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Journal ArticleDOI

Factors determining allocation of land for improved wheat variety by smallholder farmers of northern Ethiopia

31 Mar 2015-Journal of development and agricultural economics (Academic Journals)-Vol. 7, Iss: 3, pp 105-112

AbstractThis study was conducted in Northern Ethiopia, Adwa district. The main objective of the study was to examine factors influencing allocation of land for improved wheat variety by smallholder farmers of the study area. Descriptive, inferential and econometric methods were used to analyze data. Results of descriptive and inferential analyses showed that; adopters had high family size in adult-equivalent, high number of tropical livestock unit, large land size, high frequency of extension contact, access to credit service, they were followed formal schooling, and they were nearest to main road and market as compared to non-adopters. Tobit model was used to analyze factors influencing adoption of improved wheat technology econometrically. A total of thirteen explanatory variables were included in the model.  From the tested variables only eight variables (education level of household head, family size, tropical livestock unit, distance from main road and nearest market, access to credit service, extension contact and perception of household towards cost of the technology) were found to be the significant factors affecting adoption of improved wheat variety. Implication of results of this study is that any development intervention through improved wheat technologies should consider the aforementioned socioeconomic characteristics and determinants of adoption for success.   Key words: Adoption of Improved wheat variety, Adwa, smallholder farmers, Topit model.

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Journal ArticleDOI
Abstract: This study aims to identify the determinants of adoption of improved maize variety (IMV) among farmers in the northern region of Ghana and subsequently assess the factors influencing the intensity ...

43 citations


Cites methods or result from "Factors determining allocation of l..."

  • ...Gebresilassie and Bekele (2015) used Tobit regression Page 4 of 14 model to study the determinants of allocation of farmland to improved wheat variety in Northern Ethiopia....

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  • ...The positive and significant effect of education is consistent with the assertion that educated farmers can easily assimilate information and therefore adopt improved technology much more comfortable than the uneducated farmers (Diiro et al., 2015; Gebresilassie & Bekele, 2015)....

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Journal ArticleDOI
Abstract: A study was conducted to analyze factors influencing adoption of wheat technology packages by smallholder farmers in Gurawa, Meta and Habro districts in eastern Ethiopia. The analysis was based on a household survey data collected from 136 randomly selected households. A Two-limit Tobit model was used to elucidate factors affecting adoption of technology packages measured based on an index derived from five components of wheat technologies which included row planting, pesticide application, use of improved varieties, and application of inorganic fertilizers, namely, Diammonium Phosphate (DAP) and Urea. Among the variables included in the model, variation in district, gender, age of the household head, education status of the household head, farm size, distance to market, distance to FTC (Farmers’ Training Centers), cooperative membership, dependency ratio, and annual income of the households were found to significantly affect the adoption of wheat technology packages. Policy makers, planners and development practitioners should give due attention to these determinants to support smallholder farmers in wheat production and enhance gains derived from it.

11 citations


Journal ArticleDOI
06 Jul 2018
Abstract: This study examines the factors affecting the adoption of high-yield wheat varieties by wheat farmers in Sindh, Pakistan A cross-sectional data of randomly selected 240 wheat farmers from Shaheed Benazirabad and Naushahro Feroze districts in the middle region of Sindh, Pakistan were collected for this study We performed the probit model to estimate factors that influence the adoption of improved wheat varieties The results drawn from the estimations show that the adoption of improved wheat varieties by farmers in the study area was positively and significantly influenced by education, farming experience, landholding size, tube-well ownership, extension contact and access to credit The study recommends that public and private sectors should encourage access to extension service to improve of dissemination of certified seed of wheat crop among the growers through trainings, workshops and seminars

8 citations


Journal ArticleDOI
Abstract: Rice cultivation is a new practice to Tselemti district of Tigray region, Ethiopia. Adoption of rice technologies is very slow in spite of its potential in the area. This research intended to identify factors affecting adoption of rice technologies. A multistage sampling technique was employed to select 150 sample households for this study. Descriptive statistics and inferential statistics were employed to see mean and percentage differences between adopter and non-adopter categories. Besides, binary logistic regression model was employed to identify the factors affecting adoption of rice technology. Result of the descriptive and inferential analysis showed that adopters had better farm size, livestock holding, farm income, labor availability, education level, perception on rice yield, access to credit service, contacts with extension agents, participation in off-farm activities, participation in training and field days as compared to non-adopters. Moreover, the binary logistic regression model result showed that the level of education, perception on rice yield, access to credit service, participation in off-farm activities, participation on field day and participation in training were found to positively and significantly influence the adoption decision of rice technology at 1%, 5% and 10% significant level. However, market distance influences rice technology adoption negatively and significantly at 10% significant level. The variables education, rice yield, access to credit, off-farm activities, market distance, participation on field day and training determine the farmers’ continued adoption decision behavior of rice technology. Therefore, the adoption of rice technology should be sustained by paying attention and moving along with those variables which influenced the adoption significantly.

7 citations


Journal ArticleDOI
TL;DR: It is recommended that projects/programmes and policies related to the introduction and dissemination of improved maize production technologies in northern Ghana should draw lessons from studies like this to ensure improved technology uptake.
Abstract: In spite of substantial investments in developing and disseminating improved maize production technologies by successive governments and several development partners, technology adoption in Ghana remains low. The purpose of this study was to identify the factors that influence the extent of adoption of improved maize production technologies among farmers in northern Ghana. A Tobit regression model was used to analyse the determinants of the extent of technology adoption. Results of the study revealed that formal education, farming experience, extension contact, access to credit, and membership of a farmer-based organisation are significant determinants of the extent of adoption of all three technologies considered. Moreover, sex of household head did not influence the extent of adoption of improved seeds but was rather significant in the case of fertiliser application and row planting. The study recommends that projects/programmes and policies related to the introduction and dissemination of improved maize production technologies in northern Ghana should draw lessons from studies like this to ensure improved technology uptake. Key words: Adoption, improved technologies, maize, Tobit regression.

6 citations


Cites background from "Factors determining allocation of l..."

  • ...A similar finding on the effect of education on the allocation of land to improved wheat variety has been reported by Gebresilassie and Bekele (2015) in Ethiopia....

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References
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01 Jan 2008
Abstract: Agriculture is a vital development tool for achieving the Millennium Development Goal that calls for halving by 2015 the share of people suffering from extreme poverty and hunger. That is the overall message of this year's World Development Report (WDR), the 30th in the series. Three out of every four poor people in developing countries live in rural areas, and most of them depend directly or indirectly on agriculture for their livelihoods. This report provides guidance to governments and the international community on designing and implementing agriculture for development agendas that can make a difference in the lives of hundreds of millions of rural poor. The report highlights two major regional challenges. In much of Sub-Saharan Africa, agriculture is a strong option for spurring growth, overcoming poverty, and enhancing food security. Agricultural productivity growth is vital for stimulating growth in other parts of the economy. But accelerated growth requires a sharp productivity increase in smallholder farming combined with more effective support to the millions coping as subsistence farmers, many of them in remote areas. Recent improved performance holds promise, and this report identifies many emerging successes that can be scaled up. In Asia, overcoming widespread poverty requires confronting widening rural-urban income disparities. Asia's fast-growing economies remain home to over 600 million rural people living in extreme poverty, and despite massive rural-urban migration, rural poverty will remain dominant for several more decades. For this reason, the WDR focuses on ways to generate rural jobs by diversifying into labor intensive, high value agriculture linked to a dynamic rural, non-farm sector. In all regions, with rising land and water scarcity and the added pressures of a globalizing world, the future of agriculture is intrinsically tied to better stewardship of natural resources. With the right incentives and investments, agriculture's environmental footprint can be lightened and environmental services harnessed to protect watersheds and biodiversity.

3,752 citations


Book
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Abstract: Foreword Preface to the Second Edition Preface to the Third Edition Obituary INTRODUCTION AND THE LINEAR REGRESSION MODEL What is Econometrics? Statistical Background and Matrix Algebra Simple Regression *Multiple Regression VIOLATION OF THE ASSUMPTIONS OF THE BASIC MODEL *Heteroskedasticity *Autocorrelation Multicollinearity *Dummy Variables and Truncated Variables Simultaneous Equations Models Nonlinear Regression, Models of Expectations, and Nonnormality Errors in Variables SPECIAL TOPICS Diagnostic Checking, Model Selection, and Specification Testing *Introduction to Time--Series Analysis Vector Autoregressions, Unit Roots, and Cointegration *Panel Data Analysis *Large--Sample Theory *Small--Sample Inference: Resampling Methods Appendix A: *Data Sets Appendix B:* Data Sets on the Web Appendix C:* Computer Programs Index

3,690 citations


Book
06 Dec 2013
Abstract: 1. The Nature of Econometrics and Economic Data 2. The Simple Regression Model 3. Multiple Regression Analysis: Estimation 4. Multiple Regression Analysis: Inference 5. Multiple Regression Analysis: OLS Asymptotics 6. Multiple Regression Analysis: Further Issues 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 8. Heteroskedasticity 9. More on Specification and Data Issues 10. Basic Regression Analysis with Time Series Data 11. Further Issues in Using OLS with Time Series Data 12. Serial Correlation and Heteroskedasticity 13. Pooling Cross Sections Across Time: Simple Panel Data Methods 14. Advanced Panel Data Methods 15. Instrumental Variables Estimation and Two Stage Least Squares 16. Simultaneous Equations Models 17. Limited Dependant Variable Models and Sample Selection Corrections 18. Advanced Time Series Topics 19. Carrying Out an Empirical Project

1,355 citations


Posted Content
Abstract: This article examines the driving forces behind farmers’ decisions to adopt agricultural technologies and the causal impact of adoption on farmers’ integration into output market using data obtained from a random cross-section sample of 700 farmers in Ethiopia. We estimate a Double-Hurdle model to analyze the determinants of the intensity of technology adoption conditional on overcoming seed access constraints. We estimate the impact of technology adoption on farmers’ integration into output market by utilizing treatment effect model, regression based on propensity score as well as matching techniques to account for heterogeneity in the adoption decision, and for unobservable characteristics of farmers and their farm. Results show that knowledge of existing varieties, perception about the attributes of improved varieties, household wealth (livestock and land) and availability of active labor force are major determinants for adoption of improved technologies. Our results suggest that the adoption of improved agricultural technologies has a significant positive impact on farmers’ integration into output market and the findings are consistent across the three models suggesting the robustness of the results. This confirms the potential direct role of technology adoption on market participation among rural households, as higher productivity from improved technology translates into higher output market integration.

150 citations


"Factors determining allocation of l..." refers background or result in this paper

  • ...The study by Solomon et al. (2011) was consistent with this result; distance from nearest market affects adoption of improved agricultural technology negatively and significantly....

    [...]

  • ...And also according to the study by Isaiah et al. (2007), Solomon et al. (2011), Ayinde et al. (2010), Odoemenem and Obinne (2010) and Matata et al. (2010) frequency of contact with extension agent affect positively and significantly adoption decision of farmers for improved agricultural technology....

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  • ...The study by Solomon et al. (2011) confirms this result....

    [...]


Journal ArticleDOI
Abstract: This article examines the driving forces behind farmers’ decisions to adopt agricultural technologies and the causal impact of adoption on farmers’ integration into output market using data obtained from a random cross-section sample of 700 farmers in Ethiopia. We estimate a Double-Hurdle model to analyze the determinants of the intensity of technology adoption conditional on overcoming seed access constraints. We estimate the impact of technology adoption on farmers’ integration into output market by utilizing treatment effect model, regression based on propensity score as well as matching techniques to account for heterogeneity in the adoption decision, and for unobservable characteristics of farmers and their farm. Results show that knowledge of existing varieties, perception about the attributes of improved varieties, household wealth (livestock and land) and availability of active labor force are major determinants for adoption of improved technologies. Our results suggest that the adoption of improved agricultural technologies has a significant positive impact on farmers’ integration into output market and the findings are consistent across the three models suggesting the robustness of the results. This confirms the potential direct role of technology adoption on market participation among rural households, as higher productivity from improved technology translates into higher output market integration.

99 citations