Examining variations in sensitivity of cereal crop yield to climate change variables across the three regions in Northern Ghana using Multilevel and Bayesian Multilevel Modeling
Keywords:
Bayesian, Multilevel, Statistics, Climate Change, Northern Ghana
Abstract
The study applied both the multilevel and the Bayesian multilevel model approaches to investigate variations in the effects of climate variables on cereal crop yield in Northern Ghana with respect to the region of cultivation and year (time), and to compare the performance of the two models. Thirty-one years of data points on some climate variables and the annual yield of some selected cereal crops from the Meteorological Agency and the Ministry of Food and Agriculture of Ghana, respectively, were used. Results indicated significant variations in climate change impact across the regions and years of cultivation. Further, the results showed that the impact of humidity and sunshine on crop yield varies from one region to another, with humidity having the most variation. The study further revealed that the Bayesian Multilevel model performed better in its model scores and predictive ability. It concluded that there are variations in the impact of climate change on cereal crop yield in the regions in Northern Ghana and recommends that climate characteristics of the regions should be taken into account in predicting future yield and adopting mitigation strategies.
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References
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3. Amikuzuno, J., & Donkoh, S. A. (2012). Climate Variability and Yields of Major Staple Food Crops in Northern Ghana (Vol. 20, Issue 2, pp. 349–360).
4. Anang, B. T., & Amikuzuno, J. (2015). Factors Influencing Pesticide Use in Smallholder Rice Production in Northern Ghana. Agriculture, Forestry and Fisheries, 4(2), 77. https://doi.org/10.11648/j.aff.20150402.19
5. Arndt, C., Asante, F., & Thurlow, J. (2015). Implications of climate change for Ghana’s economy. Sustainability (Switzerland), 7(6), 7214–7231. https://doi.org/10.3390/su7067214
6. Asante, S., Owusu, V., & Oppong, S. (2024). Marginal Impact of climate variability on crop yields in Ghana. Scientific African, 25(April), e02314. https://doi.org/10.1016/j.sciaf.2024.e02314
7. Bolin, J. H., Finch, W. H., & Stenger, R. (2019). Estimation of Random Coefficient Multilevel Models in the Context of Small Numbers of Level 2 Clusters. Educational and Psychological Measurement, 79(2), 217–248. https://doi.org/10.1177/0013164418773494
8. Chan, C. H., & Rauchfleisch, A. (2023). Bayesian Multilevel Modeling and Its Application in Comparative Journalism Studies. International Journal of Communication, 17(111), 3700–3721.
9. Cubillos, M., Wulff, J. N., & Wøhlk, S. (2021). A multilevel Bayesian framework for predicting municipal waste generation rates. Waste Management, 127, 90–100. https://doi.org/10.1016/j.wasman.2021.04.011
10. Darfour, B., & Rosentrater, K. A. (2016). Maize in Ghana: An overview of cultivation to processing. 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016, 1–16. https://doi.org/10.13031/aim.20162460492
11. Dash, S. K. (2023). A brief introduction to Multilevel Modelling. Analytic Vidhya. https://www.analyticsvidhya.com/blog/2022/01/a-brief-introduction-to-multilevel-modelling/
12. Dessie, Z. G., Zewotir, T., Mwambi, H., & North, D. (2020). Multivariate multilevel modeling of quality of life dynamics of HIV infected patients. 2, 1–14.
13. Diez-roux, A. V. (2000). Multilevel Analysis in Public Health Research. Annu. Rev. Public Health.
14. Dumenu, W. K., & Obeng, E. A. (2016). Climate change and rural communities in Ghana: Social vulnerability, impacts, adaptations and policy implications. Environmental Science and Policy, 55(February 2018), 208–217. https://doi.org/10.1016/j.envsci.2015.10.010
15. EPA. (2021). Ghana ’ s Third Biennial Update Report to United Nations Framework Convention on Climate Change.
16. Flor, M., Weiß, M., Selhorst, T., Müller-Graf, C., & Greiner, M. (2020). Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification. BMC Public Health, 20(1), 1–10. https://doi.org/10.1186/s12889-020-09177-4
17. Gage, D., Bangnikon, J., Abeka-Afari, H., Hanif, C., Addaquay, J., Antwi, V., & Hale, A. (2012). The Market for Maize, rice, Soy, and WarehouSing in Northern Ghana. ASABE, January.
18. Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall, Boca Raton, FL.
19. Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Andrew Gelman Jennifer Hill.
20. González-Romá, V., & Hernández, A. (2023). Conducting and Evaluating Multilevel Studies : Recommendations , Resources , and a Checklist. Sage, 26(4), 629–654. https://doi.org/10.1177/10944281211060712
21. Hox, J. J. (2019). Bayesian Multilevel Modeling. Wiley StatsRef: Statistics Reference Online, 1–7. https://doi.org/10.1002/9781118445112.stat08140
22. John B. Nezlek. (2010). Multilevel Modeling and Cross-Cultural Research. In Cross-Cultural Research Methods in Psychology.
23. Kaplan, D. (2014). Bayesian Statistics for the Social Sciences (Second Edi). Guilford Press.
24. Ladislas Nalborczyk, Cédric Batailler, Hélène Loevenbruck, Anne Vilain, & Paul-Christian Bürkner. (2022). An Introduction to Bayesian Multilevel Models Using brms: A Case Study of Gender Effects on Vowel Variability in Standard Indonesian.
25. Lin, X., Chowdhury, A., Wang, X., & Terejanu, G. (2019). Approximate computational approaches for Bayesian sensor placement in high dimensions. Information Fusion, 46, 193–205. https://doi.org/10.1016/j.inffus.2018.06.006
26. Maccarthy, D. S., Adam, M., Freduah, B. S., Fosu-Mensah, B. Y., Ampim, P. A. Y., Ly, M., Traore, P. S., & Adiku, S. G. K. (2021). Climate change impact and variability on cereal productivity among smallholder farmers under future production systems in west africa. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13095191
27. McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press, Boca Raton, FL. https://doi.org/doi.org/10.1201/9780429029608
28. MoFA. (2020). Cereal Production Figures from 1992.
29. MoFA-IFPRI. (2020). Ghana’s Maize Market. International Food Policy Research Institute (IFPRI) in Ghana, 1, 1–4.
30. Mohammadi, S., Rydgren, K., Bakkestuen, V., & Gillespie, M. A. K. (2023). Impacts of recent climate change on crop yield can depend on local conditions in climatically diverse regions of Norway. Scientific Reports, 0855, 1–12. https://doi.org/10.1038/s41598-023-30813-7
31. Molina-azorín, J. F., Pereira-moliner, J., López-gamero, M. D., Pertusa-ortega, E. M., & Tarí, J. J. (2019). Multilevel research: Foundations and opportunities in management. BRQ Bus. Res. Q. https://doi.org/10.1016/j.brq.2019.03.004
32. Nalborczyk, L., Batailler, C., Loevenbruck, H., Vilain, A., & Bürkner, P. C. (2019). An introduction to bayesian multilevel models using brms: A case study of gender effects on vowel variability in standard Indonesian. Journal of Speech, Language, and Hearing Research, 62(5), 1225–1242. https://doi.org/10.1044/2018_JSLHR-S-18-0006
33. Paul-Christian Bürkner. (2017). brms : An R Package for Bayesian Multilevel Models. Journal of Statistical Software, 80(1). https://doi.org/10.18637/jss.v080.i01
34. Roback, P., & Legler, J. (2021). Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R (First). CRC Press.
35. Ropero, R. F., Rumí, R., & Aguilera, P. A. (2019). Bayesian networks for evaluating climate change influence in olive crops in Andalusia, Spain. Natural Resource Modeling, 32(1), 1–18. https://doi.org/10.1111/nrm.12169
36. Smid, S. C., McNeish, D., Miočević, M., & van de Schoot, R. (2020). Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts: A Systematic Review. Structural Equation Modeling, 27(1), 131–161. https://doi.org/10.1080/10705511.2019.1577140
37. Smith, T., & Shively, G. (2019). Multilevel analysis of individual , household , and community factors influencing child growth in Nepal. BMC Pediatrics, 1–14.
38. UNFCCC. (2011). Climate change science - the status of climate change science today. United Nations Framework Convention on Climate Change, February 2011, 1–7. https://unfccc.int/files/press/backgrounders/application/pdf/press_factsh_science.pdf
39. USDA/GAIN. (2023). Ghana Climate change report. http://africa.cimafoundation.org/documents/869
40. USDA/IPAD. (2024). USDA/IPAD Country Summary. https://ipad.fas.usda.gov/countrysummary/default.aspx?id=GH
41. Vehtari, A., & Gelman, A. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC ∗. September, 1–28.
42. Viswanathan, M., Scheidegger, A., Streck, T., Gayler, S., & Weber, T. K. D. (2022). Bayesian multi-level calibration of a process-based maize phenology model. Ecological Modelling, 474(May), 110154. https://doi.org/10.1016/j.ecolmodel.2022.110154
43. Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 3571–3594.
44. Yamana, H. (2021). Introduction to Multilevel Analysis. Annals of Clinical Epidemiology, 3(1), 5–9.
45. Yaro, J. A. (2013). The perception of and adaptation to climate variability/change in Ghana by small-scale and commercial farmers. Regional Environmental Change, 13(6), 1259–1272. https://doi.org/10.1007/s10113-013-0443-5
Published
2025-07-10
How to Cite
Nkegbe, W. K., Faisal, A., & Alhassan, A. (2025). Examining variations in sensitivity of cereal crop yield to climate change variables across the three regions in Northern Ghana using Multilevel and Bayesian Multilevel Modeling. European Scientific Journal, ESJ, 43, 34. Retrieved from https://eujournal.org/index.php/esj/article/view/19745
Section
ESI Preprints
Copyright (c) 2025 William Kofi Nkegbe, Alhassan Faisal, Abukari Alhassan
This work is licensed under a Creative Commons Attribution 4.0 International License.
