Welfare impact of adoption of improved cassava varieties by rural households in South Western Nigeria
© Afolami et al. 2015
Received: 15 August 2014
Accepted: 30 June 2015
Published: 15 July 2015
Low adoption of modern agricultural technologies amongst farmers in Nigeria has been identified as one of the main reasons for the low agricultural productivity and increase in poverty level. The general objective of this study is to examine the welfare impact of farm households adoption of improved cassava varieties in Southwestern (SW) Nigeria using poverty as an indicator. It utilizes cross-sectional farm household level data collected in 2013 from a randomly selected sample of 312 cassava producing households (186 in Ogun State and 126 in Osun State). The data obtained were subjected to descriptive and inferential statistical analysis such as Foster, Greer and Thorbecke (FGT) poverty measure and Logit regression model. The results revealed that adoption of improved cassava varieties increases the annual income and the annual consumption expenditure of producing households’ thus increasing welfare in the SW Nigeria. An analysis of the determinants of adoption with logistic regression model showed that access to improved cassava cuttings within the villages, use of radio, farming experience and farming as a major occupation are significant factors influencing adoption of improved cassava varieties in the study area. In order to achieve the much desired poverty reduction and generate an improvement in farming households’ welfare in SW Nigeria, efforts should be intensified in ensuring that farmers have access to adequate improved cassava cuttings at the right time and place. All programs, strategies and policies that would promote farmers’ education on the technology and consequently lead to improved adoption should be pursued.
KeywordsImpact Technology adoption Cassava farmers Logit model Poverty alleviation South-western Nigeria
Cassava is an important regional food source for about 200 million people (nearly one-third of the population) of sub- Saharan Africa (Abdoulaye et al., 2014). In Nigeria for instance, cassava root and leaves do not only serve as an essential source of calories but as a major source of income for rural households. Cassava provides food and income to over 30 million farmers and large numbers of processors and traders in Nigeria (Abdoulaye et al., 2014). Technological improvement (such as improved cassava varieties) is the most important factor in increasing agricultural productivity and reduction of poverty in the long-term (Solomon 2010; Solomon et al., 2011). To increase productivity, technology must be adopted in the production process and the rate of adoption of a new technology is subject to its profitability, degree of risk associated with it, capital requirements, agricultural policies and socioeconomic characteristics of farmers (Shideed and Mohammed 2005). The adoption of innovation is the last step in a decision process to make full use of an innovation having considered that such will impact positively on the livelihood of the adopter. This study is of significance because eradication of rural poverty through adoption of new agricultural technologies has been a major concern for the underdeveloped and developing countries and donors for many decades.
Intensification of better agricultural production system is one of the ways of increasing the welfare of farmers. This can be achieved if farmers take advantage of improved crop variety such as cassava. Some direct impacts of agricultural technologies (such as changes in agricultural productivity and farm income) are relatively easy to measure quantitatively, which is probably why they have been the focus of most impact research. It is however difficult to establish the causal effect of farming technology on welfare, but at the same time this is necessary if we want to know the extent of agricultural enhancement of the poor.
The general objective of this study is therefore to examine the impact of adoption of improved cassava varieties on welfare of rural farmers in SW Nigeria using poverty as an indicator.
Investigate the socio-economic characteristics of adopters and non-adopters of improved cassava varieties;
Analyze the poverty level between adopters and non-adopters of improved cassava varieties;
Determine the factors influencing the adoption of improved cassava varieties and
Make the necessary policy recommendations.
This study aims to contribute to the literature by providing a micro-perspective on the impact of agricultural technology on farm households. Assessing the impact of households’ technology adoption can assist with setting priorities, providing feedback to the research institutions and scientists, guiding government policy makers and those involved in technology transfer to have a better understanding of how technological adoption helps in reducing poverty in farming communities.
This introduction is followed by a stylized review of relevant theories and empirical evidences on the link between adoption of agricultural technologies and farmers’ welfare, poverty, food security in section two. The third section presents the study methodology, data and their sources, while the forth section presents the results and their discussions. The final section provides the study summary and conclusions.
Agriculture plays a unique role in reducing poverty through the use of new technologies (Adofu et al., 2013). Agricultural productivity growth is becoming increasingly difficult without developing and disseminating cost effective yield increasing technologies to meet the needs of increasing number of people to expand the area under cultivation or rely on irrigation (Pender and Gebremedhin, 2006; Datt and Ravallion, 1996; Hossain et al. 1992). Shideed (1998) identified two general properties of technological improvement. The first is the development of a new production function such that a greater output is achieved from a given input level. The second property is that the technological improvement must monetarily increase the discounted profits (or decrease losses) of the firm. Adoption of new technologies normally involves two stages: the decision to either adopt or not and the second stage involves how much of the new technology to adopt or use (or extent of adoption) (Mercer and Pattanayak, 2003). Farmers would never adopt an innovation if outputs are not increased from given resources, and/or if inputs are not decreased for a given output (Heady, 1952). Agricultural technology adoption is often a sequential process. Farmers may adopt a new technology in part of their land first and then adjust in later years based on what they learn from the earlier partial adoption (Xingliang and Guanming, 2011).
There is a large literature on the adoption of agricultural technology (Rogers, 2003; Sunding and Zilberman, 2001; Feder and Umali, 1993). Adoption of improved agricultural technology apparently offers opportunity to increase production and income substantially (Nweke and Akorhe, 2002) and reduce food insecurity (Nata et al., 2014). Adoption of agricultural technology depends on a range of personal, social, cultural and economic factors as well as on the characteristics of the innovation itself (Pannell et al., 2006; Omonona et al., 2006; Prokopy et al., 2008; Shiferaw et al., 2008; Eze et al., 2008; Kassie et al., 2009; Yesuf and K¨ohlin, 2008; Owusu and Donkor, 2012; Challa and Tilahun, 2014).
It is clear from this brief empirical review of literature that the impact of adoption of improved agricultural technologies on either poverty or welfare has a positive impact on poverty reduction and human welfare. For example, Hossain et al., (2003) in Bangladesh reveals that the adoption of improved varieties of rice has a positive impact on the richer households but had a negative effect on the poor, Dontsop-Nguezet et al. (2011); Kijima et al., (2008); Diagne (2006) studies on the impact of New Rice for Africa (NERICA) in Nigeria, Uganda and Cote d’Ivoire also found that the adoption of NERICA has a positive and significant influence on farmers welfare, poverty reduction and yield respectively. Likewise, Mendola (2007) and Adeoye et al. (2012) adopting the Propensity Score Matching (PSM) method and Local Average Treatment Effect (LATE) respectively confirmed the positive effect on household wellbeing arising from the impact of agricultural technology adoption on productivity and rural rice farmers’ welfare in Bangladesh and Nigeria respectively.
The study used primary data collected from a cross-sectional survey of cassava farmers from Osun and Ogun States with the aid of a structured questionnaire. The survey collected valuable information on several factors including household composition and characteristics, land and non-land farm assets, livestock ownership, household membership of different rural institutions, cassava varieties and area planted, costs of production, yield data for different crop types, indicators of access to infrastructure, household income sources and major consumption expenses.
A multistage sampling technique was used for this study. The first stage was the purposive selection of two states (Ogun and Osun States) in the SW Nigeria. The two states were purposively selected among other cassava producing states due to the intensity of cassava production in the two states. The second stage was the purposive selection of one Agricultural Development Programme (ADP) zone out of the three existing zones in Osun State and two zones out of existing four zones in Ogun. The selection was based on their agrarian nature and large production of cassava tubers and the unequal number of zone was based on the number of existing zones per state. In the third stage, we purposively selected three blocks in each of the selected zones based also on the intensity of cassava farmers. The fourth stage was the random selection of four communities in each of the selected blocks. The last stage was the random selection of eight cassava producing households from each of the selected communities in Ogun making a total of 192, while in Osun State, we randomly selected eleven cassava farmers leading to a total of 132. This selected number of farmers per community was based on the proportion of cassava farmers. The difference in the number of respondents selected per the two states was based on the existing size of cassava farmers. Out of the overall 324 cassava farmers sampled, data from 312 cassava farmers were finally used for our analysis while 12 (6 each from Ogun and Osun States) were discarded due to incomplete information supplied by the farmers.
The conceptual/analytical framework
We assumed that for a farmer to make decision on whether or not to adopt the improved cassava varieties, he/she must have first examined the benefit obtainable from the adoption and benefit derived from non-adoption. A farmer is likely to adopt the improved varieties, if the expected utility derived from adoption is greater than the expected utility from non- adoption.
Conditional on cross-sectional data availability, we estimated poverty level of cassava producing households based on their adoption of improved cassava varieties. The data obtained were subjected to descriptive and inferential statistical analysis. Descriptive statistics for this study include frequency tables, percentages and means. The inferential analyses adopted for the study are FGT (1984) poverty index measurement and logit regression model. The use of logit model in economics is based on random utility theory (Train, 2007). Households are assumed to choose the alternative(s) that maximize their utility subject to a set of constraints (Train, 2007; Mas-Colell, 1995). Indirect utility, the basis for this analysis, measures the maximum utility that a household achieves subject to some constraints (Mas-Colell et al. 1995). According to the random utility theory, indirect utility has both a deterministic component and a random (unobservable) component.
Where; z = the poverty line obtained as 2/3 mean per capita annual expenditure
q = the number of individuals below poverty line
N = the total number of individual in reference population.
Y i = the annual per capita expenditure of household i and,
α = the degree of aversion and takes on the values 0, 1, 2. In this study we only look at the poverty incidence among adopters and non- adopters of improved cassava varieties in the study area (that is when α = 0).
The poverty line is a predetermined and well-defined standard of annual income or value of consumption. In this study, the poverty line was based on the annual expenditure of the households. Two third of the mean per capita annual expenditure (2/3 of MPCHE) was used as the moderate poverty line. Respondents above this value are classified as non-poor (those spending greater than 2/3 of MPCHE) and those below it as poor.
Classification of respondents as adopters and Non-adopters and determinants of adoption of improved cassava varieties
Description of the variables used in the logit model
Gender of household head,1 male and 0 otherwise
1 if married and 0 otherwise
1 if main occupation is farming and 0 otherwise
Years of farming experience
Number of years of experience in farming
Frequency of extension contact
Number of contacts farmers had with extension agent in the last one year
Access to credit
1 if a farmer has access and 0 otherwise
Price of improved cassava cuttings
Price of a bundle of cassava stick in naira
Total area of land cultivated by farmers in Hectare
Years of education
Number of years of formal education of household head
Ownership of land
1 if farmer owns land and 0 otherwise
Membership of association
1 if a member of farmers' association and 0 otherwise
Access to improve cassava cutting within the village
1 if a farmer has access and 0 otherwise
High level of knowledge of traditional cassava varieties
1 if a farmer has high knowledge of traditional varieties and 0 otherwise
Access to radio
1 if farmer owns a radio, 0 otherwise
Proxy for total annual expenditure in naira
Logit model could be written in terms of the odds and log of odds, which enables one to understand the interpretation of the coefficients. The odds ratio implies the ratio of the probability (P i ) that a farmer adopts, to the probability (1-P i ) that the farmer is a non-adopter.
This procedure does not require assumptions of normality or homoskedasticity of errors in predictor variables (Alexopoulos, 2010). The analysis was carried out using STATA version 11.0.
Results and discussion
Socioeconomic characteristics of respondents
Respondents’ distribution by socioeconomic characteristics
Frequency (n = 312)
Gender (dummy variable)
Marital status (categorical variable)
Religion (categorical variable)
Education Level (categorical variable)
Never went to School
Attempted Primary School
Completed Primary School
Attempted Secondary School
Completed Secondary School
Attempted Tertiary Education
Completed Tertiary Education
Primary Occupation (dummy variable)
Farming experience (continuous variable in years)
Less than or equal to 10
Age (continuous variable in year)
Less than or equal to 30
Households Size (continuous variable)
The educational background of the respondents revealed that 27.2 % had never been to school, 20.4 % had at least primary education, 15.3 % attempted secondary school education and 16.6 % completed secondary school education. In addition, 3.2 % attempted tertiary education while only 7 % completed tertiary education with certificates. This implies on the aggregate that the majority of the farmers had one form of education or the other, and thus had the advantage of adopting innovation, since education helps in adopting improved agricultural technologies as observed by Ozor and Madukwe (2005). It buttresses the reason why most of the farmers adopted at least one of the improved cassava varieties. This is also corroborated by the work of Nsoanya and Nenna (2011) and Ayoade (2013).
Majority of the respondents (91.7 %) have farming as their primary occupation while only 8.3 % did not. A good proportion of the respondents, 67 % also have farming experience of about 20 years or less. The age distribution of the farmers revealed that 31.7 % were aged between 30–40 years while 29.5 % were aged between 41–50 years, 15 % of the farmers had age below 30 years and 12.8 % were between 51–60 years while 10.9 % of the respondents were above 60 years. This implies that majority of the farmers are in their active years, with an advantage of transferring innovations that enhance farm productivity. It is expected that improved varieties of cassava will be adopted at a faster rate in this area, which is in line with the observation of Onu and Madukwe (2002); Awotide et al. (2012). This is also corroborated by the work of Ayoade (2013) and Babasanya et al. (2013) who stated that farmers that are in their productive state usually experience high farm output and enhance the spread of innovation. The household size was relatively high; with 42 % of the farmers having household size that ranged between 1–4 members and 5–6 members each while 16 % had household members that are above 8. This contributes to the adoption of improved cassava technology by the farmers since having large household size brings an opportunity of expanding farm size, generating more revenue and meeting the welfare need of the households.
Adoption of improved cassava varieties
Respondents’ distribution by adoption of improved cassava varieties
Adoption of TME 419
Adoption of TMS 980505
Adoption of TMS 980815
Adoption of TMS 980326
Households’ endowments (assets) and adoption status
Respondents' adoption status by household endowments
(n = 312)
(n = 68)
(n = 244)
Owns of farm land
Have cemented house
Access to good electricity
Have access to good sanitation
Have access to television
Have access to radio
Have access to mobile Phone
Households’ assets such as radio, television, mobile phone and electricity are vital in the dissemination of information about the improved varieties which can influence adoption. Among the non-adopters only 27 % and 43 % respectively have mobile phone and access to radio, compared to respective 88 % and 96 % of the adopters. This implies that some of the non-adopters may not be aware of the improved cassava varieties through the telephone and radio hence their low adoption level. As regards access to electricity, only 10 % of the non-adopters and 16 % of the adopters had access to electricity. Lack of access to electricity could be a major constraint militating against adoption. This is because farmers could have radio and television but without adequate supply of electricity at the right time, they might be missing out on important information aired when electricity was not available. This finding is corroborated by the finding of Awotide et al., (2012).
Furthermore, households’ endowments such as: cemented house wall and floor, good sanitation, ownership of generator, motorcycle and car could all combine to improve the wellbeing of the farming household members and also encourage adoption of improved cassava varieties. However, not many of the respondents were endowed with most of these assets. For instance, only 10 % and 34 % of the respective adopters and non-adopters had access to good sanitation facility. Similarly, only 24 % and 0 % of the respective adopters and non-adopters had access to good road network. Although many of the respondents, 82 % live in their own houses, the adopters seem to be better-off as a larger percentage of 84 % of them live in cement plastered house walls and floors.
Impact of adoption of improved cassava varieties on annual income and annual consumption expenditure
Analysis of the impact of adoption of improved cassava varieties
Pooled data (n = 312)
Adopters (n = 68)
Non-adopters (n = 244)
Mean annual income from cassava production
Mean annual income from other crops
Mean annual total agricultural expenditure
Per capita annual consumption expenditure
Average farm size (ha)
Percentage of Poor producing households (P0)
In terms of the welfare impact of adoption of improved cassava varieties, a comparison was made between the consumption expenditure of adopters and non-adopters. Per capita annual expenditure reflects the effective consumption of households and therefore provides information on the food security status of households. Annual per capita consumption expenditure of the adopters was ₦36,407.8 while that of non-adopters was ₦32,969.6 with significant mean difference of ₦3,438.0 indicating that the adopters had more annual per capita consumption expenditure than non-adopters. This implies that the adopters had a better welfare than the non-adopters. The analysis of the incidence of poverty showed that about 50 % of the cassava farmers were poor. The incidence of poverty of 61.1 % among the non-adopters was however higher than those of the adopters’ of 55.9 %. These results are consistent with other related studies on the impact of agricultural technologies on poverty (Kassie et al., 2011; Asfaw 2010; Diagne et al., 2009; Awotide et al., 2012; Mendola, 2007). From all the analyses, it appears that the adopters were better-off than the non-adopters even though these comparisons did not adjust for the effects of other characteristics of the farmers that could influence the outcome.
Determinants of adoption of improved cassava varieties
Determinants of adoption of improved cassava varieties
P > |z|
Contact with Extension Agent
Access to credit
Price of improved cassava cutting
Years of education
Ownership of land
Membership of association
Access to improved cassava cutting within village
High level of knowledge of traditional cassava varieties
Use of radio
Annual total income
Prob > Chi2
The marital status of the respondents had a positive coefficient which was significant at p < 0.05, on the decision to adopt improved cassava varieties in the study area. The positive sign and significance of the estimated coefficient of marital status suggests that married farmers are more likely to adopt than unmarried farmers. As farmers get married, the likelihood of adoption of improved cassava varieties increases by 0.35 %. The empirical finding however contradicts a study by Amao and Awoyemi (2008) on adoption of improved cassava varieties by farmers in Osogbo, Osun State of Nigeria which indicated that marital status is not a significant determinant of adoption of improved cassava varieties. The increase in the probability of adopting improved cassava varieties by marital status may be due to the fact that marriage increases a farmer’s concern for household welfare and food security which is therefore likely to have a positive effect on their decision to adopt improved agricultural technology (Johnson et al. 2006; Adeoye et al. 2012; Nnadi and Akwiwu, 2008). Also, the significant positive coefficient of access to improved cassava cutting (p < 0.1) attests to its influence on the adoption of improved cassava varieties by farmers. This also is established by the work of Diagne and Demont (2007). It contributes to an increase in the probability of adoption of the new varieties by farmers, since, awareness, availability of information and planting material positively influence adoption.
Although farming as a major occupation had a significant (p < 0.01) influence on the adoption of improved cassava varieties, the effect is however negative on the probability of adoption of improved cassava varieties in the study area. The results of the marginal effect showed that those whose primary occupation is farming has a likelihood of not adopting improved cassava varieties 1.96 % times than others who are not exposed. This position was supported by the work of Diagne and Demont (2007) and could be as a result of the farmers missing out on information about improved cassava varieties since they do not attend meetings/trainings, and full time farmers spend more time on farm, thereby not availing themselves the opportunity of knowing about the improved cassava varieties. The year of farming experience significantly and positively influenced the adoption of improved cassava varieties by rural households. The likelihood of adoption by farmers increases by 0.28 % for every additional increase in year of farming experience. This result was not consistence with past empirical studies such as Adesina and Baidu-Forson (1995); Ojo and Ogunyemi (2014) who found the years of farming experience as a significant factor in adoption model. Our result could be due to the fact that farmers had learnt from experience of growing the traditional varieties and need to adopt a new innovation for a change. Information about the improved variety increases awareness—a farmer cannot adopt a technology without being aware of it (Diagne and Demont, 2007). A unit increase in access to improved cassava cutting within the villages has a likelihood of increasing farmers’ adoption of improved cassava varieties by 28 %. Use of radio can create awareness and hence increase the probability of adoption. Through the use of radio communication, information can be passed to the farmers on available sources of inputs and prices. Further, information can also be passed from one farmer to the other. This has been found to positively and significantly (at p < 0.05) influence adoption of improved cassava varieties in the study area. Farmers who use radio have a likelihood of increasing adoption of improved cassava varieties by 16.19 % in the study area.
Conclusion and policy recommendations
This study assessed the impact of adoption of improved cassava varieties on the welfare of cassava producing households in two states in the SW Nigeria. TME 419 was the most widely adopted variety among the introduced improved cassava varieties in the states with 60.2 % of the farmers adopting the variety. The results also showed that adoption of improved cassava varieties in the study area increases annual income and annual consumption expenditures of cassava farming households thus increasing their welfare. Adoption of improved cassava varieties is therefore pro-poor in nature with the adopters having a lower poverty rate than the non-adopters. A significant relationship was found between farmers’ marital status, farming as a major occupation, farming experience, access to improved cassava cutting within the villages, use of radio and adoption of improved cassava varieties.
Rural households welfare in the SW Nigeria through adoption of improved cassava varieties requires giving rural farmers more access to improved cassava cuttings within the villages where they are resident;
In as much as adoption leads to improvement in farming households’ welfare, the quest to eradicate poverty among the rural dwellers in Nigeria should incorporate strategy of educating farmers through the use of radio programme on the need to adopt improved cassava technologies. Battery should be made available for their radios since poor power supply hinders innovation dissemination;
There is need for readily available markets for the tubers through good linkage of farmers to processors to prevent commodity glut and the likelihood of offering unattractive price to the farmers . Small-holder cassava farmers should be linked to large-scale producers of high quality cassava flour, starch and industrial alcohol. The potential for sun-dried chips use in livestock and aquaculture feeds should be explored and promoted;
Suggestion for future studies is recommended in areas beyond access to credit to actual volume of credit use in production given the low level of the income of the farmers in order to ensure that fund limitation does not curtail production.
The authors gratefully acknowledge the research funding support for this study by the Tertiary Education Trust Fund (TETFUND), and the Institute of Food Security, Environmental Resources and Agricultural Research (IFSERAR), Federal University of Agriculture Abeokuta (FUNAAB) Ogun State.
- Abdoulaye T, Abass A, Maziya-Dixon B, Tarawali G, Okechukwu R, Rusike J, Alene A, Manyong V, Ayedun B (2014) Awareness and adoption of improved cassava varieties and processing technologies in Nigeria. Journal of Development Agricultural Economics 6(2):67–75View ArticleGoogle Scholar
- Adeoye BK, Olaore Y, Aliu B, Adeoye AO (2012) Family Size, Income and Marriage Types as Predictors of Healthy Living: A Case Study of Families in Ogun State. Greener Journal of Social Sciences 2(6):191–196Google Scholar
- Adesina AA, Baidu-Forson J (1995) Farmers‘Perceptions and Adoption of New Agricultural Technology: Evidence From Analysis in Burkina Faso and Guinea, West Africa. Journal of Agricultural Economics 13(13&14):1–9View ArticleGoogle Scholar
- Adisa BO, Okunade EO (2005) Women in agriculture and rural development. In: Fola AS (ed) Agricultural Extension in Nigeria., ESON. ARMTI Ilorin 69 - 77Google Scholar
- Adofu I, Shaibu SO, Yakubu S (2013) The economic impact of improved agricultural technology on cassava productivity in Kogi State of Nigeria. International Journal of Food and Agricultural Economics 1(1):63–74Google Scholar
- Akinnagbe OM, Agwu AE, Igbokwe EM (2008) Agricultural Extension policy for enhancing women participation in sustainable agricultural development in Nigeria. In: Developing Agricultural Extension Policy for Nigeria Proceedings of the 13th Annual National conference of AESON 8 -11 April 2008. Publisher AESONGoogle Scholar
- Alexopoulos G, Koutsouris A, Tzouramani I (2010) Adoption and use of ICTs among rural youth: Evidence from Greece. International Journal of ICT and Human Development, 2(3): 1–18Google Scholar
- Alston J, Norton G, and Pardey P (1995). Science under scarcity: Principles and practice for agricultural research evaluation and priority setting. Ithaca, NY: Cornell University PressGoogle Scholar
- Amao JO, Awoyemi TT (2008) Adoption of Improved Cassava Varieties and Its Welfare Effect on Producing Households in Osogbo ADP Zone of Osun State. Journal of Social Sciences 5(3):500–522Google Scholar
- Ayoade AR (2013) The Adoption Impact of Improved Cassava Varieties on The Social Life of Rural Farmers in Orire Local Government Area of Oyo State. International Journal of Humanities and Social Science 3(12):278–286Google Scholar
- Asfaw S (2010) Estimating Welfare Effect of Modern Agricultural Technologies: A Micro-Perspective from Tanzania and Ethiopia International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, KenyaGoogle Scholar
- Awotide BA, Diagne A, Awoyemi TT, Ojehomon VET (2012) Impact of Improved Agricultural Technology Adoption on Sustainable Rice Productivity and Rural Farmers’ Welfare in Nigeria: A Local Average Treatment Effect (LATE) Technique. African Economic Conference October 30- November 2, 2012, KigaliGoogle Scholar
- Babasanya B, Oladele OG, Odidi OO, Ganiyu L, Apene E, Etim J, Olafemi SO, Sirajo A (2013) Farmers’ perception and Knowledge need for adoption of new cultivars of cassava in Igabi Local Government Area (LGA), Kaduna StateGoogle Scholar
- Boahene K, Snijders TAB, Folmer H (1999) An integrated socio-economic analysis of innovation adoption: The case of Hybrid Cocoa in Ghana. Journal of Policy Modeling 21(2):167–184View ArticleGoogle Scholar
- Challa M, Tilahun U (2014) Determinants and Impacts of Modern Agricultural Technology Adoption in West Wollega: The Case of Gulliso District. Journal of Biology, Agriculture and Healthcare 4(20):63–77Google Scholar
- Datt G, Ravallion M (1996) How Important to India’s Poor is the Sectoral composition of Growth? World Bank Economic Review 10(1):1–26View ArticleGoogle Scholar
- Diagne A, Adekambi SA, Simtowe FP, Biaou G (2009a) The Impact Of Agricultural Technology Adoption On Poverty: The Case of Nerica Rice Varieties in Benin. A shorter version of the paper is being presented as contributed paper at the 27th Conference of the International Association of Agricultural Economists. August 16-22, 2009. Beijing, China
- Diagne A (2006) Diffusion and adoption of NERICA rice varieties in Cote d’Ivoire. The Development Economics 44(2):208–231View ArticleGoogle Scholar
- Diagne A, Adekambi SA, Simtowe FP (2009b) The impact of Agricultural Technology adoption on Poverty: The case of NERICA rice varieties in BeninGoogle Scholar
- Diagne A, Demont M (2007) Taking a new look at empirical models of adoption: Average treatment effect estimation of adoption rates and their determinants. Agricultural Economics 37(2-3):201–210View ArticleGoogle Scholar
- Dontsop-Nguezet PM, Diagne A, Okoruwa VO, Ojehomon VET (2011) Impact of Improved Rice Technology Adoption (NERICA varieties) on Income and Poverty among Rice Farming Households in Nigeria: A Local Average Treatment Effect (LATE) Approach. Quarterly Journal of International Agriculture 50(3):267–291Google Scholar
- Eze C, Ibekwe U, Onoh P, Nwajiuba C (2008) Determinants of Adoption Of Improved Cassava Production Technologies Among Farmers In Enugu State of Nigeria. Global Approaches to Extension Practice: A Journal of Agricultural Extension 2(1):37–44Google Scholar
- Feder G, Just ER, Zilberman D (1985) Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change 33(2):255–298View ArticleGoogle Scholar
- Feder G, Umali DL (1993) The Adoption of Agricultural Innovations: A Review. Technological Forecasting and Social Change 43(1):215–239View ArticleGoogle Scholar
- Foster J, Greer J, Thorbecke E (1984) A Class of Decomposable Poverty Measures. Econometrica 52(3):761–766View ArticleGoogle Scholar
- Greene WH (2008) Econometrics analysis, 6th edn. Pearson Prentice Hall, Upper Saddle RiverGoogle Scholar
- Green DAG, Ng’ong’ola DH (1993) Factors af fecting fertilizer adoption in less developed countries: An application of multivariate logistic analysis in Malawi. Journal of Agricultural Economics 44(1):99–109View ArticleGoogle Scholar
- Heady EO (1952) Economics of Agricultural production and Resource Use. Prentice- Hall, INC, Englewood CliffsGoogle Scholar
- Hossain S, Alamgir M, Croach R (1992) Patterns and Determinants of Adoption of Farm Practices: Some evidence from Bangladesh. Agricultural Systems 38(1):1–15View ArticleGoogle Scholar
- Johnson AJ, Lakoh AK, Saidu EN, Kobba F (2006) International. Journal of Agriculture and Food Science Technology 5(3):135–140Google Scholar
- Kassie M, Shiferaw B, Geoffrey M (2011) Agricultural Technology, Crop Income, and Poverty Alleviation in Uganda. World DevelopmentGoogle Scholar
- Kassie M, Zikhali P, Manjur K, Edwards S (2009) Adoption of Organic Farming Technologies: Evidence from Semi-Arid Regions of Ethiopia. Working Papers in EconomicsGoogle Scholar
- Kijima Y, Otsuka K, Sserunkuuma D (2008) Assessing the impact of NERICA on income and poverty in Central and Western Uganda. Agricultural Economics 38(1):327–37View ArticleGoogle Scholar
- Mas-Colell A, Whinston MD, Green JR (1995) Microeconomic Theory. Oxford University Press, New YorkGoogle Scholar
- Mendola M (2006) Agricultural technology adoption and poverty reduction: A propensity score matching analysis for rural Bangladesh. Food Policy 32(3):372–393View ArticleGoogle Scholar
- Mercer E, Pattanayak SK (2003) Agroforestry adoption by smallholders. Forests in a market economy. Forestry Sciences Series 72(1):283–299View ArticleGoogle Scholar
- Meinzen-Dick R, Adato M, Haddad L, Hazell P (2004) Science and Poverty: An Interdisciplinary Assessment of the Impact of Agricultural Research International Food Policy Research Institute Washington, D.C.Google Scholar
- Nata JF, Mjelde JW, Boadu FO (2014) Household adoption of soil-improving practices and food insecurity in Ghana. Agriculture & Food Security 3(1):17, Retrieved online November 12, 2014 at http://www.agricultureandfoodsecurity.com/content/3/1/17View ArticleGoogle Scholar
- Nkonya E, Schroeder T, Norman D (1997) Factors affecting adoption of improved maize seed and fertilizer in northern Tanzania. Journal of Agricultural Economics 48(1):1–12View ArticleGoogle Scholar
- Nnadi FN, Akwiwu CD (2008) Determinants of youths’ participation in rural agriculture in Imo State, Nigeria. Journal of Applied Sciences 2008 8(2):328–333Google Scholar
- Nsoanya LN, Nenna MG (2011) Adoption of Improved Cassava Production Technologies in Anambra East Local Government Area of Anambra. Journal of Research in National Development 9(2):36–42Google Scholar
- Nweke FL, Akorhe IA (2002) Adoption behavior of Farmers towards yam mini sets technology in Imo State Nigeria. A paper presented at the 25th annual conference of Agricultural Science Society of Nigeria. Federal University of Technology Oweri, 5–6th September 1989. Pg 18–21
- Ojo SO, Ogunyemi AI (2014) Analysis of Factors Influencing the Adoption of Improved Cassava Production Technology in Ekiti State, Nigeria. International Journal of Agricultural Sciences and Natural Resources 1(3):40–44Google Scholar
- Omonona BT, Oni OA, Uwagboe AO (2006) “Adoption of Improved Cassava Varieties and its Welfare Impact on Rural Farming households in Edo State, Nigeria. Journal of Agricultural and Food Information 7(1):39–35View ArticleGoogle Scholar
- Onu MO, Madukwe MC (2002) Adoption Levels and Information Sources of “Brood and Sell” Poultry Operators. Agro-Science Journal of Tropical Agriculture Food, Environment and Extension 3(1):66–67Google Scholar
- Owusu V, Donkor E (2012) Adoption of improved cassava varieties in Ghana. Agricultural Journal 7(2):146–151View ArticleGoogle Scholar
- Ozor N, Madukwe MC (2005) Obstacles to the Adoption of Improved Rabbit Technologies by Small-Scale Farmers in Nsukka Local Government Area of Enugu State. Journal of Tropical Agriculture, food, Environment and Extension 4(1):70–73Google Scholar
- Pannell DJ, Marshall GR, Barr N, Curtis A, Vanclay F, Wilkinson R (2006) Understanding and promoting adoption of conservation practices by rural landholders. Australian Journal of Experimental Agriculture 46(1):1407–1424View ArticleGoogle Scholar
- Pender J, Gebremedhin B (2006) Determinants of agricultural and land management practices and impacts in crop production and household income in highlands of Tigray, Ethiopia. Journal of Africa Economics 17(1):395–450Google Scholar
- Pindyck RS, Rubinfeld DL (1998) Econometric Models and Economic Forecasts, 4th edn. McGraw-Hill, SingaporeGoogle Scholar
- Prokopy LS, Floress K, Klotthor W, Baumgart G (2008) Determinants of agricultural best management practice adoption: Evidence from the literature. Journal of Soil and Water Conservation 63(5):300–311View ArticleGoogle Scholar
- Rogers EM (1995) Diffusion of innovations, 3rd edn. The Free Press, New YorkGoogle Scholar
- Rogers EM (2003) Diffusion of innovations (5th ed.). New York: Free Press
- Shakya PB, Flinn JC (1985) Adoption of modern varieties and fertilizer use on rice in the eastern Tarai of Nepal. Journal of Agricultural Economics 36(3):409–419View ArticleGoogle Scholar
- Shideed K (1998) The Impact of New Technologies: A Methodology Development. ICARDA- WARP, M/M ProjectGoogle Scholar
- Shideed KH, Mohammed E (2005) Adoption and Impact Assessment of Improved Technologies in Crop and Livestock Production Systems in the WANA Region. The Development of Integrated Crop/Livestock Production in Low Rainfall Areas of Mashreq and Maghreb Regions (Mashreq/Maghreb Project)., ICARDA, Aleppo, Syria, viii + 160 ppGoogle Scholar
- Shiferaw B, Kebede TA, You L (2008) Technology adoption under seed access constraint and the economic impacts of improved pigeonpea varieties in Tanzania. Agricultural Economics 39(1):1–15View ArticleGoogle Scholar
- Solomon A, Bekele S, Franklin S, Mekbib GH (2011) Agricultural technology adoption, seed access constraints and commercialization in Ethiopia. Journal of Development and Agricultural Economics 3(9):436–447Google Scholar
- Solomon A (2010) Estimating Welfare Effect of Modern Agricultural Technologies: A Micro-Perspective from Tanzania and Ethiopia. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), NairobiGoogle Scholar
- Sunding D, Zilberman D (2001) The Agricultural Innovation Process: Research and Technology Adoption in a Changing Agricultural Sector. In: Gardner B, Rausser G (eds) Handbook of Agricultural Economics., Vol.1, Elsevier Science B.VGoogle Scholar
- Torimiro DO (2005) Children exploitation in labour empirical exposition from Ile – Ife, Nigeria. Research Report summated to the council for department of socio science Research in Africa (CODEESRIA), SenegalGoogle Scholar
- Train KE (2007) Discrete Choice Models with Simulation. Cambridge University Press, New YorkGoogle Scholar
- Xingliang M, Guanming S (2011) A Dynamic Adoption Model with Bayesian Learning: An Application to U.S. Soybean Farmers., Selected Paper prepared for presentation at the Agricultural and Applied Economics Associations 2011 AAEA and NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24-26Google Scholar
- Yesuf M, K¨ohlin G (2008) Market imperfections and farm technology adoption decisions: a case study from the highlands of Ethiopia. Environment for Development Discussion Paper Series 4, GothenburgGoogle Scholar
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