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Determinants of Climate Smart Agriculture Technology Practices in Ghana: Application of Multinomial Logistic Regression

Received: 19 November 2023     Accepted: 9 December 2023     Published: 22 December 2023
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Abstract

The study primarily examined the determinants of Climate Smart Agriculture technology practices on maize production. Data on socio-demographic and farming characteristics were obtained from the Climate Change, Agriculture and Food Security Partnership for Up Scaling the project’s targeted communities (Bompari, Dazuuri and Toto) in the Lawra municipality of the Upper West Region of Ghana. A total of 300 peasant farmers completed the questionnaire. Results from the model building confirmed models 1 and 2 to have strong explanatory power. Notwithstanding that, further evaluation with the adoption of Likelihood Ratio and log-likelihood favoured model 1 Furthermore, the post estimation results (Average Marginal Effects) from model 1 revealed that farming experience and household head status have no significant impact on predicting Climate Smart Agriculture technology practices. The results also confirmed that farmers who have practiced Climate Smart Agriculture technology for 6 to 10 years were found to be accompanied by a low probability (15.47%) of using improved variety/treated seeds as compared to those farmers who have practiced the technology for a period of 1–5 years. Also, tied ridges as Climate Smart Agriculture technology practiced by farmers resulted in a high probability of 11.44% for high yields relative to low yields. We recommend the need for further study to investigate the underlying reasons, if any, based on the non-significant relationship established at the 5% level between the determinants of mineral chemical fertiliser and monoculture respectively.

Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 6)
DOI 10.11648/j.ajtas.20231206.15
Page(s) 187-194
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), 2023. Published by Science Publishing Group

Keywords

Climate Change, Climate Smart Agriculture, Multinomial Logistic Regression, Predictions

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Cite This Article
  • APA Style

    Hashim, I., Alhassan, A., Puurbalanta, R., Akurugu, E., Iddrisu, Y., et al. (2023). Determinants of Climate Smart Agriculture Technology Practices in Ghana: Application of Multinomial Logistic Regression. American Journal of Theoretical and Applied Statistics, 12(6), 187-194. https://doi.org/10.11648/j.ajtas.20231206.15

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    ACS Style

    Hashim, I.; Alhassan, A.; Puurbalanta, R.; Akurugu, E.; Iddrisu, Y., et al. Determinants of Climate Smart Agriculture Technology Practices in Ghana: Application of Multinomial Logistic Regression. Am. J. Theor. Appl. Stat. 2023, 12(6), 187-194. doi: 10.11648/j.ajtas.20231206.15

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    AMA Style

    Hashim I, Alhassan A, Puurbalanta R, Akurugu E, Iddrisu Y, et al. Determinants of Climate Smart Agriculture Technology Practices in Ghana: Application of Multinomial Logistic Regression. Am J Theor Appl Stat. 2023;12(6):187-194. doi: 10.11648/j.ajtas.20231206.15

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  • @article{10.11648/j.ajtas.20231206.15,
      author = {Ibrahim Hashim and Abukari Alhassan and Richard Puurbalanta and Edward Akurugu and Yahaya Iddrisu and Salifu Hussein},
      title = {Determinants of Climate Smart Agriculture Technology Practices in Ghana: Application of Multinomial Logistic Regression},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {12},
      number = {6},
      pages = {187-194},
      doi = {10.11648/j.ajtas.20231206.15},
      url = {https://doi.org/10.11648/j.ajtas.20231206.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231206.15},
      abstract = {The study primarily examined the determinants of Climate Smart Agriculture technology practices on maize production. Data on socio-demographic and farming characteristics were obtained from the Climate Change, Agriculture and Food Security Partnership for Up Scaling the project’s targeted communities (Bompari, Dazuuri and Toto) in the Lawra municipality of the Upper West Region of Ghana. A total of 300 peasant farmers completed the questionnaire. Results from the model building confirmed models 1 and 2 to have strong explanatory power. Notwithstanding that, further evaluation with the adoption of Likelihood Ratio and log-likelihood favoured model 1 Furthermore, the post estimation results (Average Marginal Effects) from model 1 revealed that farming experience and household head status have no significant impact on predicting Climate Smart Agriculture technology practices. The results also confirmed that farmers who have practiced Climate Smart Agriculture technology for 6 to 10 years were found to be accompanied by a low probability (15.47%) of using improved variety/treated seeds as compared to those farmers who have practiced the technology for a period of 1–5 years. Also, tied ridges as Climate Smart Agriculture technology practiced by farmers resulted in a high probability of 11.44% for high yields relative to low yields. We recommend the need for further study to investigate the underlying reasons, if any, based on the non-significant relationship established at the 5% level between the determinants of mineral chemical fertiliser and monoculture respectively.
    },
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Determinants of Climate Smart Agriculture Technology Practices in Ghana: Application of Multinomial Logistic Regression
    AU  - Ibrahim Hashim
    AU  - Abukari Alhassan
    AU  - Richard Puurbalanta
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    AU  - Yahaya Iddrisu
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    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
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    EP  - 194
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20231206.15
    AB  - The study primarily examined the determinants of Climate Smart Agriculture technology practices on maize production. Data on socio-demographic and farming characteristics were obtained from the Climate Change, Agriculture and Food Security Partnership for Up Scaling the project’s targeted communities (Bompari, Dazuuri and Toto) in the Lawra municipality of the Upper West Region of Ghana. A total of 300 peasant farmers completed the questionnaire. Results from the model building confirmed models 1 and 2 to have strong explanatory power. Notwithstanding that, further evaluation with the adoption of Likelihood Ratio and log-likelihood favoured model 1 Furthermore, the post estimation results (Average Marginal Effects) from model 1 revealed that farming experience and household head status have no significant impact on predicting Climate Smart Agriculture technology practices. The results also confirmed that farmers who have practiced Climate Smart Agriculture technology for 6 to 10 years were found to be accompanied by a low probability (15.47%) of using improved variety/treated seeds as compared to those farmers who have practiced the technology for a period of 1–5 years. Also, tied ridges as Climate Smart Agriculture technology practiced by farmers resulted in a high probability of 11.44% for high yields relative to low yields. We recommend the need for further study to investigate the underlying reasons, if any, based on the non-significant relationship established at the 5% level between the determinants of mineral chemical fertiliser and monoculture respectively.
    
    VL  - 12
    IS  - 6
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Author Information
  • CSIR-Savanna Agricultural Research Institute (CSIR-SARI), Wa, Ghana

  • Department of Statistics, University for Development Studies, Tamale, Ghana

  • Department of Statistics, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana

  • Department of Statistics, University for Development Studies, Tamale, Ghana

  • CSIR-Savanna Agricultural Research Institute (CSIR-SARI), Wa, Ghana

  • Department of Statistical Sciences, Tamale Technical University, Tamale, Ghana

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