




































In ternationa l
Scholars
Journa ls

 

Global Journal of Business Management ISSN 6731-4538 Vol. 2 (2), pp. 001-007, February, 2008. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
 
 

 

Full Length Research Paper 

 

Determinants of technology adoption in the 

production of horticultural export produce in Kenya 
 

J. N. Nzomoi1*, J. K. Byaruhanga1, H. K. Maritim2, P. I. Omboto2
 

 
1
Masinde Muliro University of Science and Technology, P.O BOX 190, Kakamega, Kenya 

2
Moi University, P. O. Box 3900, Eldoret, Kenya. 

 
Accepted 06 December, 2007 

 
The horticultural sector is currently the second largest foreign exchange earner in Kenya after tea. It employs more 
than a half a million people in the formal sector and over two million people in the informal sector. The major export 
destinations demand some minimum quality levels of the produce entering their market. There have been 
technological innovations with regard to seed priming, treatment, disease and insect resistance and overall quality 
improvement. However the increased profitability associated with such technological advancement has not been fully 
enjoyed in Kenya and other developing countries due to non- adoption, partial adoption or inappropriate adoption of 
the improved technologies. This necessitates the application of the full range of the available technology in the 
production process to maximize output and profits. This paper investigates the determinants of technology adoption 
in the production of horticultural export produce. Multistage and random sampling methods were used to select the 
study areas and horticultural export producers respectively. One hundred and twelve (112) respondents were 
interviewed using structured questionnaires. The study covered Nakuru, Kiambu, Laikipia, Thika and Machakos 
districts. The data was analyzed using the omnibus logistic function employing the SPSS software Version 10. 
Regression results indicated positive coefficients for levels of education, role of government, funds availability and 
membership to professional bodies by the firms alongside other variables. Results further revealed that failure to 
utilize technologies was due to financial constraints, inappropriate technologies, technical knowledge, nature of the 
businesses, poor infrastructure, access to information as well as the technologies themselves not being available. 
This paper concludes that education levels, government involvement, access to finance and membership to 
professional bodies positively influence technology adoption. 

 
  Key words: Adoption, technology and horticulture 

 
INTRODUCTION 

 
Kenya’s horticultural industry has been rated as the most 
successful in Africa (Kenyaweb, 2004). This success is 
attributed to the country’s geographical location that gives 
it both a temperate and tropical climate that allows gro-
wing of a wide variety of crops. The horticultural export 
business in the country is in the hands of about 200 
registered active exporters, majority of who are members 
of industry associations such as the Kenya Flower 
Council (KFC) and the Fresh Produce Exporters 
Association of Kenya (FPEAK). Flowers constitute 60%  
 
 

 
*Corresponding author. E-mail: nnzomoi@yahoo.co.uk. Mobile: 
0722 225015 

 
 
 
 
 

 
of the horticultural exporters and out of 100 registered flo-
wer exporters, 80 % of the flowers are exported by 50 
companies, which are members of KFC. FPEAK is lar-
gely represented by 60 fruit and vegetable exporters who 
have contracted thousands of farmers across the country 
to grow specified crops for export. Four large exporters – 
Kenya Horticultural Exporters, Vegpro, Sunripe and 
Everest Enterprises, control the biggest percentage of 
exports. The European Union is the largest buyer of 
Kenya’s horticultural produce led by Britain, Netherlands 
and Germany in that order. Other buyers include the US, 
Japan, the Middle East and South Africa. 

Modern technology may be divided into four Principal 

types, (Knight et al., 1972) . First we have biological tech-

nology which includes new crop varieties as well as other 



 
 
 

 

technologies which incorporate materials of a biological 
nature. For instance we have technologies generated 
through biological breakthroughs such as breeding of 
new High Yielding Varieties (HYVs), disease resistant 
varieties or even drought resistant varieties. Second there 
is chemical technology which includes chemical 
fertilizers, chemical means for pest and weed control 
such as fungicides, herbicides and insecticides. Thirdly 
we have mechanical technology including farm machi-
nery and farm equipment used during land preparation in 
such activities as tilling, pest control, weeding, spraying 
and transportation among others. Finally there is 
management technology which includes the knowledge 
concerned with decision-making and management of 
farming activities without directly involving the use of new 
materials.  

Although the various technology types are distinct, 
there is a considerable overlap among them when it 
comes to their adoption. For instance the application of 
chemical, biological and mechanical technologies nec-
essarily entails the application of managerial technology. 
For this reason, this study does not address itself to a 
specific type of a technology but aggregates together the 
four technology types simply as “technology”. Thus, the 
study investigates why technology is not utilized and not 
which technology is not utilized. In this study horticulture 
is defined to include fruits, flowers and vegetables only.  

In the area of horticultural production there have been 
technological innovations with regard to irrigation, 
chemical fertilization, pruning techniques and harvesting 
among others. The introduction and adoption of tissue 
culture has also become a standard technique for 
propagation of fruits, vegetables, ornamental plants and 
some tree crops (such as oil palm) in both developed and 
developing nations. According to Hudson (1980), the 
production of virus-free seedlings is important in pro-
ducing healthy plants especially such crops as stra-
wberries, potatoes, bananas and orchids. Shoot-tip 
grafting in such fruit crops as citrus is an improved tec-
hnique, which has spread to India, China, Chile, Brazil 
and Egypt. Recent advances in bio- technology including 
genetic engineering and protoplast fusion will undoub-
tedly make incorporation of the desirable characters in a 
breeding program more feasible and faster than present 
conventional means. However the increased profitability 
associated with such technological advancement has not 
been fully enjoyed in Kenya and other developing 
countries due to non-adoption, partial adoption or 
inappropriate adoption of the improved technologies. 
Consequently it is obvious that despite these develop-
ments there are constraints that have hindered the full 
utilization of these production and marketing innovations. 
This implies that the actual marketed output levels have 
remained far below the potential levels if such technolo-
gical innovations were fully made use of. This denies the 
country the much-needed foreign exchange. Hence the 
need for an investigation into the determinants of techno- 

 
 
 
 

 

logy adoption in the production of horticultural produce in 

Kenya. 
 
 
RESEARCH DESIGN AND METHODOLOGY 
 
This study utilized primary data. A structured questionnaire contain-
ing both closed and open-ended questions was designed to elicit 
information relevant to the issue under investigation. The Sampling 
Frame, from where the sample was drawn, was obtained from the 
Horticultural Crops Development Authority (HCDA). It consisted of 
all registered producers of flowers, fruits and vegetables for export. 
This list consisted of both small-scale and large-scale producers.  

Multistage, random sampling procedure was employed in select-
ing the sample from where the data was collected. This method 
ensures a high degree of representativeness by providing the 
elements with equal chances of being selected as part of the 
sample (Babbie, 1994). The survey data was collected from firms 
located in various parts of the country. The sample data was 
distributed as follows; Naivasha (29) (especially flower growers-
where nearly 75% of total flower producers in Kenya are located), 
Nairobi (23), Kikuyu (12), Thika (12), Ruiru (10), Limuru (9), Nakuru  
(8), Athi River (4), Machakos (3) and Ngong Hills (2). A multiple 

regression equation was estimated by use of the omnibus logistic 
function under the SPSS software Version 10. Both qualitative and 
quantitative methods were employed in the data analysis. 

 
CHOICE OF THE ECONOMETRIC MODEL 
 
To adopt or not to adopt technology is a discrete choice. Discrete 
choice econometric models have been widely used in estimating 
models that involve discrete economic decision problems (Guerre 
and Moon, 2004). Similarly, Feder et al. (1985) recommend the use 
of qualitative response models in these studies, mainly the Tobit 
and Probit models. It is however difficult to choose between these 
two models owing to the statistical similarities between them 
(Amemiya, 1981). Baker (1992) utilized a multivariate logit model in 
studying computer adoption and awareness in Mexico while 
Heissey et al. (1993) used the same model to determine adoption of 
new wheat varieties in Pakistan. 

This study utilized the logit model because the dependent 
variable is dichotomous and the model is computationally simpler. 
The probit model was not used because of the nature of the 
variables used in the study since it assumes cumulative normal 
distribution. Kipsat (2002) also rejects the use of the probit model 
on the grounds that it leads to inefficient estimators and that the 
estimated probabilities are not constrained to lie between the (0, 1) 
range demanded by probability theory.  

The logistic regression, like the log-linear model, is part of a 
category of statistical models called Generalized Linear Models. 
This broad class of models includes ordinary regression and 
ANOVA as well as multivariate statistics such as ANCOVA. Logistic 
regression enables a researcher to predict a discrete outcome such 
as group membership, from a group of variables that may be 
continuous, discrete, dichotomous or a mixture of any of these. The 
predictor variable in logistic regression can take any form. This is 
because logistic regression makes no assumption about the 
distribution of the independent variables; -they do not have to be 
normally distributed, linearly related, or of equal variance within 
each group. 

 

SPECIFICATION AND ESTIMATION OF THE LOGIT MODEL 
 
This model is based on the cumulative logistic probability function.  
Its specification takes the following form: 



 
 
 

 

Table 1. Summary of expected signs of the independent variables. 
 

  Expected sign of 
 

Variable Definition its coefficient 
 

X1 Educational Level Positive (+) 
 

X2 Local Technology Positive (+) 
 

X3 Professional Positive (+) 
 

X4 Membership Negative (-) 
 

X5 Financial Constraints Positive (+) 
 

X6 Government Positive/Negative (+/-) 
 

X7 Involvement Positive (+)  

Land Type 
 

  
 

 Land/Farm Size  
 

Source: Own computation  
 

 

 

Pi = F (Zi) = F (+Xi) = 1/1+ e
-zi

 = 1/1+ e
- (
+Xi). ………….. (1) 

 
Where in this notation e represents the base of natural logarithms 
which is approximated at 2.718. Pi is the probability that an 
individual will make a certain choice, in this study whether to adopt 
a certain technology or not. In estimating equation (1) stated above, 

we multiply both sides by (1 + e
-z

) Pi = 1 so that dividing by Pi and 
then subtracting 1 yield: 
 

e
-zi

 = 1/ Pi – 1 =1-Pi /Pi . …………………………………………. (2) 
 
However since e

-z
 = 1/ e

zi
, then e

zi
 = Pi /1-Pi so that by taking the 

natural logarithm on both sides of the equation we obtain: 
 
Zi = Log Pi /1-Pi or from equation (1) presented above, we have:  
Log Pi /1-Pi=Zi = +Xi…………………………………………. (3) 
Where: 
 
Log Pi /1-Pi = the log of the odds that a certain decision will be 
made.  
 = The constant of the equation
 
 = The coefficient of the predictor variables (Pindyck and 
Rubinfeld, 1991).
Unlike the probit model, one special feature of the logit model is that 
it transforms the problem of predicting probabilities within a 0.1 
range of the real line. The slope of the cumulative logistic distribu-
tion is greatest at P = ½ implying that changes in independent 
variables will have their greatest effect on the probability of 
choosing a given option at the midpoint of the distribution. Because 
of the advantages it has over the probit model, the logit model was 
used in this adoption study.

Based on theoretical and empirical evidence, the determinants of 
adoption decisions in this study were hypothesized, apriori, to 
include the following: education level, local technology (technology 
developed with the participation of the intended beneficiaries), 
membership to professional organizations, financial constraints, 
government facilitation, land tenure system and land size.

Thus, the reduced-form technology adoption model was specified 

as:

 
Where: Zi = f (Ed, TecL, ProfM, Fc, Gov, LanT, LanS,).  
Zi = Probability of technology adoption (1 if adopted, otherwise 
0) Ed = Education level of adopter/farmer 
TecL = Local technology 
ProfM = Professional Membership  
Fc = Financial Constraints 

  
  

 
 

 
Gov = Government Involvement 
LanT = Land Type/Tenure 
LanS = Land/Farm Size 
 
Following Gujarat (1995), the specific model estimated was further 
assumed to take the log-linear form, which tends to give the best 
results. It is also very convenient for elasticity calculations. Since 
the study utilized cross- sectional data we specified the equation 
without time lags as here below shown:  
Zi=B0+B1lnX1+B2lnX2+B3lnX3+B4lnX4+B5lnX5+B6lnX6+B7lnX7+e…… 
……(4) 
Where the independent variables are: 
X1 = Education level of the adopter/ farmer 
X2 = Local technology 
X3 = Professional Membership 
X4 = Financial constraints 
X5 = Government Involvement 
X6 = Land Type 
X7 = Land/Farm Size 
e = Error term  
Table 1 
 

 

RESULTS AND DISCUSSION 
 

Taking technology adoption, Zi, to be LnP (x)/(1- (P (x)) 

as the dependent variable in the logit model, the resulting 

multiple regression equation becomes; 

Zi = 13.743 + 1.537X1 + 0.463X2 + 1.646X3 – 3.591X4 
+ 1.559X5 – 1.269X6 – 13.674X7  
Based on the definitions of the variables as indicated 
earlier, the equation is presented as: 
Technology Adoption (Zi) = 13.743 + 1.537[Education] +  
0.463[Local Technology] + 1.646[Professional 
Membership] – 3.591[Financial Constraints] +  
1.559[Government Involvement] – 1.269[Land 
type/Tenure] – 13.674[Farm/Land Size]. 

The interpretation and discussion of each independent 

variable, as it relates to the dependent variable is exp-

lained here below: 
 

Education: This variable took a positive sign (+1.537) 
implying that highly educated farmers and marketers are 
better adopters of improved technologies than less 
educated ones. This is consistent with our theoretical 
expectations and conforms to the findings of other earlier 
researchers (Williams, 1958; Salasya et al., 1996) as 
observed when reviewing literature. This positive correla-
tion shows the influence education has on technology 
adoption. Educated producers and marketers have 
exposure to new technologies and innovations, are more 
receptive to new ideas and are more willing to adopt, 
hence the positive correlation between education and 
technology adoption. The education levels of the respon-
dents ranged from secondary to university graduates. 
However even for the educated ones, some technologies 
are product-specific or site-specific and the adopters may 
need to be provided with information on their usage. 
 
Local Technology: This variable took a positive sign (+ 

0.463) as expected. This showed that technology adop- 



      
 

   PROFESSIONAL MEMBERSHIP AND SALES  
 

 
60          

 

          
 

 50          
 

    50       
 

 40          
 

 30          
 

           
 

      28     
 

 20          
 

          
ANINCOME  

        
16  

 

          
 

 10         
Less than 1m  

C
o
u
n
t 

         
 

  7        
 

0         More than 
 

   Yes No  
  

 
PROFMEMB 

 
Chart 1. 

 

 

tion was dependent on whether the technology was 
locally developed or imported. We had hypothesized that 
farmers do not easily adopt foreign technologies due to 
their complexities. The hypothesized sign for the local 
technology was positive due to the expectation that 
technologies that are developed with the participation of 
the intended beneficiaries do not pose difficulties when 
being adopted. This finding confirms the need for partici-
patory technology development between the innovators 
and the farmers as recommended by Reijntejees et al. 
(1992) and Laurens (1997). In this regard technology 
itself need not necessarily be developed in the adopting 
country as long as the two parties can work together to 
ensure suitability, relevance and appropriateness of the 
technology. This is emphasized by the fact that individual 
respondents identified numerous problems associated 
with technologies that are not locally developed such as 
inappropriateness, lack of qualified personnel to imple-
ment them, and high costs associated with acquisition 
and utilization of such technologies. 
 

Professional membership: This variable had a positive 
coefficient of 1.646 as hypothesized. The positive coeffi-
cient implied a positive correlation between technology 
adoption and membership to professional organizations. 
Producers who were members of professional organiza-
tions such as the Kenya Flower Council and the Fresh 
Produce Exporters Association of Kenya among others 
were better placed to adopt new technologies than those 
who did not belong to any organization. This is confirmed 
by the graphical output (Chart 1). Membership to such 
organizations enabled farmers to acquire information on 
any new product in the market as well as attend seminars 
and workshops at which stakeholders would meet and 
exchange ideas. This should be encouraged for both 
small-scale and large-scale producers. 

 
 
 
 

 

Financial constraints: This variable took a negative sign 
(-3.591). This is in conformity with our expectations 
presented while generating the model. This hypothesis 
was based on the understanding that some innovations 
may be too costly for the adopter to access and make 
use of them. For this reason the variable of financial 
constraints was expected to negatively influence techno-
logy adoption. This meant that firms facing financial 
constraints are less likely to adopt new technologies than 
firms with sufficient funding. Based on our data set for 
this study, the negative sign of the variable confirms our 
expectations. These findings are supported by similar 
findings by earlier researchers (Heady, 1952; Salam, 
1985; Salasya et al., 1998; Nandwa et al., 1997) who also 
identified costs as key determinants of adoption of 
improved technologies or improved varieties of seeds, 
fertilizers, soil conservation methods and irrigation 
methods among others. 
 
Government role: Role of government took a positive 
sign (+1.559) as expected. The justification was that 
successful adoption of some technologies might require 
government facilitation. This showed that government 
plays a significant role that enables firms to enhance their 
production and marketing strategies. For technologies to 
be utilized there is need for government involvement in 
making it possible for the users to conveniently benefit 
from the availability of the new technology. It may also be 
said that failure to utilize technologies by the various 
intended beneficiaries can be blamed on the govern-
ment’s inability or reluctance to facilitate the same. The 
positive correlation means that the higher the level of 
government involvement the better it is for the farmers to 
adopt new technologies. Intervention measures to 
enhance technology adoption should therefore be 
designed to include appropriate government role. This 
government role should be the very bare minimum since 
excessive government meddling can actually curtail 
productivity. 
 
Land type/ land tenure system: The variable on land 
ownership policy was expected to take either positive or 
negative sign. It was thus indeterminate depending on 
whether land was privately owned, leased or rented. For 
example farmers who are squatters may avoid adopting 
technologies that are expensive and are of long-term 
nature while those who own land may have the motiva-
tion to adopt new technologies even when such techno-
logies are expensive. Regression results showed a 
negative sign (-1.269) implying that land ownership 
system negatively influenced technology adoption. How-
ever, it can be seen from the graphical output (Chart 2) 
that farmers who privately own land get the highest 
incomes from sales of their produce than those who have 
leased or rented land. Thus the positive correlation 
between financial strength and technology adoption 
would lead us to expect a positive sign if a majority of the 
farmers own the land on which they operate. Indeed our 



 
 
 

 

data analysis show that 50.9% of the respondents 

operated on privately owned land while the rest either 
leased or rented the land. The negative sign obtained 

from our results was however not entirely unexpected 
since it was expected to take either sign. 
 
Land size/farm size: This variable was expected to 
positively influence technology adoption. The basis of this 
expectation was that for a firm to grow and acquire a 
large tract of land on which to operate it might also be in 
a position to utilize new technology compared to smaller 
firms, perhaps on account of large-scale production. This 
variable however took a negative sign (-13.674) implying 
that technology adoption is negatively influenced by the 
size of the firm. It was expected that the advantages of 
large-scale production would positively influence techno-
logy adoption and vice versa so that smaller firms would 
find it difficult to adopt technology than large ones. 
Empirical evidence allows us to reject the hypothesis. 
Notwithstanding this rejection however, it is clear that a 
large firm in terms of establishment and output levels, is 
definitely better placed to acquire and adopt new 
technologies than a smaller firm. Perhaps this unexpec-
ted outcome can be attributed to the definition of farm 
size in this study which emphasized physical acreage 
rather than the volume of output which would have 
implied financial strength arising from the sales of the 
output. The second aspect to this finding is that 
technology adoption is not in any way dependent on farm 
size but is indeed a function of factors exogenous to the 
farm. 
 
 
SUMMARY OF REGRESSION RESULTS AND 

MULTIPLE RESPONSE ANALYSIS 
 
Post-harvest handling: The quality of horticultural 
produce is dependent on how the produce is handled and 
transported to the final consumers. Our data analysis 
revealed that 87.3% of the respondents experienced 
post-harvest losses before marketing their produce. 
Given their perishability, there is need to carefully handle 
the produce so as to reduce post-harvest losses and 
ensure it is in good condition by the time it reaches both 
local and foreign consumers. In the case of vegetables 
for example some produce such as onions and potatoes 
can stand transportation and are grown in areas of 
concentration where climate and soils suit them best. 
Producing vegetables and fruits in areas far away from 
the market necessitated careful transportation. In this 
case shelf life is a critical factor and in the case of 
tomatoes the varieties grown need to have thicker skins 
to prevent excessive losses during transportation.  

The shape and colour of ripe produce is also an issue 
and farmers should avoid harvesting too late since this 
makes the fruits to go bad by the time they reach 
consumers. With a tendency to buy daily rather than buy 
in bulk and stock, the consumer expects to see ripe pro- 

  
  

 
 

 

duce in the market but this has high cost implication for 
the producer. There is therefore need to look at the 
harvest cycle and determine the appropriate time to 
select produce to minimize spoilage. In the case of 
flowers however they are carefully packed and transport-
ted in containers with cooling effect so that they are fresh 
by the time they reach their overseas destinations. 
 

Financial constraints: A perennial problem faced by 
farmers is access to working capital. In the production 
chain the various processes require the usage of funds 
so that a financially stable producer will realize higher 
output than a less financially endowed one. A majority of 
the respondents (55.5%) relied on private savings as their 
main source of funds for their activities. This was perhaps 
the case for the established firms who ploughed back 
profits to expand their businesses and rarely relied on 
bank loans. Indeed we see that only 17.3% of the 
respondents relied mainly on bank loans. In this study 
most farmers identified financial incapacitation as a major 
constraint in their operations. Indeed with regard to 
technology adoption 96.3% of the respondents were 
unanimous that it required funds to implement. 
 
Poor commodity prices: Respondents identified poor 

and often fluctuating commodity prices as a major 
problem especially for the fruits and vegetable producers. 
This is a serious constraint given that farmers tend to 
produce more the higher the price. A persistently low 
price would be a disincentive to the farmers who may opt 
to produce other commodities attracting higher prices. A 
substantial number of flower producers however were 
contented with the prevailing prices offered for their 
produce. This was partly because of the foreign market 
outlet for the flowers where better prices are offered. A 
number of solutions were suggested to this problem 
including improved bargaining power in world markets. To 
this end 61.9% of the respondents wanted the gov-
ernment to assist in improving their bargaining power in 
the world markets. Another way of improving commodity 
prices was to improve its quality relative to that of your 
competitors. 
 
Storage facilities: Due to lack of suitable and appro-
priate storage facilities on the farms and the perishability 
nature of horticultural produce, farmers are often forced 
to market produce at peak production periods to avoid 
incurring losses when the produce goes bad. This was 
the case especially with fruits and vegetables. It is 
however possible for the farmers to extent the season by 
controlled environment production such as the use of 
irrigation or early and late varietal selection. This is 
another strategy that farmers can adopt to maximize 
returns from their produce. In addition storage facilities 
need to be available at the destinations to ensure the 
produce is of good quality when it is sold. This ensures 
the right price is paid for the produce and enhances the 
financial position of the farmer. In this study 75.9% of the 



 
 
 

 
LAND TYPE AND SALES 

 
50  

 

40 41 

 

30  

 

20   
       SALES 

 

 
10 

15      
 

      
Less than 1m  

C
o

un
t 

     
9  

0   4 5 More than 
 

   6    8 
 

       
 

  Pri  
Re  

Lea mb 
 

  vat   
 

   

nte  

sed  

  ely   inat  

   

d   
 

  ow    ion  

      
 

  ned     of 
 

       the 
  

a 
 

LANDTYP 
 

Chart 2. 
 

 

firms interviewed relied mainly on foreign market outlets 
for their produce implying that there is need to ensure 
sufficient storage facilities in the importing country. This 
ensures that the produce is of high quality by the time it 
changes hands. 
 
Market infrastructure: Accessibility is crucial to any form 
of business enterprise whether agricultural or industrial. 
In this study our findings indicate that only 10.7% of the 
respondents were accessible while the rest either had 
poor access roads or were simply inaccessible. This was 
especially the case for small-scale producers who lack 
the financial capacity to improve access roads leading to 
their enterprises.  

The hub of any horticultural marketing network is the 
wholesale market. Wholesale markets are located in most 
urban centers but suffer from a number of serious 
constraints. For instance the physical size of the market 
may be too small to cope with the volume of trade. In 
other markets the problem is the availability of storage 
space to handle cargo as it arrives and leaves the 
market. The most needed facility for vegetable and fruit 
wholesalers is access to cold storage facilities. This 
greatly reduces spoilage of produce and allows traders to 
effectively market their produce in a measured way, 
rather than having to sell all deliveries on the day of 
arrival.  

Respondents also mentioned poor road network as a 
constraint regarding where they can source their produce 
and where they can sell it. Poor road network also dam-
ages the fresh consignments and greatly reduces the life 
span of the vehicles carrying the produce due to tear and 
wear. For export purposes the air transport ensures 
timely delivery of produce to various international destina-
tions. Even then, such produce would need to be deliver- 

 
 
 
 

 

ed to the airport from the various production points in 

good condition so as to fetch more in international 

markets when exported. 
 
Market information: Access to accurate market informa-

tion is crucial to horticultural farmers. Respondents 
identified market information as one of the benefits they 
obtain from being members of professional organizations. 
However for the small-scale producers who do not belong 
to such organizations, this is a serious constraint. For 
such farmers the HCDA as a regulator in this field should 
assist in acquisition of accurate, timely and relevant 
information to help them plan production and marketing 
levels. There is need for the farmers to understand why 
prices change both in the short run and the long run as 
well as how to interpret prices and respond appropriately 
in terms of what and when to produce. 

 

Conclusion and Policy Implications 
 

From the above discussion it can be seen that the 
variables that influence technology adoption positively 
include level of education, local technology, professional 
membership and government involvement. On the other 
hand variables that influence the dependent variable neg-
atively are financial constraints, land tenure system and 
land size or farm size. Thus, it is clear that before ado-
pting any new technology farmers considered financial 
implications, relevance, profitability and efficiency. In this 
regard technology adoption can be said to be dependent 
on farmer characteristics and other factors exogenous to 
the firm. 

The analysis also reveals that most production is done 
on small scale basis and that flower producers get more 
income than fruit or vegetable growers.  

Based on study findings, several implications may be 
drawn. There is need to improve the education levels of 
the producers and to enhance and rationalize govern-
ment role in the production process. There is also need to 
assist farmers and exporters to access credit as well as 
support professional organizations in the horticulture sub-
sector. In addition, there is also need to strengthen the 
existing extension system and to promote the use of post-
harvest technologies and facilities. Finally the provision of 
accurate market information as well as the provision of 
sound infrastructure will enhance production of 
horticultural export produce. 

 
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