Bas Boen project
 Production model
 Principal components
 Estimates of principal compone...
 Discrete variable responses
 Adjustments among the continuous...
 Predicted production gains

Group Title: Florida Agricultural Experiment Station Journal series ; no. 5352
Title: Evaluation of Haitian agricultural development with the use of principal components
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00080687/00001
 Material Information
Title: Evaluation of Haitian agricultural development with the use of principal components
Series Title: Florida Agricultural Experiment Station Journal series ;, no. 5352
Physical Description: 29 leaves ; 28 cm.
Language: English
Creator: Ward, Ronald W.
Zahalka, Ahmed.
Publisher: University of Florida, Agricultural Experiment Station
Publication Date: 198-?
Subject: Agricultural extension work -- Evaluation. -- Haiti
Spatial Coverage: Haiti
General Note: Caption title.
General Note: Includes bibliographic references (leaf 29).
 Record Information
Bibliographic ID: UF00080687
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 163577174

Table of Contents
        Page 1
    Bas Boen project
        Page 2
        Page 3
        Page 4
        Page 5
    Production model
        Page 6
    Principal components
        Page 7
    Estimates of principal components
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
    Discrete variable responses
        Page 16
        Page 17
    Adjustments among the continuous inputs
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
    Predicted production gains
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
Full Text

Evaluation of Haitian

Agricultural Development

with the Use of Principal Components

Ronald WV Ward and Ahmed Zahalka *

Considerable experimental agricultural development

efforts have been undertaken in Haiti (Zurekas, pp. 80-120).

Many of the Haitian programs have been direc-ted toward

development through intensive extension efforts with the

primary purpose to provide local producers with improved

production and management techniques. Frequently such

experiments are localized and designed to emphasize the

development methods while giving less consideration to

evaluating the overall project performance. The Haitian Bas

Boen region development project is a case in point (Prires,

p. 76).

In this paper the Eas Doen project is considered with

the primary objective to show the relationship between

production responses to intensive agricultural development

programs. A statistical technique using principal

components is used to link production responses to those

inputs changed through the extension efforts.

Ronald W. Ward is a professor in the Food and Resource
Economics Department, University of Florida. Mr. Ahmed
Zahalka is a market development consultant in Barbados.
The authors acknowledge the comments from the journal
Florida Agricultural Experimen-t Station Journal Series
No.. 5

-l l. r ... .

Bas Soen Project

The Bas Boen project began in 1969 with an agreement

signed by the Organization of American States, Israel and

Haiti (La Gra, p. 4). It was a pilot project with the major

objective to enhance rural farm incomes through improved

production and management practices. Between 1969 and 1973

experiments in collective farming using a variety of

nontraditional commodities were considered with little

apparent success. Because of crop failures and changes in

government policies toward exports,- the project was

redirected back to the production of traditional crops such

as corn, sorghum, beans, cane, and tobacco.

The role of agriculture in Haiti's economy has received

increasing attention in recent years, reflecting a growing

awareness that the country's economic problems cannot be

solved without a solution to the problem of agricultural

stagnation. Zurekas advocated the necessity of a farm-level

study in Haiti where the study would help determine the

productivity increases obtainable from purchased inputs and

new production practices (p. 342). Gislason noted the

important potential impact the Bas Boen project could have

on productivity by training small producers to adopt new

technologies to small plots of land (p. 38). Prires

describes the Bas Boen project's gradual approach in

achieving the goal of improving the welfare of the farmers

through intensive extension efforts (p. 76). Finally, La

Gra documented the changes at Bas Boen that were felt to be

a result of the project between 1969 and 1972. Each of

these studies emphasis the need to have better empirical

analysis of the work completed

In 1.974 an intensive extension system using para-

extension personnel was integrated into the project.

Because of the changes between 1969 to 1973, the early

stages of the development efforts have little relevance to

the subsequent production gains except to redirect the

overall emphasis. Hence, the analytical focus of this

research is for the periods 1974-78 (Gislason, p. 10).

The overall project consists of seven villages,

including the Bas Boen area which received the initial

intensive extension assistance. The educational levels,

incomes, family characteristics, and cultural and religious

practices differ little among the local farmers. Hence, for

statistical purposes the population studied can be

considered homogeneous. A test later in the analysis will

provide support for the homogeneity assumption.

While a total of 597 producers were in some way

exposed to the extension input over the four years, data

were collected on.a continuous basis for a random sample of

30 producers. The professional extension staff recorded

production data from the outset of the project. Hence for

those randomly selected producers monitored, the information

recorded should be highly reliable since it was coded by

trained extension personnel. The analysis is limited to the

small number of producers where the'extension programs were

overseen on a continuous bases (Zahalka, pp. 53-56).

Information on a wide range of crops was documented;

however, cane, corn, beans, sorghum and tobacco accounted

for over 90 percent of the acreage and resulting'income.

The subsequent analysis is limited to these five crops.

The analysis includes data pooled over cross sections

of producers and time series over production periods. The

producers differed primarily by the size of their operations

and were very similar with respect to demographic

characteristics. If a data pooling problem exists it would

most probably be related to size differences. Hence, a test

for pooling bias will be made later in the analysis by

defining the cross sections by size of the land farmed.

Table 1 provides a description of the data collected

during the project life. Production yields (YD) are

expressed in output per hectare for each of the five crops.

Guidance in the use of each of the variables in Table 1 was

provided on a continuous basis by the resident extension

specialist. A large part of the changes in the use of these

input variables can be attributed to the extension efforts.

This is especially true when compared to earlier uses of

these inputs. The first six input variables (SD through CL)

represent physical inputs where continuous levels can be

applied. Whereas, the remaining variables are discrete in

that they either were or were not used (eg., they can be

expressed as dummy variables). Variables (ML, CL, VY, IN,

SR, CL, MD) were measured as discrete management practice

Table 1. List of variables and unit of measurement.

Variable Symbol Unit of Measurement

Yield YD Yields in kilograms per hectare

Seeds SD Founds of seeds per hectare

Fertilizers FZ Pounds of basic fertilizer
per hectare

Nitrogen NG Pounds of urea 46% per hectare

Water WR Hours of irrigation per hectare

Labor LR Day-work of manpower per hectare

Cleaning CL Times of cleaning with units of
1, 2, 3,and 4 times per crop season

Multiple ML Number of crops planted in the
Cropping same field per year

Rotation RN Crop rotation plan (code with 1=
recommended rotation, and 0=
unrecommended rotation)

Variety VY Variety of seeds (code with 1=
hybrid seeds, and O=Iocal seeds)

Insecticide IN Code with 1=insecticide applies
and 0=no application

Supervision SR Code with l=under supervision
and 0=no supervision

Method MD Method of planting (code with
1=planted on rows and 0=planted
in square or pockets)
Entries below the dash line are discrete type management

type inputs in contrast to the varying levels of the other


Production Model

The variables in Table 1 include those major inputs

that should theoretically influence the yields for each of

the five commodities. Furthermore, complementarity probably

exists among some of the inputs. For example, the gains

from cleaning probably depend on irrigation efforts,

fertilizer use, etc. Such complementarity points to the use

of a multiplicative type production function. If X. is

defined to be those continuous input variables and X. the
discrete inputs, then the multiplicative function in

equation (1) can be adapted to the current problem.

(1) B. (Bo + j XK. + e)
YD = IX. exp J J

Each input should have a positive effect on yields such that

3YD / a X. = B.YDIX. > 0 AND 2 YD/~X. 9Xk B. B YD/X. X > 0
I I I I k 1 k I k

where k j j. Likewise, for the discrete variables, the

yield adjustment from some level YD due to using one or

more of the inputs follows as in (2):

E am m
(2) YDI = YD0 (exp 1) > 0

where m is a subset of the j discrete variables that have

been changed. The gains in both cases depend on the levels

of the other inputs.

The time and cross sectional notational has not been

included in (2). Using the variables discussed earlier for

this model requires the pooling of cross sectional and time

series data. A explicit test for the appropriateness of

pooling the data will be addressed in the empirical

analysis. Clearly, equation (1) can be transformed into a

linear estimable function as shown in (3).

(3) log(YD) = Bi log(X.) + ABjXj + Bg + e.

Define the vector Y as the transformed yields in

equation (3) and X as a matrix of the transformed inputs,

then yields are related to the inputs where

(4) Y = X B + E.

Clearly if the vector B is known, the effect of each input

on production can be measured.

Development efforts in the Bas Boen project were

designed to influence the values of X. If the extension

efforts were completely successful in stimulating the

sampled producers to respond to the recommendations, then it

is likely that the levels of various inputs would be highly

correlated. Estimation problems associated with

multicollinearity are well known and principal components

procedures provide a useful way for dealing with the data


Principal Components

Theoretically there exists some vector of weights

applied to the exogenous variables such that

(5) (X'X hI)W = 0, and W'W = I.

Note that the latter product matrix assumes the vectors of W

to be orthogonal and h is a vector of eigenvalues and W is

a matrix of weights or eigenvectors. In then follows that

W'X'XW = hi or P'P=hl where P = XW. P represents the

principal components of X and are orthogonal since P'P is a

diagonal matrix. Hence, there exist a matrix W such that X

can be transformed into a set of orthogonal vectors. The

vector of the first principal component is defined as F1 =

XW1. The variability among the independent variables is

equal to tr(X'X). Furthermore, tr(P'P) = tr(W'X'XW)

tr(X'XW'W) = tr(X'X). That is, the variance of the input

variables can be explained by a set of orthogonal vectors P.

For the present problem, the values of h and W are of

particular importance.

Estimates of Principal Components

Equation (3) differs slightly with each of the five

crops since some of the inputs were not present or did not

change for a particular crop. Hence, selected B's are

restricted to zero for different crops as will be shown

subsequently. For the variables included in each crop, the

correlations ranged from near zero up to .97.

Given the correlations and the results from equation

For a detailed discussion of principal components see
Dhrymes, pp.53-65. The above discussion of the trace of X'X
assumes that the vectors of X have been first normalized.
Subsequent regression estimates wil', however, be for the
original values of X.

(4), both h and W are shown in Tables 2 and 3. The

eigenvalues for the principal component of each crop are

shown in Table 2. The sum of the eigenvalues equals the

variance of the normalized X and the ratio of each

eigenvalue to this total shows the contribution of each

principal component in representing the variability of the

initial input variables (X). For example, over 75 percent

of the variability of corn inputs can be explained with the

first three principal components (see the % columns).

Similarly over 90 percent of the cane-- input variability is

explained with the first three components. Hence, for most

of the commodities it is possible to estimate the

relationships between the yields and inputs by using a

limited number of orthogonal vectors to represent the input


The eigenvectors W give a weighted contributed of each

input variable in calculating the principal components,

recalling that P=XW. These weights represent loading factors

useful for showing the importance of each input to .the

principal components. The weights of the first component

are usually easy to interpret, while values for the higher

components are more difficult to explain.

Consider in Table 3, the weights for the first

principal component for. the commodities point to the

importance of each input. For cane, WR, LR, RN, and SR are

The correlations are not reported in this paper but are
available upon written request to the authors.

Table 2. Eigenvalues (EV) and cumulative percentage of variance.

PC I Corn 1 Sorghum Beans I Cane 1 Tobacco
SEV % I EV % EV % EV % EV %
----------+----+------4----- -----+---------- ---+--------------
1 5.83 .53 7.43 .68 5.20 .58 4.20 .70 2.32 .39
2 1.46 .66 1.10 .78 .92 .68 .88 .85 1.73 .68
3 .93 .75 .80 .85 .82 .77 .40 .91 .90 .83
4 .73 .82 .53 .90 .80 .86 .36 .97 .72 .95
5 .51 .87 .37 .93 .50 .92 .20 1.00 .33 1.00
6 .45 .91 .31 .96 .40 .96
7 .36 .94 .24 .98 .21 .99
8 .29 .96 .13 .99 .13 1.00
9 .24 .98 .09 1.00
10 .18 .99
11 .02 1.00

First 3 PC for the five basic crops.

Crop Var Principal 1 Principal 2 Principal 3




Corn FZ 0.28950
NG 0.32684
WR 0.32845
LR 0.29877
ML 0.29391
RN 0.35818
VY 0.22994
IN 0.19356
SR 0.35937
CL 0.31147
MD 0.28469



-0. 17184

-0. 14611
-0. 16358

0. 14954
-0. 19131

Beans SD 0.40305 -0.18764
WR 0.33562 0.06775
LR 0.24755 -0.00865
ML 0.30149 0.37215
RN 0.37777 -0.44569
IN 0.22478 0.42501
SR 0.38931 -0.39782
CL 0.39444 0.07228
MD 0.26896 0.52808

Tobacco FZ 0.36294 0.44544
NG 0.57102 -0.34881
WR 0.58554 -0.31123
LR 0.24094 0.42583
ML 0.33911 0.04305
CL 0.16218 b.63235



0. 12608

-0. 11885
-0. 16672


Table 3.

weighted almost equally while ML and CL are slightly less.

Each input for corn is weighted very close except for

insecticides, that is they have similar loading factors for

calculating the first principal component. The remaining

weights for the other commodities lead to a similar

observation that the inputs are of relative close importance

with only a few exceptions. The weight of nitrogen use for

sorghum was low relative to the other factors. Possibly

part of this low value results because some of the nitrogen

inputs were included in FZ. Data on -specific FZ analyses

were not available.

The second principal component and the resulting

weights (col. 2 of Table 3) are used to show the importance

of the inputs to explaining the residual variability in X

not explained by the first component. If the first

principal component explains a large portion of the

variability of X, then the interpretation of the remaining

components become much more tentative. What is generally

seen in both the second and third components is that only a

few of the inputs are of major importance and that no

consistent weighting is apparent across the commodities.

Given the different biological requirements of these

commodities, the resulting differences in weights beyond the

first principal component would be expected.

The most important aspect of the weights in Table 3 is

that they are used to calculate the principal components.

The parameters of (4) can then be estimated using a limited

set of principal components, say up to the first r


(6) Y = Pr A + E

=X Wr A + E

where B = WrA, Wr is (k x r), A is (r x 1), and Pr =X Wr,

assuming there are k variables and r components (i.e., r <=

k). Recall that the vectors of P are orthogonal, hence A

can be estimated in (6). Given A and W, then B of (4)

immediately follows.

The final regression results using the above procedures

are reported in Table 4 for each crop. The parameters

correspond to the multiplicative function set forth in

equation (1). The values were calculated by multiplying

the weights of W in Table 3 by the principal component

parameters (A). The A parameters are not shown since their

values alone are of limited interest. Standard errors are

reported in parenthesis and the R2 values apply to YD

relative to YD. The signs of all parameters but one are

correct and the significance levels are acceptable. The

impact of nitrogen on sorghum is negative but statistically

insignificant. Where parameter values are missing, that

variable did not enter a particular crop model.

For those continuous variables (SD, FZ, NG, WR, LR, CL)

the parameters can be readily interpreted as the percentage

yield adjustment resulting from a percentage increase in

each particular input. The actual effect of each input

differs considerably across commodities as will be shown

Table 4. Estimated coefficients using principal components.

Var. Crop

Corn Sorghum Beans Cane Tobacco

cept 2.486 3.101 1.954 7.921 -8.223

SD .4982

FZ .0127 .0093 .3534
(.0013) (.0004) (.1776)

NG .0158 -.0158 .0-066
(.0008) (.0125) (.0160)

WR .3771 .0199 .2804 .0123 .2691
(.0263) (.0000) (.0208) (.0000) (.3750)

LR .7140 .8344 .2475 .5599 2.0732
(.0618) (.2895) (.0282) (.0000) (.5712)

ML .2344 .1840 .2071 .0894 .4310
(.0145) (.0085) (.0264) (.0634) (.2718)

RN .1662 .1444 .0756 .0535
(.0148) (.0055) (.0177) (.0064)

VY .1287 .1512
(.0400) (.0070)

IN .1192 .1512 .0995
(.0460) (.0070) (.0473)

SR .1680 .1509 .0782 .1141
(.0153) (.0074) (.0153) (.0128)

CL .1342 .1446 .1334 .2453 .5868
(.0198) (.0162) (.0067) (.1474) (.1771)

MD .1376 .1353 .1260
(.0115) (.0040) (.0200)

RE .8592 .9140 .7185 .6958 .3882
# PC 3 3 3 3 3
SSE 2.5114 1.1002 1.9162 .3960 2.6202
MSE .0230 .0154 .0183 .0073 .1007
# OBS 113 75 109 58 30
G TEST .0291 5.6709 20.9444 8.7470 1.2063
F TEST .2427 .3935 .6406 .0848 .3283

later. The explanatory power of the estimates were

reasonable except for the tobacco equation where the R =

.38. The lower value is of importance to tobacco producers

in that considerable more variability in yields could be

expected even with well planned use of those inputs included

in the tobacco equation.

Three principal components (i.e., r=3) were used in

each equation. Estimates with more principal components

were nearly identical to those reported in Table 4.

The potential problem from pooling the producers over

the production years was recognized earlier. If there are

differences among the cross sections, the error sums of

squares should differ among the cross sections. Breusch and

Pagan provide one test of this difference where their

weights of the errors (see E in equation (4)) follows a Chi-

Square distribution. The G-test values shown in Table 4 are

the weighted errors over cross sections and without

exception they are less than the table Chi-Square values,

assuming a .05 significance level. One further test of the

homogeneity property is to adjust the equations in Table 4

by using a covariance model accounting for both cross

sectional and time series effects. Pindyck and Rubinfeld

(p.252) outline a well known F test procedure comparing the

difference in the error sums of squares from the covariance

and fixed parameters models. If the F value is not

significant, then the error structures are statistically no

different with the adjustments for either cross sections and

time series. These F values shown at the bottom of Table 4

are statistically insignificant for each commodity. With

these results using size of the farm as the pooling

criteria, the homogeneity assumption cannot be rejected and

pooling presents no particular estimation problem. Given

these parameter values, the problem of evaluating the

success of the Bas Boen project can be addressed.

Discrete Variable Responses

A number of the input variables were discrete as noted

in Table 1. If YDo is defined as the level of yields

without some particular discrete input and YD1 as the level

with that input, then the percentage adjustment by adding

that input is easily shown (Halvorsen and Palmquist, p.


(7) ((YD YD )/YD ) = exp 1

These percentage adjustments are given in Table 5 for the

four crops where the discrete variables were present. Both

corn and sorghum included all five discrete inputs. The

last column in Table 5 shows the total gain expected if all

discrete variables were added. For corn and sorghum, nearly

a 78 percent increase in yields would be expected over the

level YD while the gain would be considerably less for

beans and cane. The importance of crop rotation (RN) and

supervision (SR) by the extension specialist is seen where a

15-18 percent increase in yields would be projected with

each of these inputs. Gains resulting from planting in rows

Table 5. Response of crop yields to the discrete inputs.

Crops Variables
RN % VY % IN % SR % MD % TOTAL %
Corn 18.08 13.74 12.66 18.30 14.75 77.53
Sorghum 15.54 16.30 16.29 16.28 13.35 78.16
Beans 7.86 10.46 8.13 13.43 39.88
Cane 5.50 12.09 17.59
The variables are: RN = rotation, VY = variety, IN = insecticide,
SR = supervision, and MD = method of planting.
See Table 1 for a complete definition of variables.

versus squares or pockets are nearly equal for the three

crops where a change in the planting method (MD) occurred.

Overall what is evidence in Table 5 is the significant

productivity improvements that can be realized with those

inputs characterized with the discrete variables. The

expected gains among the four major crops provide guidelines

for redirecting the extension emphasis.

Adjustments Among the Continuous Inputs

The effects among the continuous input variables

differed considerably across the crops. In order to

illustrate these responses, the yields are again indexed

where YD is the predicted yields over values of the Xi and

YD0 is the yield with the lowest level of that input, say Xi

(low) Define:

(8) IN = YD /YD0

= ( X. I X.(low) )

thus showing the predicted percentage gain relative to the

lowest predicted yield for that input. The following

figures illustrate the index over selected continuous

variable initially identified in Table 1.

Figure 1 shows the indexed values as irrigation (WR) is

measured from a low of 10 hours per hectare to a high of 25

hours. Corn, beans and tobacco show large yield

improvements over this range of irrigation. Corn production

responded with nearly a 40 percent increase while beans and

tobacco yields increased around 25 percent. Both sorghum














I /



/1 1

10 12 14 16 18 20 22 2






Sor ghum

and cane were primarily rain-fed crops and hence did not

show the same levels of response to irrigation.

Yield changes in response to more labor input are

recorded in Figure 2. Tobacco showed the greatest gain

since it is a labor intensive crop. Output increased 120

percent over the data range considered. The positive

response among the other crop remained considerably below

that of tobacco.

Indexed production gains for both multiple cropping and

cleaning are shown in Figures 3 and 4. Multiple cropping

represented the number of crops grown on the same land

within a season. A positive response was expected since

land fertility and overall management skills are likely to

improve through the multiple cropping practices. The

positive adjustments to both cleaning and multiple cropping

followed similar relative patterns. In each of these

figures, the index, represents the expected gain from changes

in one or more inputs. The index should not, however, be

interpreted as a prediction of yields since in each figure

all variables are held fixed except for the one being

analyzed. Rather it is an index of gains that can be

expected with adjustments in one particular input.

Finally, responses to fertilizer and nitrogen are not

shown since tobacco was the only crop showing large

production gains. The levels of application were generally

low for both corn and sorghum and this possibly attributed

to low but significant yield responses reported in Table 4.

2.4 -4

2.2 -



1 '

1, -4--
1. 0 4


I1.0 ..... l" ..... 'I '''" '' I"......I" '...... ....... ''' '' I
80 85 90 95 100 105 i10 115 120





































/ Beans

2.14 3.0

3.6 14.2







Predicted Production Gains

The prior results established a strong analytical

production relationship with inputs, yet it the actual gains

realized over the four years of the Bas Boen project were

not shown. While actual yields are known, it is necessary

to calculate the predicted yields in order to show the

effects from those input variables most directly influenced

by the intensive extension efforts. As pointed out earlier,

the input variables were of two types: physical inputs (SD,

FZ, NG, WR, LR) and general management practice variables

(CL, ML, RN, VY, SR, MD). Both variables clearly require

management decisions. The decision as to how much of the

physical inputs to use must be made. Second, the use of

different production practices must be considered. The

project goal was to provide guidelines for influencing both

types of decisions. The specified model (eq. (1))

facilitates separating the effects of these two variable

types given the above distinction.

To calculate the expected gains two indices are

developed. Define YD1 now to be the predicted yield based

on the average values of all input for each of the four

years. The yield YD2 represents the predicted production

with the annual average level of all management practice

variables while holding all other inputs at the 1974 level.

Finally, YD0 is the predicted value for 1974 using average

input levels for that year. Then:

(8) IT = YDI /YD0,

and (9) IL = YD2/YD,.

Index IT shows the yield gains relative to the 1974 initial

period of the experiment. Whereas, IL shows the gains

predicted given changes only in the management practice

variables. This index remains the same if it is calculated

relative to the base period of 1974 as shown in equation (9)

or it is calculated relative to what the difference would be

each year if management practices where added relative to

that used in 1974 letting the other inputs change. That is,

YD3 is the predicted yield holding all-management practices

at the 1974 level, then YD1 / YD3 is equivalent to YD2 /


These indices are estimated by crop in Table 6.

Estimated corn yields are shown to increase by nearly 170

percent from the 1974 level. To the extent that the

increase use of the inputs resulted from the extension

efforts, then this index provides strong evidence of the

success of the' efforts. The effects of changing only the

management practice variables while holding the other

variables fixed are also shown in Table 6. By 1978

approximately 60 percent of the corn production gains were

from the management practice variables. Between 1974 and

1975 the management variables were the only inputs changed

for sorghum. By 1978 estimated sorghum gains exceeded 200

percent and the management practice variables accounted for

nearly 80 percent of this gain. Production gains for beans

showed similar improvements resulting from increases in both


Table 6. Estimated yield responses over time.

Years Corn : Sorghum I Beans I Cane I Tobacco


1974 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1975 2.017 1.647 1.185 1.185 1.551 1.327 1.024 1.024 1.346 1.026
1976 2.428 1.852 2.602 2.340 1.809 1.560 1.044 1.044 1.640 1.129
1977 2.689 2.001 3.035 2.570 1.989 1.645 1.394 1.214 1.569 1.151

See equations (8) and (9) for definitions of IT and IL.

type inputs.

The cane crop was cultivated under traditional farming

practices without significant intervention from the project

management until 1977. The response in productivity

following 1976 is readily seen where a 40 percent gain over

the base period is observed. Again a large share of the

increase was due to improved management practices.

Finally, tobacco required considerable physical inputs

where FZ, NG, WR, and LR were changed. Hence, most of the

predicted gains are attributable to these inputs.


The analytical model set forth provides a direct

measure of the productivity gains that were realized in Das

Boen region of Haiti. Using principal component procedures,

both the effects for changing management practices and

applications of physical inputs were measured. In most

crops, over 60 percent of the productivity gains are related

to improved management practices. Such results are of

paramount importance in that it points to the potential

gains that can be achieved through extension guidance.

Furthermore, the model clearly shows the strong

complementarity among the inputs and the overall gains

achieveable through coordinated management of all inputs.

These results were based on a sample of farmers within

a small study area. The question of the applicability of

the empirical estimates to a broader base is logically

raised. Given the homogeneous nature of the producers and

the similarity of resource distribution throughout the

region, the results should have useful applications to other

agricultural efforts in the country. It is ,however, again

emphasized that the estimates are based on a small subset of

farmers within the region. Clearly, additional empirical

research is needed on a broader scale. Also, follow up

efforts are needed to judge the continued use of the

production technology in subsequent years after the project

was terminated.

The experimental extension program-is one of a number of

development plans designed to assist the LDC's. The results

of this analysis do not provide data for judging the

relative merits of alternative development methods. Rather,

they illustrate the gains that can be realized when both

capital investments are coordinated with direct personalized

management assistance.


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Dhrymes, Phoebus J. Econometrics. Springer-Verlag. New
York, N.Y. 1974.

Gislason, Conrad. The Bas Doen Project--The Lessons
Learned. Washington D.C: 1978 (unpublished).

Halvorsen, Robert and Raymond Palmquist. ''The
Interpretation of Dummy Variables in Semilogarithmic
Equations.'' American Economic Review. 70(1980):474-475.

La Gra, Jerry. Feasibility of Expanding the Integrated
Cooperative Project at Bas Doen. Haiti: 1972 (unpublished).

Pindyck, Robert and Daniel Rubinfeld. Econometric Models
and Economic Forecasts. McGraw-Hill Book Company. New York,
N.Y. (2nd Ed.). 1981.

Prires, Moshe Z. A Project of International Cooperation in
Agricultural Development, Valley of Cul-de-Sac, Haiti.
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