Article Tools
Email this article (Login required)
Email the author (Login required)
About The Authors

Eba Muluneh Sorecha
Haramaya University, Ethiopia

Lecturer, Haramaya University, Ethiopia

Birhanu Bayissa
Haramaya University, Ethiopia

Lecturer, Haramaya University, Ethiopia

Information for Author
Visitor Statistic

Journal Content


Dry spell length analysis for crop production using Markov-Chain model in Eastern Hararghe, Ethiopia

Eba Muluneh Sorecha, Birhanu Bayissa
  J. Degrade. Min. Land Manage. , pp. 891-897  
Viewed : 106 times


The information on the length of dry spells could be used for deciding a particular crop or variety, supplementary irrigation water demand and for others agricultural activities. The study was conducted in three districts: Babile, Haramaya and Kersa, eastern Hararghe, Ethiopia. The aim of the study was to analyze dry spell lengths and its implications on crop production in eastern Hararghe, so as to minimize unexpected damage due to long dry spells and to have effective and efficient planning for farming communities. Thirty years of rainfall data for each district were collected form National Meteorological Agency of Ethiopia. Data quality control has been done prior to analysis. Markov-Chain model were employed to analyze the collected data. The result of the study revealed that dry spells were highly hitting Babile district comparing to the other two districts. The probability of dry spell lengths of 5 and 7 days in Babile district was found to be about 99 and 80%, respectively. Whereas, in Haramaya district, the probability of dry spell length of 5 days was found to be 80% during 181(Days of the Year) DOY, then it falls to below 50 % by 221DOY. Moreover, the probability of the occurrences of dry spells of 10, 15, and 20 days were below 5% in Haramaya district during the main rainy season. The study also investigated that in Kersa district, the probability of occurrences of the dry spell lengths of 5, 7, 10, 15, and 20 days were estimated to fall below 30%, showing that the area was better in crop production as compared to the rest districts. The annual rainfalls in all the districts were decreasing as per the trend line and variable in all the districts: Babile, Haramaya and Kersa districts, having the CV values of, 41, 34 and 31%, respectively. Information regarding dry spell length analysis has to be well understood at grass root levels to ensure food security via lifesaving irrigation schemes or any other options. 


crop production; dry spells; Markov-chain model; Ethiopia

Full Text:



Abeysekera, S., Senevirahtne, K.E., Leaker, A. and Stern, R.D. 1983. “Analysis of rainfall data for agricultural purposes”.11(2):165-183.

Barron, J. 2004. “Dry spell mitigation to upgrade semi-arid rain fed agriculture: Water harvesting and soil nutrient management for smallholders maize cultivation in Machakos Kenya”. Doctoral Thesis in Natural Resource Management, Stockholm University Sweden.

Barron, J., Rockstrom, J., Gichuki, F. and Hatibu, N. 2003. Dry spell analysis and maize yields for two semi-arid locations in east Africa. Agril for Meteorol., 117: 23-37.

Gabriel, K. R. amd,J., Neumann. 1957. “On a distribution of weather cycles by length”. Quarterly Journal of the Royal Meteorological Society 83: 375-380.

Hadgu, G., Tesfaye, K., Mamo, G. and Kassa, B. 2013. Trend and variability of rainfall in Tigray, Northern Ethiopia: Analysis of. Academia Journal of Agricultural Research, 1(6):088-100.

IPCC. 2008. Climate Change, summary for policymakers: A report of working group of the Intergovernmental Panel on Climate Change, Montreal, Canada.

Jayawardene, H.K.W.I., Sonnadara, D.U.J. and Jayewardene, D.R. 2005. Trends in rainfall in Sri Lanka over the last century. Sri Lankan Journal of Physics. 6:7-17.

Mathlouthi, M., Lebdi, F. 2008. “Characterization of dry spell events in a basin in the North of Tunisia”.

Medhi, J. 2009. “Stochastic Processes”. New Age Science.

Seleshi, Y. and Zanke, U. 2004. Recent Changes in Rainfall and Rainy days in Ethiopia. International Journal of Climatology, 24:973– 983.

Seleshi, Y. and Camberlin, P. 2006. Recent changes in dry spell and extreme rainfall events in Ethiopia. Theor. Appl. Climatol. 83:181–191.

Sharma, T. C. 1996. “ Simulation of the Kenyan longest dry and wet spells and the largest rain sums using a Markov Model”. Journal of Hydrology 178 (55-67).

Sivakumar, M. 1991. "Empirical analysis of dry spells for agricultural applications in West Africa " .Journal of Climate: 532-539.

Simane, B. and Struik, P. C. 1993. Agroclimatic analysis: A tool for planning sustainable wheat (Triticum turgidum var. durum) production in Ethiopia.. Agri. Economics Env, 47:31-46.

Sorecha, E.M., Bayissa, B., Toru, T. 2017. Characterization of Rainfall Indices for Crop Production in Kersa District, Eastern Ethiopia: Farmers’ Advisory. Acad. Res. J. Agri. Sci. Res. 5(2): 134-139.

Taley, S.M. and Dalvi, V. B. 1991. “Dry-spell analysis for studying the sustainability of rainfed agriculture in India – The case study of the Vidarbha region of Maharashtra state”. Large Farm Development Project.

Usman, M. and C. Reason. 2004. "Dry spell frequencies and their variability over sourthern Africa." Climate Research 26: 199-21.

Wilhite, D. A. Glantz, M. H. 1985. “Understanding the drought phenomenon; The role of definitions”. Water International. 10:111-120.


  • There are currently no refbacks.

Copyright (c) 2017 Journal of Degraded and Mining Lands Management

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Indexed By