Adoption determinants of row planting for wheat production in Munesa District of Oromia Region, Ethiopia
28 Feb 2019-Journal of Agricultural Extension and Rural Development (Academic Journals)-Vol. 11, Iss: 2, pp 25-34
TL;DR: In this paper, the Tobit econometric model was used in estimating the determinants of adoption and intensity of use of row planting for wheat production in the Munesa district of Oromia region, Ethiopia.
Abstract: Agriculture takes the lion’s share in the economic development of many developing countries, including Ethiopia. Agricultural policy of the years has focused on supporting the introduction of improved technologies to boost production and reduce food insecurity. However, outcomes of such agricultural policies have been influenced by different factors of which low adoption of improved agricultural technology is a major constraint. The objective of this study was therefore, to analyze the determinants of adoption and intensity of use of row planting for wheat production. Data were obtained from both primary and secondary sources. Multi-stage sampling technique was used to select 140 wheat producer household heads from the Munesa district of Oromia region, Ethiopia. Data were collected through the administration of semi-structured questionnaires. Data were analyzed using both descriptive statistics and the Tobit econometric model. Descriptive result shows that, from 140 sampled households 97 are adopters of wheat row planting while the remaining are non-adopters. The model was used in estimating the determinants of adoption and intensity of use of row planting for wheat production. The model results revealed that education level, labor availability, extension contact, credit use, participation in training and access to improved seed had positively and significantly influenced adoption and intensity of use of row planting for wheat production. Based on the results of this study, it can be concluded that, policy and development interventions should focus on improving economic and institutional support system for high rates of adoption and intensity leading to improved productivity and income among smallholder farmers.
Key words: Adoption, row planting, Tobit model, Munesa, wheat.
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01 Jan 2016
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TL;DR: In this paper, the authors provided empirical evidence on the impact of a climate-smart agricultural practice (row planting) on the welfare of rural households and showed that adoption of row planting technology has a positive and significant impact on per capita consumption and on crop income per hectare.
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TL;DR: In this article, the authors examined the impact of adopting climate-smart agricultural technology, which refers to a joint application of row planting methods and the use of chemical fertilizers, on the multidimensional poverty status of rural households in Ethiopia.
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TL;DR: In this article, the authors analyzed the perceptions, impacts, and rewards of farmers who adopted row planting for the production of teff as a result of being exposed to a technology promotion campaign for row planting in the Oromia region of Ethiopia.
Abstract: This study analyzes the perceptions, impacts, and rewards of farmers who adopted row planting for the production of teff as a result of being exposed to a technology promotion campaign for row planting of teff in the Oromia region of Ethiopia.
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TL;DR: In this article , the authors focused on the adoption and intensity of adoption of artificial insemination (AI) technology in Saesie-tsaedaemba District of Tigray Region, Ethiopia.
Abstract: Abstract Background The study was focused on the adoption and intensity of adoption of artificial insemination (AI) Technology in Saesie-tsaedaemba District of Tigray Region, Ethiopia. AI is one of the most important and valuable dairy technology that has been used for genetic improvement for several years in the study area. However, there was little empirical information about major factors affecting adoption decision and intensity of AI in the study area. The purpose of this study was to evaluate the status of AI technology adoption and its intensity and to identify major factors influencing the adoption and intensity of use of AI technology. Methods A multistage sampling technique was applied to select study sites and sample households. A structured interview was used to collect data from a total of 204 sample farmers. Besides, key informants interview was used to triangulate, validate, and enrich the findings of the household interview. Results Results of the tobit model regression revealed that households’ level of literacy, milk yield, income, training, access to extension service, mobile ownership, supplementation of concentrated feed and hybrid cattle ownership were found to have a positive and statistically significant relationship with adoption and intensity of AI technology, whereas distance to farmer training centre (FTC) office had shown a negative relationship. Conclusions Adoption of context-based AI technology plays a paramount importance in achieving farm household's food security. The extension system should give more emphasis to the capacity building which is pivotal for introducing, adoption, and scaling out of best practices of dairy technologies. Besides the effort of the government, the participation of the private sector in AI technology is important to achieve wider adoption of AI technology.
3 citations
References
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Book•
01 Jan 1983
TL;DR: In this article, the authors present a survey of the use of truncated distributions in the context of unions and wages, and some results on truncated distribution Bibliography Index and references therein.
Abstract: Preface 1. Introduction 2. Discrete regression models 3. Probabilistic-choice models 4. Discriminant analysis 5. Multivariate qualitative variables 6. Censored and truncated regression models 7. Simultaneous-equations models with truncated and censored variables 8. Two-stage estimation methods 9. Models with self-selectivity 10. Disequilibrium models 11. Some applications: unions and wages Appendix: Some results on truncated distributions Bibliography Index.
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6,456 citations
"Adoption determinants of row planti..." refers methods in this paper
...The use of linear programming models, logistics and probit models were therefore inappropriate (Tobin, 1958)....
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31 Jan 1982
TL;DR: This article reviewed various studies which have provided a description of and possible explanation to patterns of innovation adoption in the agricultural sector and highlighted the diversity in observed patterns among various farmers' classes as well as differences in results from different studies in different socioeconomic environments.
Abstract: This paper is a revised version of Staff Working Paper 444 It reviews various studies which have provided a description of and possible explanation to patterns of innovation adoption in the agricultural sector It therefore covers both empirical and theoretical studies The discussion highlights the diversity in observed patterns among various farmers' classes as well as differences in results from different studies in different socio-economic environments, and reviews the attempts to rationalize such findings Special attention is given to the methodologies which are commonly used in studies of innovation adoption, and suggestions for improvements of such work through the use of appropriate economometric methods are provided The diversity of experiences with different innovations in different geographical and socio-cultural environments suggest that studies of adoption patterns should provide detailed information on attributes of the institutional, social and cultural setting and their interactions with economic factors These may be an important element in explaining conflicting experiences
3,145 citations
TL;DR: In this article, the authors review various studies which have provided a description and possible explanation to patterns of innovation adoption in the agricultural sector, and point out that the tendency of many studies to consider adoption in dichotomous terms (adoption/nonadoption) may not be appropriate in many cases where the actual decisions are defined over a more continuous range.
Abstract: This paper reviews various studies which have provided a description and possible explanation to patterns of innovation adoption in the agricultural sector. The survey points out that the tendency of many studies to consider innovation adoption in dichotomous terms (adoption/nonadoption) may not be appropriate in many cases where the actual decisions are defined over a more continuous range. More attention needs to be given to the socio-cultural and institutional environment in area studies so that their interrelation with economic factors affecting adoption can be inferred. The presence of several interrelated innovations is another aspect that needs to be considered more carefully in future research, since a number of simultaneous decisions may be involved. Furthermore, the possibility of regular sequential patterns in adopting components of a new technological package should be specifically addressed in future studies. Finally, the impact of differential adoption rates on land holding distribution merits attention in future research.
2,845 citations
"Adoption determinants of row planti..." refers methods in this paper
...The adoption and intensity of use of wheat row planting was estimated based on the approach by Roger (1962) and Feder et al. (1985) using the Tobit model. The Tobit model was used since the proportion of area under row planting had a censored distribution. The use of linear programming models, logistics and probit models were therefore inappropriate (Tobin, 1958). Solomon et al. (2011)...
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...Econometric estimation of adoption and intensity of use of row planting The adoption and intensity of use of wheat row planting was estimated based on the approach by Roger (1962) and Feder et al. (1985) using the Tobit model....
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...The adoption and intensity of use of wheat row planting was estimated based on the approach by Roger (1962) and Feder et al. (1985) using the Tobit model....
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Book•
01 Jan 1984TL;DR: In this paper, the authors introduce the concept of Bivariate association for nominal-and ordinal-level variables, and present a set of measures of central tendency and Chi-square distribution.
Abstract: 1. Introduction. PART I: DESCRIPTIVE STATISTICS. 2. Basic Descriptive Statistics: Tables, Percentages, Ratios and Rates, and Graphs. 3. Measures of Central Tendency. 4. Measures of Dispersion. 5. The Normal Curve. PART II INFERENTIAL STATISTICS. 6. Introduction to Inferential Statistics: Sampling and the Sampling Distribution. 7. Estimation Procedures. 8. Hypothesis Testing I: The One-Sample Case. 9. Hypothesis Testing II: The Two-Sample Case. 10. Hypothesis Testing III: The Analysis of Variance. 11. Hypothesis Testing IV: Chi Square. PART III BIVARIATE MEASURES OF ASSOCIATION. 12. Bivariate Association for Nominal- and Ordinal-Level Variables. 13. Association Between Variables Measured at the Interval-Ratio Level. PART IV: MULTIVARIATE TECHNIQUES. 14. Elaborating Bivariate Tables. 15. Partial Correlation and Multiple Regression and Correlation. Appendix A: Area Under the Normal Curve. Appendix B: Distribution of t. Appendix C: Distribution of Chi Square. Appendix D: Distribution of F.
351 citations