Applied and Computational Mathematics

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The Impact of Increasing Input Costs to the Farmers in Cotton Production in Tanzania

Received: 28 August 2015    Accepted: 11 September 2015    Published: 25 September 2015
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Abstract

The persistence of increasing input costs has effects to the farmers in the process of cotton production. These input costs can be in terms of pesticides, seeds, fertilizers, sprayers and the like, that have a real contribution towards cotton outputs per hectare. This paper intended to analyze effects of increasing input costs to the cotton farmers in Tanzania. The primary data were collected through questionnaires, which were distributed to 35 respondents, who are knowledgeable enough about cotton production in Shinyanga-Bariadi. Secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute and Shinyanga Regional Commissioners. A log-linear model was estimated by using Panel data method consisting of 24 observations from Mwanza-Misungwi and Shinyanga-Bariadi from 2003 to 2014. The Hausman test was applied to evaluate the fixed effect and random effect by using STATA 11 and EXCEL. The elasticity for every variable was computed to evaluate how a change in input price might increase or decrease the costs to the cotton farmers in Tanzania.

DOI 10.11648/j.acm.20150405.18
Published in Applied and Computational Mathematics (Volume 4, Issue 5, October 2015)
Page(s) 379-386
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), 2024. Published by Science Publishing Group

Keywords

Input Costs, Elasticity (Coefficient), Cobb-Douglas, Cotton Farmers

References
[1] Tanzania Cotton Board, "The Seond Cotton Sector Development Strategy (CSDS II): 2009- 2015: A Stakeholder Roadmap for Increased Production,Productivity and Profitability of Cotton," Dar es salaam, March, 2010.
[2] FAO, "Analysis of Incentives and Disincentives for Cotton in the United Republic of Tanzania," MAFAP, Tanzania, January 2013.
[3] A. Hina, A. Muhammad, and A. Huma, "Economic Analysis of Input Trend in Cotton Production Process in Pakistan," Asian Economic and Financial Review 2(4):553-561, pp. 2(4):553-561, 2012.
[4] M. H. Ghasemi, "Economic Modeling and Sensitivity Analysis of the Costs of Inputs for Alfalfa Production in Iran: A case study from Hamedan province," Ozean Journal of Applied Sciences 3(3),, 2010.
[5] N. Banaeian, and M. Zangeneh, "Estimating Productio Function of Walnut Production in Iran using Cobb-Douglas Method," Agricultura Tropica Et Subtropica, p. Vol. 44 (4), 2011.
[6] E. I. Mwangulumba, and M.K. Buluma, "Tanzania Cotton Production and Productivity," A paper presented at the SEACF Conference in Nyeri-Kenya, 26th to 29 August 26th to 29 August 2012.
[7] M. A. Hamjah, "Climatic Effects on Cotton and Tea Productions in Bangladesh and Measuring Efficiency using Multiple Regression and Stochastic Frontier Model Respectively," Mathematical Theory and Modeling www.iiste.org, Vol.4, No.3, 2014.
[8] M. Barungi, J. Mugisha, I. Nalukenge, "Analysis of input demand by smallholder cotton producers in eastern Uganda," Eastern Africa Journal of Rural Development, pp. Vol.21,No.1, 2005.
[9] K. Bakhsh, I. Hassan, and A. Maqbool, "Factors Affecting Cotton Yield: A Case Study of Sargodha(Pakistan).," Journal of Agriculture &Social Sciences, pp. Vol. 1, No. 4, 2005.
[10] M. Zahedi, H. E. Reza, and F. Mondani, "Energy Use Efficiency and Economical Analysis in Cotton Production System in an Arid Region: A Case Study for Isfahan Province, Iran," International Journal of Energy Economics and Policy, pp. Vol. 4, No. 1, pp.43-52, 2014.
[11] R. Moradi, P. R. Moghaddam, and H. Mansoori, "Energy Use and Economical Analysis of Seedy Watermelon Production for Different Irrigation system in Iran," Elsevier, Energy Report1 (2015), pp. 36-42, 2014.
[12] A. D. Patience, "Profitability and Economic Efficiency of Groundnuts Production in Benue State, Nigeria," African Journal of Food, Agriculture, Nutrition and Development, pp. Vol.13, No.4, 2013.
[13] A. Michael, "Estimation of Irrigation Water Demand in Rice Production," IISTE, pp. Vol.4, No.7, 2014.
Author Information
  • Department of Applied Mathematics, The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania

  • Department of Applied Mathematics, The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania

  • Department of Applied Mathematics, The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania

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    Busheni Dome, Dmitry Kuznetsov, Yaw Nkansah-Gyekye. (2015). The Impact of Increasing Input Costs to the Farmers in Cotton Production in Tanzania. Applied and Computational Mathematics, 4(5), 379-386. https://doi.org/10.11648/j.acm.20150405.18

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    ACS Style

    Busheni Dome; Dmitry Kuznetsov; Yaw Nkansah-Gyekye. The Impact of Increasing Input Costs to the Farmers in Cotton Production in Tanzania. Appl. Comput. Math. 2015, 4(5), 379-386. doi: 10.11648/j.acm.20150405.18

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    AMA Style

    Busheni Dome, Dmitry Kuznetsov, Yaw Nkansah-Gyekye. The Impact of Increasing Input Costs to the Farmers in Cotton Production in Tanzania. Appl Comput Math. 2015;4(5):379-386. doi: 10.11648/j.acm.20150405.18

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  • @article{10.11648/j.acm.20150405.18,
      author = {Busheni Dome and Dmitry Kuznetsov and Yaw Nkansah-Gyekye},
      title = {The Impact of Increasing Input Costs to the Farmers in Cotton Production in Tanzania},
      journal = {Applied and Computational Mathematics},
      volume = {4},
      number = {5},
      pages = {379-386},
      doi = {10.11648/j.acm.20150405.18},
      url = {https://doi.org/10.11648/j.acm.20150405.18},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.acm.20150405.18},
      abstract = {The persistence of increasing input costs has effects to the farmers in the process of cotton production. These input costs can be in terms of pesticides, seeds, fertilizers, sprayers and the like, that have a real contribution towards cotton outputs per hectare. This paper intended to analyze effects of increasing input costs to the cotton farmers in Tanzania. The primary data were collected through questionnaires, which were distributed to 35 respondents, who are knowledgeable enough about cotton production in Shinyanga-Bariadi. Secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute and Shinyanga Regional Commissioners. A log-linear model was estimated by using Panel data method consisting of 24 observations from Mwanza-Misungwi and Shinyanga-Bariadi from 2003 to 2014. The Hausman test was applied to evaluate the fixed effect and random effect by using STATA 11 and EXCEL. The elasticity for every variable was computed to evaluate how a change in input price might increase or decrease the costs to the cotton farmers in Tanzania.},
     year = {2015}
    }
    

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    AB  - The persistence of increasing input costs has effects to the farmers in the process of cotton production. These input costs can be in terms of pesticides, seeds, fertilizers, sprayers and the like, that have a real contribution towards cotton outputs per hectare. This paper intended to analyze effects of increasing input costs to the cotton farmers in Tanzania. The primary data were collected through questionnaires, which were distributed to 35 respondents, who are knowledgeable enough about cotton production in Shinyanga-Bariadi. Secondary data were collected from the Tanzania Cotton Board, Ukiliguru Agriculture Training Institute and Shinyanga Regional Commissioners. A log-linear model was estimated by using Panel data method consisting of 24 observations from Mwanza-Misungwi and Shinyanga-Bariadi from 2003 to 2014. The Hausman test was applied to evaluate the fixed effect and random effect by using STATA 11 and EXCEL. The elasticity for every variable was computed to evaluate how a change in input price might increase or decrease the costs to the cotton farmers in Tanzania.
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