Adoption of row planting technology and household welfare in southern Ethiopia, in case of wheat grower farmers in Duna district, Ethiopia
Article Sidebar
Main Article Content
Abstract
Adoption of yield enhancing row planting technologies is crucial for achieving agricultural growth and development. This study attempts to investigate the factors affecting adoption decision and its impact on income and expenditure in Duna district cross-sectional field survey data gathered in 2018-2019. However, technology adoption and farmers livelihood were poorly related due to lack comprehensive measure of agricultural productivity. A cross-sectional field survey was conducted among 375 wheat growers in Duna district, southern Ethiopia. Descriptive statistics and econometric methods such as binary logit regression and propensity score matching was employed for the data analysis. Sensitivity analysis developed to ensure the validity of the conditional independence assumption. The results of binary logit regression revealed that the technology participant was significantly affected by age, education status, size of family, off-farm income, land holding, livestock holding, quantity of fertilizer used, farmers training center, access to credit and extension services. Adoption was associated with a significantly higher crop yield and expenditure. Sensitivity analysis indicated that there is no hidden bias. The findings suggest that the role of technology adoption at farm level due to higher yield and income could translate in to reduced poverty. Extension office and another concern body should give an important attention to adoption decision which base for enhancing yield. The summary of this technique by policy makers and plan designers could bring better enhancement on wheat cultivator. Improving such a technique is crucial option to enhance wheat grower income, consumption expenditure and crop yield.
Article Details
References
Hill R, Eyasu T. Ethiopia poverty assessment. [Final report]. Washingtong, DC: Poverty Global Practice
Africa Region, World Bank Group; 2015.
Abraham B, Araya H, Berhe T, Edwards S, Gujja B, Khadka RB, et al. The system of crop intensification: reports from the field on improving agricultural production, food security, and resilience to climate change for multiple crops. Agric Food Secur. 2014;3(1):4.
Abate GT, Bernard T, Brauw A, Minot N. The impact of the use of new technologies on farmers’ wheat yield in Ethiopia: evidence from a randomized control trial. Agric Econ. 2018;49(4):409-421.
Beyene LM, Legesse EE. Public investment in irrigation and training for agriculture-led development: a CGE approach for Ethiopia. SSRN Electron J. 2013;2:1-78.
ATA Ethiopia [Internet]. Addis Ababa: The Agency; c2010. [cited 2019 Oct 15]. Results of 2012 New Tef Technology Demonstration Trials. Available from: http://sri.cals.cornell.edu/aboutsri/othercrops/teff
/Ethiopi a_SCI_tef_2012_trials.pdf.
Central Statistics Agency [Internet]. Addis Ababa: The Agency; c1963. [cited 2019 Oct 15]. Area and production of major crops. Available from: https://www.statsethiopia.gov.et/wp-content/uploads/2019/06
Agricultural-Sample-Survey-Area-and-Production-Meher-Season-2013.pdf.
FAO [Internet]. Rome: The Organization; c1945. [cited 2019 Oct 15]. Feeding the world in 2050. Available from: http://www.fao.org/tempref/docrep/fao/meeting/018/k6021e.pdf.
Rashid S. Staple food prices in Ethiopia. RePEc. 2010;1:2-13.
Jabbar MA, Saleem MA, S, Erkossa T. Technical and economic performance of animal-drawn implements for minimum tillage: experience on vertisols in Ethiopia. Exp Agric. 2002;38(2):185-196.
Chauhan BS, Abeysekera AS, Wickramarathe MS, Kulatunga SD, Wickrama UB. Effect of rice establishment methods on weedy rice infestation and grain yield of cultivated rice (O. Sativa L.) in Sri Lanka. Crop Prot. 2014;55:42-49.
Bandiera O, Rasul I. Social networks and technology adoption in northern Mozambique. Econ J. 2006;116(514):869-902.
Simtowe F, Berresaw MK, Diagne A, Silim S, Muange E, Asfaw S, et al. Determinants of agricultural technology adoption: the case of improved pigeonpea varieties in Tanzania. Q J Int Agric. 2011;50(4):325-345.
Vandercasteelen J, Dereje M, Mart MM, Seyoum TA. The impact of the promotion of row planting on farmers’ teff yields in Ethiopia. Econ. 2013;344:3-27.
Mulugeta T, Hundie B. Impacts of adoption of improved wheat technologies on households' food consumption in southeastern Ethiopia. In: Bernard L, editor. International Association of Agricultural Economists (IAAE) Triennial Conference; 2012 Aug 18-24; Foz do Iguaçu, Brazil. Boston: Wiley-Blackwell; 2012. p. 2-13
Akudugu MA, Guo E, Dadzie SK: Adoption of modern agricultural production technologies by farm households in Ghana: what factors influence their decisions. Int J Agric Biol. 2012; 2(3):1-13.
Yonas B. The impact of row planting of teff crop on rural Household income: a case of Tahtay Maychew wereda, Tigrai, Ethiopia. Econ. 2015;1:9-58.
Awotide BA, Diagne A, Omonona BT. Impact of improved agricultural technology adoption on sustainable rice productivity and rural farmers’ welfare in Nigeria: a local average treatment effect (LATE) technique. Business. 2012;1:2-23
Kasirye I. Constraints to agricultural technology adoption in Uganda: evidence from the 2005/06-2009/10 Uganda national panel survey. African J Agric Resour Econ. 2013;8(2):1-18.
Abebe B, Workayehu T. Effect of method of sowing on yield and yield components of Tef (Eragrostis Tef (Zucc) trotter) at Shebedino, southern Ethiopia. Glob J Chem. 2015;2(1):37-44.
Duressa D. Analysis of factors influencing adoption of Quncho Tef: the case of Wayu Tuqa district. Int J Afr Asian Stud. 2015;12:1-10.
Gebregewergs EYaB: Effect of wheat row planting technology adoption on small farms yield in Ofla Woreda, Ethiopia. Int J Agric Extension Social Dev.2016:3(5):184-196.
Rauniyar GP, Goode FM. Technology adoption on small farms. World Dev. 1992;20(2):275-282.
Alemu T, Emana B, Haji J, Legesse B. Impact of wheat row planting on yield of smallholders in Arsi zone of Ethiopia. IJEER. 2014;4(5):386-393.
Yamane T. Statistics: an introductory analysis. 3rd ed. New York: Happer & Row publisher;1973.
Wooldridge JM. Introductory econometrics: a modern approach 5th ed. Ohio: South-Western publisher; 2013.
Rosembaum P, Rubin D.B. The central role of propensity scores in estimating dose-response functions. Biometrika. 1983;70(1):41-55.
Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat. 2004;86(1):4-29.
Khandker SR, Koolwal GB, Samad HA. Handbook on impact evaluation quantitative methods and practices. [Internet]. New York: The World Bank; 1944 [cited 2019 Jun 15]. Available from: http://documents1.worldbaank.org/curated/en/650951468335456749/pdf/520990PUB0EPI1101Official0Use0Only1.pdf.
Caliendo M, Kopeinig SS. Some practical guidance for the implementation of propensity score matching. J Econ Surv. 2008;22(1):31-72.