Like in many other sub-Saharan Africa countries, agriculture in Ethiopia is a basis for the entire socioeconomic structure of the country and has a major influence on all other economic sectors and development processes and hence it plays a crucial role in poverty reduction. Despite the marginal decline in its share of GDP in recent years, it is still the single largest sector in terms of its contribution to GDP as agricultural GDP constitutes 41% of total country's GDP. As to Gebru 2006 citing CSA 2003, out of the total production of agriculture, about 70% comes from crop production. According to Abegaz 2011, cereal crops constitute the largest share of farming household’s production and consumption activities. Accordingly citing Alemayehu et al., 2009, only five major cereals account for about 70% of area cultivated and 65% of output produced. Fertilizer use is also concentrated on cereals followed by pulses and oilseeds respectively according to Endale 2011 citing CSA 1995/96-2007/08. On the other hand, according to Endale 2011, data from the Ethiopian Seed Enterprise show that improved seeds are mostly used in wheat and maize cultivation with an average of 89 and 42 thousand quintal in the period 1994/95 to 2005/06, respectively. Moreover, Abegaz 2011 citing the Household Income, Consumption and Expenditure Survey of CSA indicated that the five major cereal crops account for 46% of household’s total consumption. Therefore, a closer look at what is happening in cereal production has an important welfare and policy implication in Ethiopia. According to Ketema and Kassa 2016 citing Shiferaw et al. 2013, wheat contributes about 20% of the total dietary calories and proteins worldwide. Ethiopia is the second largest wheat producer in sub-Saharan Africa next to South Africa. Mann and Warner 2017 citing Minot et al. 2015 indicated that there are approximately 4.7 million farmers growing wheat on approximately 1.6 million hectares representing between 15 and 18% of total crop area and less than 1% of all wheat production takes place outside the four main regions of Ethiopia according to recent estimates. Wheat is one of the major staple crops in the country in terms of both production and consumption. According to Kelemu 2017 citing FAO 2014, it is the second most important food in the country behind maize in terms of caloric intake. cereal production in 2007/08.
Table of Contents
1. INTRODUCTION
2. REVIEW OF THE EMPIRICAL EVIDENCE
3. MATERIALS AND METHODS
3.1. Analytical Framework for Evaluation of Adoption of Wheat Variety Impact on Productivity
3.2. Data and Variables
4. RESULTS AND DISCUSSIONS
4.1. Descriptive Statistics
4.2. Propensity Scores Estimation using Probit Model
4.3. Assessing Matching Quality
4.4. Average Treatment Effects Estimation
5. CONCLUSION AND RECOMMENDATION
Objectives and Research Themes
The primary objective of this study is to analyze the impact of adopting improved wheat varieties and management information on wheat productivity in Ethiopia, while identifying disparities in these impacts across different administrative regions and agro-ecological zones.
- Impact evaluation of improved wheat technologies on agricultural productivity.
- Application of propensity score matching to address selection bias in technology adoption.
- Comparative analysis of regional and agro-ecological zone performance.
- Examination of demographic and socio-economic determinants of technology adoption.
- Assessment of the robustness of various econometric impact estimation methods.
Excerpt from the Book
3.1. Analytical Framework for Evaluation of Adoption of Wheat Variety Impact on Productivity
The correct evaluation of the impact of a treatment like adoption of a technology will require identifying the “average treatment effect on the treated” defined as the difference in the outcome variables between the treated objects like farmers and their counterfactual. A counterfactual is defined as “knowledge of what would have happened to those same people if they simultaneously had not received treatment” (Olmos A., 2015 citing Shadish et al., 2002). In this context, as to González et al. 2009, if Y represents the outcome variable and if D is a dummy variable that takes the value of 1 if the individual was treated and 0 otherwise, the “average treatment effect on the treated” will be given by:
(1) TATT= E[Y (1) / D =1]− E[Y (0) / D =1]
However, accordingly, given that the counterfactual (E[Y (0) / D = 1]) is not observed, a proper substitute has to be chosen to estimate TATT. Using the mean outcome of non-beneficiaries-which is more likely observed in most of the cases-do not solve the problem given that there is a possibility that the variables that determine the treatment decision also affect the outcome variables. In this case, the outcome of treated and non-treated individuals might differ leading to selection bias (González et al., 2009). To clarify this idea, the mean outcome of untreated individuals has to be added to (1) from which the following expression can be easily derived:
(2) TATT={E[Y (1) / D =1]− E[Y (0) / D =0]}−{E[Y (0) / D =1]− E[Y (0) / D =0]}
Here E[Y (0) / D= 1]−E[Y (0) / D= 0] represents the selection bias which will be equal to zero if treatment was given randomly which can be achieved through the use of experimental approach.
Summary of Chapters
1. INTRODUCTION: Provides an overview of the role of agriculture in the Ethiopian economy and establishes the necessity of increasing wheat productivity through improved technology adoption.
2. REVIEW OF THE EMPIRICAL EVIDENCE: Reviews existing impact assessment studies regarding technology adoption in Ethiopia to identify knowledge gaps and methodological lessons.
3. MATERIALS AND METHODS: Details the analytical framework based on propensity score matching and describes the data sources and variables used for the evaluation.
4. RESULTS AND DISCUSSIONS: Presents the descriptive statistics, the estimation of propensity scores, the assessment of matching quality, and the estimated average treatment effects at various levels.
5. CONCLUSION AND RECOMMENDATION: Concludes that while technology adoption has a positive impact, it is not homogeneous across regions, and recommends strengthening research and extension systems accordingly.
Keywords
Wheat productivity, Ethiopia, Propensity score matching, Improved wheat varieties, Technology adoption, Impact assessment, Probit model, Agricultural extension, Agro-ecological zones, Smallholder farmers, Yield gap, Selection bias, Econometric evaluation, Wheat management practices, Cereal production.
Frequently Asked Questions
What is the core focus of this research?
The work examines the impact of adopting improved wheat varieties and related management information on wheat productivity per unit of land in Ethiopia.
What are the primary themes addressed?
The themes include agricultural productivity growth, the effectiveness of technology adoption, and the differences in these impacts across various Ethiopian administrative regions and agro-ecological zones.
What is the main research question or goal?
The main goal is to identify the causal impact of improved wheat variety adoption on productivity and to uncover regional and agro-ecological disparities in that impact.
Which scientific method is employed?
The study utilizes the propensity score matching (PSM) technique, a robust quasi-experimental method, to control for observed selection bias in technology adoption.
What is covered in the main body of the work?
The main body covers the theoretical framework, data description, descriptive statistics of the variables, propensity score estimation using a probit model, quality checks for matching, and estimation of average treatment effects.
Which keywords characterize this work?
Key terms include wheat productivity, technology adoption, propensity score matching, Ethiopia, smallholder farmers, and agro-ecological zones.
How does the study address regional heterogeneity?
The research conducts separate analyses for different administrative regions and agro-ecological zones to show that the impact of the same technology package varies significantly due to local conditions.
What is the primary conclusion regarding the effectiveness of these technologies?
The study concludes that while improved varieties have a positive impact on average, the benefits are not uniform, suggesting a need for more tailored agricultural interventions that suit specific local conditions.
- Quote paper
- Fitsum Daniel (Author), Baye Belay (Author), 2018, Impact of Improved Wheat Varieties & Information's Adoption on Productivity in Ethiopia, Munich, GRIN Verlag, https://www.grin.com/document/443127
