TY  - JOUR
T1  - Phenology-Aware Machine Learning for Wheat Yield Prediction  under Climate Variability: Central Anatolia, Türkiye
TT  - Phenology-Aware Machine Learning for Wheat Yield Prediction  under Climate Variability: Central Anatolia, Türkiye
AU  - Güngüneş, Ramazan
AU  - Ateş, Volkan
AU  - Erol, Taşkın
AU  - Özek, Rojin
PY  - 2025
DA  - October
Y2  - 2025
DO  - 10.19159/tutad.1740059
JF  - Türkiye Tarımsal Araştırmalar Dergisi
JO  - TÜTAD
PB  - Siirt University
WT  - DergiPark
SN  - 2148-2306
SP  - 287
EP  - 295
VL  - 12
IS  - 3
LA  - en
AB  - This study aims to develop a phenology-aware machine learning framework for accurately predicting wheat yields in Türkiye’s Central Anatolia Region. The research integrates provincial wheat yield data from the Turkish Statistical Institute (TurkStat) (2004-2023) with fourteen agro-climatic and soil parameters retrieved from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) platform (2003-2023). To enhance model sensitivity, all variables were segmented into five key phenological stages of wheat growth, and for each stage, the minimum, maximum, and mean values were calculated. Three classical machine learning algorithms-Gradient Boosting (GB), Random Forest (RF), and Multilayer Perceptron (MLP)-were implemented using Python (Scikit-learn and TensorFlow libraries) under a “global training-local testing” strategy. The results show that GB consistently achieved the highest predictive accuracy across all provinces, with R2 values ranging from 0.96 to 0.99, mean absolute error (MAE) between 3.6 and 6.8 kg da-1, and root mean square error (RMSE) below 7.1 kg da-1. The RF model performed slightly lower (R2= 0.81-0.90) yet remained robust in most regions. In contrast, the global MLP model exhibited heterogeneous performance, particularly in Karaman Province, where non-climatic management factors dominate (R2= -1.25; MAE ≈ 26 kg da-1). When retrained with local data, the MLP model’s accuracy improved substantially, raising R2 to 0.79 and reducing MAE to approximately 10-15 kg da-1. These findings confirm that integrating phenological segmentation within ensemble learning approaches-particularly Gradient Boosting-substantially enhances wheat yield forecasting performance. The study highlights the importance of local calibration to capture irrigation and management effects and provides a robust methodological foundation for developing climate-resilient agricultural decision-support systems.
KW  - Phenology
KW  - yield predictions
KW  - machine learning algorithms
KW  - artificial intelligence
N2  - This study aims to develop a phenology-aware machine learning framework for accurately predicting wheat yields in Türkiye’s Central Anatolia Region. The research integrates provincial wheat yield data from the Turkish Statistical Institute (TurkStat) (2004-2023) with fourteen agro-climatic and soil parameters retrieved from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) platform (2003-2023). To enhance model sensitivity, all variables were segmented into five key phenological stages of wheat growth, and for each stage, the minimum, maximum, and mean values were calculated. Three classical machine learning algorithms-Gradient Boosting (GB), Random Forest (RF), and Multilayer Perceptron (MLP)-were implemented using Python (Scikit-learn and TensorFlow libraries) under a “global training-local testing” strategy. The results show that GB consistently achieved the highest predictive accuracy across all provinces, with R2 values ranging from 0.96 to 0.99, mean absolute error (MAE) between 3.6 and 6.8 kg da-1, and root mean square error (RMSE) below 7.1 kg da-1. The RF model performed slightly lower (R2= 0.81-0.90) yet remained robust in most regions. In contrast, the global MLP model exhibited heterogeneous performance, particularly in Karaman Province, where non-climatic management factors dominate (R2= -1.25; MAE ≈ 26 kg da-1). When retrained with local data, the MLP model’s accuracy improved substantially, raising R2 to 0.79 and reducing MAE to approximately 10-15 kg da-1. These findings confirm that integrating phenological segmentation within ensemble learning approaches-particularly Gradient Boosting-substantially enhances wheat yield forecasting performance. The study highlights the importance of local calibration to capture irrigation and management effects and provides a robust methodological foundation for developing climate-resilient agricultural decision-support systems.
CR  - Anonymous, 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK.
CR  - Anonymous, 2008. Agro-Meteorological Crop Yield Forecast Bulletin. No: 2008/1 Ankara, Türkiye, (https://arastirma.tarimorman.gov.tr/tarlabitkileri/Belgeler/bulten/B%C3%BClten%20No%202008-1%20(3 1%20Mart%202008)%20Agro-Meteorolojik%20%C 3%9Cr%C3%BCn%20Verim%20Tahmini%20B%C3%BClteni.pdf), (Accessed Date: 06.01.2025). (In Turkish).
CR  - Anonymous, 2025. Agricultural Statistics Database, (TÜİK), (https://biruni.tuik.gov.tr/medas/?kn=76), (Accessed Date: 06.01.2025). (In Turkish).
CR  - Breiman, L., 2001. Random forests. Machine Learning, 45(1): 5-32.
CR  - Eddamiri, S., Bassine, F.Z., Ongoma, V., Epule, T., Chehbouni, A., 2024. An automatic ensemble machine learning for wheat yield prediction in Africa. Multimedia Tools and Applications, 83(25): 66433-66459.
CR  - Hatfield, J.L., Prueger, J.H., 2015. Temperature extremes: effect on plant growth and development. Weather and Climate Extremes, 10(1): 4-10.
CR  - Jägermeyr, J., Müller, C., Ruane, A.C., Elliott, J., Balkovic, J., Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J.A., Fuchs, K., Guarin, J.R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A.K., Kelly, D., Khabarov, N., Lange, S., Lin, T.-S., Liu, W., Mialyk, O., Minoli, S., Moyer, E.J., Okada, M., Phillips, M., Porter, C., Rabin, S.S., Scheer, C., Schneider, J.M., Schyns, J.F., Skalsky, R., Smerald, A., Stella, T., Stephens, H., Webber, H., Zabel, F., Rosenzweig, C., 2021. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nature Food, 2(11): 873-885.
CR  - Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H., Yun, K., Butler, E.E., Timlin, D.J., Shim, K., Gerber, J.S., Reddy, V.R., Kim, S., 2016. Random forests for global and regional crop yield predictions. PLoS One, 11(6): e0156571.
CR  - Ji, Z., Yang, H., Yu, Q., 2021. Prediction of crop yield using phenological information derived from remote sensing time series. Agricultural and Forest Meteorology, 311(1): 108684.
CR  - Khaki, S., Wang, L., 2019. Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10(1): 621.
CR  - Kim, N., Lee, Y.W., 2016. Machine learning approaches to corn yield estimation using satellite images and climate data: a case of Iowa State. Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography, 34(4): 383-390.
CR  - Lesk, C., Rowhani, P., Ramankutty, N., 2016. Influence of extreme weather disasters on global crop production. Nature Communications, 529(1): 84-87.
CR  - Lobell, D.B., Asseng, S., 2017. Comparing estimates of climate change impacts from process-based and statistical crop models. Environmental Research Letters, 12(1): 015001.
CR  - Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J.W., Rötter, R.P., Boote, K.J., Ruane, A.C., Thorburn, P.J., Cammarano, D., Hatfield, J.L., Rosenzweig, C., Aggarwal, P.K., Angulo, C., Basso, B., Bertuzzi, P., Biernath, C., Brisson, N., Challinor, A.J., Doltra, J., Gayler, S., Goldberg, R., Grant, R.F., Heng, L., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, R.C., Kersebaum, K.C., Müller, C., Kumar, S.N., Nendel, C., O'leary, G., Olesen, J.E., Osborne, T.M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M.A., Shcherbak, I., Steduto, P., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., White, J.W., Wolf, J., 2015. Multimodel ensembles of wheat growth: many models are better than one. Global Change Biology, 21(2): 911-925.
CR  - Pei, J., Li, Y., Wang, C., Zhang, X., Liu, D., 2025. The role of phenology in crop yield prediction using machine learning time-window segmentation. Field Crops Research, 361(1): 110340.
CR  - Ray, D.K., Mueller, N.D., West, P.C., Foley, J.A., 2013. Yield trends are insufficient to double global crop production by 2050. PLoS One, 8(6): e66428.
CR  - Raza, A., Shahid, M.A., Zaman, M., Miao, Y., Huang, Y., Safdar, M., Maqbool, S., Muhammad, N.E., 2025. Improving wheat yield prediction with multi-source remote sensing data and machine learning in arid regions. Remote Sensing, 17(5): 774.
CR  - Rufaıoğlu, S.B., Tunc, M., 2025. Machine learning-based yield and quality predictıon models. IFEC 2O25 II. International Future Engineering Conference, April 28-29, Bakü Azerbaycan.
CR  - Sacks, W.J., Deryng, D., Foley, J.A., Ramankutty, N., 2010. Crop planting dates: an analysis of global patterns. Global Ecology and Biogeography, 19(5): 607-620.
CR  - Sajid, S.S., Shahhosseini, M., Huber, I., Hu, G., Archontoulis, S.V., 2022. County-scale crop yield prediction by integrating crop simulation with machine learning models. Frontiers in Plant Science, 13(1): 1000224.
CR  - Shahhosseini, M., Hu, G., Huber, I., Archontoulis, S.V., 2021. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Scientific Reports, 11(1): 1606.
CR  - Sishodia, R.P., Ray, R.L., Singh, S.K., 2020. Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19): 3136.
CR  - Tack, J., Barkley, A., Nalley, L.L., 2015. Effect of warming temperatures on US wheat yields. Proceedings of the National Academy of Sciences, 112(22): 6931-6936.
CR  - Tari, A.F., 2016. The effects of different deficit irrigation strategies on yield, quality, and water-use efficiencies of wheat under semi-arid conditions. Agricultural Water Management, 167(1): 1-10.
CR  - Tattaris, M., Reynolds, M.P., Chapman, S.C., 2016. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Frontiers in Plant Science, 7(1): 1131.
CR  - Thong-un, N., Wongsaroj, W., 2022. Productivity enhancement using low-cost smart wireless programmable logic controllers: a case study of an oyster mushroom farm. Computers and Electronics in Agriculture, 195(15): 106798.
CR  - Tittonell, P., Giller, K.E., 2013. When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Field Crops Research, 143(1): 76-90.
CR  - Van Klompenburg, T., Kassahun, A., Catal, C., 2020. Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177: 105709.
CR  - Vicente-Serrano, S.M., Beguería, S., López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7): 1696-1718.
CR  - You, J., Li, X., Low, M., Lobell, D., Ermon, S., 2017. Deep gaussian process for crop yield prediction based on remote sensing data. Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, San Francisco, California USA, pp. 4559-4565.
CR  - Zhang, C., Dong, J., Ge, Q., 2022. Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products. Scientific Data, 9(1): 407.
CR  - Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D.B., Huang, M., Yao, Y., Bassu, S., Ciais, P., Durand, J., Elliott, J., Ewert, F., Janssens, I.A., Li, T., Lin, E., Liu, Q., Martre, P., Müller, C., Peng, S., Peñuelas, J., Ruane, A.C., Wallach, D., Wang, T., Wu, D., Liu, Z., Zhu, Y., Zhu, Z., Asseng, S., 2017. Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Acedemy of Sciences, 114(35): 9326-9331.
UR  - https://doi.org/10.19159/tutad.1740059
L1  - https://dergipark.org.tr/en/download/article-file/5045173
ER  -