This study employed a log-linear model derived from the Semi-log functional form as the econometric model specified and the best fit for explaining adoption rate of NRCRI Cocoyam Production packages among women in the study area. A multi-stage random sampling technique was used to select 120 cocoyam farmers (Females) in the state in 2008. The study found farm size and membership of cooperative society to be positively and significantly related to rate of adoption at 5.0% level of probability. Farming experience coefficient also had a positive relationship with rate of adoption and was significant at 1.0%. Household Size was significant and negatively related to rate of adoption rate at 10.0%. No significant relationship was found between adoption rate and marital status, age, education and extension contact. The results call for policies aimed at redistribution of land by making more land available to the women farmers, encouraging the experienced farmers for increased adoption of cocoyam packages and programmes through the cooperative societies, which will motivate farmers to invest in and increased adoption of these packages

Akinpelu, A.O

Asumugha, G.N

Chinaka, E.C

Okoye, B.C

English

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveDeterminants of Adoption of NRCRICocoyam Production Packages amongSmall-Holder Women Farmers in EnuguStateChinaka, E.C and Akinpelu, A.O and Okoye, B.C andAsumugha, G.NNational Root Crops Research Institute, Umudike, Abia StateMarch 2009Online at https://mpra.ub.uni-muenchen.de/17420/MPRA Paper No. 17420, posted 26 Feb 2020 09:13 UTCDETERMINANTS OF ADOPTION OF NRCRI COCOYAM PRODUCTION PACKAGES AMONG SMALL-HOLDER WOMEN FARMERS IN ENUGU STATE  Chinaka, E.C., A.O. Akinpelu, B.C. Okoye and G.N. Asumugha  National Root Crops Research Institute Umudike, Umuahia Abia State.  Abstract This study employed a log-linear model derived from the Semi-log functional form as the econometric model specified and the best fit for explaining adoption rate of NRCRI Cocoyam Production packages among women in the study area. A multi-stage random sampling technique was used to select 120 cocoyam farmers (Females) in the state in 2008. The study found farm size and membership of cooperative society to be positively and significantly related to rate of adoption at 5.0% level of probability. Farming experience coefficient also had a positive relationship with rate of adoption and was significant at 1.0%. Household Size was significant and negatively related to rate of adoption rate at 10.0%. No significant relationship was found between adoption rate and marital status, age, education and extension contact. The results call for policies aimed at redistribution of land by making more land available to the women farmers, encouraging the experienced farmers for increased adoption of cocoyam packages and programmes through the cooperative societies, which will motivate farmers to invest in and increased adoption of these packages.  Key Words: Adoption Rate, NRCRI Cocoyam production Packages and Enugu State   Introduction Cocoyam (Xanthosoma sp., Colocasia sp.) is an important staple cultivated in the south-eastern and south-western parts of Nigeria (Ojiako et al., 2007). It is also an important food security crop in Nigeria and variously grown by resource poor farmers, mostly women, who intercrop cocoyam with yam, maize, plantain, banana, vegetables and rice (Ikwelle et al., 2003). Currently, Nigeria is the world’s largest producer of cocoyam. The average production figure for Nigeria is 5,068,000mt which accounts for about 37% of total world output of cocoyam (FAO, 2007).  Rogers (1995) demonstrates that adoption of technologies depends on their characteristics: compatibility with the existing values and norms, complexity, observability, trialability, and relative advantage. This definition pertains to technologies in a variety of disciplines, and may be as relevant in other fields as it is in agricultural related technologies. Earlier studies by Dorp and Rulkens (1993), Agwu and Anyaeche (2007), Springer et al. (2002), Kimenju et al. (2005) and Nwawuisi, et al (2007) show that farmers decision to use particular crop cultivars were influenced by a number of reasons, some of which are market-driven or socio-culturally based. The objective of this paper therefore is the unveiling of factors that determined adoption of NRCRI Cocoyam Packages among women farmers in Enugu State of South-Eastern Nigeria.   Methodology The study was conducted in Enugu State of the South-Eastern Nigeria using the multi-stage purposive random sampling technique. In the first stage, 2 LGA’s were randomly selected in the state. In the second stage, 6 communities were randomly selected from each LGA. In the third stage, 10 women cocoyam farmers were purposively randomly selected in each community giving a sampling frame of 120 respondents for the study. In order to determine the socio-economic characteristics of the farmers, questions were asked with respect to age, household size, level of education, farming experience and farm size. Percentages were used to describe the socio-economic characteristics of the farmers. The functional forms of multiple regressions were tried. Statistical and econometric criteria such as values of R2, F-Ratio, number and level of significant of the best fit equation, based on the value R2 (coefficient of multiple determination), F-ratio and the conformity of the signs of the coefficient with apriori expectations. Adoption rate (AR) was measured as a percentage of the 10 production packages (plant population, minisett technology, weed control, fertilizer application, stand geometry, compatible crop mixtures, mounding/ridging, pest control methods, early planting and mulching), this was used as the dependent variable with values ranging from 0-100% The log-linear model derived from the  Semi-log functional form was the econometric model specified and the best fit for explaining adoption rate in the study. The model is described thus: AR=f (AGE, MAST, HHS, EDU, EXT, EXP, HA and COOP). Where; AGE = Age of farmer, MAST=Marital status, HHS =Household size, EDU =Educational level in years, EXT=Number of extension contacts, EXP=Farming experience in years, HA=Farm Size in ha and COOP=Membership of cooperatives (dummy variable; 1=member, 0=otherwise).  Results and Discussions The data in Table I show that majority (58.4%) of the respondents in the State were less than 51years of age. The average age of the respondents was 48years, indicating that most of the respondents were young and in their most productive years. Age is said to be a primary latent characteristic in adoption decisions. (Bonabana-Wabbi, 2002; Nwaru, 2004; Nwawuisi et al., 2007; Agwu and Afieroho, 2007) found out that the ability of a farmer to break risk, be innovative decreases with age.  Table 1. Distribution of Respondents according to Socio-Economic Characteristics in Enugu State. Variable Percentage Mean Age   18-28 6.7  29-39 21.7  40-50 30.0  51-61 30.0  > 61 11.6 48.0 Household Size   1-5 40.0  6-10 58.3  11-15 1.7  > 15 0.0 6.0 Level of Education   None 61.7  1-6 21.7  7-12 13.3  13-18 1.7  > 18 1.6 3.1 Farming Experience   1-5 11.7  6-10 5.0  11-15 11.7  16-20 11.7  > 20 59.9 26.7 Farm Size   0.01-0.05 0.0  0.06-0.10 0.0  0.20-0.60 8.5  0.70-1.10 45.8  > 1.10 45.7 1.6 Source: Field Data, 2008  A large percentage (60%) of the respondents had household size of 6-10 persons and 25.1% had household size of 1- 5 persons. The average household size of the respondents was 6 persons. Larger households are more likely to provide the labor that might be required   as a result of adoption of these technologies. Effiong, (2005) and Idiong (2005) reported that a relatively large household size enhance the availability of labour.  Data in table 1 show that 61% of the farmers had no formal education. This show that majority of the farmers had high degree of illiteracy among them. The average educational level of the farmers was about 3years. Educated farmers are  expected to be more receptive to improved farming techniques, while farmers with low level of education or without education would be less receptive to improved farming techniques (Okoye, et al. 2004, Ajibefun and Aderinola, 2004).   The average farming experience for respondents was 26.7years. A farmer’s experience can generate or erode confidence. With more experience, a farmer can become more or less averse to the risk implied by adopting a new technology; thus this variable can have a positive or negative effect on a farmer’s decision to adopt an improved weed control technology.   The table also indicated that majority (91.5%) of the respondents had cocoyam holdings of more than 0.6ha. the average land size holding was 1.6ha. The result, indicate that cocoyam production in the study area is dominated by small-holder scale producers.  Farm size affects adoption costs, risk perceptions, human capital, credit constraints, labor requirements, tenure arrangements and more. With small farms, it has been argued that large fixed costs become a constraint to technology adoption (Abara and Singh, 1993) especially if the technology is costly.  Determinants of adoption of NRCRI Cocoyam Packages among Women in Enugu State.  To ascertain the influence of some personal characteristics and institutional factors on adoption of NCRI cocoyam packages among women, regression analysis using the semi-logarithmic form was used.   Table 3 Estimates of Adoption rate of NRCRI Cocoyam Packages among Women in Enugu State  Variables Coefficient t-value Constant term -218.3854 -1.60 In MAST 66.7673 1.60 In AGE 1.2608 0.08 In HHS -19.7682 -1.69* In EDU 0.6840 0.12 In HA 25.7676 2.05** In EXT -2.3058 -0.34 In EXP 23.9205 5.28*** In COOP 15.3201 2.67**  Note:*, **, and *** means significant at 10%, 5% and 1% respectively ln = represents the natural logarithm   R2(coefficient of multiple  determination) was 0.81, show that 81% variation in the dependent variable (adoption rate) is accounted for by the independent variables included in the model indicates goodness of fit.  The signs of the coefficients for farm size and membership of cooperative societies were positive and significant at 5% level. Farm size affects adoption costs, risk perceptions, human capital, credit constraints, labor requirements, tenure arrangements and more. With small farms, it has been argued that large fixed costs become a constraint to technology adoption (Abara and Singh, 1993). Sofranke, (1984) stated that adoption of innovation is membership of farmer association due to its group dynamic effects.  The coefficient for farming experience was positive relationship with adoption rate and highly significant at 1% level. With more experience, a farmer can become more or less averse to the risk implied by adopting a new technology.   The coefficient for household size was negative and significant at 10% level. Large house hold holdings which is largely synonymous with those who are married may be bedeviled with a lot of family responsibilities which may lead to decreased adoption of  innovations.  Conclusion The findings of this study show that decision to adopt the cocoyam packages is influenced by farm size, membership of cooperative societies, farming experience and household size. The results call for policies aimed at land reform to  make more land available to the women farmers, encouraging the experienced farmers for increased adoption of cocoyam packages. There is an urgent need to organize programmes especially through the cooperative societies, which will motivate farmers to invest in and increased adoption of these packages.  References Abara, I. O. C. and S. Singh. (1993) “Ethics and Biases in Technology Adoption: The Small Farm Argument.” Technological Forecasting and Social Change. 43 : 289-300.  Agwu, A.E and Anyaeche, C. L (2007) Adoption of Improved Cassava Varieties in six rural Communities of Anambra State, Nigeria. Academic Journals (African Journal of Biotechnology) Vol. 6(2) pp 089-098. Ajibufun, I.A and Aderinola, E.A. (2004): Determinates of Technical efficiency and policy  implication in Report preservation at B-annual Research  workshop of Africa Economic Research Consortium. Ajibufun, I.A and Aderinola, E.A. (2004): Determinates of Technical efficiency and policy  implication in Report preservation at B-annual Research  workshop of Africa Economic Research Consortium. Banabana-Wabbi, J (2002) Assessing Factors affecting Adoption of Agricultural Technologies: The Case of Integrated Pest Management (IPM) in Kumi District, Eastern Uganda. Unpublished M.Sc Thesis, Dept. of Agricultural and Applied Economics, Virginia Polythecnic Institute and State University USA. Dorp M, Rulkens T (1993). Farmer crop selection criteria and gene bank collections in Indonesia, In: Boef W, Amanor K, Wellard K,Bebbington A (Eds). Cultivating knowledge: Genetic diversity, farmerexperimentation and crop research. London, intermediate technology publications. Pp. 119-127 Effiong ,E.O(2005). Efficiency of production in selected livestock enterprise in Akwa-Ibom State.Nigeria .unpublished Ph.D Dissertation. Department of agricultural economic,M ichael Okpara University of agriculture, Umudike FAO (2007) FAOSTAT Statistics Division of the Food and Agriculture Organization, http://faostat.fao.org Idiong,I.C(2005), Evaluation of Technical ,Allocation, and Economic Analysis unpublished Ph.D Dissertation. Department of agricultural economic,M ichael Okpara University of agriculture, Umudike. Ikwelle, M. C., Ezulike, T.O and Eke-okoro, O.N (2003). Contribution of Root and Tuber Crops to the Nigerian Economy. Proc. 8th Triennial Symposium of the International Society for Tropical Root Crops-Africa Branch (ISTRC-AB) held at the International Institute of Tropical Agriculture, Ibadan, Nov. 12-16, 2001. pp 13-18 Kimenju SC, De Groote H, Karugia J, Mbogoh S, Poland D (2005) Consumer awareness and attitudes toward GM foods in Kenya. Afr.J. Biotechnol. Vol. 4 (10): pp. 1066-1075. Nwaru, J.C. (2004) Rural Credit markets and Arable Crop production in Imo State of Nigeria. Unpublished Ph.D Dissertation Michael Okpara University of Agriculture, Umudike, Nigeria. Nwawuisi, J.U., B.C. Okoye and C.O. Odaji. (2007). Adoption of Improved Cassava Varieties (TMS 30211 and TMS 3001) in Ivo L.G.A of Ebonyi State.  Paper presented at the Proceedings of the 41st Conference of the Agricultural Society of Nigeria, Samaru  2007  Pp 527 – 530 Ojiako, I.A, Asumugha, G.N, Ezedimma, C and Uzokiwe N E (2007) .Analysis of production trends in the major root and tuber crops in Nigeria, 1961-2005. Res in crops 8(2) pp 371-380 Okoye, B. C., Okorji, E. C and Asumugha, G. N (2004) Outlook on Production Economics of Paddy Rice under Resource constraints in Ebonyi State. Proc. of the 38th Annual Conference of the Agricultural Society of Nigeria. (ASN), 17- 21 Oct. 2004, Lafia Nasarawa State. Pp 337-342. Rogers, E.M (1995) Diffusion of Innovations. 3rd Edition. New York: The Free Press, 1983.__4th Edition. New York Sofranko, A.J. (1984). Introducing Technological Change: The social Setting. In: Swanson, B.E. (ed)  Agriccultural Extension: A Reference Manual. Second Edition. Food and agricultural Organisation of United Nations, Rome pp 65-76. Springer A, Mattas K, Papastefanou GT, Tsioumanis A (2002) Comparing Consumer Attitudes towards Genetically Modified Food in Europe. Mimeo. 

Determinants of Adoption of NRCRI Cocoyam Production Packages among Small-Holder Women Farmers in Enugu State

Munich Personal RePEc Archive

Munich Personal RePEc Archive
Determinants of Adoption of NRCRI
Cocoyam Production Packages among
Small-Holder Women Farmers in Enugu
State
Chinaka, E.C and Akinpelu, A.O and Okoye, B.C and
Asumugha, G.N
National Root Crops Research Institute, Umudike, Abia State
March 2009
Online at https://mpra.ub.uni-muenchen.de/17420/
MPRA Paper No. 17420, posted 26 Feb 2020 09:13 UTC
DETERMINANTS OF ADOPTION OF NRCRI COCOYAM PRODUCTION PACKAGES 
AMONG SMALL-HOLDER WOMEN FARMERS IN ENUGU STATE 
 
Chinaka, E.C., A.O. Akinpelu, B.C. Okoye and G.N. Asumugha 
 
National Root Crops Research Institute Umudike, Umuahia Abia State. 
 
Abstract 
This study employed a log-linear model derived from the Semi-log functional form as the econometric 
model specified and the best fit for explaining adoption rate of NRCRI Cocoyam Production packages 
among women in the study area. A multi-stage random sampling technique was used to select 120 cocoyam 
farmers (Females) in the state in 2008. The study found farm size and membership of cooperative society to 
be positively and significantly related to rate of adoption at 5.0% level of probability. Farming experience 
coefficient also had a positive relationship with rate of adoption and was significant at 1.0%. Household 
Size was significant and negatively related to rate of adoption rate at 10.0%. No significant relationship 
was found between adoption rate and marital status, age, education and extension contact. The results call 
for policies aimed at redistribution of land by making more land available to the women farmers, 
encouraging the experienced farmers for increased adoption of cocoyam packages and programmes through 
the cooperative societies, which will motivate farmers to invest in and increased adoption of these 
packages. 
 
Key Words: Adoption Rate, NRCRI Cocoyam production Packages and Enugu State  
 
Introduction 
Cocoyam (Xanthosoma sp., Colocasia sp.) is an 
important staple cultivated in the south-eastern 
and south-western parts of Nigeria (Ojiako et al., 
2007). It is also an important food security crop 
in Nigeria and variously grown by resource poor 
farmers, mostly women, who intercrop cocoyam 
with yam, maize, plantain, banana, vegetables 
and rice (Ikwelle et al., 2003). Currently, Nigeria 
is the world’s largest producer of cocoyam. The 
average production figure for Nigeria is 
5,068,000mt which accounts for about 37% of 
total world output of cocoyam (FAO, 2007). 
 
Rogers (1995) demonstrates that adoption of 
technologies depends on their characteristics: 
compatibility with the existing values and norms, 
complexity, observability, trialability, and 
relative advantage. This definition pertains to 
technologies in a variety of disciplines, and may 
be as relevant in other fields as it is in 
agricultural related technologies. Earlier studies 
by Dorp and Rulkens (1993), Agwu and 
Anyaeche (2007), Springer et al. (2002), 
Kimenju et al. (2005) and Nwawuisi, et al (2007) 
show that farmers decision to use particular crop 
cultivars were influenced by a number of 
reasons, some of which are market-driven or 
socio-culturally based. The objective of this 
paper therefore is the unveiling of factors that 
determined adoption of NRCRI Cocoyam 
Packages among women farmers in Enugu State 
of South-Eastern Nigeria. 
  
Methodology 
The study was conducted in Enugu State of the 
South-Eastern Nigeria using the multi-stage 
purposive random sampling technique. In the 
first stage, 2 LGA’s were randomly selected in 
the state. In the second stage, 6 communities 
were randomly selected from each LGA. In the 
third stage, 10 women cocoyam farmers were 
purposively randomly selected in each 
community giving a sampling frame of 120 
respondents for the study. In order to determine 
the socio-economic characteristics of the 
farmers, questions were asked with respect to 
age, household size, level of education, farming 
experience and farm size. Percentages were used 
to describe the socio-economic characteristics of 
the farmers. The functional forms of multiple 
regressions were tried. Statistical and 
econometric criteria such as values of R2, F-
Ratio, number and level of significant of the best 
fit equation, based on the value R2 (coefficient of 
multiple determination), F-ratio and the 
conformity of the signs of the coefficient with 
apriori expectations. Adoption rate (AR) was 
measured as a percentage of the 10 production 
packages (plant population, minisett technology, 
weed control, fertilizer application, stand 
geometry, compatible crop mixtures, 
mounding/ridging, pest control methods, early 
planting and mulching), this was used as the 
dependent variable with values ranging from 0-
100% The log-linear model derived from the 
 Semi-log functional form was the econometric 
model specified and the best fit for explaining 
adoption rate in the study. The model is 
described thus: 
AR=f (AGE, MAST, HHS, EDU, EXT, EXP, 
HA and COOP). 
Where; 
AGE = Age of farmer, MAST=Marital status, 
HHS =Household size, EDU =Educational 
level in years, EXT=Number of extension 
contacts, EXP=Farming experience in years, 
HA=Farm Size in ha and COOP=Membership of 
cooperatives (dummy variable; 1=member, 
0=otherwise). 
 
Results and Discussions 
The data in Table I show that majority (58.4%) 
of the respondents in the State were less than 
51years of age. The average age of the 
respondents was 48years, indicating that most of 
the respondents were young and in their most 
productive years. Age is said to be a primary 
latent characteristic in adoption decisions. 
(Bonabana-Wabbi, 2002; Nwaru, 2004; 
Nwawuisi et al., 2007; Agwu and Afieroho, 
2007) found out that the ability of a farmer to 
break risk, be innovative decreases with age. 
 
Table 1. Distribution of Respondents according to Socio-Economic Characteristics in Enugu State. 
Variable Percentage Mean 
Age   
18-28 6.7  
29-39 21.7  
40-50 30.0  
51-61 30.0  
> 61 11.6 48.0 
Household Size   
1-5 40.0  
6-10 58.3  
11-15 1.7  
> 15 0.0 6.0 
Level of Education   
None 61.7  
1-6 21.7  
7-12 13.3  
13-18 1.7  
> 18 1.6 3.1 
Farming Experience   
1-5 11.7  
6-10 5.0  
11-15 11.7  
16-20 11.7  
> 20 59.9 26.7 
Farm Size   
0.01-0.05 0.0  
0.06-0.10 0.0  
0.20-0.60 8.5  
0.70-1.10 45.8  
> 1.10 45.7 1.6 
Source: Field Data, 2008 
 
A large percentage (60%) of the respondents had 
household size of 6-10 persons and 25.1% had 
household size of 1- 5 persons. The average 
household size of the respondents was 6 persons. 
Larger households are more likely to provide the 
labor that might be required   as a result of 
adoption of these technologies. Effiong, (2005) 
and Idiong (2005) reported that a relatively large 
household size enhance the availability of labour. 
 
Data in table 1 show that 61% of the farmers had 
no formal education. This show that majority of 
the farmers had high degree of illiteracy among 
them. The average educational level of the 
farmers was about 3years. Educated farmers are 
 expected to be more receptive to improved 
farming techniques, while farmers with low level 
of education or without education would be less 
receptive to improved farming techniques 
(Okoye, et al. 2004, Ajibefun and Aderinola, 
2004).  
 
The average farming experience for respondents 
was 26.7years. A farmer’s experience can 
generate or erode confidence. With more 
experience, a farmer can become more or less 
averse to the risk implied by adopting a new 
technology; thus this variable can have a positive 
or negative effect on a farmer’s decision to adopt 
an improved weed control technology.  
 
The table also indicated that majority (91.5%) of 
the respondents had cocoyam holdings of more 
than 0.6ha. the average land size holding was 
1.6ha. The result, indicate that cocoyam 
production in the study area is dominated by 
small-holder scale producers.  Farm size affects 
adoption costs, risk perceptions, human capital, 
credit constraints, labor requirements, tenure 
arrangements and more. With small farms, it has 
been argued that large fixed costs become a 
constraint to technology adoption (Abara and 
Singh, 1993) especially if the technology is 
costly. 
 
Determinants of adoption of NRCRI 
Cocoyam Packages among Women in Enugu 
State.  
To ascertain the influence of some personal 
characteristics and institutional factors on 
adoption of NCRI cocoyam packages among 
women, regression analysis using the semi-
logarithmic form was used. 
 
 
Table 3 Estimates of Adoption rate of NRCRI Cocoyam Packages among Women in Enugu State  
Variables Coefficient t-value 
Constant term -218.3854 -1.60 
In MAST 66.7673 1.60 
In AGE 1.2608 0.08 
In HHS -19.7682 -1.69* 
In EDU 0.6840 0.12 
In HA 25.7676 2.05** 
In EXT -2.3058 -0.34 
In EXP 23.9205 5.28*** 
In COOP 15.3201 2.67** 
 
Note:*, **, and *** means significant at 10%, 5% and 1% respectively 
ln = represents the natural logarithm  
 
R2(coefficient of multiple  determination) was 
0.81, show that 81% variation in the dependent 
variable (adoption rate) is accounted for by the 
independent variables included in the model 
indicates goodness of fit. 
 
The signs of the coefficients for farm size and 
membership of cooperative societies were 
positive and significant at 5% level. Farm size 
affects adoption costs, risk perceptions, human 
capital, credit constraints, labor requirements, 
tenure arrangements and more. With small 
farms, it has been argued that large fixed costs 
become a constraint to technology adoption 
(Abara and Singh, 1993). Sofranke, (1984) stated 
that adoption of innovation is membership of 
farmer association due to its group dynamic 
effects. 
 
The coefficient for farming experience was 
positive relationship with adoption rate and 
highly significant at 1% level. With more 
experience, a farmer can become more or less 
averse to the risk implied by adopting a new 
technology.  
 
The coefficient for household size was negative 
and significant at 10% level. Large house hold 
holdings which is largely synonymous with those 
who are married may be bedeviled with a lot of 
family responsibilities which may lead to 
decreased adoption of  innovations. 
 
Conclusion 
The findings of this study show that decision to 
adopt the cocoyam packages is influenced by 
farm size, membership of cooperative societies, 
farming experience and household size. The 
results call for policies aimed at land reform to 
 make more land available to the women farmers, 
encouraging the experienced farmers for 
increased adoption of cocoyam packages. There 
is an urgent need to organize programmes 
especially through the cooperative societies, 
which will motivate farmers to invest in and 
increased adoption of these packages. 
 
References 
Abara, I. O. C. and S. Singh. (1993) “Ethics and 
Biases in Technology Adoption: The 
Small Farm Argument.” Technological 
Forecasting and Social Change. 43 : 
289-300. 
 Agwu, A.E and Anyaeche, C. L (2007) 
Adoption of Improved Cassava 
Varieties in six rural Communities of 
Anambra State, Nigeria. Academic 
Journals (African Journal of 
Biotechnology) Vol. 6(2) pp 089-098. 
Ajibufun, I.A and Aderinola, E.A. (2004): 
Determinates of Technical efficiency 
and policy  implication in Report 
preservation at B-annual Research  
workshop of Africa Economic Research 
Consortium. 
Ajibufun, I.A and Aderinola, E.A. (2004): 
Determinates of Technical efficiency 
and policy  implication in Report 
preservation at B-annual Research  
workshop of Africa Economic Research 
Consortium. 
Banabana-Wabbi, J (2002) Assessing Factors 
affecting Adoption of Agricultural 
Technologies: The Case of Integrated 
Pest Management (IPM) in Kumi 
District, Eastern Uganda. Unpublished 
M.Sc Thesis, Dept. of Agricultural and 
Applied Economics, Virginia 
Polythecnic Institute and State 
University USA. 
Dorp M, Rulkens T (1993). Farmer crop 
selection criteria and gene bank 
collections in Indonesia, In: Boef W, 
Amanor K, Wellard K,Bebbington A 
(Eds). Cultivating knowledge: Genetic 
diversity, farmerexperimentation and 
crop research. London, intermediate 
technology publications. Pp. 119-127 
Effiong ,E.O(2005). Efficiency of production in 
selected livestock enterprise in Akwa-
Ibom State.Nigeria .unpublished Ph.D 
Dissertation. Department of agricultural 
economic,M ichael Okpara University 
of agriculture, Umudike 
FAO (2007) FAOSTAT Statistics Division of the 
Food and Agriculture Organization, 
http://faostat.fao.org 
Idiong,I.C(2005), Evaluation of Technical 
,Allocation, and Economic Analysis 
unpublished Ph.D Dissertation. 
Department of agricultural economic,M 
ichael Okpara University of agriculture, 
Umudike. 
Ikwelle, M. C., Ezulike, T.O and Eke-okoro, 
O.N (2003). Contribution of Root and 
Tuber Crops to the Nigerian Economy. 
Proc. 8th Triennial Symposium of the 
International Society for Tropical Root 
Crops-Africa Branch (ISTRC-AB) held 
at the International Institute of Tropical 
Agriculture, Ibadan, Nov. 12-16, 2001. 
pp 13-18 
Kimenju SC, De Groote H, Karugia J, Mbogoh 
S, Poland D (2005) Consumer 
awareness and attitudes toward GM 
foods in Kenya. Afr.J. Biotechnol. Vol. 
4 (10): pp. 1066-1075. 
Nwaru, J.C. (2004) Rural Credit markets and 
Arable Crop production in Imo State of 
Nigeria. Unpublished Ph.D Dissertation 
Michael Okpara University of 
Agriculture, Umudike, Nigeria. 
Nwawuisi, J.U., B.C. Okoye and C.O. Odaji. 
(2007). Adoption of Improved Cassava 
Varieties (TMS 30211 and TMS 3001) 
in Ivo L.G.A of Ebonyi State.  Paper 
presented at the Proceedings of the 41st 
Conference of the Agricultural Society 
of Nigeria, Samaru  2007  Pp 527 – 530 
Ojiako, I.A, Asumugha, G.N, Ezedimma, C and 
Uzokiwe N E (2007) .Analysis of 
production trends in the major root and 
tuber crops in Nigeria, 1961-2005. Res 
in crops 8(2) pp 371-380 
Okoye, B. C., Okorji, E. C and Asumugha, G. N 
(2004) Outlook on Production 
Economics of Paddy Rice under 
Resource constraints in Ebonyi State. 
Proc. of the 38th Annual Conference of 
the Agricultural Society of Nigeria. 
(ASN), 17- 21 Oct. 2004, Lafia 
Nasarawa State. Pp 337-342. 
Rogers, E.M (1995) Diffusion of Innovations. 
3rd Edition. New York: The Free Press, 
1983.__4th Edition. New York 
Sofranko, A.J. (1984). Introducing 
Technological Change: The social 
Setting. In: Swanson, B.E. (ed) 
 Agriccultural Extension: A Reference 
Manual. Second Edition. Food and 
agricultural Organisation of United 
Nations, Rome pp 65-76. 
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https://mpra.ub.uni-muenchen.de/17420/1/MPRA_paper_17420.pdf

Determinants of Adoption of NRCRI Cocoyam Production Packages among Small-Holder Women Farmers in Enugu State

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