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Research Article |

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

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.

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

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.

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

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

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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