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Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models

Received: 26 January 2021     Accepted: 6 February 2021     Published: 10 February 2021
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Abstract

Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.

Published in International Journal of Data Science and Analysis (Volume 7, Issue 1)
DOI 10.11648/j.ijdsa.20210701.11
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Poultry Farming, Auto-Regression, Fractional Integration, Long-Memory, Augmented Dickey Fuller (ADF) Test, Random Mean Squared Error (RMSE)

References
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[3] Ahmad, H. A. (2012). Egg production forecasting: Determining efficient modeling approaches. Journal of Applied Poultry and Research, 20 (4); 463–473. doi: 10.3382/japr.2010-00266.
[4] Hanus, A., Hanusoval, E., Oravcova, M. & Hrncar, C. (2017). Factors affecting growth in Oravka chicken breed. Slovak J. Anim. Sci., 50 (3); 112–117.
[5] Yakubu, A., Oluremi, O. A. & Ibrahim, Z. N. (2018). Modelling egg production in Sasso dual-purpose birds using linear, quadratic, artificial neural network and classification regression tree methods in the tropics. Livestock Research for Rural Development 30 (10).
[6] Nosike, R. J., Okoro, V. M. O. & Ukwu, H. O. (2016). Statistical Modelling of Body Weight and Linear Body Measurements in Nigerian Indigenous Chicken. Journal of Agriculture and Veterinary Science, 7 (1); 27-30.
[7] Abiyu, T. (2019). Statistical Modelling of Indigenous Chicken with Body Weight and Linear Body Measurements in Bench Maji Zone, South Western Ethiopia. International Journal of Environmental Sciences and Natural Resources, 22 (2). doi: 10.19080/IJESNR.2019.22.556083.
[8] Luis, G. V., Mario, C. M., Daniel, R. & Jose, M. C. (2013). Using the distributed-delay model to predict egg production in laying hens. Rev Colomb Cienc Pecu, 2013 (26); 270-279.
[9] Shakeel, N. & Masood, A. K. (2014). Modeling and Forecasting of Beef, Mutton, Poultry Meat and Total Meat Production of Pakistan for Year 2020 by using Time Series ARIMA Models. European Scientific Journal, 3 (special issue).
[10] Raji, A. O., Alade, N. K. & Duwa, H. (2014). Estimation of Model Parameters of the Japanese Quail Growth Curve using Gompertz Model. Arch. Zootec. 63 (243); 429-435.
[11] Isife, J. K., Ukwani, C. & Sani, G. (2019). Design and Simulation of an Automated Poultry Feed Mixing Machine Using Process Controller. Global Scientific Journals, 7 (1); 537-602.
[12] Ahmad, H. A. (2019). Poultry Growth Modeling using Neural Networks and Simulated data. Journal of Applied Poultry Research, 18; 440-446. doi: 10.3382/japr.2008- 00064.
[13] Semara, L., Mouffok, C. & Belkasmi, F. (2019). Comparison of some Non-Linear Functions for Describing Broiler Growth Curves of Cobb500 Strain. Poultry Science Journal, 7 (1); 51-61. doi: 10.22069/psj.2019.15965.1386.
[14] Abdul, S. M., Bonsu, F. R. K., Abunyuwah, I. & Serekye, A. Y. (2020). Relative Economic Value Estimates of Guinea Fowls (Numida meleagris) Production Traits. World Journal of Advanced Research and Reviews, 7 (1); 273-281. doi: 10.30574/wjarr.2020.7.1.0265.
[15] Dzungwe, J. T., Gwaza, D. S. & Egahi, J. O. (2018). Statistical Modeling of Body Weight and Body Linear Measurements of the French Broiler Guinea Fowl in the Humid Tropics of Nigeria. Poultry, Fisheries and Wildlife Sciences, 6 (2); 1-4. doi: 10.4172/2375-446X. 1000197.
Cite This Article
  • APA Style

    Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. (2021). Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. International Journal of Data Science and Analysis, 7(1), 1-7. https://doi.org/10.11648/j.ijdsa.20210701.11

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    ACS Style

    Cecilia Mbithe Titus; Anthony Wanjoya; Thomas Mageto. Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. Int. J. Data Sci. Anal. 2021, 7(1), 1-7. doi: 10.11648/j.ijdsa.20210701.11

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    AMA Style

    Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. Int J Data Sci Anal. 2021;7(1):1-7. doi: 10.11648/j.ijdsa.20210701.11

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  • @article{10.11648/j.ijdsa.20210701.11,
      author = {Cecilia Mbithe Titus and Anthony Wanjoya and Thomas Mageto},
      title = {Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models},
      journal = {International Journal of Data Science and Analysis},
      volume = {7},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.ijdsa.20210701.11},
      url = {https://doi.org/10.11648/j.ijdsa.20210701.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210701.11},
      abstract = {Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models
    AU  - Cecilia Mbithe Titus
    AU  - Anthony Wanjoya
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    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    EP  - 7
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20210701.11
    AB  - Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.
    VL  - 7
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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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