 WAGE RATE

Change in OutputExisting State Projected State For Existing Producers

Cro Production Production }mount Acres!!(thousand pounds (thousand pounds (thousand pounds)

Tomatoes 4,560 11,763 -164 5


Manoa lettuce 1,744 2,410 - 16 .4

Snapbeans 1,286 1,772 - 88 3

Y See footnote a, Table 41.

Of the three patterns only IA allows papaya to be produced. The

reasons for increased production not affecting existing papaya producers

have been discussed previously. The reduction in output of existing

producers is very small for the other crops, because of the highly

inelastic nature of the computed supply functions. In terms of fa~

units, none of these patterns would force more than the equivalent of

three producers out of the production of these crops. In reality this

change ~~uld probably t&<e the form of shifting to the production of

crops other than those included in the analysis. Another likely outcome

107

of any development plan would be for some of the existing producers to

move to the newly developed land. A shift of this type is not only

possible but encouraged by the state as part of the development program.

An analysis of the ramifications of this possibility is beyond the scope

of the present study.

Changes in Producer and State Income

Even though the output of existing producers does not appear to

change significantly under the different patterns, their income does.

For the purposes of this comparison only changes in gross income will be

considered. Gross income is defined as the Honolulu ~olesale price

multiplied by marketable output. Total state income is computed in the

same manner. Table 44 indicates the estimated income changes associated

with land use pattern IA.

TABLE 44. CHANGES IN INCCME RESULTING FRCM LAND USE PATTERN IA

THAT ASSUMES A 25-ACRE UNIT AND A $1.25 WAGE RATE

EKisting Gross Projected Gross Projected Income ofCrop State Income State Income EKisting Producers

Tomatoes $ 925,680 $2,042,400 $ 591,330

Cantaloupe 0 343,896 0

Manoa lettuce 284,272 234,069 155,505

Snapbeans 344,648 340,578 207,501

Papaya 947,250 l~408 ,860 947,250

TOTALS $2,501,850 $4,369,803 $1,901,586

It is apparent from Table 44 th~t development of the study lands

according to pattern IA would reduce the income of existing producers by

nearly 25 per cent (39 per cent if papaya is excluded). Total state

108income, on the other hand, rose by approximately 75 per cent (90 per

cent if papaya is excluded). This income increase is actually under-

stated because it does not include the value added between Wholesale

and retail. These values are used here for purposes of comparison only,

and should not be considered measures of state income attributable to

the various crops. The multiplier effect has also been ignored in this

discussion. Some of the increase in expenditure for diversified crops

would doubtlessly be at the expense of impo~ted produce. The plugging

of this income "leak ll would tend to increase state income and would be

subject to a multiplier effect. In general, the amount of state income

thus created would depend upon how much leaked to the mainland on each

round of subsequent spenJing.

Table 45 indicates the income changes associated ~th land use

pattern IB. For pattern IB the income of existing producers fell by

34 per cent while total state income rose 88 per cent. Interpretation

of the results is subject to the same limitations as pattern IA.

TABLE 45. CHANGES IN INCCME RESULTING FRCM LAND USE PATTERN IB

THAT ASSUMES A 25-ACRE UNIT AND A $1.50 WAGE RATE FOR HOOLEHUA

Existing Gross Projected Gross Projected Income ofCrop State Income State Income Existing Producers

Tomatoes $ 925,680 $2,010,767 $ 634,608

Cantaloupe ° 333,269 °Manoa lettuce 284,272 239,633 162,071

Snapbeans 344,648 347,041 222,829

TOTALS $1,554,600 $2,930,710 $1,019,508

109

Table 46 indicates the income changes associated with land use

pattern ID.

TABLE 46. CHANGES IN INCOME RESULTING FROM LAND USE PATTERN ID

THAT ASSUMES A FOUR-ACRE UNIT AND A $1.25 WAGE RATE

Existing Gross Projected Gross Projected Income ofCrop State Income State Income Existing Producers

Tomatoes $ 925,680 $1,909,135 $ 713,471

Cantaloupe 0 299,062 0

Manoa lettuce 284,272 255,701 183,341

Snapbeans 344,648 351,565 237,683

TOTALS $1,554,600 $2,815,463 $1,134,495

Pattern ID, as expected, has the smallest effect on existing vegetable

producer income. In this case, existing producer income falls 28 per

cent While total state income increases 81 per cent. Again, interpreta-

tion of these results is subject to the same limitations as for pattern

IA.

Changes in the Wholesale Price Level

Each of the land use patterns considered in this report would cause

reductions in the wholesale price level for most of the included crops.

Although a consideration of how these changes might effect retail prices

and consequently the consumer is beyond the scope of this study, an

approximation can be made. For convenience it is assumed that the

retail margin is constant. That is, a one cent drop in the price at

wholesale would reduce retail price by a like amount. For patterns lA,

IB, and IC, the vegetable price changes given in Table 47 occurred.

110

TABLE 47. ESTIMATED WHOLESALE PRICES FOR SELECTED LAND USE PATTERNS

era

Tomatoes

Cantaloupe

Manoa lettuce

Snapbeans

20.3

20.6

16.3

26.8

13.80

13.85

9.02

17.72

14.69

15.19

9.39

18.82

16.23

17.53

10.61

19.84

It is evident from Table 47 that the expected price reductions are

substantial. Pattern IB, for example, would reduce wholesale price by

5.6, 5.4, 6.9, and 8.0 cents per pound for tomatoes, cantaloupe, Manoa

lettuce, and snapbeans, respectively. If the assumption of a constant

retail margin holds a similar decline would occur at the retail level.

111

CHAPTER VII

Sili~E ADDITIONAL CROPS THAT APPEAR TO OFFER POTENTIAL

FOR ACREAGE EXPANSION

This study has considered in some detail the possibility of

expanding the production of a relatively small number of crops. In this

chapter expansion of several additional crops (zucchini squash, eggplant,

Irish potatoes, passion fruit, and macadamia nuts) ~ll be discussed in

a more general manner. Some major limitations are inherent in the

approach used here. First, a demand curve was not constructed for any

of these crops. In fact, only for zucchini squash and eggplant is

sufficient data available from which to estimate a demand relationship.

Secondly, only limited cost of production and yield data are available.

Within these limitations, the following discussion will attempt to

point out some of the relevant factors influencing the possibility of

expanding the production of these crops in the areas under study.

Vegetable Crops

One objective of this study was to discover what the effect of

bringing new vegetable land into production would be on crop prices. A

selected group of four crops was used to provide a basis from which to

consider vegetable production in general. It can be argued that such a

small number of crops is not an adequate basis upon which to make

generalizations. While this may be true, it is also true that the vast

majority of vegetables that could be grown in these areas could utilize

only very small acreages as long as the Honolulu market must absorb all

additional production. During the 1963-64 season the Hawaii Agricul

tural EKperiment Station Demonstration Farm at Eoolehua conducted trials

112

(on 1/5 acre plots) for eggplant, cucumbers, zucchini squash, bell

peppers (two plots), and tomatoes (t\~ plots). From this group of five

crops eggplant, zucchini squash, and tomatoes appeared to be promising

(35). Eggplant returned over $1,400 per 1/5 acre plot based on actual

sales in the Honolulu market (35). The test plot upon which this is

based is small, therefore any error is compounded when stated in per

acre terms. The state is nearly self-supporting in this crop \~.th a

supply of slightly over 700,000 pounds per year. With the yield level

recorded by the Demonstration Farm, Which represents above average

management, the total existing market supply could be grown on 11 acres

with one crop or 5.5 acres with two crops per year. On this basis, it

appears safe to assume that additional production would affect price

markedly and that only limited acreage could be brought into production

without reducing price below per unit cost. Zucchini squash indicated a

return of nearly $500 per 1/5 acre (35) but again it appears that this

crop could profitably utilize only a limited acreage. The total

Honolulu supply of all squash could be grown on 10 acres (at the

reported 28,300 pound yield per acre) with one crop, or five acres with

two crops per year.

There are three vegetable crops, potatoes, dry onions, and carrots,

for which production could be expanded without reducing price. This is

true because most of the market supply is currently imported from the

U. S. mainland. Of the three, cost of production and yield estimates

are available only for potatoes (12). This material is based on planta

tion yield experience during WOrld War II and synthesized cost data.

According to this study the cost of producing potatoes in Hawaii is

nearly three times as high as the California cost but because of high

113

yields (potentially high, as current yield levels are far below those

assumed) and lower transportation cost a net return of from $12 to $728

per acre appears possible. This return level depends upon wholesale

price, yield, and cost structure, and does not include costs for such

items as transportation from Molokai and real property tax, which should

total about $60. The study cited uses wholesale prices of $6.04 and

$8.33 per cwt. and defines a high and low cost unit. The acreage

required to supply the Honolulu market by 1965 was estimated at from

1,400 to 2,000 acres based upon a 15-20,000 pound per acre yield, one

crop per year, and population and consumption estimates. If the price

and cost data upon Which this discussion is based are reliable, the

expansion of potato production in Hawaii is feasible.

The three crops discussed here are generally suited to the areas

being considered. Potato production in Hawaii or at least in the areas

of study may be restricted to one crop per year because of the summer

heat.

Orchard Crops

Two orchard crops which appear to have a future in Hawaii as export

crops, and that could be grown in the areas under study, are passion

fruit and macadamia nuts. The discussion in this section is based upon

cost and return budgets prepared by Joseph T. Keeler (29, 31).

Converting the passion fruit budget to a base comparable to that

used in this study; i.e., $1.25 per hour for labor; $2,400 return to

management; a 25-acre unit; and $330 for water, irrigation labor, and

transportation expenses, allows it to be considered in comparison with

the other crops. Assuming a 30,000 pound yield per year, the price per

pound could drop from the current 5.5 to 4.8 cents per pound and still

114

cover production costs. Data on wholesale price and quantity sold are

not available for this crop so it is nOw known how many additional acres

could be brought into production before price wouid fall to this level.

Scott (56) shows that a market could be developed for the production of

2,250 acres (assuming a 30,000 pound yield which is sUbstantially higher

than average) with a comprehensive promotional campaign. It is probably

realistic to assume that the acreage could be expanded gradually, with

sufficient promotion, without adversely affecting price. The immediate

concern, however, is with the effect on price of a current increase in

output and this cannot be determined from available data.

Macadamia nuts offer another orchard crop possibility for these

lands. Converting the budget prepared by Keeler to a comparable base,

using the procedure outlined for passion fruit, indicates that with a

marketable yield of 5,865 pounds (in shell - 2500 pounds shelled) and a

price of around 20 cents per pound, macadamia nuts could be profitably

grown. Scott (57) provides an estimate, for 10 years hence, of 20

million pounds, or the production of 13,300 acres (assuming a 1500 pound

yield - shelled). This level of sales could be achieved by the time

current plantings reach maturity.

The two orchard crops discussed here are not equally suited to the

study areas. The HAES Demonstration Farm at Hoolehua uses passion fruit

windbreaks that yield well and show no wind damage, this crop appears to

be equally suited to the other areas. Macadamia nuts on the other hand

can be grown in all of the areas but are susceptible to wind damage and

are not gro\~ to best advantage under irrigated conditions. Current

acreages of both crops are far below the estimated requirements. In

January 1963 there were 4,420 acres in macadamia nuts and 290 acres in

115

passion fruit.

It is possible to include any crop in the program for which the

necessary demand, sup~ly, and budget data are available. Of the five

crops discussed in this section only eggplant and zucchini squash are in

this r ~gory. These crops could utilize such a small acreage that it

was not ~.'nsidered worthwhile to include them in the detailed analysis.

\

116

CHAPTER VIII

SUMMARY AND CONCLUSIONS

Summary

Evaluation of a land development project necessitates estimation

of the land use pattern that will probably result in the project area.

Ideally, this estimation would include all of the production alterna

tives faced by the producer. In practice, however, a complete analysis

of this type would seldom be possible. The present study considers a

selected group of crops that includes those most likely to be grown in

important quantities.

The estimating procedure utilizes an iterative linear programming

model to allocate lands of varying quality among uses in such a way as

to assure that each physical land class is being utilized in its best

agricultural use. The best agricultural use is defined as that use

earning the highest net return. Market demand functions for each crop

were incorporated in the solution procedure because the level of return

changes with quantity for all crops with a downward-sloping demand.

Market supply functions were also included for these crops, as it was

necessary to measure the effects of expanded production on existing

produce~s.

Land use patterns were estimated for four- and 25-acre units, two

different development plans, and for various labor assumptions. These

patterns indicate that with 25-acre units, and assuming that the entire

area will be developed, the announced first increment at Hoolehua

(Honolulu Advertiser, May 24, 1965) can be utilized only if a $1.25 per

hour wage rate prevails. There is some doubt that sufficient labor

would be available on Molokai at this wage. If Hoolehua only were

117

developed, the producers could pay as much as $2.00 per hour and utilize

the entire area (300 acres). Hired labor demand curves, derived for

Hoolehua under both development plans, indicate that the 91 persons

currently unemployed on Molokai could be given work at $1.50 per hour.

Under the several development plans, the output of existing

producers changes only slightly. Total state output, on the other hand,

increases greatly (40 to 200 per cent). The gross income earned by

existing producers is reduced by 25-30 per cent while total gross state

income after development increases by 75-90 per cent. Wholesale prices

are reduced substantially in each case.

Most crops not considered in the study could utilize only a limited

acreage. Notable exceptions to this were carrots, potatoes, and dry

onions which could be expanded greatly without reducing price. These

crops can only be grown as one crop per year, during the winter.

Certain orchard crops appear to offer some potential, but expansion

depends upon the level of market promotion.

Emplications

Differences between the several land use patterns occur as the

result of wage rate variations, changes in assumed unit size, imposed

labor restrictions, and modification of the assumed area to be develope~

The State Department of Land and Natural Resources, as the planning

agency in this venture, can control only two of these variables, unit

size and area to be developed. Even in the case of unit size the degree

of control is not absolute because a farmer with a 25-acre unit, for

example, may choose to utilize only part of it in actual production.

Recent evidence (Honolulu Advertiser, May 24, 1965) seems to

indicate that the state is committed to an initial development plan for

118

the Hoolehua lands only. The first increment of this development is to

be composed of l2-25-acre units. For immediate planning needs, those

patterns estimated for the Hoolehua lands only and based on a 25-acre

unit appear to be the most appropriate. The only difference between

these patterns is the wage rate. Selection of the pattern that would

most closely approximate reality depends upon the wage that would have

to be paid to obtain the required labor. The author feels that plan IIB

($1.50 wage rate) probably comes the nearest to accomplishing this.

Labor would not be restrictive if the wage was sufficiently high to

attract workers to Molokai. On the basis of pineapple company expe

rience, a wage of $1.50 per hour is sufficient to do this. It should be

restated, however, that if only the Hoolehua lands are developed for

diversified crop production, the new faDners could pay up to $2.00 per

hour for labor, while still utilizing the 300 acres of LCI (see pattern

IIC). Pattern IIB could utilize over 400 acres of LCI while paying

$1.50 per hour for all labor. Another approach to estimating a land use

pattern for Hoolehua would be to assume a fixed amount of available

labor and then compute the use pattern. This was done for the entire

area, ~th the labor restriction imposed only at Hoolehua (pattern IE),

but could not be computed for Hoolehua alone because the programming

model will not accept problems involving a single land class and more

than one restriction. It is possible to anticipate what would happen if

a corresponding pattern were computed for Hoolehua. To begin with,

papaya drops out of the pattern IE because it cannot compete for scarce

labor. Tomatoes are grown on 130 acres and Manoa lettuce on four acres

of LCI. If only Hoolehua were developed, with the same labor restric

tions used in pattern IE, more crops, probably all of the vegetable

119

crops, would be grown at Hoolehua but total acreage would not increase.

Actually fewer acres would probably be utilized because the labor

requirement for tomatoes is one of the lowest of the vegetable crops

being considered. This means that approximately 50 per cent of each

unit would remain idle. Obviously, this idle land results from labor

becoming restrictive. In the case of pattern IE, labor is restrictive

in the second and fourth quarters only, with about 27,000 hours left

unused in each remaining quarter. If the assumption of prevailing

management practices is relaxed, in reference to the prevailing cropping

patterns used in this study, it would be possible to reconstruct the

budgets and demand curves on a quarterly basis, thus allowing quarterly

patterns to be estimated.

Validity of Findings

Production costs were estimated, for the various land classes,

from existing cost of production studies and are assumed to accurately

represent conditions in Hawaii. If these costs are in error it would,

of course, effect the land use patterns. All known existing data, as

well as materials based upon personal interviews with Experiment Station

and Extension Service specialists, have been incorporated in the cost

budgets. IE in the future conflicting but more reliable data should be

made available or if costs should change, the land use ~atterns can be

recomputed utilizing the new cost structure.

The yield estimates used in this study probably tend to be conser

vative for high quality irrigated land. It was felt, however, that

because of the lack of information regarding the effects of ~~nd and the

difficulty in ~ssessing the contribution of management in those

instances where high yields have been recorded, it would be better to

120

retain the above yield estimates. Two of the study crops in particular

(tomatoes and bananas) appear to offer potentially higher yields.

Tomatoes have yielded 30-50 tons per acre on an annual (2-crop) basis

(varieties N-52 and N-55) under irrigated conditions. One of the

highest recorded yields was on the Molokai Demonstration Farm at

Hoolehua. The level of management on this farm is quite different from

the prevailing levels assumed in this study. Irrigated bananas have

also recorded yields several times larger than those used here, but

these yields were in sheltered areas. ~indbreaks at Hoolehua that are

sufficient to protect bananas will not be available for some time, if

at all (51).

The estimated demand functions, for ~ich some statistical tests

of reliability are provided in Appendix C, have been previously

discussed in some detail. If even one of these equations is in error

the computed land use patterns would indicate incorrect acreages and

returns for all crops. In this analysis no attempt has been made to

quantify the changes that would occur from variations in these functions.

The supply functions used in this study were estimated with a

relatively simple model. Although the fits obtained ~th this model

leave much to be desired statistically, there did not appear to be a

practical alternative to the procedure employed. Errors in these func

tions would influence the acreage that could be brought into production

at a given price. The more inelastic the true relationship is, the less

significant this source of error becomes. The elasticity of supply

measured over the range of price change associated with land use

pattern IA (~ich reduces price more than any of the other patterns)

indicates inelastic supply for all crops for which supply was estimated.

121

The coefficients of elasticity are .16, .25, and .02 for tomatoes,

3napbeans, and Manoa lettuce, respectively.

The computed land use patterns are only as reliable as the data

used in the programming model. In this section an attempt has been made

to establish the type of error that would occur in the estimated land

use patterns as a result of errors in the input data. It should be

re-emphasized at this point that computed net return levels would be in

error if the factors influencing their measurement are not adequately

controlled. As the actual level of return does not enter in the evalua

tion of the respective'patterns, this possibility should not pose a

serious problem. Far more important than the actual level of returns is

the relative level between uses.

Suggestions for Further Research

The land use patterns estimated in this study are based'on data for

a single year, 1963. Amore satisfactory planning approach would be to

project levels of demand and production costs to some future period,

computing land use patterns for each intervening year, so that a land

development scheme could be devised. This scheme should represent

changes in population, income, competitiveness of the several crops, and

any other factor that might significantly affect one or more of the

crops.

There are a number of other areas that could easily justify

additional research. In terms of the input data a logical next step

would be to const);llct a set of budgets for better than average or

perhaps optimum input levels. The possibility of export markets for

winter vegetables should be explored more fully, perhaps to the extent

of estimating demand curves for selected export markets. With these

122

data, the patterns could be recomputed.

The programming procedure developed for this study should be

tested on other types of problems. For example, estimating a land use

pattern for an entire state or region would be a good test of the

efficiency of the program in handling larger problems.

123

APPENDIX A

DESCRIPTION OF LAND TYPES

The following land type descriptions are from the detailed land

classification reports (3, 44) published by the Land Study Bureau,

University of Hawaii.

Molokai

Land Type

1

Description

Deep, red (Molokai) soils; nonstony to slightlystony; slopes less than 10%; average annualrainfall (AAR) less than 20 inches; moderate tostrong winds.

3 Moderately deep, red (Molokai) soils; stony;slopes less than 10%; AAR less than 25"; moderateto strong winds.

7 Moderately deep, red (Molokai and Lahaina) soils;slopes less than 40%; eroded; AAR less than 25".

17 Moderately deep; nonstony to slightly stony, dark(Kawaihapai and Hanalei) soils; nearly levelcoastal flat areas.

Oahu 3 Included are nonstony, moderately deep to deep,well-drained, moderately fine to fine-texturedlands having brownish-red to red surface soil overred subsoil. The soils have developed on uplandpositions from residuum of basic igneous rocks andfrom old alluvium. Soil reaction is slightly acidto mildly alkaline. Slopes range from 0 to 10 percent. Soil series include Maliko, Mamala (greaterthan 20 inches deep), Molokai, and Lahaina. Landsare easily worked. Median annual rainfall variesfrom 15 to 40 inches. Elevations range from 0 to1000 feet.

4 Included are non-stony, deep, well-drained, mediumto moderately fine-textured lands having brownsurface soil over yellowish-brown subsoil. Thesoils have developed from volcanic ash and cinderswashed from adjacent uplands. Soil reaction isneutral to mildly alkaline. Slopes range from 0 to10 per cent. Included is the Koko soil series.Lands are easily worked. Median annual rainfallvaries from 10 to 20 inches. Elevations rangefrom 0 to 200 feet.

O~u

Land TYEe

5

l~

Description

Included are ncnstony, deep, well-drained, mediumto fine-textured lands having brown to darkreddish-brown surface soil over dark brovm subsoil.These young soils are derived from recent alluvium.Soil reaction is slightly acid to neutral~ Slopesrange from 0 to 10 per cent. Soil series includedare the Kawaihapai and Pulehu. Lands are easilyworked. Median annual rainfall varies from 10 to40 inches. Elevations range from 0 to 600 feet.

8 Included are nonstony (except for Kaena),moderately deep, moderately well to imperfectlydrained, fine-textured lands having dark grayishbrown to grayish-black surface soil over subsoilin which gray and brown colors are often mottledwith tints of yellows, browns, and reds. The soilshave developed from old alluvium and residuum ofbasic igneous rocks under the influence ofrestricted drainage. Soil reaction is neutral tomildly alkaline. Slopes range from 0 to 10 percent. Soil series and complexes included areHonouliuli, Honouliuli-Mamala Complex, Kaena,Kalihi, Keaau, Kokokahi, Lualualei, Makalapa,Papaa, and Waimanalo. Lands are difficult to workbecause the soils are very plastic and sticky whenwet and very hard when dry. In localized areas,the impeded internal drainage restricts growth ofcrops that require good drainage. Median annualrainfall varies from 15 to 60 inches. Elevationsrange from 0 to 500 feet.

9 Included are nonstony, deep, well-drained, finetextured lands having dark reddish-brown to brownsurface soil over red subsoil. The soils havedeveloped on upland positions (including highterraces) from old alluvium or residuum from basicigneous rocks. Soil reaction is strongly to slightly acid. Slopes range from 0 to 10 per cent.Included are the Alaeloa, Kaneohe, and Lolekaaseries. Lands are easily worked. Median annualrainfall varies from 40 to 100 inches. Elevationsrange from 0 to 800 feet.

19 Included are nonstony, deep, well-drained,moderately fine to fine-textured lands having darkreddish-brown surface soil over red subsoil. Thesoils have developed on upland positions fromresiduum of basic igneous rocks. Soil reaction ismedium acid. Slopes range from 11 to 20 per cent.Included are the Alaeloa, Kaneohe, and Lolekaaseries. Lands are difficult to work because of

125

Land Type Description

Oahu slope. Median annual rainfall varies from 40 to80 inches. Elevations range from 10 to 1000 feet.

23 Included are stony, deep, well-drained, moderatelyfine to fine-textured lands having red to reddishbrown surface soil over red to brown subsoil. Thesoils have developed from recent and old a11uviunl.Soil reaction is slightly acid to neutral. Slopesrange from 0 to 10 per cent. Soil series includedare Ewa, Kahuku, Kamananui, Pu1ehu, and Waialua.Lands are difficult to work because of stoniness.Median annual rainfall varies from 15 to 40 inches.Elevations range from 0 to 400 feet.

32 Included are nonstony, deep, well-drained,moderately fine to fine-textured lands havingreddish-brown to dark red surface soil and redsubsoil. They have developed on upland positionsfrom residual lava or old alluvium. Soil reactionis strongly acid. Slopes range from 21 to 35 percent. Soil series included are Kahana, Kunia, andWahiawa. Lands are difficult to work because ofslope. Median annual rainfall varies from 30 to60 inches. Elevations range from 250 to 1200 feet.

35 Included are stony, deep, imperfectly to welldrained, medium to fine-textured lands having darkreddish-brown to dark grayish-brown surface soilover brown subsoil. The soils have developed onbottomland positions from recent alluvium. Soilreaction varies from medium acid to neutral.Slopes range from 0 to 10 per cent. Soil seriesincluded are Hanalei and Kawaihapai. Lands aredifficult to ,~rk because of the stoniness. TheHanalei soils are subject to occasional flooding.Median annual rainfall varies from 30 to 80 inches.Elevations ranges from 0 to 300 feet.

37 Included are stony, deep, well-drained, moderatelyfine to fine-textured lands having dark reddishbrown surface soil over brown subsoil. The soilshave developed on old alluvial terraces. Soilreaction is very strongly acid to strongly acid.Slopes range from 0 to 10 per cent. Included isthe Lokekaa soil series. Lands are difficult towork because of the stoniness. Median annual rainfall varies from 40 t~ 90 inches. Elevations rangefrom 20 to 500 feet.

Oahu

Land Type

41

126

Description

Included are stony, deep, vrell-drained, medium tofine-textured lands having dark reddish-brownsurface soil over reddish-brown subsoil. The soilshave developed on old alluvial terraces or volcaniccinder deposits. Soil reaction is very strongly tostrongly acid. Slopes range from 11 to 20 percent. Included are the Lolekaa and Ualakaa soilseries. Lands are difficult to work because of theslope and stoniness. The layer of soil materialover cinders may be rather thin in the case of theUalaka~ soils. The Lolekaa soils are usuallydeficient in bases including potassium and calcium,and phosphorus fixation is high. Median annualrainfall varies from 40 to 90 inches. Elevationsrange from 20 to 500 feet.

42 Included are stony, moderately deep, imperfectly-drained, fine-textured lands having very dark graysurface soil over brown, gray, yellowish-brown, andolive, mottled subsoil. The soils have developedOn upland positions from old alluvium or lavasweathered in place, under conditions of restrictedinternal drainage. Soil reaction is neutral tomildly alkaline. Slopes range from 11 to 20 percent. Soil series and complexes included areHonouliuli, Kaena, Lualualei, Lualualei-Ewa Complex,Makalapa, and Papaa. Lands are difficult to workbecause of the slope, stoniness, and stickinesswhen wet. Median annual rainfall varies from 15 to40 inches. Elevations range from 0 to 500 feet.

47 Included are nonstony, deep, well-drained moderatelyfine to fine-textured lands with dark reddish-brownsurface soil over reddish-brown to red subsoil.The soils have developed on upland positions fromlava weathered in place. Soil reaction is mediumacid. Slopes range from 21 to 35 per cent. Soilseries included are Alaeloa, Kaneohe, Lolekaa,Nakalele, and Paaloa. Lands are difficult to workbecause of slope. Special problems range fromacidity to erodibility. Median annual rainfallvaries from 40 to 80 inches. Elevations range from10 to 1000 feet.

56 Included are rocky, deep, imperfectly to welldrained, medium to fine-textured l&;ds having darkred to very dark brown surface soil and red to graysubsoil. The soils have developed on uplands,upland talus positions, or alluvial flats fromresiduum of basic igneous rocks or from alluvium.Soil reaction ranges from slightly acid to neutral.

127

Land Type Description

Oahu Slopes range from 0 to 35 per cent. Soil seriesand complexes included are the Ewa, Hanalei,Honouliuli, Kaena, Kahana, Kahuku, Kawaihapai,Kawaihapai-Lualualei Complex, Koko, Lahaina,Lualualei, Lualualei-Ewa Complex, Mahana, Makalapa,Mamala, Molokai, Nakalele, Pulehu, Rockland Coral,Rocky lands, Stony lands, and Rough Broken LandSoil Complexes. Lands are virtually impossible towork because of the rocks, and in some cases,slope. Median annual rainfall varies from 25 to80 inches. Elevations range from 0 to 1500 feet.

57 Included are stony or rocky, shallow, well-drained,medium to fine-textured lands having variablesurface and subsoil colors. The soils havedeveloped on uplands or upland alluvial positionsfrom residuum of basic igneous rocks or fromalluvium. Soil reaction is quite variable. Slopesrange from 36 to 80 per cent. Soil series andcomplexes included are Ewa, Ewa-Lualualei Complex,Kaena, Kunia, Lahaina, Lolekaa, Lualualei, Lualualei -Ewa Comple."<:, r.1ahana, Manana, Molokai, Waikapu,and the (Rough Broken Land) Soil Complexes. Landsare virtually impossible to work because of theslope. In some cases, erosion is a serious problem.Median annual rainfall varies from 15 to 100 inches.Elevations range from nearly sea level to 1500 feet.

58 Included are generally steep pali lands havinglittle o~ no soil mantle over the rock material.Drainage is usually good because of the slope.Color varies with the rock composition and degreeof weathering. Slopes generally do not ~xceed

35 per cent, although some inclusions may be moresteep. Many of these lands occur on the sides ofgulches which dissect the open cultivated lands,but large areas belonging to this unit are found inthe Waianae and Koolau mountain ranges. Because ofthe slope and the lack of soil materials, theselands are not suited for agricultural uses. Amountand distribution of rainfall are highly variablethroughout the area of occurrence. Median annualrainfall varies from 15 to about 250 inches.Elevations range from 150 to 4000 feet.

128

APPENDIX B

IRRIGATION REQUIREMENTS

Waimanalo

Irrigation requirements for the study crops produced at Waimanalo

were estimated by multiplying inches of pan evaporation by an average

growing period consumptive use coefficient then deducting effective

rainfall. This procedure is based upon materials supplied by the Soil

Conservation Service. Table B-1 illustrates the calculation of these

requirements for the Waimanalo area. To complete the estimation of

water requirements for this area it was necessary to know during Which

months the "typical" vegetable farmer would grow the various crops. The

follo~ng cropping pattern was established from interviews with

EXperiment Station specialists (Horticulture):

Manoa lettuce (4 crops) April-May, June-July,

August-September, October-November

Snapbeans (3 crops) February-March-April, May-June-July,

August-September-October

Tomatoes (2 crops) February-March-April-May,

September-October-November-December

Cantaloupe (3 crops) February-March-April, May-June-July,

August-September-October

TABLE B-1. IRRIGATION REQUIR~1ENTS FOR STUDY CROPS AT WAIMANALO

Pan Evaporation Average Growing Period Rainfall Irrigation bl~ Inches per Month~(E) Consumptive Use (K) (E)·(K) Total Effective Requirement--Other Other Other Other

Bananas crops£! Bananas Crops£! Bananas Crops£! Bananas crops£!

January 4.5 .85 .58 3.82 2.61 2.0 1.34 1.09 2.48 1.52

February 4.6 .85 .58 3.90 2.66 1.4 1.00 .70 2.90 1.96

March 4.9 .85 .58 4.19 2.86 1.9 1.34 1.15 2.85 1.71

April 6.0 .85 .58 5.08 3.l~7 1.2 1.05 .70 4.03 2.77

Hay 7.3 .85 .58 6.24 4.26 .8 .70 .60 5.54 3.56

June 8.9 .85 .58 7.54 5.14 .5 .50 .40 7.04 4.74

July 9.0 .85 .58 7.61 5.19 .6 .50 .45 7.11 4.74

August 7.8 .85 .58 6.60 4.50 .6 .50 .35 6.10 4.15

September 7.3 .85 .58 6.20 4.23 .8 .72 .70 5.48 3.53

October 6.6 .85 .58 5.59 3.82 1.1 .80 .68 4.79 3.14

November 4.3 .85 .58 3.68 2.51 1.8 1.30 1.10 2.38 1.41Decembei:' 4.3 .85 .58 3.68 2.51 2.5 1.70 1.50 1.98 1.01

-~ Pan (!vaporation data obtained from reference (l~7).

£I There are 27,154 gallons of water in one acre inch.

£! Other crops consist of snapbeans, Manoa Lettuce, tomatoes, cantaloupe and papaya.

.....N\0

130

Waianae Kai and Hoolehua

Pan evaporation data are not available for Hoolehua. However,

Soil Conservation Service and Experiment Station specialists felt that

conditions at Hoolehua were similar to those found at Waianae Kai;

consequently, Waianae Kei data are used for both areas. Estimating

procedures are similar to those described above for Waimanalo and are

summarized in Table B-2. The Waianae Kai-Hoolehua cropping patterns

were established through interview with Experiment Station specialists

(Horticulture):

Manoa lettuce (5 crops) February-March, April-May,

JUne-July, August-September, October-November

Snapbeans (4 crops) January-February-March, April~ay-June,

July-August-September, October-November-December

Tomatoes (2 crops) February-March-April-May,

September-October-November-December

Cantaloupe (3 crops) January-February-March,

April-May-June, July-August-September

APPENDIX C

DEMAND CURVE TABLES

The average monthly sales for each of the included crops are given in Table C-l. Tables C-2 through

C-6 present the vegetable and orchard crop dem~.d functions used in this study. The interpretations of

these equations is discussed in the text under Demand Analysis~

TABLE C-l. AVERAGE MONTHLY SALES FOR SELECTED CROPS 1959-63~

Month Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. DecoCrop

Snapbeans 100 91 114 l24E! 13oE1 117E! ll~ 116 119 122 lO~ 98E1

Tomatoes 433E! 31~ 367E! 45lE! 440 422 395 358 274 297 307'E1 446E!-..

Manoa lettuce 131 112 151 151 152 140 126E1 133£/ 124E! 125E! 117£/ 134E1

Apple banana 564 448 453E1 42o'E! 444E1 443E! 523E! 612£1 641 709 644 630

~ The demand curve for cantaloupe is fitted without a seasonal shift.

£! High price months.

....WI-.:>

TABLE C-2. MULTIPLE REGRESSION OF SNAPBEAN PRICES

Source DF SS MS F R Sguare-Regression 3 0.463920E 04 O. 154640E 04 0.918705E 02 O. 703788 E 00

Residual 116 0.195256E 04 0.168324E 02

Source ~ Coefficient Standard Error Mean Std. Dev. R T

X( 1) Snafbeanpr ce -0.130279E 04 0.266521E 03 0.217817E 02 0.410273E 01

X( 2) Snapbeansupply -0. 170272E-00 0.125671E-01 0.1l7800E 03 0.308762E 02 -0.782468E 00 -0.135490E 02

X( 3) Year +o.686561E 00 0.135946E-00 0.195850E 04 0.281652E 01 0.390582E-00 0.505024E 01X(lO) Season !0.127980E 01 0.759452E 00 0.500000E 00 0.502096E 00 -0. 287164E-00 -0.337034E 01

TABLE C-3. MULTIPLE REGRESSION OF TOMATO PRICES

Source OF SS MS F R Square- -Regression 3 0.891258E 03 0.297086E 03 0.242024E 02 00 384965E-00

Residual 116 0.142391E 04 0.122751E 02

Source ~ Coefficient Standard Error Mean ~d. Dev. R T-X( 1) Tomato

price -0.975692E 03 0.230460E 03 0.179683E 02 0.350358E 01x( 4) Total

tomatosupply -0.100688E-01 0.423231E-02 0.525508E 03 0.839358E 02 -00 276l79E-00 -0.237902E 01

X( 7) Year +O.510700E 00 0.117940E-OO 0.195850E 04 0.281652E 01 0.279957E-00 Oo432473E 01X( 9) Season to.201171E 01 0.68 7060E 00 0.500000E 00 0.502096E 00 -0.525909E 00 -0.585599E 01

....\.oJW

TABLE C-4. MULTIPLE REGRESSION OF MANOA LETTUCE PRICES

Source OF SS MS F R Square

Regression 3 0.972885E 03 0.324295E 03 0.353622E 02 00477681E-00

Residual 116 0.106380E 04 0.917067E 01

Source Name Coefficient Standard Error Mean Std. Oevo R T- -X( 1) Manoa

lettuceprice -O.216604E 04 00251244E 03 00142208E 02 0.302831E 01

X( 2) Manoalettucesupply -0.103198E-00 0.124034E-Ol 0.1l6592E 03 0.293697E 02 -0. 264500E-00 -0.832014E 01

X( S) Year +0.111937E 01 0.128769 E-OO 0.195850E 04 0.281652E 01 00 292692E-00 0.869289E 01X(ll) Season ±0.088456E-00 0.557059E-Ol O.SOOOOOE 01 0.502096E 01 00283391E-00 0.317585E 01

TABLE C-5. SIMPLE REGRESSION OF CANTALOUPE PRICES

Source OF SS MS F R SquareRegression 1 0.405046E 03 0.405046E 03 0.714851E 02 0.817112E 00Residual 16 0.906585E 02 0.5666l6E 01

Source Name Coeffi cient Standard Error Mean Std. Oev. R T-X( 1) Cantaloupe

price 0.257213E 02 0.105730E 01 0.18l444E 02 0.238037E 01

X( 2) Cantaloupesupply -0.579616E-01 0.685540E-02 0.D0722E 03 0.842146E 02 -00903942E 00 -O.845489E 01

l-'W~

TABLE C-6. MULTIPLE REGRESSION OF APPLE BANANA PRICES

Source DF SS MS F !L2.suare

Regression 3 0.971206E 02 0.323735E 02 0.853634E 02 0.688248E 00

Residual 116 0.439923E 02 0.379244E-00

Source Name Coefficient Standard Error Mean Std. Dev. R T-X( 1) Apple

bananaprice -0.571858E 03 0.391831E 02 0.770833E 01 0.615828 E 00

X( 2) Bananasupply -00285318E-02 0.645183E-03 0.531167E 03 0.982895E 02 -0. 270148E-OO -0.442228E 01

X( 6) Year +0. 296703E-00 0.200205E-Ol 0.195847E 04 0.282843E 01 0.750378E 00 0.148200E 02X( 8) Season ;to.166684E-00 O.125964E-00 O.SOOOOOE 00 0.502096E 00 O.262817E-00 00264653E 01

I-'WVI

APPENDIX D

SUPPLY CURVE TABLES

The vegetable and orchard crop supply functions used in this study are presented in Tables 0-1

through D-4. The interpretation of these equations is discussed in the text under Supply Analysis.

TABLE D-l. MULTIPLE REGRESSION OF SNAPBEAN SUPPLY

~:!. DF SS MS F ~uare

Regression 5 0.306112E 05 0.612225E 04 0.832160E 01 0.267390E-00

Residual 114 0.838705E 05 0.735706E 03

Source Name Coefficient Standard Error Mean Std. Dev. R T-X( 2) Snapbean

supply 0.108516E 03 0.923215E 01 0.118450E 03 00271239E 02

X( 3) Year -0.354848E 01 0.101126E 01 0.525000E 01 0.291692E 01 -0. 198908E-00 -0.350899E 01

X( 6) Price L3 0.106113E 01 0.445023E-00 0.215267E 02 0.756999E 01 0.124484E-00 0.238444E 01

X(24) Q1 -0.124097E 02 0.705645E 01 0.250000E-00 0.434828E-00 -0.354218E-00 -0.175863E 01

X(25) Q2 0.178070E 02 0.747482E 01 0.250000E-00 0.4348 28 E-OO 0.323065E-00 0.238227E 01

X(26) Q3 0.174843E 02 0.7.39757E 01 0.250000E-00 0.434828E-00 0.108104E-00 0.236352E 01

I-'Lv~

TABLE D-2. MULTIPLE REGRESSION OF TOMATO SUPPLY

Source DF SS MS F R Square-Regression 5 O.321132E 06 0.642263E 05 0.756745E 01 0.249196E-00

Residual 114 0.967538E 06 0.848718E 04

Source Name Coefficient Standard Error Mecm Std. Dev. R T

X( 1) Tomatosupply O.322263E 03 0.384%7E 02 0.390650E 03 O.921259E 02

X( 2) Price I.4 O.349411E 01 0.239723E 01 0.177458E 02 O.446624E 01 0.239857E-00 0.145756E 01

X( 4) Year -0.433861E 01 O.314590E 01 0.550000E 01 0.288432E 01 -0. 737721E-01 -0.1379l3E 01

X( 5) Q1 0.222650E 02 0.237868E 02 0.250000E-00 0.434828 E-OO -0.8979l3E-Ol 0.936024E 00

X( 6) Q2 0.105257E 03 0.268256E 02 0.250000E-00 0.434828 E-OO 0.473099E-00 0.392375E 01

X( 7) Q3 -0.654879E 01 0.2580S1E 02 0.250000E-00 0.434828 E-OO -0.169276E-00 -0. 253779E-OO

t-'W'-I

TABLE 0-3. MULTIPLE REGRESSION OF MANOA LETTUCE SUPPLY

Source OF SS MS F !LSquare

Regression 5 0.481190E OS 0.962381E 04 0.201202E 02 0.468782E-00

Residual 114 0.s4s280E OS 0.478316E 03

Source ~ Coeffi ci ent . Standard Error ~ Std. Oev. R T

X( 1) Manoalettucesupply 0.717328E 02 0.949723E 01 0.116s92E 03 0.218704E 02

X( 2) Price L1 0.219835E-00 0.s54011E 00 0.141950E 02 0.414117E 01 0.112576E-00 0.396807E-OO

X( 4) Q1 -0. 22s937E-00 0.s68900E 01 0.2s0000E-:00 0.43413 28 E-OO -0.143283E-00 -0. 397147E-01

X( 5) Q2 0.204013E 02 0.608664E 01 0.250000E-00 0.434828E-00 0.251526E-00 0.335182E 01

X( 6) Q3 0.870310E 01 0.589378E 01 0.2s0000E-00 0.4348 28 E-OO 0.2s1691E-01 0.147666E 01

X( 8) Year 0.627612E 01 0.734415E 00 0.550000E 01 0.288432E 01 0.625601E 00 0.854573E 01

....W0:>

TABLE D-4. MULTIPLE REGRESSION OF BANANA SUPPLY

Source DF SS MS F !L29.uare

Regression 4 0.444118E 06 0.111029E 06 0.175244E 02 0.378707E-00

Residual 115 0.728604E 06 0.633569E 04

Source Name Coefficient Standard Error Mean Std. Coeff. R T-X( 1) Banana

supply 0.596352E 03 0.627750E 02 0.532000E 03

X( 2) Price 1.6 0.198201E 01 0.754258E 01 0.748417E 01 0.208938E-01 0.244394E-00 0.262777E-00

X( 4) Q1 -0.110807E 03 0.213367E 02 0.250000E-00 -0.485357E-00 -0.186499E-00 -0.518840E 01

X( 5) Q2 -0.157833E 03 0.221446E 02 0.250000E-00 -0.691340E 00 -0.464885E-00 -0.712737E 01

X( 6) Q3 -0.481039E 02 0.210228E 02 0.250000E-00 -0. 210705E-00 0.181827E-00 -0.228817E 01

I-"W-0

140

APPENDIX E

CC»IPUTING FACILITIES REQUIRED FOR DATA ANALYSIS

The Statistical and Computing Center at the University of Hawaii

is presently equipped vnth an IBM 7040 computer. This installation was

used for all of the data analysis included in this study.

Computing Time by Phase

The approximate computing time required for each phase of the

investigation is as follows:

Regression analysis • • • • • • • • • • • • • • • 1.5 hours

De-bugging the linear programming system •••• 2.5 hours

Estimation of land use patterns • • • • • • • • • 1.0 hours

In addition to this machine time a substantial amount of programmer time

was supplied by the Center to assist in writing and de-bugging the

linear programming system.

Computing Time Required for Estimating Land Use Patterns

Computing time required for the different land use patterns varied

from two to 10 minutes depending upon the nature of the problem. The

longest runs occurred with patterns IB, IC, and IE where either the high

cost or restrictiveness of Hoolehua labor forced the crops to compete

for the limited Oahu lands. In patterns IA and ID the large acreage of

high quality land (680 acres of Lel) at Hoolehua entered early allovnng

rapid convergence. The approximate computing times for the several

patterns are listed below.

141

Pattern

IA ($1.25 ~vage; 25-acre units) • • 0 • • • • • • 0 • • •

IB ($1.50 wage at Hoolehua; 25-acre units) • • • • • • •

IC ($2.00 ~vage at Hoolehua; 25-acre units) • • • • • • •

ID ($1.25 wage; four-acre units) • • • • • · • • · • • •

Computing Time(minutes)

2

3

10

2

IE ($1.25 wage; 25-acre units; quarterly laborrestrictions at Hoolehua ••••••••• • • • • • 7

In cases of slow convergence it is possible to obtain preliminary

results by interrupting the solution process. Frequently, these results

will contain sufficient data for deleting one or more of the crops

before attempting a subsequent run. Crops could be deleted if they are

entering on insignificant acreages or if their acreages change little

from one stage of the solution to the next. If they are deleted for the

latter reason the acreage in the regions where they are to be grown must

be reduced appropriately. It may also be found that the system is close

enough to a solution for the intermediate results to be used as they

are. To reduce the likelihood of slow convergence the program could be

modified as follows:

(a) Price need not be modified according to the production

costs of the several producing regions as was done in this

study. If price is changed by a uni'form amount at each

step of the solution process the major cause of slow

convergence is removed. However, this modification of

the procedure will affect the accuracy of the results as

the final price for each crop may be higher or lower than

the actual price.

(b) An approximate solution could be accepted by writing a

degree of allowable error into the program. There are a

number of ways of doing this; perhaps the most obvious is

to accept a solution that is ~thin a given range of the

required market supply.

142

143

REFERENCES

1. Adams, R. L. Farm Management Crop M~. Universi ty ofCalifornia Press, 1953.

2. • Farm Managemant Livestock Manual. University ofCalifornia Press, 1954.

3. Baker, H. L. Molokai: Present and Potential Land Use. L.S.B.Bulletin No.1. University of Hawaii, August, 1960.

4. Barlowe, Raleigh. Land Resource Economics. Prentice-Hall, Inc.,1958.

5. Bembo1:ver, William. Banana Production in Hawaii. CooperativeEXtension Service, University of Hawaii, June, 1962.

6~ Case, Frederick Eo Real Estate. Allyn and Bacon, Inc., 1962.

Civilian Population, Births Deaths, and Migration Data of Hawaiiby Geographic Area, 1950-l9~3. Hawaii Department of Health, April,1963.

8.

9.

Dorfman, Robert, Paul A. Samuelson and Robert M. Solow. LinearProgramming and Economic Analysis. New York: McGraw-Hill Book Co.,Inc., 1958.

Egbert, Alvin C. and E. O. Heady. Regional Adjustments in GrainProduction. U. S. Department of Agriculture in Cooperation withIowa Agricultural EXperiment Station, Technical Bulletin No. 1241and Supplement, June 1961.

10. Ely, Richard T. and George S. wehrwein~ Land Economics. TheMacmillan Co., 1940.

11. Ezekiel, Mordecal and Karl A. Fox. Methods of Correlation andRegression Analysis. New York: John Wiley & Sons, Inc., 1961.

12. Feasibility Study on Potatoes. Unpublished report from the filesof the Hawaii State Department of Agriculture, November, 1964.

13. Foytik, Jerry. Demand Characteristics for Vine Vegetables inHonolulu, Hawaii, 1947-61. Agricultural Economics Bulletin No. 23,University of Hawaii, December, 1964.

14. • Demand Characteristics for Selected Fruits in Honolulu,Hawaii, 1947=61. Agricultural Economics Bulletin No. 24, Unive;';si ty of Haws~ i:- December, 1964.

15. • Intraseasonal Demand Shifts for Fruits and Vegetables.Paper presented at the annual meeting of the Western Farm EconomicsAssociation, San Luis Obispo, July 15-17, 1964.

144

16. Hamilton, Richard A. Papayas Grown on Shallow Clay Soil. HawaiiFarm Science, Agricultural Experiment Station, University o£Hawaii, January, 1954.

17. Heady, Earl O. Economics o~ ~ricultura1 Production and Resource~. Prentice-Hall, Inc., 1964,.

18. Heady, Earl 0., et...!l. Agricultural Supply Functions. Ames, Iowa:Iowa State University Press, 1961.

19. Heady, Earl O. and Yalfred candler. Linear Programming Methods.Ames, Iowa: Iowa State University Press, 1960.

20. Hogg, Howard C. ''Returns to Land and Gross State Income FromRanching Operations in Hawaii." L.S.B. Unpublished Report, June,1964.

21. Hogg, Howard C. and Harold L. Baker. Ranching Costs and Returns Maui. L.S.B. Miscellaneous Report No.1, University of Hawaii,NOV'einber, 1961.

22. • Ranching Costs and Returns - Kauai. L.S.B. Miscella-neous Report No.2, University of Hawaii, February, 1962.

23. Ran~hing Costs and Returns - Hawaii. L.S.B. Miscella-neous Report No.3, University of Hawaii, March, 1962.

24. Ranching Costs and Returns - Oahu. L.S.B. MiscellaneousReport No.4, University of Hawaii, June, 1962.

25. • Ranching Costs and Returns - Mo1okai. L.S.B. Miscella-neous Report No.5, University of Hawaii, September, 1962.

26. Honolulu Unloads. United States Department of Agriculture, MarketNews Service Cooperating with Hawaii Department of Agriculture.Annual. 1954-63.

27. Johnston, J. Econometric Methods. New York: McGraw-Hill BookCo., Inc., 1963.

28. Judge, G. G. and T. D. Wallace. Estimation of Spatial PriceEqUilibrium Models. Journal of Farm Economics, Vol. XL, No.4.November, 1958.

29. Kee~er, Joseph T. Cost and Returns Budget for Macadamia Nuts.Department of ~gricultural Economics, University of Hawaii. Unpublished Report, 1964.

\\

Department of AgriculUnpublished Report, 1964.

30. Cost and Returns for Bananas.tural Economics, University of Hawaii.

145

31. Keeler, Joseph T. Cost and Returns for Passion Fruit. Departmentof Agricultural Economics, University of Hawaii. UnpublishedReport, 1964.

32. Cultural Methods and Costs in Growing Puna Papayas.Department of Agricultural Economics, University of Hawaii. Unpublished Report, 1964.

33. Keeler, Joseph T. and Donald M. Kinch. Diesel Versus Gasoline inSmall Wheel Tractors. Agricultural Economics Report No. 43,University of Hawaii, January, 1960.

34. Keeler, Joseph T., K. Mihata and W. Nakashima. Economic FactorsAffecting the Production of Papayas in Waimanalo, Oahu. Agricultural Economics Report No. 49, University of Hawaii, June, 1961.

35. Larson, A. B. and L. B. Rankine. Report on Molokai DemonstrationFarm for 1963-64. Agricultural Economics Progress Report, University of Hawaii. In process.

36. Lefeber, Louis. Allocation in Space: Production, Transport, andIndustrial Location. Amsterdam: North-Holland Publishing Co.,1958.

37. Long-Term Projections of Demand and Supply for Selected Agricultural Commodities. New De1.hi: National Council of AppliedEconomic Research, 1962.

38. Lucas, E. C. Demand and Price Structure of Tomato in Hawaii andIts Implication for Control of Market Supply. Department ofAgricultural Economics, University of Hawaii. Unpublished Paper,January, 1965.

39. McCOnnell, Douglas J. Preliminary Studies on the Feasibility ofProducing Vegetables on Mo1okai. Hawaii Agricultural ExperimentStation Report. Progress Report Nos. 1, 2, and 3, University ofHawaii, March, 196~.

40. Mollett, J. ~ Cost of Producing Lettuce in Hawaii. AgriculturalEconomics Report No. 54, University of Hawaii, July, 1961.

41. Cost of Producing Tomatoes in Hawaii. AgriculturalEconomics Report No. 61, University of Hawaii, January, 1963.

42. Nakagawa, Yukio. Gro~~ng Tomatoes in Hawaii. Extension Circular374, University of Hawaii, April, 1957.

43. • Growing Lettuce in Hawaii. Extension Circular 376,University of Hawaii, April, 1957.

44. Nelson, L. A. Detailed Land Classification - Island of Oahu. L.S.&Eulletin No.3, University of Hawaii, January, 1963.

45. Nerlove, Marc. Distributed Lags and Estimation of Long-Run Supplyand Demand Elasticities: Theoretical Considerations. Journal ofFarm Economics, Vol. XL, No.2. May 1958.

46. Nerlove, Marc and William Addison. Statistical Estimation of LongRun Elasticities of Supply and Demand. Journal of Farm Economics,Vol. XL, No.4. November, 1958.

47. Pan Evaporation Data - State of Hawaii. Department of Land andNatural Resources, State of Hawaii, September, 1961.

48. Panaewa Farm Development. Panaewa Farm Development Committee.Hilo, Hawaii. March, 1964.

49. Peters, C. W., J. A. Mollett, and WOodrow Y. Nakashima. Mainland~rkets for Hawaiian Winter Vegetables. Agricultural EconomicsReport No. 51, University of Hawaii. June, 1961.

50. Peters, C. W., Robert H. Reed, and C. Richard Creek. Margins,Shrinkage, and Pricing of Certain Fresh Vegetables in Honolulu.Agricultural Economics Bulletin No.7) University of Hawaii. June,1954.

51. Preliminary Plan for Establishment of Windbreaks at Molokai FarmLots. Department of Land and Natural Resources, State of Hawaii.Unpublished Report~ Jul~ 1964.

52. Ratcliff, Richard U. A Restatement of Appraisal Theory. University of Wisconsin, 1963.

53. Renne, Roland R. Land Economics. Harper and Brothels, 1947.

54. P~jko, Anthony S. Time Series Anal sis in Measurement of Demand.Agricultural Economics Research, Vol. XIII, No.2. April, 19 1.

55. Schrader, Lee F. &ld Gordon A. King. Regional Location of BeefCattle Feeding. Journal of Farm Economics, Vol. XLIV, No.1.February, 1962.

56. Scott, F. S., Jr. An Analysis of Market DevelOpment for FrozenPassion Fruit Juice. Agricultural Economics Bulletin No. 11,University of Hawaii, June, 1958.

57. Sales Potentials and Methods and Costs of MarketDevelOpment for Macadamia Nuts. Paper presented at conference onMacadamia Nuts sponsored by the Honolulu Chamber of Commerce andThe Hawaii Macadamia Producers Association.

58 0 Shoji, Kobe, Masao Nakamura and Mitsuo Matsumura. Growth and Yieldof Papaya in Relation to Fertilizer Application. AgriculturalExperiment Station, University of Hawaii, July, 1958.

59. Smi th, V. L.?qui libri Ulll.

Minimization of Economic Rent in §patlal PriceReview of Economic Science, Vol. 30, 1963.

147

60. Soil Survey - Territory of Hawaii. U. S. Department of Agricultureand Hawaii Agricultural EKperiment Station, 1955.

61. Statistics of Hawaiian Agriculture. Hawaii Crop and LivestockReporting Service Cooperating with United States Department ofAgriculture. Annual 1954-63.

62. Takeyama, T. and G. G. Judge.Programming. Journal of Farm1964.

63. Tallaferro, W. J. Rainfall of the Hawaiian Islands. Hawaii ~ater

Authority, State of Hawaii.

64. Tomek, ~lliam G. Using Zero-One Variables with Time Series Datain Regression Equations. Journal of Farm Economics, Vol. 45, No.4.November, 1963.

65. Tramel, Thomas E. and A. D. Seale, Jr. Reactive Programming of- Supply and Demand Relations--Application to Fresh Vegetables.

Journal of Farm Economics, Vol. XLI, No.5. December, 1959.

66. Whittlesey, Norman K. and Melvin D. Skold. Production Quotas andLand Values: Dmportance of the Dual in a Spatial LinearProgramming Problem. Journal of Farm Economics, Vol. 46, No.5.December, 1964.

67. Yeron, Dan and E. O. Heady. Approximate and Exact Solution to Non~

Linear Programming Problem with Separable Objective Function.Journal of Farm Economics, Vol. XLIII, No.1. February, 1961.

68. Yee, ~alter S. Mu1reg-A General Multiple Regression Program for the}BM 7040. Hawaii Institute of Geophysics, University of Hawaii,June, 1964.

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