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

Ibrahim Hashim; Abukari Alhassan; Richard Puurbalanta; Edward Akurugu; Yahaya Iddrisu; Salifu Hussein

doi:doi:10.11648/j.ajtas.20231206.15

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

Ibrahim Hashim, Abukari Alhassan, Richard Puurbalanta^*, Edward Akurugu, Yahaya Iddrisu, Salifu Hussein

Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 6)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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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
AU  - Edward Akurugu
AU  - Yahaya Iddrisu
AU  - Salifu Hussein
Y1  - 2023/12/22
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231206.15
DO  - 10.11648/j.ajtas.20231206.15
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 187
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
ER  -

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Author Information

Ibrahim Hashim

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

Department of Statistics, University for Development Studies, Tamale, Ghana
Richard Puurbalanta

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

Contact Email
Edward Akurugu

Department of Statistics, University for Development Studies, Tamale, Ghana
Yahaya Iddrisu

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

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

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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}
}

Copy | Download

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
AU  - Edward Akurugu
AU  - Yahaya Iddrisu
AU  - Salifu Hussein
Y1  - 2023/12/22
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231206.15
DO  - 10.11648/j.ajtas.20231206.15
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 187
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
ER  -

Copy | Download