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Liczba wyników
2017 | z. 4 (46) | 835--841
Tytuł artykułu

Efficient Planning of Sorghum Production in South Africa - Application of The Box-Jenkin's Method

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Estimation and forecasting of crop production are crucial in supporting policy decisions regarding food security and development issues. The present study examines the current status of sorghum production in South Africa. Univariate time series modelling using ARIMA model was developed for forecasting sorghum production. Box and Jenkins linear time series model, which involves autoregression, moving average, and integration, also known as ARIMA (p, d, q) model was applied. The annual production series of sorghum from 1960 to 2014 exhibited a decreasing trend while prediction of sorghum production between 2017 and 2020 showed an increasing trend. The study has shown that the best-fitted model for sorghum production series is ARMA (1, 0, 4). The model revealed a good performance in terms of explaining variability and forecasting power. This study has also shown that sorghum could contribute to the household and national food security because of its drought-tolerant properties. (original abstract)
Rocznik
Numer
Strony
835--841
Opis fizyczny
Twórcy
  • University of Limpopo, South Africa
  • University of Limpopo, South Africa
Bibliografia
  • Awal, M. A., Siddique, M. A. B. (2011). Rice production in Bangladesh employing by ARIMA model. Bangl. J. Res., 36(1), 51-62.
  • Badmus, M. A., Ariyo, O. S. (2011). Forecasting cultivated area and production of Maize in Nigerian using ARIMA Model. Asian J. Agric. Sci., 3(3), 171-176.
  • Biswas, R., Bhattacharyya, B. (2013). ARIMA modelling to forecast area and production of rice in West Bengal. J. Crop Weed, 9(2), 26-31.
  • Box, G. E. P., Jenkin, G. M. (1976). Time Series of analysis: Forecasting and control. Jon Wiley & Sons.
  • DAFF (2010). Sorghum production guidelines. Obtainable from Resource Centre Directorate Agricultural Information Services Private Bag X144 Pretoria 0001.
  • DAFF (2015). Abstract of Agricultural Statistics. Retrieved August 20th 2016 from: www.daff.gov.za/pdf
  • Ehab, A., Frah, H. (2016). Sudan Production of Sorghum; Forecasting 2016-2030 Using Autoregressive Integrated Moving Average ARIMA Model. Am. J. Math. Stat., 6(4), 175-181.
  • Dlamini, N. R. (2007). Effect of sorghum type and processing on the antioxidant properties of sorghum (Sorghum bicolor L. Moench) based foods. PhD Thesis, Texas A&M University, College Station, TX, USA.
  • Groenewald, J. A. (2000). The Agricultural Marketing Act: A Post - Mortem. South Afr. J. Econ., 68(3), 161-176.
  • Gujarati, D. N., Porter, D. C. (2009). Basic Econometrics, 5th Edition. New York: McGraw-hill, USA.
  • Jambhulkar, N. N. (2013). Modelling of rice production in Punjab using ARIMA model. Int. J. Sci. Res., 2(8), 2-3.
  • Kok, P., Collinson, M. A. (2006). Migration and urbanization in South Africa. Statistics South Africa; Pretoria. Report 03-04-02.
  • LeBourvellec, C., Renard, C. M. G. C. (2012). Interactions between Polyphenols and Macromolecules: Quantification Methods and Mechanisms. Crit. Rev. Food Sci. Nutr., 52, 213-248.
  • Lüder, J., Brauer, A., Jurisch, R. (2012). Breakpoint detection within the time series: modelling approach upon paleoclimatic proxy data. Hist. Soc. Res., 37, 315-325.
  • Radebe, H. (2013). Biofuels offer sorghum new growth hopes. Bus. Day Live, Jan 4th.
  • Taylor, J. R. N. (2003). Overview: Importance of sorghum in Africa, in Afripro: Workshop on the Proteins of Sorghum and Millets: Enhancing Nutritional and Functional Properties for Africa, Pretoria, 2-4 April 2003. Retrieved Nov 2013 from: www.afripro.org.uk
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171525863

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