The analysis of daily rainfall data for agricultural purposes

Leaker, Ann; Abeyasekera, S.; Stern, R. D.; Seneviratne, E.K.

doi:10.4038/jnsfsr.v11i2.8380

Cited by 8 publications

(10 citation statements)

References 3 publications

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“…Some authors [3][4][5] has discussed fitting models to the transition probabilities to smoothen the statistical fluctuations and using the fitted values in the model. This was carried out by fitting 2 separate 12 degree polynomials to P(W/W) and P(D/D) (see figure 8) and using the fitted results in the model.…”

Section: Resultsmentioning

confidence: 99%

“…Over the last 40 years several models have been developed with increasing degrees of sophistication to capture the underlying physical dynamics that govern rainfall [1][2][3][4][5][6][7][8] . All these models can be classified in to three types, namely, empirical statistical models, dynamic models and intermediate stochastic models 2 .…”

Section: Introductionmentioning

confidence: 99%

“…The reliability of predicting the amount first depends on the accuracy of the prediction of wet and dry days. However, results of work published so far lack in showing how accurately the models predict wet and dry days for any given area or any given year [2][3][4][5][6][7][8] . Very often the work is limited to applying the techniques only to data from one station or irrigation site or showing how well it fits by calculating confidence limits for a given year or two.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Occurrence of Rainfall in Selected Weather Stations in the Wet and Dry Zones of Sri Lanka

2002

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Two models were developed based on the Markov method to predict the occurrence of rainfall. Daily rainfall data from 9 meteorology stations in Sri Lanka namely, Colombo, Ratnapura, Kandy, Galle, Hambanthota, Batticaloa, Anuradhapura, Badulla and Trincomalee were analysed using these models. The results show that the models can forecast the status of a given day at an average of 73%. Higher agreement is seen for stations in the dry zone of Sri Lanka when compared with those in the wet zone. No significant difference was observed between the results obtained using the 1 st order Markov process and the 2 nd order Markov process. The models were not sensitive to the range of data used or the short-term fluctuations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting the Occurrence of Rainfall in Selected Weather Stations in the Wet and Dry Zones of Sri Lanka

2002

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“…The probabilities of the dry spell lengths exceeding 5, 7, 10, 15 and 20 days were computed using Markov model of order two under the assumption of binomial error structure (Abeysekera et al,1983). For the study, rainfall data from 1980-2013of all districts: Babile, Haramaya and Kersa collected were used.…”

Section: Research Approachmentioning

confidence: 99%

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

Sorecha

Bayissa

2017

J.Degrade.Min.Land Manage.

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Abstract: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 understoodat grass root levels to ensure food security via lifesaving irrigation schemes or any other options. Keywords: crop production, dry spells, Ethiopia, Markov-chain modelTo cite this article: Sorecha, E.M. and Bayissa, B. 2017. Dry spell length analysis for crop production using Markov-Chain model in Eastern Hararghe, Ethiopia. J. Degrade. Min. Land Manage. 4(4): 891-897,

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“…This was so for 1982, 2006 and 2007. Consequently, a 23 year rainfall data set was analyzed following the standard procedure for analysing rainfall data for agricultural purposes which involves summarising the daily data to obtain monthly totals and then annual totals (Abeyasekera et al, 1983). This was done partly to reduce the volume of data in subsequent analyses.…”

Section: Statistical Analysis Of Rainfall Datamentioning

confidence: 99%

The Nature of Rainfall at a Typical Semi-Arid Tropical Ecotope in Southern Africa and Options for Sustainable Crop Production

Mzezewa¹,

Gwata²

2012

Crop Production Technologies

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The analysis of daily rainfall data for agricultural purposes

Abstract: It is demonstrated that a comprehensive analysis of rainfall data to give useful agronomic results should use the daily measurements. Twomethods of analysis are described and are illustrated using daily rainfall records For 56 years from Maradankadawala in'the Anuradhapura district.

Cited by 8 publications

References 3 publications

Forecasting the Occurrence of Rainfall in Selected Weather Stations in the Wet and Dry Zones of Sri Lanka

Forecasting the Occurrence of Rainfall in Selected Weather Stations in the Wet and Dry Zones of Sri Lanka

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

The Nature of Rainfall at a Typical Semi-Arid Tropical Ecotope in Southern Africa and Options for Sustainable Crop Production

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