Embed Size (px)
Citation preview
This article was downloaded by: [Bibliothèques de l'Université de Montréal]On: 08 December 2014, At: 09:12Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
The Journal of Agricultural Educationand ExtensionPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/raee20
Determinants of Adoption of ImprovedFish Production Technologies amongFish Farmers in Delta State, NigeriaA.U. Ofuoku a , N.F. Olele b & G.N. Emah ca Department of Agricultural Economics and Extension , DeltaState University , Asaba Campus, Nigeriab Department of Animal Science and Fisheries , Delta StateUniversity , Asaba Campus, Nigeriac Department of Agricultural Economics and Extension , RiversState University of Science and Technology , Port-Harcourt, RiversState, NigeriaPublished online: 21 Oct 2008.
To cite this article: A.U. Ofuoku , N.F. Olele & G.N. Emah (2008) Determinants of Adoption ofImproved Fish Production Technologies among Fish Farmers in Delta State, Nigeria, The Journal ofAgricultural Education and Extension, 14:4, 297-306, DOI: 10.1080/13892240802416186
To link to this article: http://dx.doi.org/10.1080/13892240802416186
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
Determinants of Adoption of ImprovedFish Production Technologies among FishFarmers in Delta State, Nigeria
A.U. OFUOKU*, N.F. OLELE$ and G.N. EMAH%
*Department of Agricultural Economics and Extension, Delta State University, Asaba Campus, Nigeria,$Department of Animal Science and Fisheries, Delta State University, Asaba Campus, Nigeria,%Department of Agricultural Economics and Extension, Rivers State University of Science and
Technology, Port-Harcourt, Rivers State, Nigeria
ABSTRACT This study was conducted to isolate the determinants of improved fish productiontechnologies in Delta State, Nigeria. Data were collected from a sample population of 250 fishfarmers from ten randomly selected Local Government Areas of Delta State. The data wereelicited from respondents with the use of structured interview schedule while descriptive statisticsand multiple regression analysis were used to analyze the data. The recommended fish farmingtechnologies at different stages of adoption process were pH testing and regulation, testing ofdissolved oxygen level, feed formulation, polyculture, practices integrated fish/poultry or ricefarming, re-circulation method, cage system, spawning and stocking density. The level of adoptionwas low. The grand mean adoption score and adoption index are 1.02 and 0.10, respectively. Thelow level of adoption was attributed to cost of the technologies, their complexities and lack ofextension contact. The level of education, age of farmers, farm size, farm income and extensioncontact were the major determinants of fish production technologies adoption at 0.05 level ofsignificance.
KEY WORDS: Determinants, Adoption, Improved, Fish, Production, Technologies, Fishfarmers, Delta states, Nigeria
Introduction
There is a steady rise in human population in Nigeria and this has not been matchedby the corresponding increase in food production (Ekokotu and Ekelemu, 1999).
According to Akagbajo-Samson (1997) while human population growth is rising at a
rate of about 4�5%, livestock production is trailing behind at a rate of 2�3%. This
shows that there is a wide gap between supply and demand of animal protein. The
consequences of the scenario are the soaring cost of animal protein. This has made it
almost impossible for the poverty stricken Nigerians to meet their animal protein
needs. Thus, the need arises to explore alternative avenues for accessing animal
protein as a way of increasing protein consumption. This alternative source could be
Correspondence address: A.U. Ofuoku, Department of Agricultural Economics and Extension, Delta State
University, Asaba Campus, Nigeria. Email: [email protected]
1389-224X Print/1750-8622 Online/08/040297-10 # 2008 Wageningen University
DOI: 10.1080/13892240802416186
Journal of Agricultural Education and Extension
Vol. 14, No. 4, 297�306, December 2008
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
achieved if farmers could adopt better aquaculture technologies in Delta State in
particular and Nigeria in general.
The bulk of the total fish produced in the country accounted for capture fisheries
was 97% (Federal Department of Fisheries (FDF), 2004), while culture fisheries
contributed a mere 3% of the fish produced in the last decade. There was a gross
short fall in fish production compared with the projected mean annual demand figure
of 752,297 metric tons (Utomakili, 1987). To close the gap between fish protein
supply and demand in Nigeria, suggested a number of options including the
development of efficient, rural based, low external input and sustainable small-scale
fish culture (Ekokotu and Ekelemu, 1999). The ease with which culture fisheries was
practiced and the fact that fisher folks also practice integrated fisheries will further
improve food availability and protein sustainability in the country. This initiative will
arrest food shortage. This reform is also practiced outside Nigeria, hence Gupta
(1990) observed that the economic status of the rural dwellers especially women in
Bangladesh, who practice integrated fishery have been considerably improved
through small-scale fish farming carried out in ditches, ponds, road side canals
and borrow pits (Gupta, 1990). In Nigeria, where integrated fishery is being practiced
by the fisher folks who produce over 65% of the nations food and occupy about 85%
of our land mass, the nation’s need for food and fiber is adequately met. According to
Akinbode (1982), rather than engage in direct production, the Agricultural
Development Programme (ADP) was designed to stimulate and motivate small-
scale farmers to the use of modern techniques of fish farming through farm extension
education. The extension service has a vital role of increasing and improving fish
production through their linkage between researchers and end-users. Without
extension services most research endeavor will be a futile exercise (Adebolu and
Ikotun, 2001). The adoption level of modern fish management methods is an index of
the success of extension delivery service.
Theoretical Framework
Farmers undertake decision-making on daily basis to settle questions which arise
from the day-to-day and season-to-season operations of the farm (Agbamu, 2006). It
implies mental confrontation with the structure of ideas, problems and the settlement
of these issues into concrete action guidelines or actionable opinions. It involves
taking into account all factors neither the farm’s production and social environment,
making choices, discriminating on the basis of feasibility, and have identifying
consequences for alternative actions.Farmers’ decision-making on technology adoption is prompted by such problems
or challenges they confront in their business from time-to-time. In this process, the
farmers’ sources of information fundamentally shape the kinds of decisions they
make. Sources of information and acquired knowledge from those sources constitute
the foundation on which many decisions of farmers are based (Agbamu, 2006).
Consequently, the sources of information which farmers depend on to improve their
production techniques gird the theoretical issues in decision-making of the farmers.
A conceptual framework for decision-making in fish farming technologies adapta-
tion is shown in Figure 1. It consist of (i) sources of technology information to
farmers; (ii) major fish farming technologies to use; (iii) the decision criteria that the
298 A.U. Ofuoku et al.
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
farmers must use; (iv) socio-economic factors affecting farmers’ decisions; (v) the
farm management decision tools; (vi) stages of adoption; and (vii) the preserved
benefits from the adoption of recommended technologies.
It is expected that the perceived benefits of adaptation of fish farming technologies
can be attained if the farmers did not encounter constraints in decision-making. The
conceptual framework for analyzing decision-making in Figure 1 is therefore:
i. educational level;ii. age of farmers;
iii. farm size;
iv. household size;
Farmer’s Technology Adoption Decisions
Sources of informationExtension workers other farmersProgressive farmer Farmers’ groupMass Media Neighbour/Friend
Use of technology to adopt improved breedsPH testing and reaction Testing of O2 level/regulationFeed formulation Polyculture practicesIntegrated fish/poultryOr rice farmingRecirculation methodCage systemSpawmingStocking density
What to produceWhat fish enterprise
How much input to use Brood stock, PH meter’Seedfish.,lime, fertilizer, water, credit
Socio-economic factorsEducational level, Age, farm size,Household size, Farm income,Extension contact
Decision criteriaHigher Yield Availability rapid growth Attractiveness Lesser cost Accessibility Ease of use
Decision toolsComparative advantage partial budget Factor-ProductFactor-FactorProduct-Product
Stages of AdoptionAwarenessInterestEvaluation TrialAdoption/rejection
Benefits from adoption Increased productivityIncreased procitReduced Cost Improved living standard
Figure 1. Conceptual framework for analyzing decision making on technology adoption and itseffects.
Determinants of Adoption of Improved Fish Production Technologies 299
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
v. farm income; and
vi. extension contact
Ofuoku et al. (2005) observed that the farmer’s decision for or against the adoption
of any science-based production technology was described as a mental process,
consisting of several stages. Such activity will provide firm knowledge on which
action could be based, with regard to persuading farmers to try the new technologies
to provide the information necessary for actual implementation, and provide
information needed to assess results of decisions and hopefully to confirm it.
Apart from the characteristics of the technology which are already known to affect
farmer’s decision to adopt, it is suspected that the socio-economic characteristics of
the farmer play major roles in adoption of decision-making.
The available culture fish production technologies include improved breeds,
stocking density, polyculture fishery integration feed formulation among others.
There is need to advance reasons why fish farmers do not adopt these technologies.
The study was carried out to investigate the factors that influenced the adoption of
improved fish farming technologies and specifically to determine the level of
adoption of improved fish farming technologies in; ascertain the adoption index
and identify the variables that determined adoption improved fish farming
technologies among farmers in Delta state. It is therefore hypothesized that the
socio-economic characteristics of the farmers do not significantly influence their
adoption of improved fish farming technology.
Methodology
This study was carried out in Delta State. The state is divided into three agricultural
zones namely: north, central and south agricultural zones with their zonal offices
located in Agbor, Effurun and Warri respectively. It consists of 25 local government
areas. The multi-stage random sampling techniques was applied in the selection of
respondents. Ten local government areas were randomly selected from the pool of 25
local government areas. Farmers were then selected randomly from a list of farmers
in each of the local government areas under the study. This list was provided by Delta
State Agricultural Programmed (DTADP) extension agents in each of the local
government areas selected. This gave a total number of 250 respondent fish farmers.
The data were collected by using structured interview schedule for the fish farmers.
The services of agricultural science teachers in the local government areas of study
was employed. The collected data were analyzed using descriptive statistics
percentages and means; and multiple regression analysis. The determinants adopts
were classified into low (0�4 years technologies) medium (4�6 technologies) and high
(above six technologies). The adoption levels which formed the dependent variable
were determined by computing the number of the technologies adopted by the fish
farmers in the study area. The adoption index was arrived at, by dividing the overall
mean adoption score by the number of adoption stages (Map 1).
The multiple regression model is implicitly specified as follows:
/Y �f (X1; X2; X3; X4; X6; U)
Where Y�level of adoption of improved fish farming technologies (number of
technologies adopted).
300 A.U. Ofuoku et al.
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
X1�Level of education (years spend in school)
X2�Age of farmers (years)
X3�Farm size (population of fish)
X4�Household size (number of persons in the household)
X5�Farm income (Naira)X6�Extension contact (number of visits by extension agents)
U�Error term
The level of significance used was 0.05.
Results and Discussion
Level of Adoption of Improved Fish Farming Technologies
The computation revealed that 74% of the farmers adopted 0�4 technologies, 18.40%
adopted between 4 and 6 technologies while only 7.60% of the respondent’s farmers
adopted more than six technologies.
The results imply revealed that the adoption level was low generally since 74% of
the respondents fell under the low adoption category. This is confirmed by the
adoption score and index (Table 2) that are likewise low. This is attributed to the
traditional training and visit system where farmers do not participate in technology
development and farmers are not easily reached by Extension Agents. The low
adoption of innovations is an index of the poor extension approach to agricultural
development.
However, the results of further analysis (Table 2) revealed that 58.9% of the fish
farmers were already using some of the improved breeds of fish like Holland Clarias,
Clarias and Heteroclarias, etc. Polyculture was being practiced by 45.80% of
respondents, while 46.70% adopted the cage aware that they can system the fish
culture. On awareness bases, the table revealed that over 90% of the respondents
Figure 2. Map of Delta State, Nigeria
Determinants of Adoption of Improved Fish Production Technologies 301
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
formulate their feeds, over 80% aware of culture improved breeds, while 75% have the
awareness of the practice of polyculture fishery. The mean adoption score for using
improved fish breeds, polyculture and cage system of fish culture was computed as
0.24, 1.10 and 1.20, respectively. The grand mean adoption score was computed as
0.96, while the adoption index was 0.10.
Tables 1 and 2 shown that the fish farmers are yet to fully adopt most of the
recommended fish production technologies. This is confirmed by the low adoption
index. Shingi et al. (2004) opined that moving diffusion processes were the major
challenges to extension services. While 66% of the respondents revealed that the
adoption levels were attributed to the cost of the technologies, 56% attested to the
scarcity and complexity of the technologies. More than half of the respondents (68%)
complained about non-availability of extension agents to introduce, teach and
demonstrate the technologies. These observations were confirmed by the works of
Ofuoku et al. (2005) who maintained that lack of extension contact was the most
serious problem. This has implications for a change of approach to extension
delivery. The farmer field school and farmer-group approaches that emphasize
participatory demonstration and technology development are very useful in this
situation. With these approaches, the extension workers will be able to reach many
farmers at the same time and consequently, every farmer in his block will be reached.
As these approaches employ the principles of adult and experimental learning, the
Extension Agent becomes a facilitator of knowledge creation, a situation that makes
farmers to be more interested in further learning and technology development
through experimental learning. If these approaches can be used to replace the
traditional, World Bank training and visit system, where contact farmers are used to
reach other farmers, the adoption index will be enhanced in the future as the new
approaches promote motivation through technology development and adult learn-
ing. The use of contact farmers does not help the present day situation since the
contact farmer do not have the teaching skills of extension delivery and his own
situation may be different from those of other farmers in his cell. He also cannot
transfer the technology to the farmers exactly the way it is given to him. This has the
implication of message distortion which affects the results of farmers’ trials.Other agricultural innovations could be adopted if among other factors, the input
and output relationships are favorable, ensure that the procurement cost is low, risk
of adoption is low, success of the innovation is glaring sooner or later and the
innovation simple to handle (Heidues, 1994).
Table 1. Percentage distribution of fish farmers with respect to fish farming technologiesadoption score
Adoption scores of fish production technologies Frequency Percentage (%)
Low (1�4) 185 74.00Medium (4�6) 46 18.40High (�6) 19 7.60Total 250 100
Source: Field survey (2006).
302 A.U. Ofuoku et al.
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
Table 2. Percentage distribution of fish farmers according to adoption decision process
Adoption stage Uses ofimprovedbreeds of
fish HollandCharias
pH testingand reaction
Testingof O2 levelregulation
Feedingformulation
Polyculturepractices
Int. fishpoultry or
rice farming
Recircula-tion method
Cagesystem
Spawning Stockingdensity
Awareness 80.30 32.40 56.00 91.20 74.30 39.60 46.30 75.60 38.40 80.20Interest 44.60 24.50 43.00 63.40 60.30 62.00 24.00 60.50 47.00 48.30Evaluation 64.80 41.00 44.30 68.30 61.35 46.20 12.40 66.10 41.60 33.20Trial 62.30 41.30 46.20 56.10 54.11 51.30 10.40 54.80 62.30 44.50Use 58.90 23.20 18.10 7.20 45.80 14.00 7.20 46.70 23.40 30.20Total 310.90 162.40 207.60 350.20 295.86 213.10 100.30 303.70 212.70 236.40Mean adoptionscore
0.24 0.10 0.07 1.03 1.10 0.11 0.03 0.20 0.10 0.12
The overall mean (grand mean) adoption score�1.02.
Adoption index�0.10.
Source: Field Survey (2006).
Determ
ina
nts
of
Ad
op
tion
of
Imp
roved
Fish
Pro
du
ction
Tech
no
log
ies3
03D
ownl
oade
d by
[B
iblio
thèq
ues
de l'
Uni
vers
ité d
e M
ontr
éal]
at 0
9:12
08
Dec
embe
r 20
14
Determinants of the Adoption of Fish Farming Innovations
The hypothesis of this study states that socio-economic characteristics of the fish
farmers do not determine the adoption of improved fish production technologies.
Multiple regression analytical tool using the ordinary least squares (OLS) method
was applied.
The linear function was used as the lead equation because its equation indicated
goodness-of-fit considering the quality of its coefficient, R-squared adjusted R-
squared, and F-ratio. The parameters of the estimated linear regression model is
shown in (Table 3) revealed that the level of formal education (X1) was positively
correlated with the adoption of improved fish farming technology at 0.05 level of
significance. The implication is that educated farmers readily adopt improved fish
production technologies. This is in consonance with Ewuola and Ajibefun (2000),
Lemchi et al. (2003) who noted that technological change is achieved through formal
education. Formal education enables farmers to obtain useful information from
bulletins, agricultural newsletters and other sources (Agbamu, 2006). The formal
education usually aid farmers and lead them to accept new farm technologies more
readily to enhance their income than those farmers without formal education.
In developing countries like Nigeria, a general characteristic of farmers is that they
are tradition bound. They are understandably afraid of costly risk and will not take
them until they are convinced that the new methods are safe, will pay off and will not
violate their values. Agbamu (2006) posited that there was the contention that most
farmers are tradition bound because of their low level of education. In situations like
this where farmers are tradition bound, a lot has to be done by the extension agent to
erase some of these superstitious beliefs. This calls for the application of the Farmer-
Group Approach. This will make agricultural extension service more client-driven
thus reducing the risk of introducing an innovation that is at variance with the
tradition of the farmers.The age of the fish farmers (X2) revealed a negative correlation with adoption at
0.05 level of significance. This implied that the older farmers are unwilling to accept
improved technologies, because they are afraid of risks involved with new
technologies. Lemchi et al. (2003) and Maduakor (2001). It has implication for the
Table 3. Linear regression model estimates of the determinants of fish farmingtechnologies
Variables Coefficient Standard error T-Value
Constant 0.997 0.320 3.115*Level of education (X1) 6.542E�02 0.014 4.66*Age (X2) �1.559E�02 0.006 �2.60*Farm size (X3) 0.427 0.070 6.10*Household size (X4) �2.386E�02 0.015 �1.60Farm income (X5) 9.047E�07 0.000025 3.648*Extension contact (X6) 0.156 0.017 9.176*
R2�0.708; F�98.287.
*Significance at 0.05 level.
Source: Field survey (2006).
304 A.U. Ofuoku et al.
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
present extension approach of training and visit as the farmers do not make input in
the required technology. The change of approach to a client-driven one will erase the
fear inherent in the older generation. They will readily accept any technology they
contribute to its development.
Farm size (X3) revealed a positive correlation which revealed that fish farmers with
large farms more readily adopt improved technologies than farmers with small farm
sizes. This is where economics of scale in aqua cultural production comes in to play.Farm income (X5) also revealed a positive correlation with adoption of fish
farming technologies. According to Madukwe (1993), wealth and adoption of
innovation go hand in hand. This is the reason why even when new technologies are
costly to adopt because it requires large amount of money initially, the wealthy
farmers readily adopt them. The fish farmers who adopted most of the improved
technologies were the rich farmers and this was observed in the study area.
Extension contact (X6) is positively correlated with the adoption of fish farming
technologies. This was because the more extension agents visited the fish farmers andeducated them on the more recent technologies the better they understand and
adopted them. This observation was in agreement with Asiabaka (1996) who
reported that the frequency of extension contact influences, the adoption behaviors
of farmers. Such frequent contact is nutured by a change to the farmer field school
approach of extension delivery, as the participation of the farmers in the technology
development, based on adult learning principles and experimental learning motivates
them. This has facilitated farmer demand for knowledge, and offered opportunity for
the farmers to choose, test and adapt technologies according to their needs.
Conclusion
The study revealed that DTADP had ten recommended fish farming technologies, but
the adoption levels were generally low, recording a mean point of 8.53 out of 30 points.
The overall or grand mean adoption score was computed as 0.96 while the adoption
index was 0.191. Some of the reasons for non-adoption of the improved technologies
were given as dearth of the technologies; complexity of the technologies and lack ofextension contacts, prompted by the use of contact farmers to reach out to other
farmers and technologies tube adopted by farmers dictated by extension agencies.
The adoption score and index were not encouraging as both were low, which
suggests that there are a lot that need to be done in the extension system with regard
to empowering the farmers educationally.
The level of education, farm size, farm income, and extension contact were
positively correlated with adoption of the fish production technologies, while age was
negatively correlated with regard to fish production technologies. There is the need tojettison the present training and visit system for farmer-group and farmer field
school approach which suits the present day dispensation.
Recommendations
i. More fisheries extension agents should be employed by the DTADP.ii. Efforts should be made to adopt the current progressive approaches of farmer-
group and farmer field school.
Determinants of Adoption of Improved Fish Production Technologies 305
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
iii. Interest-free loans should be disseminated to fish farmers so that they will be
able to expand and by eventually be able to adopt costly innovation.
iv. Extension agents should increase the frequency of their visit to fish farmers.
v. Adult education programs should be organized for the fish farmers who are not
literate.
References
Adebolu, V.O. & Ikotun, S.J. (2001) The Role of Research in Sustainable Fisheries Development towards
Poverty Alleviation in Nigeria. Proceedings of National Workshop for Agricultural and Rural
Development in Nigeria, Jos, Nigeria, pp. 21�33.
Agbamu, J.U. (2006) Essentials of Agricultural Communication in Nigeria. Lagos, Nigeria: Malthouse Press.
Akagbajo-Samson, Y. (1997) Introduction to Aquaculture and Fisheries Management in Nigeria. Abeokuta:
Good Educational.
Akinbode, A. (1982) A Critical Analysis of the Management of Agricultural Extension in Nigeria. Journal
of Agricultural Administration, 10, pp. 45�60.
Asiabaka, C.C. (1996) Factors Influencing the Adoption of Cassava Plant Protection among Farmers in
Nigeria. IITA, Benin: ESCAPP.
Ekokotu, P.A. & Ekelemu, J.K. (1999) An Introductory Guide to Artisanal Fresh Water Fish Culture in
Nigeria. In: Omeje, S.I. (Ed.), Issues in Animal Science. Enugu: Raykendy Scientific, pp. 197�200.
Ewuola, S.O. & Ajibefun, I.A. (2000) Selected Media and Socio-economic Factors Influencing Innovation
Adoption by Small-Scale Farmers: Empirical Evidence form Ondo and Ekiti States, Nigeria. Applied
Tropical Agriculture, 1(2), pp. 24�26.
Federal Department of Fisheries (2004) Federal Department of Fisheries Statistics for 1995. Abuja,
Nigeria: Federal Department of Fisheries.
Gupta, M.V. (1990) Rural Women in Agriculture, Bangladesh. Manila, Philippines: ICLARM Quarterly.
Heidues, F. (1994) The Special and Economic Analysis of the Adoption of Agricultural Innovation in
Niger State. Special Research Programme, 4, pp. 21�33.
Lemchi, J.M., Ishiunza, M. & Tankouano, A. (2003) Factors Driving the Intensity and Rate of Cooking
Banana Adoption in Nigeria. Journal of Agriculture and Social Research, 3(2), pp. 135�166.
Maduakor, C.O. (2001) Adoption Behaviour of Cassava Farmers in Anambra State of Nigeria. Global
Issues in Agricultural Extension, 2(1), pp. 23�27.
Madukwe, M.C. (1993) Indigenous Technology for Rural Activities. Indigenous Knowledge and Develop-
elopment Monitor, 1(3), pp. 23�25.
Ofuoku, A.U., Emuh, F.N. & Osuagwu, C.N. (2005) Adoption of Improved Varieties of Soya Beans
(Glycine max) among Rural Female Farmers in Ndokwa West and Ukwuani Local Government
Areas of Delta State, Nigeria. Proceedings of the 19th Annual Conference of FAMAN Held
18th�20th October, at the Delta State University, Asaba Campus, Asaba.
Shingi, P.M., Fliegel, F.C. & Kelvin, J.F. (2004) Agricultural Technology and the Issue of Unequal
Distribution of Rewards: An Indian Case Study. Rural Sociology, 46, pp. 430�445.
Utomakili, B. (1987) An Economic Analysis of Fish Farming in Nigeria. Unpublished PhD Thesis, O.A.U
Ile-Ife, Nigeria.
306 A.U. Ofuoku et al.
Dow
nloa
ded
by [
Bib
lioth
èque
s de
l'U
nive
rsité
de
Mon
tréa
l] a
t 09:
12 0
8 D
ecem
ber
2014
