MODELING FACTORS INFLUENCING BARLEY YIELD IN ETHIOPIA: AUGMENTED COBB-DOUGLAS PRODUCTION FUNCTION APPROACH

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Abera Gayesa Tirfi

Abstract

Barley production in Ethiopia is constrained by climatic and non-climatic factors. The objective of this study was to examine the influence of climate change and non-climatic inputs on barley yield in Ethiopia. The study employed an augmented Cobb-Douglas production function approach to model factors influencing barley yield in the country. The results revealed that short/belg-season rainfall and temperature variables showed positive relationship with barley yield, having minimal positive impact on yield of barley. The positive elasticity of short/belg-season rainfall is justified by the fact that short duration barley crops are grown in the highlands of Bale, North Central Shewa, and Wollo zones contributing less than 10% of total grain production.Conversely, long/main-season rainfall showed negative impact on yield of barley, which due to extreme rain events such as high rainfall above optimum requirement of the crop as well as scarcity of rainfall in some pocket areas. The result infers that cultivation of barley in Ethiopia moderately depends on rainfall. Among the non-climatic variables, irrigated land area under barley cultivation, fertilizer quantity used, and improved barley seed used had positive impact on barley yield. Fertilizer and improved seed inputs had positive and significant impact on barley yield. The result implies that barley yield is highly responsive to use of fertilizer and improved barley seed inputs and moderately responsive to irrigation input. Conversely, land area cultivated under barley crop had negative impact on barley yield, although not significant.

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Alemu, G. and Haji, J. (2016). Economic Efficiency of Sorghum Production for Smallholder Farmers in Eastern Ethiopia: The Case of Habro District; Journal of Economics and Sustainable Development, Vol. 7 (15); pp 44 – 51.
Araya, A. et al. (2021). Modeling the effects of crop management on food barley production under a midcentury changing climate in northern Ethiopia; Climate Risk Management, Vol. 32; pp 1 – 15.
Asfaw, Z. (2000); The barleys of Ethiopia; In: S. B. Brush (edit), 2000; GENES in the FIELD: On-Farm Conservation of Crop Diversity, International Development Research Centre and International Plant Genetic Resources Institute, Lewis Publishers, New York.
Aragie, AE (2013). Climate change, growth and poverty in Ethiopia. Volume-3.
Bekele, K., et al, (2019). Modeling Climate Change and its Impacts on Food Barley (HorduemvulgareL.) Production using Different Climate Change Scenarios in Lemubilbilo District, Oromia Regional State, Ethiopia, International Journal of Research in Environmental Science, Vol. 5 (3), pp 33 – 40.
CSA, (2020). Report on area and production of major crops, Agricultural Sample Survey 2019/20, Bulletin 587, Addis Ababa, Ethiopia.
Chen, C. et al, 2004; Yield variability as influenced by climate: A statistical investigation. Climate Change Vol. 66: pp 239-261.
Dickey, D. A. and W.A. Fuller, 1979; Distributions of the estimators for autoregressive time series with a unit root, Journal of American Statistical Association, Vol. 74: pp 427- 431.
Dushko, J., L. Darko and K. Cane, 2011; Cobb-Douglas production function revisited, VAR and VECM analysis and a note on Fischer/Cobb-Douglass paradox, Munich Personal RePEc Archive, available at: https://mpra.ub.uni-muenchen.de/33576/.
FAO (Food and Agriculture Organization), (2015). Adaptation to climate risk and food security: Evidence from smallholder farmers in Ethiopia. Food and Agriculture Organization, Rome, Italy.
Gujrati, D. 2004; Basic Econometrics, fourth edition. The McGraw-Hill, New York, USA.
Gupta, S., Sen, P., and Srinivasan, S. (2012). Impact of climate change on Indian economy: Evidence from food grain yields. Centre for Development Economics Working Paper 218, New Delhi.
Kim, M-K and Pang, A. (2009): Climate Change Impact on Rice Yield and Production Risk. Journal of Rural Development, Vol. 32 (2): pp 17 – 29.
Kumar, A. and Sharma, P. (2013). Impact of Climate Variation on Agricultural Productivity and Food Security in Rural India; Economics Discussion Papers, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/discussionpapers/2013-43.
MoA, (2001). 2001 Belg Season in North-Central Ethiopia, Report of a joint UN-EUE/MoA/FAO mission undertaken from 19 – 29 March 2001; https://www.africa.upenn.edu-belg.
Mojapelo, M. C. et al. (2019). Estimation of sorghum supply elasticity in South Africa; Journal of Agribusiness and Rural Development, Vol. 2 (52); pp 131 – 138.
Phillips, P.C. and P. Perron, 1988; Testing for a Unit Root in Time Series Regression. Biometrika, Vol. 75(2), pp 335-46.
Sharma, S. and S. Singh, 2019; The Validity of Wagner’s Law in India: A Post-liberalisation Analysis, M.J.P. Rohilkhand University, Uttar Pradesh, India.
Singh, A. K. and Sharma, P. (2018). Measuring the productivity of food-grain crops in different climate change scenarios in India: An evidence from time series investigation; Research, Vol. 4 (16): pp 661 – 673.
Yawson, D.O., Adu, M. O. & Armah, F. A. (2020). Impacts of climate change and mitigation policies on malt barley supplies and associated virtual water flows in the UK; Scientific Reports: http://www.nature.com/scientificreports