Predicting Rainfall Variability and Cereal Crop Yields Using Climate Oscillation Indices and Machine Learning in Northwestern Nigeria
DOI:
https://doi.org/10.47514/kjg.2026.08.01.033Keywords:
Artificial Neural Network, Random Forest Model, Large-Scale Climatic Oscillation, Rainfall, Crop yield, PredictionAbstract
Northwestern Nigeria, a semi-arid Sahelian environment, is highly vulnerable to rainfall variability due to its strong dependence on rain-fed agriculture. This study evaluated the predictive capability of Artificial Neural Network (ANN) and Random Forest (RF) machine learning models in forecasting annual rainfall variability and aggregate cereal crop yields using some large-scale climate oscillation indices, specifically the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and the Atlantic Multi-decadal Oscillation (AMO). Historical data (2000-2024) were sourced from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resource (POWER) database for rainfall, the National Agricultural Extension and Research Liaison Services (NAERLS) for crop yields, and the National Oceanic and Atmospheric Administration (NOAA) for climate indices. Data preprocessing included temporal aggregation, normalisation, and quality control procedures before model development. The models were trained and validated using a chronological 80:20 data split, while predictive performance was evaluated using the coefficient of determination (R2), Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results showed that the ANN model achieved slightly superior rainfall prediction performance (R² = 0.811; MAE = 101.82 mm), while the RF model produced higher predictive accuracy for aggregate cereal crop yields (R² = 0.893; MAE = 0.04375 T/Ha). The findings demonstrate the effectiveness of machine learning approaches in modelling complex climate-crop interactions across northwestern Nigeria. However, limitations associated with the relatively short temporal dataset and the exclusion of agronomic variables are acknowledged. The study highlights the potential application of machine learning models in climate-informed agricultural forecasting, early warning systems, and climate adaptation planning within vulnerable semi-arid regions.
References
Abdullahi, S. A., & Marafa, A. A. A. (2023). Impact of Climate Change on Agricultural Production in Nigeria. Jigawa Journal of Social and Management Sciences, 1(2), 1–13. https://doi.org/10.1201/9781003245285-9
Adedibu, P. A., Opeyemi, A. A., Lawrence, A. J., Paul, J. I., & Oguntoye, E. (2022). Savanna Biomes in Nigeria: Indicator Species and Plant Adaptation Strategies. Department of Plant Biology, University of Ilorin, Ilorin, Nigeria. https://doi.org/10.14293/S2199-1006.1.SOR-.PPDXHKL.v1
Adejuwon, J. O., & Ogundiminegha, Y. G. (2019). Impact of Climate Variability on Cassava Yield in the Humid Forest Agro-Ecological Zone of Nigeria. Journal of Applied Sciences and Environmental Management, 23(5), 903–908. https://doi.org/10.4314/jasem.v23i5.21
Adeniyi, M. O., Ogunsola, O. E., Nymphas, E. F., & Oladiran, E. O. (2009). Food Security Measures during Uncertain Climatic Conditions in Nigeria. African Journal of Food, Agriculture, Nutrition and Development (AJFAND), 9(2), 652–677.
Ati, O. F., Iguisi, E. O., & Mohammed, S. O. (2010). Effects of El Niño/Southern Oscillation (ENSO) on rainfall characteristics in Katsina, Nigeria. The African Journal of Agricultural Research, 5(23), 3273–3278. https://doi.org/10.5897/AJAR08.455
Barcikowska, M. J., Kapnick, S. B., & Feser, F. (2018). Impact of large-scale circulation changes in the North Atlantic sector on the current and future Mediterranean winter hydroclimate. Climate Dynamics, 50(5–6), 2039–2059. https://doi.org/10.1007/s00382-017-3735-5
Bello, A. A., & Mamman, M. B. (2018). Monthly rainfall prediction using artificial neural network: A case study of Kano, Nigeria. Environmental and Earth Sciences Research Journal, 5(2), 37–41. https://doi.org/doi.org/10.18280/eesrj.050201
Borgel, F., Frauen, C., Neumann, T., & Meier, M. (2020). The Atlantic Multidecadal Oscillation controls the impact of the North Atlantic Oscillation on North European climate. Environmental Research Letters, 15(10). https://doi.org/10.1088/1748-9326/aba925
Cao, J., Zhang, Z., Xie, J., Luo, Y., Han, J., Mitchell, P. J., & Tao, F. (2024). Tailoring wheat agronomic management to ENSO phases to manage climate variability in Australia at 5-minute resolution. Agricultural and Forest Meteorology, 356(110168). https://doi.org/doi.org/10.1016/j.agrformet.2024.110168
Chinazor, O. F. (2021). North Atlantic Oscillation and Rainfall Variability in Southeastern Nigeria: A Statistical Analysis of 30 Year Period. Journal of Atmospheric Science Research, 4(4), 42–49. https://doi.org/10.30564/jasr.v4i4.3843
Darman, L. P., Januhariadi, Yudha, M. P., & Aslan. (2024). Assessment of NASA POWER reanalysis products as data resources alternative for weather monitoring in West Sumbawa, Indonesia. E3S Web of Conferences, 485, 06006, 1–14. https://doi.org/doi.org/10.1051/e3sconf/202448506006
Ding, R., Nnamchi, H. C., Yu, J., Li, T., Sun, C., Li, J., Tseng, Y., Li, X., Xie, F., Feng, J., Ji, K., & Li, X. (2023). North Atlantic Oscillation controls multidecadal changes in the North Tropical Atlantic−Pacific connection. Nature, 14(862), 1–10. https://doi.org/10.1038/s41467-023-36564-3
Egbuawa, O. I., Anyanwu, J. C., Amaku, G. E., & Onuoha, I. C. (2017). Assessment of the Teleconnection Between El Niño Southern Oscillation (ENSO) and West African Rainfall. African Research Review, 11(4), 17–29. https://doi.org/10.4314/afrrev.v11i4.3
Ekpoh, I. J., & Nsa, E. (2011). Extreme Climatic Variability in North-western Nigeria: An Analysis of Rainfall Trends and Patterns. Journal of Geography and Geology, 3(1), 51–62. https://doi.org/10.5539/jgg.v3n1p51
Gbode, I. E., Adeyeri, O. E., Menang, K. P., Intsiful, J. D. K., Ajayi, V. O., Omotosho, J. A., & Akinsanola, A. A. (2019). Observed changes in climate extremes in Nigeria. Meteorological Applications, 26(4), 642–654. https://doi.org/10.1002/met.1791
González-González, M. A., & Corrales-Suastegui, A. (2024). Monthly Precipitation Outlooks for Mexico Using El Niño Southern Oscillation Indices Approach. Atmosphere, 15(981), 1–14. https://doi.org/doi.org/10.3390/atmos15080981
Hashidu, U. S., & Badaru, S. I. (2021). Relationship between El-Niño southern oscillation and rainfall in Sudano-Sahelian Region of Northern Nigeria. Journal of Agricultural Economics, Environment and Social Science (JAEESS), 7(2), 211–216. http://www.jaeess.com.ng
Henchiri, M., Igbawua, T., Javed, T., Bai, Y., Zhang, S., Essifi, B., Ujoh, F., & Zhang, J. (2021). Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño – Southern. Remote Sensing, 13(4730), 1–32. https://doi.org/doi.org/10.3390/rs13234730
Igbawua, T., Ujoh, F., Mkighirga, K. S., & Adagba, G. (2024). Assessment of relationship between sea surface temperature (SST) changes and precipitation types in Nigeria from 2000 to 2022. Results in Earth Sciences, 2(100031), 1–15. https://doi.org/10.1016/j.rines.2024.100031
Kagabo, J., Kattel, G. R., Kazora, J., Shangwe, C. N., & Habiyakare, F. (2024). Application of Machine Learning Algorithms in Predicting Extreme Rainfall Events in Rwanda. Atmosphere, 15(6), 1–22. https://doi.org/10.3390/atmos15060691
Kumar, D. N., Reddy, M. J., & Maity, R. (2007). Regional Rainfall Forecasting using Large-Scale Climate Teleconnections and Artificial Intelligence Techniques. Journal of Intelligent Systems, 16(4), 307–322.
Lukwasa, Z. A., Zeleke, T. T., Beketie, K. T., & Ayal, D. Y. (2022). Spatio-temporal rainfall variability and its linkage with large scale climate oscillations over the Republic of South Sudan. Climate Services, 28(100322), 1–11. https://doi.org/10.1016/j.cliser.2022.100322
Martin, E. R., & Thorncroft, C. D. (2014). The Impact of the AMO on the West African monsoon annual cycle. Quarterly Journal of the Royal Meteorological Society, 140(678), 31–46. https://doi.org/10.1002/qj.210 7
Masao, K. (2024). Oceanic Oscillations and their Influence on Regional Climate Variability in Japan. American Journal of Climatic Studies (AJCS), 4(1), 32–42. https://doi.org/doi.org/10.47672/ajcs.2037
Meenal, R., Michael, P. A., Pamela, D., & Rajasekaran, E. (2021). Weather prediction using random forest machine learning model. Indonesian Journal of Electrical Engineering and Computer Science, 22(2), 1208–1215. https://doi.org/10.11591/ijeecs.v22.i2.pp1208-1215
Nassah, H., Daghor, L., Chatoui, H., Tounsi, A., Khoulaid, F., Fakir, Y., Erraki, S., & Said, K. (2022). Climate Change Impact on Agricultural Production in the Sahel Region. In H. Chatoui, H. Moummou, M. Tilaoui, N. Saadaoui, & A. Brhich (Eds.), Nutrition and Human Health (pp. 3–11). Springer, Cham. https://doi.org/_10.1007/978-3-030-93971-7_1
National Bureau of Statistics (NBS). (2010). Annual Abstract of Statistics, 2010, Federal Republic of Nigeria. www.nigerianstat.gov.ng
NBS. (2020). 2020 Demographic Statistics Bulletin. In National Bureau of Statistics (NBS) (May). https://nigerianstat.gov.ng/download/1241121
Nicholson, S. E. (2013). The West African Sahel: A Review of Recent Studies on the Rainfall Regime and Its Interannual Variability. International Scholarly Research Notices (ISRN) Meteorology, 2013, 1–32. https://doi.org/10.1155/2013/453521
Nobre, G. G., Hunink, J. E., Baruth, B., Aerts, J. C. J. H., & Ward, P. J. (2019). Translating large-scale climate variability into crop production forecast in Europe. Scientific Reports, 9(1277), 1–13. https://doi.org/10.1038/s41598-018-38091-4
Nye, J. A., Baker, M. R., Bell, R., Kenny, A., Kilbourne, K. H., Friedland, K. D., Martino, E., Stachura, M. M., Van Houtan, K. S., & Wood, R. (2014). Ecosystem effects of the Atlantic Multidecadal Oscillation. Journal of Marine Systems, 133, 103–116. https://doi.org/10.1016/j.jmarsys.2013.02.006
Ogundele, O. T., Akpulu, H. I., Ngwu, S., & Odo, K. O. (2024). Analysis of Rice Farmers’ Awareness and Adaptation to Climate Variability in Kano State, Nigeria. FUDMA Journal of Agriculture and Agricultural Technology, 10(2), 62–70. https://doi.org/10.33003/jaat.2024.1002.10
Oguntunde, P. G., Lischeid, G., Abiodun, B. J., & Dietrich, O. (2014). Analysis of spatial and temporal patterns in onset, cessation and length of growing season in Nigeria. Agricultural and Forest Meteorology, 194(15), 77–87. https://doi.org/10.1016/j.agrformet.2014.03.017
Okumura, Y. M. (2019). ENSO Diversity from an Atmospheric Perspective. Current Climate Change Reports, 5, 245–257. https://doi.org/10.1007/s40641-019-00138-7
Olalere, G. E., Bulama, L., & Umar, A. A. (2021). Rainfall Anomalies Pattern in Northwestern Nigeria (NWN). Journal of Geography, Environment and Earth Science International, 25(4), 43–52. https://doi.org/10.9734/jgeesi/2021/v25i430282
Olayide, O. E., Onyeneke, R. U., Tasie, O., Ayuba, G., Shuaibu, A., Illo, F. G., Odunola, O., Oyekunle, O., & Jacob, Y. (2023). Kebbi State Climate Smart Agriculture Profile.
Prince, A. I., Nzechie, O., Obiorah, J., Ehi, O. E., & Idakwoji, A. A. (2023). Analysing the Critical Impact of Climate Change on Agriculture and Food Security in Nigeria. International Journal of Agriculture and Earth Science (IJAES), 9(4), 1–27. https://doi.org/10.4018/979-8-3693-3272-6.ch017
Raj, E. E., Kumar, R. R., & Ramesh, K. V. (2020). El niño–southern oscillation (ENSO) impact on tea production and rainfall in south India. Journal of Applied Meteorology and Climatology, 59(4), 651–664. https://doi.org/10.1175/JAMC-D-19-0065.1
Rilwanu, T. Y., & Adamu, G. K. (2022). Water Management Strategy for Climate-Smart Agriculture in Semi-Arid Northern Nigeria. In Y. Y. Obadaki, J. O. Folorunsho, M. A. Gada, R. D. Abu, & T. Y. Rilwanu (Eds.), Integrated Environmental Management Issues: A Festschrift in Honour of Professor Edwin Osawe Iguisi, Professor of Geomorphology & Hydrology (March, pp. 66–83). Ahmadu Bello University Press.
Rodrigues, G. C., & Braga, R. P. (2021). Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate. Agronomy, 11(1207), 1–17. https://doi.org/doi.org/10.3390/agronomy11061207
Salau, O. R., Fasuba, A., Aduloju, K. A., Adesakin, G. E., & Fatigun, A. T. (2016). Effects of changes in ENSO on seasonal mean temperature and rainfall in Nigeria. Climate, 4(1), 1–12. https://doi.org/10.3390/cli4010005
Schillerberg, T. A., Tian, D., & Miao, R. (2019). Spatiotemporal patterns of maize and winter wheat yields in the United States: predictability and impact from climate oscillations. Field Crops Research, 238, 17–28. https://doi.org/doi.org/10.1016/j.fcr.2019.04.002
Shehu, A. U., Yelwa, S. A., Sawa, B. A., & Adegbehin, A. B. (2016). The Influence of El-Niño Southern Oscillation (ENSO) Phenomenon on Rainfall Variation in Kaduna Metropolis, Nigeria. Journal of Geography, Environment and Earth Science International, 8(1), 1–9. https://doi.org/10.9734/JGEESI/2016/27287
Shi, P., Yang, T., Xu, C., Yong, B., Shao, Q., Li, Z., Wang, X., Zhou, X., & Li, S. (2017). How do the multiple large-scale climate oscillations trigger extreme precipitation? Global and Planetary Change, 157, 48–58. https://doi.org/10.1016/j.gloplacha.2017.08.014
Simon, S. M., Glaum, P., & Valdovinos, F. S. (2023). Interpreting random forest analysis of ecological models to move from prediction to explanation. Scientific Reports, 13(3881), 1–12. https://doi.org/10.1038/s41598-023-30313-8
Singh, S., Abebe, A., Srivastava, P., & Chaubey, I. (2021). Effect of ENSO modulation by decadal and multi-decadal climatic oscillations on contiguous United States streamflows. Journal of Hydrology: Regional Studies, 36(100876). https://doi.org/10.1016/j.ejrh.2021.100876
Ta, S., Kouadio, K. Y., Ali, K. E., Toualy, E., Aman, A., & Yoroba, F. (2016). West Africa Extreme Rainfall Events and Large-Scale Ocean Surface and Atmospheric Conditions in the Tropical Atlantic. Advances in Meteorology. https://doi.org/10.1155/2016/1940456
Takele, R., Tesfaye, K., & Traore, P. C. S. (2020). Seasonal Climate Predictability in Ethiopia: Review of best predictor sets for sub-seasonal to seasonal forecasting. (301). https://www.ccafs.cgiar.org
Tayyeh, H. K., & Mohammed, R. (2023). Analysis of NASA POWER reanalysis products to predict temperature and precipitation in Euphrates River basin. Journal of Hydrology, 619(129327). https://doi.org/doi.org/10.1016/j.jhydrol.2023.129327
Tullu, G. M. (2024). Spatiotemporal Rainfall Analysis of El Niño, Neutral and La Niña Years Across Belg and Kiremt Seasons Over Oromia Region, Ethiopia. Journal of Environmental Science and Agricultural Research, 2(3), 1–9. https://doi.org/doi.org/10.61440/JESAR.2024.v2.15
Uddin, M. J., Li, Y., Tamim, M. Y. B., Miah. M., & Ahmed, S. M. S. (2022). Extreme Rainfall Indices Prediction with Atmospheric Parameters and Ocean – Atmospheric Teleconnections Using a Random Forest Model. Journal of Applied Meteorology and Climatology, 61, 651–667. https://doi.org/10.1175/JAMC-D-21-0170.1
Umar, A. T., & Kehinde, M. O. (2020). Relationship of El-Nino Southern/Oscillation to Rainfall Patterns in Nigeria. Open Journals of Environmental Research (OJER), 1(1), 1–20. https://openjournalsnigeria.org.ng/pub/ojer20200101.pdf
Yilmaz, I., & Kaynar, O. (2011). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications, 38(5), 5958–5966. https://doi.org/10.1016/j.eswa.2010.11.027
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Copyright (c) 2026 Isah Abdullahi Tanko, Tayo Iyanda Yahaya, Aishetu Abdulkadir, Joel Aghaegbunam Ezenwora, Ishiaku Ibrahim, Abdulrazak Tijjani Kura (Author)
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