Determinants of Barley Output Supply Response in Ethiopia: Application of Ardl Bound Cointegration Approach

Abera Gayesa Tirfi

Department of Agriculture and Animal Health, University of South Africa, Ethiopia Regional Learning Centre, Ethiopia

DOI: https://doi.org/10.36956/rwae.v3i3.580

Received: 1 July 2022; Received in revised form: 12 August 2022; Accepted: 18 August 2022; Published: 5 September 2022

Copyright © 2022 Abera Gayesa Tirfi. Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


Abstract

This study investigated barley output supply response determinant factors in Ethiopia. An ARDL bound test approach was employed as a method using secondary data from 1981-2020. The study demonstrated that barley output supply was affected positively and significantly by zero-order lagged seasonal rainfall and crop growing period temperature. The study supports the findings of researchers who reported that warming temperature followed by an increase in the amount of rainfall had a positive impact on barley output supply. The positive impact of temperature was induced because of a rise in the ocean and earth’s surface average temperature, causing more evaporation that increases overall rainfall while reaching over the highland areas. Studies confirm that ENSO and moist winds coming from the Atlantic and Indian Oceans influence the occurrence of rainfall in the western, southeastern, central, and northern highlands of Ethiopia. The study further exhibited that CSMRF and CGPMT had a positive effect on barley output both in the long-run and short-run, implying that climate parameters have minimal effect on barley production. Non-climatic variables demonstrated that both lagged and current year’s producer prices had a positive significant effect on barley output supply in both the long-run and short-run, implying that barley output supply is highly responsive to any price incentive strategies announced before re-allocation of the area towards barley cultivation. Conversely, the study explored that the use of fertilizer in first-order lag had a negatively significant impact on barley output supply in both seasons; implying that increased use of fertilizer in lagged periods may reduce barley output as a result of inappropriate fertilizer application by farmers. The results generated by this study are a useful addendum to the repository of knowledge on the elasticity of crop supply at an aggregate level, which can be used in designing strategies and measures for the mitigation and adaptation of climate change.

Keywords: Changing climate, Supply response, Barley output, ARDL model


References

[1] Gardi, M.W., Memic, E., Zewudu, E., et al., 2022. Simulating the effect of climate change on barley yield in Ethiopia with the DSSAT-CERES-Barley model. Agronomy Journal. 114, 1128-1145.

[2] AR5 Climate Change 2014: Impacts, Adaptation, and Vulnerability [Internet]. IPCC (Intergovernmental Panel on Climate Change); 2014. Available from: https://www.ipcc.ch/report/ar5/wg2/

[3] Martinez, R., Hemming, D., Malone, L., et al., 2012. Improving Climate Risk Management at Local Level: Techniques, Case Studies, Good Practices and Guidelines for World Meteorological Organization Members [Internet]. IntechOpen. DOI: https://doi.org/10.5772/51554#

[4] Winthrop, M., Kajumba, T., McIvor, S., 2018. Ethiopia Country Climate Risk Assessment Report, Irish Aid [Internet]. Available from: https://www.climatelearningplatform.org/sites/default/files/resources/ethiopia_country_climate_risk_assessment_report

[5] Yawso, 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. 10, 376.

[6] CSA (Central Statistical Agency), 2012. Annual Statistics Bulletin. Addis Ababa, Ethiopia.

[7] CSA (Central Statistical Agency), 2020. Report on area and production of major crops in Ethiopia. Agricultural Sample Survey 2019/2020, Addis Ababa, Ethiopia.

[8] Lakew, B., Semeane, Y., Alemayehu, F., et al., 1997. Exploiting the diversity of barley landraces in Ethiopia. Genetic Resources and Crop Evolution. 44, 109-116.

[9] Bekele, B., Alemayehu, F., Lakew, B., et al., 2005. Food barley in Ethiopia. Food barley: Importance, uses and local knowledge. ICARDA: Aleppo, Syria. pp. 53-82.

[10] Asfaw, Z., 2000. GENES in the FIELD: On-Farm conservation of crop diversity. International Development Research Centre: Ottawa; International Plant Genetic Resources Institute: Rome; Lewis Publishers: New York.

[11] Wosene, G.A., Berhane, L., Bettina, I.G.H., et al., 2015. Ethiopian barley landraces show higher yield stability and comparable yield to improved varieties in multi-environment field trials. Academic Journals. 7(8), 275-291.

[12] Samson, J., Berteaux, D., McGill, B.J., et al., 2011. Geographic disparities and moral hazards in the predicted impacts of climate change on human populations. Globa Ecology and Biogeography. 20(4), 532-544. DOI: https://doi.org/10.1111/j.1466-8238.2010.00632.x

[13] Bekele, B., Wu, W., Yirsaw, E., et al., 2019. Climate change and its effect on land use change in the Central Rift Valley of Ethiopia. Applied Ecology and Environmental Research. 17(4), 7693-7713.

[14] Overview [Internet]. World Bank; 2021. Available from: https://www.worldbank.org/en/country/ethiopia/overview

[15] World Population Dashboard Ethiopia [Internet]. United Nations Population Funds; 2021. Available from: https://www.unfpa.org/data/world-population/ET

[16] CSA, 2018. Report on area and production of major crops in Ethiopia. Agricultural sample survey 2017/18, Addis Ababa.

[17] Gashaw, G., Tura, K., 2015. Review of barley value chain management in Ethiopia. Journal of Biology, Agriculture and Healthcare. 5(10), 84-98.

[18] Muluken, B., Jemal, E., 2011. Achievements in food barley breeding research in the early production systems of northwest, Ethiopia. Review of Barley Research and Development in Ethiopia. Proceedings of the 2nd National Barley Research and Development Review Workshop, 2006 Nov 28-30; HARC, Holetta, Ethiopia.

[19] Cammarano, D., Hawes, C., Squire, G., et al., 2019. Rainfall and temperature impacts on barley (Hordeum vulgare L.) yield and malting quality in Scotland. Field Crop Research. 241, 107559. DOI: https://doi.org/10.1016/j.fcr.2019.107559

[20] Climate Change: The Scientific Basis [Internet]. IPCC (Intergovernmental Panel on Climate Change); 2001. Available from: http://www.ipcc.ch/

[21] Deressa, T.T., 2007. Measuring the economic impact of climate change on Ethiopian agriculture: Ricardian approach. Policy Research Working Paper. DOI: https://doi.org/10.1596/1813-9450-4342

[22] Zaremba, Ł., 2018. Cobweb theorem in relation to the fruit market. AgEcon Search. 20(3), 190-195. DOI: https://doi.org/10.5604/01.3001.0012.1516

[23] Brianzoni, S., Mammana, C., Michetti, E., et al., 2008. A stochastic cobweb dynamical model. Discrete Dynamics in Nature and Society. Article ID 219653. DOI: https://doi.org/10.1155/2008/219653

[24] Mendelsohn, R., Tiwari, D., 2000. Two essays on climate change and agriculture: A developing country perspective. FAO Information Division: Rome, Italy.

[25] Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Economics. 16(3), 289-326.

[26] Johansen, S., Juselius, K., 1990. Maximum likelihood estimation and inference on cointegration—with application to the demand for money. Oxford Bulletin of Economics. 52, 169-210.

[27] Hassler, U., Wolters, J., 2006. Autoregressive distributed lag models and cointegration; Working Paper, No. 2005/22, Free University of Berlin, School of Business and Economics: Berlin.

[28] Duasa, J., 2010. Real exchange rate and trade variables: Asymmetric cointegration relationship. International Journal of Applied Business and Economic Research (IJABER). 8(1), 1-14.

[29] Frimpong, M.J., Oteng, E.F., 2006. Bound Testing Approach: An Examination of Foreign Direct Investment, Trade and Growth Relationships [Internet]. Available from: https://mpra.ub.uni-muenchen.de/352/1/MPRA_paper_352.pdf

[30] Dickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association. 74(366a), 427-431.

[31] Gujarati, D., 2004. Basic econometrics. McGraw Hill: New York.

[32] Heij, C., de Boer, P., Franses, P.H., et al., 2004. Econometric methods with applications in business and economics. Oxford University Press: Oxford.

[33] Wooldridge, J.M., 2013. Introductory econometrics: A modern approach. Cengage Learning: Boston. pp. 868.

[34] Enders, W., 2010. Applied econometric time series (3rd ed.). John Wiley & Sons, Inc.: Hoboken, New Jersey.

[35] Akter, N., Hong, S.J., 2011. Education and economic growth in Haryana (India): Application of cointegration analysis and vector error correction model on supply response of pulses in Bangladesh. South Asian Studies. 17(1), 277-295.

[36] Ssekuma, R., 2011. A study of cointegration models with applications [Master’s thesis]. Pretoria: University of South Africa.

[37] Sharma, S., Singh, S., 2019. The validity of Wagner’s Law in India: A postliberalisation analysis. South Asian Journal of Social Studies and Economics. 4(2), 1-13.

[38] Climate Change 2013: The Physical Science Basis [Internet]. IPCC; 2013. Available from: https://www.ipcc.ch/report/ar5/wg1/

[39] Conway, D., 2000. Some aspects of climate variability in the North East Ethiopian Highlands, Wolli and Tigray. Ethiopian Journal of Science. 23(2), 139-161.

[40] Fischer, G., Velthuizen, H.T.V., 1996. Climate Change and Global Agricultural Potential Project: A Case Study of Kenya [Internet]. International Institute for Applied Systems Analysis: Laxenburg. Available from: https://pure.iiasa.ac.at/id/eprint/4956/

[41] Dumrul, Y., Kilicarslan, Z., 2017. Economic impacts of climate change on agriculture: Empirical evidence from ARDL approach for Turkey. Journal of Business, Economics and Finance (JBEF). 6(4), 336-347.

[42] Lobell, D.B., Schlenker, W., Costa-Roberts, J., 2011. Climate trends and global crop production since 1980. Science. 333, 616-620.

[43] Schlenker, W., Roberts, M., 2009. Nonlinear temperature effects indicate severe damages to U.S. crops yields under climate change. Proceeding of the National Academy of Science. 106, 15594-15598.

[44] Chandio, A.A., Gokmenoglu, K.K., Ahmad, F., 2021. Addressing the long- and short-run effects of climate change on major food crops production in Turkey. Environmental Science and Pollution Research. 28, 51657-51673.

[45] Ketema, A.M., 2020. Determinants of agricultural output in Ethiopia: ARDL approach to co-integration. International Journal of Business and Social Research. 10(3), 1-10.

[46] Taye, B.A., Asfaw, F.F., Yirsaw, B.G., et al., 2021. Modeling the impact of climate and fertilizer on barley production. American Journal of Biological and Environmental Statistics. 7(2), 44-51.

[47] Chandio, A.A., Rehman, A., Jiang, Y., et al., 2020. Short and long-run impacts of climate change on agriculture: An empirical evidence from China. International Journal of Climate Change Strategies and Management. 12(2), 201-221.

[48] Elbeydi, K., Eljadi, A.A., 2007. Measuring the supply response function of barley in Libya. African Crop Science Conference Proceedings. 8, 1277-1280.

Online ISSN: 2737-4785, Print ISSN: 2737-4777, Published by Nan Yang Academy of Sciences Pte. Ltd.