Introduction
Rice has become an important economic crop and the major staple food for millions of people in Sub-Sahara Africa in general and Nigeria in particular (WARDA, 2006). As a matter of fact, Africa has become a big player in international rice markets, accounting for 32% of global imports in 2006, at a record level of 9 million tons that year (WARDA, 2008). Due to the growing importance of the crop and the increasing challenges of attainment of food security, it has been estimated that annual rice production needs to increase from 586 million metric tons in 2001 to meet the projected global demand of about 756 million metric tons by 2030 (FAO, 2002). In the West Africa sub region, Nigeria has witnessed a well-established growing demand for rice as propelled by rising per capita consumption and consequently the insufficient domestic production had to be complemented with enormous import both in quantity and value at various times (Erenstein et al., 2004; Daramola, 2005). Ogundele et al., (2004) also noted that due to rapid population growth, urban residents’ exposure to dietary patterns of foreign cultures, urban lifestyles with preference for foods which require less time to prepare and rising household income of the urban population, the demand for rice in Nigeria keeps increasing.

In Nigeria, the status of rice in the average diet has been transformed from being a luxury food item that it was at independence, in 1960 to that of a staple, taking the place of cassava, yam among other staples, as both the rich and the urban poor rely on it as a major source of calories (WARDA, 2003-2004; Daramola, 2005). Rice consumption has risen tremendously since 1970 (+10.3 percent per annum), a result of the accelerating population growth rate (+2.8 percent per annum) and increased per capita consumption (+7.3 percent per annum) leading to an increase in domestic demand over domestic supply (Akande, 2002; UNEP, 2005). In response to this, the Nigerian government resorted to importation to bridge the gap, with this shift in the Nigerian rice economy from a self-sufficient nation to an importing nation; rice has become a strategic commodity in Nigerian economy (Akande 2002; Nkang et al., 2006). Notwithstanding, in recent years, rice production in Nigeria has been expanding at the rate of 6% per annum, with 70% of the production increase due mainly to land expansion and only 30% being attributed to an increase in productivity (Fagade, 2000; Falusi, 1997; WARDA, 2007; 2008; Okoruwa et al., 2007). Despite the continued expansion of land production to increase yield and output, none of these has materialized to reduce the demand and supply gaps. Thus, increasing agricultural productivity and hence production using improved agricultural technologies is foreseen as the only way by which this gap can be reduced.

Furthermore, Diagne (2006) and Diagne & Demont (2007) reveals that awareness of existence of a technology is a sine qua non for its adoption (i.e. use), while, in principle, one can start using a new technology while knowing nothing about its characteristics or performance. It is this fact (i.e. that awareness is a prerequisite for adoption) that makes accounting for awareness fundamental in adoption studies. It is therefore the contention of this study to examine the awareness and adoption rate of these improved rice varieties in the study area. The contribution is therefore to profile the various improved rice varieties grown in the study area, and to determine the factors that would influence awareness, adoption and intensity of adoption of improved rice varieties. This study will also provide information on the current improved rice varieties cultivated in the study area, the exposure rate of improved rice varieties, which factors have the greatest influence on improved rice varieties adoption and the intensity of adoption. The result is expected to help researchers make more informed decisions on how to promote the improved rice varieties adoption and contribute towards design of appropriate policies enabling developing the rice sub sector by the government.

1 Results and Discussion
1.1 Description of rice farmers’ socio-economic characteristics by adoption status
Table 1 gives the report of descriptive statistics disaggregated by farmers’ adoption status and socio-economic characteristics for 149 surveyed rice farmers. Generally, adopters are defined as farming households that planted at least one of the improved rice varieties and specifically, a household is defined as an adopter of a particular variety if it plants that variety in at least one of its rice plots. The result showed that majority (87.9%) of the respondents were male and forms (76.5%) of the adopters and (11.4%) of the non-adopters, thereby re-validating the age long dominance of males in agriculture. This is one prominent characteristic where most farm operations are carried out manually and therefore more demanding in terms of physical strength especially for labour intensive crops such as rice. In terms of educational level of the household heads, (32.2%) of the total respondents had no formal education. The proportion with no formal education was not significantly different between the adopters and non-adopters. The implication of this is that since majority of the rice farmers had no formal education it implies non-availability to appreciate potential for improved managerial capabilities among them. This can also pose limitations to technical skills and decision- making prowess.

Table 1 Description of rice farmers’ socio-economic characteristics by adoption status

The result also shows that a larger percentage of the farmers are within the productive ages of 31~60 years thereby implying availability of strength and by extension, mental alertness for adoption of productivity enhancing innovations or technologies and can also be amenable for adoption of productivity-enhancing technologies. Average age of the respondents was 50 years; the mean age of the adopters (50 years) was not significantly different from the non-adopters (54 years).  On the overall, majority (86.6%) of the respondents had Agriculture as major occupation with 76.5% of the adopters having agriculture as main occupation.  The average household size was 8 persons per household. This could have positive and negative effect on adoption status. The positive effect could arise if the large household size is used as a source of family labour, thereby reducing the cost of labour and will also want to adopt improved varieties. However, a large household size could also worsen the adoption status of farming household particularly if it is composed of a large number of dependants, which means the family will rely on hired labour and this can lower the adoption status as a result of cost of labour.

In terms of membership to association, there was no statistical difference between membership to a group and likelihood of adoption. This implies that probability of adoption was more or less the same for both adopters and non-adopters, (43.0%) of the farmers in the population belong to an association while (36.9%) and (6.0%) are adopters and non-adopters respectively. About 78.5% of the population had access to media, with 72.3% adopters and 6.0% non-adopters, there exist a statistical difference between adopters and non-adopters access to media significant at 1% hence the adopters were able to easily access information about the existing or improved technologies than the non-adopters. The proportion of the farmers surveyed that had access to mobile phone was 46.3%, with 41.6% adopters and 4.7% non-adopters.

Also shown in table 1, about (34.2%) of the farmers had possibility of obtaining credit with 32.9% being adopters and 1.34% non-adopters. There is also a significant difference in access to credit between the adopting and the non-adopting households at 5%. About 14.1% of the farmers in the population ever attended agricultural training with 10.7% and 3.4% being adopters and non-adopters respectively. On the overall, about (12.1%) of the farmers in the population ever attended rice farming training with 8.7% and 3.4% being adopters and non-adopters respectively. Distribution of rice farming training among adopters and non-adopters was statistically significant at 5%.

1.2 Description of rice farmers farm characteristics by adoption status
Table A3 shows farm characteristics of the respondents by adoption status. Agricultural Development Programme (ADP) categorization of farm size was used in this study, less than 2 ha (small farm size), 2~5 ha (medium sized farm) and greater than 5 ha (large farm size). The results revealed that average farm size cultivated by the respondents during the survey year was 3.0 ha; the most common farm size cultivated among the rice farmers studied was between 2~5 ha (49.0%) with 43.0% and 6.0% adopters and non-adopters respectively, followed by those that cropped less than 2 ha (34.2%) while 11.4% of the farmers cropped greater than 5 ha. There was no statistical difference between farm size and adoption status of the respondents. About 73.8% of the farmers planted Improved NARS varieties (sipi), 24.8% planted other improved varieties while 34.3% of the farmers planted traditional varieties. NERICA variety was planted by only 0.67% of the farmers. Distribution of rice farmers by varieties cultivated and adoption status shows statistical different at 1% for traditional, improved NARS and Other Improved varieties.

In the study area rice was mainly grown in lowland and upland areas. Distribution of rice farmers by ecology cultivated to rice in the study area revealed that 55.0% of the rice farmers carried out their activities in the lowland area, 17.5% in the upland area while 27.5% carried out their activities in both upland and lowland areas. Distribution of rice farmers by ecology cultivated to rice between adopters and non-adopters shows statistical difference at 5% for those who cultivated on upland only and lowland only. Table 2 further shows that 95.3% of the rice farmers surveyed have knowledge of the improved rice varieties grown in the study area while 4.7% had no knowledge of the improved rice varieties. About 87.3% of those aware of the improved rice varieties adopted at least one of the improved rice varieties at the time of visit while 8.1% of those aware do not adopt, this disaggregation was statistically significant at 1%.

Table 2 Description of rice farmers farm characteristics by adoption status

The result also revealed that on the overall population, 87.3% of the rice farmers surveyed had planted at least one of the improved rice varieties grown in the study area while 12.8% has not planted any of the improved rice varieties on their rice plots.

1.3 Determinants of awareness of improved rice varieties
To estimate factors that affect the propensity of exposure to improved rice varieties a probit regression was used. Table 3 depicts results from a probit estimation of the determinants of getting exposed to at least one improved rice varieties. The log likelihood of -21.74 and LRChi2 of 13.0 significant at 1% level show that the model is fitted. The coefficient for access to media was positive and statistically significant at 5%. The marginal effect was 0.108. This implies that a unit increase in farmer’s access to media will increase the probability of being aware of improved rice varieties by 0.108. Although, years of formal education does not influence awareness significantly however, its influence was positive. Farmers with more years of education are more likely to get expose or acquire information about agricultural technologies. Membership to association was also positive but not significant, implying that farmers in social group tend to be more aware of agricultural technologies as they share ideas among members of the group. Having agriculture as main occupation shows positive though does not significantly influence awareness.

Table 3 Probit estimates of the determinants of awareness of improved rice varieties

1.4 Determinants of adoption and intensity of improved rice varieties
This study adopted the Heckman two-stage model to assess the socio-economic/demographic characteristics that influence the farmers’ adoption and intensity of adoption of improved rice varieties. The first stage examined the determinants of probability of adoption and the dependent variable was whether a farmer used (Adopted) improved rice variety or not. The second stage examined the intensity of adoption and the dependent variable was the proportion of area cultivated to the improved rice variety. To test for sample selection bias, the relationship between the residuals for the two stages, the selection and outcome equation was examined. The Wald test of independent equations rejects the null hypothesis of no correlation (rho=0) between the two disturbance terms (i.e. in the regression equation and selection equation) at 1% level of significance. The negative sign of rho shows that the unobservable factors that reduce the probability of adoption of improved rice variety increase the intensity of adoption and vice versa. The result as presented in Table 4 also shows that the sigma is statistically significant, implying that the choice of explanatory variables included in the heckman model explained the level of adoption of improved rice varieties. The inverse mills ratio (lambda) was significant implying that covariates that condition the proportion of area grown to improved rice variety is conditional on the probability of adoption implying that there is no sample selection bias problem.

Table 4 Determinants of adoption of improved rice varieties

The results of the analysis showed that access to credit, access to media and agricultural income significantly influence the probability of adoption. Access to credit positively and significantly (p≤0.05) influenced adoption. This implies that farmers that had access to credit faces lessen cash constraint; this enables them to purchase inputs such as improved seeds. In line with a priori expectation, access to media had a positive and significant (p≤0.05) influence on the probability of adoption. This implies that a high level of awareness was created through media. The study also revealed that agricultural income had a positive and significant (p≤0.01) influence on the probability of adoption meaning that farmers’ with higher agricultural income have the likelihood of re-investing back into the farm production activities by purchasing more modern productive inputs like improved seeds.

Table A5 also revealed that gender, household size, farm size and agricultural income significantly influence the intensity of adoption of improved rice variety. Gender of the household head was positive and significant (p≤0.1) which implies proportion of area cultivated to improved rice variety by the male headed households increases by 52.6% as against female headed households. The implication of this could be due to the fact that female headed households have poor access and control over resources in general and have shortage of farm labour. Farm size had a positive and significant (p≤0.01) influence on intensity of adoption implying that the proportion of area cultivated to improved rice variety increases as farm size increases. If the farm size increases by one hectare the area allocated to improved rice variety will increase by 94.9%. This may be due to the fact that farmers operating larger farms tend to have greater financial resources and a high probability of receiving credit to further enhance production than those of small farm size.

Similarly, household size had a negative and significant (p≤0.1) influence on the level of adoption which means that an additional member to the family will reduce the area cultivated to improved rice variety intensity by 4.1%. This is in agreement with Alene et al. (2008) that household size explains the family labour supply for prediction and household consumption levels. A positive sign implies that a larger household provides cheaper labour  while a negative sign on the other hand means that a larger household is labour inefficient hence will rely on hired labour which will eventually increase the cost of production. Agricultural income also had a positive and significant (p≤0.1) influence on the intensity of adoption, this implies that proportion of area cultivated to improved rice variety increases with an increase in agricultural income. This could be an incentive for the farmers to produce more, create wealth, boost their output and ultimately increase household income.

Although years of formal education did not influence adoption significantly, however, its influence was positive, which implies that highly educated farmers are better adopters, one cogent reason for this is that with an increase in the number of years of education, the ability of farmers to use resources efficiently increases. Allocative effect of education also enhances farmer’s ability to obtain, analyze and interpret information. Several studies reviewed by Feder et al., (1985) indicate that education level enhances farmers’ ability to acquire, interpret and use information, including information about agricultural technologies, and hence leads to earlier and faster adoption.

2 Conclusion
This study examined the factors influencing the awareness and adoption of improved rice varieties in North central Nigeria. The awareness of improved varieties was mainly accelerated by access to media which suggests that there is potential for increasing the diffusion of new varieties through existing formal institutions and methods in the dissemination of information on improved rice varieties. Farm size cultivated to rice significantly affected intensity of adoption, therefore factors aimed at increasing available farm land for rice production will go a long way to improve the adoption and intensity of improve rice varieties. The study has also shown that the propensity of cultivating (adopting) at least one improved rice variety is high among farmers that have access to credit. These findings point to the importance of improving farmer’s access to financial markets that enable them to acquire credit to purchase seed and complementary inputs for improved rice.

Based on the findings of this study, it is recommended that credit facilities should be provided to the farmers at no collaterals with very low interest rates by government and private sectors for improved adoption in the study area. Thus, rice farmers should form cooperatives through which they can partner with financial institutions to facilitate release of credit towards increasing rice production and subsequently generate more agricultural income Necessary efforts such as creation of awareness about the potential benefits inherent in the adoption of improved rice seed, increase in farmers’ education, more publicity about the varieties released through the media should be intensified. They should also be encouraged to diversify into other agricultural production as the agricultural income significantly influences both the probability and the intensity of adoption.

3 Methodology
3.1 Study area and data
This study was conducted in North central Nigeria in two states Benue and Nassarawa states. The North Central zone is the largest producer of rice in Nigeria, accounting for 47% of the total rice output (PCU, 2001; Chuma, 2012). These states where selected based on their large rice production area and easy accessibility to sampling. Nasarawa and Benue states are located in the north central zone of Nigeria within the sub-humid region which lies within the guinea savannah region of the country with very fertile soils for rice production. They share in the benefits of the Benue river valley for rice production and are highly agrarian with a large percentage of their populace (75%) engaged in rice farming and other agricultural activities. Rainfed upland and rainfed lowland are the major rice production ecologies in the study area. The study made use of primary data from the rice baseline survey designed by AfricaRice and conducted by NCRI.

3.2 Sampling Technique
A multistage sampling technique was employed by AfricaRice for this survey. Nasarawa and Benue states were purposively selected based on its large involvement in rice production before the random sampling approach was used in selecting two local government areas each from the states. A total of 149 rice producing households were used for data analysis. The questionnaire was structured to capture the socioeconomic characteristics of the farmers, farmers’ knowledge of rice varieties cultivated, production activities, agronomic characteristics and farm characteristics of the respondents among others.

3.3 Analytical Procedure
The data analytical techniques that were used in this study comprised of descriptive statistics, probit regression and Heckman model.

3.3.1 Probit regression model
The probit regression model was used to identify factors determining the awareness (exposure) of improved rice varieties by farmers in the study area (table 5) The following model used by Lidia Dandedjrohoun et al., (2012); Dontsop Nguezet et al. (2010) was adopted for the analysis.


Where:
Y_i* = Knowledge of Improved Variety (1=yes and 0 otherwise)
β₀ = Intercept
β_i =A vector of parameter estimates
X_i =A vector of explanatory variables which include;
X₁=Age of the household head (years)
X₂=Education level of the household head (years)
X₃=Household size (number of people in household)
X₄=Membership in association (Yes=1, 0 otherwise)
X₅=Access to media (Yes=1, 0 otherwise)
X₆=Ownership of mobile Phone (Yes=1, 0 otherwise)
X₇ =Main Occupation (farming=1, 0 otherwise)

Table 5 Description of the variables specified in the model

3.3.2 Heckman two-stage sample selection model
The Heckman two-stage sample selection model was used to identify the determinants of adoption and its intensity among the respondents (table 5). The first stage employed the probit regression model with the dependent variable specified as dichotomous assuming value 1 if the farmer adopts one or more of the improved rice varieties and 0 otherwise (Usman et al., 2011, Awotide et al., 2011)





Z_i*=whether a farmer adopts improved rice variety or not (1=yes and 0 otherwise)
X_i=vector of factors influencing the adoption of improved rice varieties.
X₁=Gender of the farmer
X₂=Age of the household head
X₃=Education level of the household head
X₄=Household size (number of people in household)
X₅=Membership in association
X₆=Access to media 
X₇=Ownership of mobile Phone
X₈=Farm Size     
X₉=Main Occupation
X₁₀=Access to credit
X₁₁=Access to improved rice seeds
X₁₂=Agricultural training
X₁₃=Off- farm employment
X₁₄=Agricultural income
Equation (2) is estimated by maximum likelihood as an independent probit model from the entire sample of adopters and non-adopters; X is a vector of factors influencing the decision to adopt. The sample selection bias is what Heckman (1979) refers to as the inverse Mill’s ratio (λ), is computed from the parameter estimates of the selection equation for each observation in the selected sample (Greene 1993), and is represented by:


Where  α and  δ are respectively, the density and distribution functions.
The level of adoption, Yi, specified in equation (4), is observed only if  >0, and is estimated by ordinary least squares, where the vector of inverse Mill’s ratios is included as an additional regressor in order to correct for potential selection bias.


Y= proportion of area under improved rice variety. The independent variables were as defined in the selection model above.

Acknowledgement
Financial support by Africa Rice is gratefully acknowledged.

References
Adeogun O.A., Ajana A.M., Ayinla O.A., Yarhere M.T., and Adeogun M.O., 2008, Application of logit model in adoption decision: A study of hybrid clarias in Lagos, Nigeria, Journal of Agricultural & Environmental Sciences, 4(4): 468-472

Africa Rice Center (WARDA), 2007, African Rice Trends: Overview of Recent Development in the Sub-Saharan Africa Rice Sector. Africa Rice Center Brief. Cotonou, Benin: WARDA. 8pp.

Africa Rice Center (WARDA)/FAO/SAA, 2008, NERICA: the New Rice for Africa-a Compendium. EA Somado, RG Guei and SO Keya (eds.). Cotonou, Benin: Africa Rice Center (WARDA); Rome, Italy: FAO; Tokyo, Japan: Sasakawa Africa Association. pp. 210 

African Rice Centre (WARDA), 2006, Progress Report 2003-2005. Joint Interspecific Hybridization Project. WARDA, Cotonou, Bennin.

Akande S.O., 2002, An Overview of the Nigerian Rice Economy, NISER, Ibadan.

Awotide B.A., Diagne A., Awoyemi T.T., and Ojehomon V.E.T., 2011, Impact of Access to Subsidized Certified Improved Rice Seed on Income: Evidence from Rice farming Households in Nigeria. OIDA International Journal of Sustainable Development, 2(12): 43-60

Bwire Joseph, 2008, Factors affecting adoption of improved meat goat (boer) production in Rangelands of Sembabule District. Unpublished M.Sc thesis Makerere University Kampala.

Chuma Ezedinma, 2012, Impact of Trade on Domestic rice Production and the Challenge of Self Sufficiency in Nigeria.
https://www.google.com.ng/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CDYQFjAA&url= http%3A%2F%2Fwww.warda.cgiar.org%2Fworkshop%2Fricepolicy%2Fchuma.e%2Fchuma.e.nigeria.paper.pdf&ei= DTIrUsegOoqX1AXz5IHgBQ&usg=AFQjCNGyzxzZJgUioyAtII3qbJOzbAC70g&sig2=5_P16Z1TApHFY31lskiG-A&bvm=bv.51773540,d.d2k Accessed 7^th sept., 2013.

Daramola B., 2005, Government Policies and Competitiveness of Nigerian Rice Economy. A Paper presented at the `Workshop on Rice Policy & Food Security in Sub-Saharan Africa’ organized by WARDA, Cotonou, Republic of Benin, November, 07-09.

Diagne A., 2006, Taking a New Look at Empirical Models of Adoption: Average Treatment Effect Estimation of Adoption Rates and their Determination. Paper presented at the 26 conference of the International Association of Agricultural Economics August 12-18, at Gold Coast, Austria.

Diagne A., and Demont M., 2007, Taking a New Look at Empirical Model of Adoption: Average Treatment Effect Estimation of Adoption Rates and their Determinants. Agricultural Economics, 37: 201- 210
 http://dx.doi.org/10.1111/j.1574-0862.2007.00266.x

Dimara E., and Skurus D., 1998 Adoption of New Tobacco Varieties in Greece: Impacts of Empirical Findings on Policy Design: Agricultural Economics, 19: 297-307
http://dx.doi.org/10.1016/S0169-5150(98)00041-3

Dow W., and Norton E., 2003, Choosing Between and Interpreting the Heckit and Two- Part Models for Corner Solutions, Health Services & Outcome Research Methodology, 4: 5-18
http://dx.doi.org/10.1023/A:1025827426320

Erenstein O., Lançon F., Osiname O., and Kebbeh M., 2004, Operationalising the strategic framework for rice sector revitalization in Nigeria. Project report -The Nigerian Rice Economy in a Competitive World: Constraints, Opportunities And Strategic Choices. Abidjan: WARDA-The Africa Rice Centre. pp. 11-35.

Fagade S.O., 2000, Yield Gaps and Productivity Decline in Rice Production in Nigeria. Paper Presented at the Expert Consultation on Yield Gap and Production Decline in Rice, 5-7 September, 2000. FAO, Rome, Italy. Pp.15

Falusi A.O., 1997, Agricultural Development and Food Production in Nigeria: Problems and Prospects. In: B., Shaid, N.O., Adedipe, M. Aliyu and Jir, M. (eds.) Integrated Agricultural Production in Nigeria: Strategies and Mechanism (NARP Monograph No. 5. pp. 151-170

Food and Agricultural Organization 2002: www.fao.org.

Heckman J., 1979, Sample Selection Bias as a Specification Error, Econometrica, 47:1 (153- 161)

Jackline Bonabana-Wabbi, 2002, Assessing Factors Affecting Adoption of Agricultural Technologies: The Case of Integrated Pest Management (IPM) in Kumi District, Eastern Uganda. Unpublished M.Sc thesis Virginia Polytechnic Institute and State University Blacksburg, Virginia.

Keuneman E.A., 2006, Improved rice production in a changing environment: from concept to practice. Int. Rice Commission Newslett. 5: p. 2

Nkang N.M., Abang S.O., Akpan O.E., and Offem K.J., 2006, Co integration and Error Correction Modelling of Agricultural Export Trade in Nigeria: The case of Cocoa. Journal of Agriculture and Social Sciences.

Ogundele O.O., Ojehomon V.E.T., Momoh S., Ogunremi L.T., Tiamiyu S.A., and Wayas J.W., 2004, Rice Policy and Cognate Economic Issues. The Nigeria Rice Memorabilia Project Synergy Limited, Abuja, Nigeria xxxvi 1145 pages. Pg.7

Oyekale A.S., and Idjesa E., 2009, Adoption of improve maize seed and the efficiency in River State, Nigeria, Academic Journal of Plant Sciences, 2(1): 44-50

PCU2001, Terms of reference: social and beneficiary impact assessment. Sheda: Project Coordinating Unit.

Saha A., Alan Love H., and Schwart R., 1994, Adoption of Emerging Technologies under Output Uncertainty. American Journal of Agricultural Economics, 76(4): 836-846
http://dx.doi.org/10.2307/1243745

Saka J.O., and Lawal B.O., 2009, Determinants of Adoption and Productivity of Improved Rice varieties in Sourthwestern Nigeria, African Journal of Biotechnology, 8(19): 4923-4932

Saka J.O., Okoruwa V.O., Lawal B.O., and Ajijola S., 2005. Adoption of Improved Rice Varieties among Small-Holder Farmers in South-Western Nigeria World Journal of Agricultural Sciences 1 (1): 42-49

UNEP, 2005, Integrated Assessment of the Impact of Trade Liberalization. A Country Study on the Nigerian Rice Sector. United Nations Environment Programme. ISBN 92-807-2450-9

Usman S., Umar A.S.S., and Goni M., 2011, Farmers’ Awareness and Adoption of Improved Sesame Seeds in Jigawa State Nigeria: An Application of Heckman’s Sample Selection Model Savannah. Journal of Agriculture, 6(2): 67-72

West Africa Rice Development Centers (WARDA), 2003, Annual Report.

Yirga C.T., 2007, The Dynamics of Soil Degradation and Incentives for Optimal Management in Central Highlands of Ethiopia. Unpublished PhD thesis. Department of Agricultural Economics, Extension, and Rural Development, University of Pretoria, South Africa, pp. 216