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Assessing the impact of climate variability on maize yields in the different regions of Ghana—A machine learning perspective

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30. Gyamerah S. A., Ngare P., and Ikpe D., “Probabilistic forecasting of crop yields via quantile random for- est and Epanechnikov Kernel function,” Agric For Meteorol, vol. 280, p. 107808, Jan. 2020. https://doi. org/10.1016/j.agrformet.2019.107808 31. Crane-Droesch A., “Machine learning methods for crop yield prediction and climate change impact assessment in agriculture,” Environmental Research Letters, vol. 13, no. 11, p. 114003, Oct. 2018. https://doi.org/10.1088/1748-9326/aae159 32. Appiah-Badu N. K. A., Missah Y. M., Amekudzi L. K., Ussiph N., Frimpong T., and Ahene E., “Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana,” IEEE Access, vol. 10, pp. 5069–5082, 2022. https://doi.org/10.1109/ACCESS.2021.3139312 33. Cedric L. S. et al., “Crops yield prediction based on machine learning models: Case of West African countries,” Smart Agricultural Technology, vol. 2, p. 100049, Dec. 2022. https://doi.org/10.1016/j. atech.2022.100049 34. Lee J., Kim J., and Ko W., “Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method,” Applied Sciences, vol. 9, no. 6, p. 1231, Mar. 2019. https://doi.org/10.3390/app9061231 35. LI Q., XU S., ZHUANG J., LIU J., ZHOU Y., and ZHANG Z., “Ensemble Learning Prediction of Soybean Yields in China Based on Meteorological Data,” J Integr Agric, Feb. 2023. https://doi.org/10.1016/j.jia. 2023.02.011

Trang 23 · Đoạn 64Vị trí nguồn

36. Abdul-Razak M. and Kruse S., “The adaptive capacity of smallholder farmers to climate change in the Northern Region of Ghana,” Clim Risk Manag, vol. 17, pp. 104–122, 2017. https://doi.org/10.1016/j. crm.2017.06.001 37. Owusu-Sekyere J. D., Alhassan M., and Nyarko B. K., “ARPN Journal of Agricultural and Biological Sci- ence ASSESSMENT OF CLIMATE SHIFT AND CROP YIELDS IN THE CAPE COAST AREA IN THE CENTRAL REGION OF GHANA,” vol. 6, no. 2, 2011, [Online]. www.arpnjournals.com 38. Armah F. A., Odoi J. O., Yengoh G. T., Obiri S., Yawson D. O., and Afrifa E. K. A., “Food security and climate change in drought-sensitive savanna zones of Ghana,” Mitig Adapt Strateg Glob Chang, vol. 16, no. 3, pp. 291–306, Mar. 2011. https://doi.org/10.1007/s11027-010-9263-9 39. Tachie-Obeng E., Akponikpè P. B. I., and Adiku S., “Considering effective adaptation options to impacts of climate change for maize production in Ghana,” Environ Dev, vol. 5, pp. 131–145, Jan. 2013. https:// doi.org/10.1016/j.envdev.2012.11.008 40. Lu X., Zhou W., Ding X., Shi X., Luan B., and Li M., “Ensemble Learning Regression for Estimating Unconfined Compressive Strength of Cemented Paste Backfill,” IEEE Access, vol. 7, pp. 72125– 72133, 2019. https://doi.org/10.1109/ACCESS.2019.2918177 41. Feng L. et al., “Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning,” Remote Sens (Basel), vol. 12, no. 12, p. 2028, Jun. 2020. https://doi.org/10.3390/ rs12122028 42. Gyamerah S. A., Asare C., Mintah D., Appiah B., and Kayode F. A., “Exploring the optimal climate con-

Trang 23 · Đoạn 65Vị trí nguồn

ditions for a maximum maize production in Ghana: Implications for food security,” Smart Agricultural Technology, vol. 6, p. 100370, Dec. 2023. https://doi.org/10.1016/j.atech.2023.100370 43. Preety K., Prasad A. K., Varma A. K., and El-Askary H., “Accuracy Assessment, Comparative Perfor- mance, and Enhancement of Public Domain Digital Elevation Models (ASTER 30 m, SRTM 30 m, CAR- TOSAT 30 m, SRTM 90 m, MERIT 90 m, and TanDEM-X 90 m) Using DGPS,” Remote Sens (Basel), vol. 14, no. 6, p. 1334, Mar. 2022. https://doi.org/10.3390/rs14061334 44. A. C. Tan and D. Gilbert, “Ensemble machine learning on gene expression data for cancer classifica- tion,” 2003. 45. Ceamanos X., Waske B., Benediktsson J. A., Chanussot J., Fauvel M., and Sveinsson J. R., “A classi- fier ensemble based on fusion of support vector machines for classifying hyperspectral data,” Int J Image Data Fusion, vol. 1, no. 4, pp. 293–307, Dec. 2010. https://doi.org/10.1080/19479832.2010. 485935 46. Qi C. and Tang X., “A hybrid ensemble method for improved prediction of slope stability,” Int J Numer Anal Methods Geomech, vol. 42, no. 15, pp. 1823–1839, Oct. 2018. https://doi.org/10.1002/nag. 2834 47. Meharie M. G., Mengesha W. J., Gariy Z. A., and Mutuku R. N. N., “Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects,” Engineering, Con- struction and Architectural Management, vol. 29, no. 7, pp. 2836–2853, Aug. 2022. https://doi.org/10. 1108/ECAM-02-2020-0128

Trang 23 · Đoạn 66Vị trí nguồn

48. A. A. Neloy, H. M. S. Haque, and Md. M. Ul Islam, “Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring,” in Proceedings of the 2019 11th Interna- tional Conference on Machine Learning and Computing, New York, NY, USA: ACM, Feb. 2019, pp. 350–356. PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 23 / 24