- Din Pajouh, Y., & Allahvardipour, P. (2025). Monitoring and forecasting of evapotranspiration changes in the Moghan Plain according to the sixth IPCC report. Environment and Water Engineering, 11 (1), 47-56. (In Persian).
- Karimi, S. R., Nasrolahi, A., & Iranshahi, M. (2024). Investigating the effects of climate change on reference evapotranspiration based on the SSP scenarios. Iranian Journal of Soil and Water Research, 54(11), 1759-1777. (In Persian).
- Shahin Rukhsar, P., Alizadeh, A., Ansari, H., & Ghorbani, M. (2020). Study of the group uncertainty of atmospheric general circulation models in meteorological data simulation (Case study of Rasht synoptic station). Irrigation and Drainage of Iran, 13 (6), 1897-1909. (In Persian).
- Moradi, R., & Naghizadeh, M. (2023). Growth and Yield Response of Wheat (Mihan variety) to Future Climate Change in Kerman and Ardebil. Journal Of Agroecology, 15(1), 1-16. (In Persian).
- lotfi, M., Kamali, G. A., Meshkatee, A. H., & Varshavian, V. (2021). Evaluation of Yield Changes and Length of Dryland Wheat Phenological Stages under RCP Scenario Using DSSAT and AquaCrop Models in Western Iran. Iranian Journal of Soil and Water Research, 52(10), 2665-2677. (In Persian).
- Aini Nargeseh, H., Rahimi Moghadam, S., Azizi, K., Qarnjik, A., & Amiri, R. (2024). Adaptation of rainfed winter wheat to climate change in arid and cold regions using optimal planting date and supplementary irrigation. Agricultural Sciences Research in Arid Regions, 6 (4), 277-293. (In Persian).
- Borzo, F., Ramezani Edelali, H., & Kaviani, A. (2023). The effect of climate change under climate change conditions on winter wheat yield. Iranian Irrigation and Drainage, 17 (5), 995-979. (In Persian).
- Shiyokhi Soghanloo, S., Mousavi Bayegi, M., Torabi, B., & Raeni Sarjaz, M. (2021). Evaluation of the impact of climate change on the yield of irrigated wheat cultivar Mehregan under drought stress conditions (case study: Varamin). Agricultural Meteorology, 9 (2), 15-28. (In Persian).
- Iqbal, N., Shahzad, M. U., Sherif, E. S. M., Tariq, M. U., Rashid, J., Le, T. V., & Ghani, A. (2024). Analysis of wheat-yield prediction using machine learning models under climate change scenarios. Sustainability, 16(16), 6976.
- Mirshekari, S., Yaghoubi, F., & Hashemi, SA. (2025). Climate Change Impacts on Wheat Yields in Arid Regions of Iran: A Multimodel Approach for Adaptation Strategies. Int. J. Plant Prod.
- Eddamiri, S., Bouras, E.H., & Amazirh, A. (2024). Modeling the impact of climate change on wheat yield in Morocco based on stacked ensemble learning. Model. Earth Syst. Environ., 10, 6413-6433.
- O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461-3482.
- Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., & Wang, R. H. J. (2020).The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev., 13, 3571-3605.
- Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937-1958.
- Thrasher, B., Depsky, N., Taylor, M. A., & Nemani, R. (2022). NASA-NEX-GDDP-CMIP6 Dataset. NASA Earth Exchange.
- Semenov, M.A., & Stratonovitch, P. (2010). Use of Multi-Model Ensembles from Global Climate Models for Assessment of Climate Change Impacts. Climate Research, 41, 1-14.
- Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric., 177, 105709.
- Bento, V. A., Ribeiro, A. F., Russo, A., Gouveia, C. M., Cardoso, R. M., & Soares, P. M. (2021). The impact of climate change in wheat and barley yields in the Iberian Peninsula. Scientific reports, 11(1), 15484.
- Ahmadi, M., Etedali, H. R., Salem, A., Al-Mukhtar, M., & Elbeltagi, A. (2024). Simulation of wheat water footprint using AquaCrop model under the climate change, case study in Qazvin plain. Applied Water Science, 14(12), 264.
- Karatayev, M., Clarke, M., Salnikov, V., Bekseitova, R., & Nizamova, M. (2022). Monitoring climate change, drought conditions and wheat production in Eurasia: The case study of Kazakhstan. Heliyon, 8(1).
- Yanagi, M. (2024). Climate change impacts on wheat production: Reviewing challenges and adaptation strategies. Advances in Resources Research, 4(1), 89-107.
- Fashoto, S.G., Mbunge, E., Ogunleye, G., & den Burg, J.V. (2021). Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination. Precis. Agric., 6, 679-697.
- Pang, A., Chang, M.W., & Chen, Y. (2022). Evaluation of random forests (RF) for regional and local-scale wheat yield prediction in southeast Australia. Sensors, 22, 717.
- Sahbeni, G., Székely, B., Musyimi, P.K., Timár, G., & Sahajpal, R. (2023). Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal. AgriEngineering, 5, 1766-1788.
- Tariq, A., Yan, J., Gagnon, A.S., Riaz Khan, M., & Mumtaz, F. (2023). Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spat. Inf. Sci, 26, 302-320.
- Fei, S., Hassan, M.A., Xiao, Y., Su, X., Chen, Z., Cheng, Q., Duan, F., Chen, R., Ma, Y. (2023). UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precis. Agric., 24, 187-212. &
- Li, Q.C., Xu, S.W., Zhuang, J.Y., Liu, J.J., Yi, Z.H., & Zhang, Z.X. (2023). Ensemble learning prediction of soybean yields in China based on meteorological data. J. Integr. Agric., 22, 1909-1927.
- Blair, T. A. (1919). A statistical study of weather factors affecting the yield of winter wheat in Ohio. Monthly Weather Review, 47(12), 841-847.
- Du, X., Gao, Z., Sun, X., Bian, D., Ren, J., Yan, P., & Cui, Y. (2022). Increasing temperature during early spring increases winter wheat grain yield by advancing phenology and mitigating leaf senescence. Science of the Total Environment, 812, 152557.
- Arunrat, N., Sereenonchai, S., Chaowiwat, W., & Wang, C. (2022). Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand. Science of the Total Environment, 807, 150741.
- Crane-Droesch, A. (2018). Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environmental Research Letters, 13(11), 114003.
- Ciscar, J. C., Fisher-Vanden, K., & Lobell, D. B. (2018). Synthesis and review: An inter-method comparison of climate change impacts on agriculture. Environmental Research Letters, 13(7), 070401.
- Hu, T., Zhang, X., Bohrer, G., Liu, Y., Zhou, Y., Martin, J., ... & Zhao, K. (2023). Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield. Agricultural and Forest Meteorology, 336, 109458.
- Ray, D., Gerber, J., MacDonald, G., & West, P. (2015). Climate variation explains a third of global crop yield variability. Nat. Commun., 6, 5989.
- Rezaei, E. E., Siebert, S., Manderscheid, R., Müller, J., Mahrookashani, A., Ehrenpfordt, B., et al. (2018). Quantifying the response of wheat yields to heat stress: the role of the experimental setup. Field Crop Res., 217, 93-103.
- Chen, Y., Zhang, Z., & Tao, F. (2018). Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0°C. Earth Syst. Dynam., 9, 543-562.
- Zheng, B., Chenu, K., Dreccer, M., & Chapman, S. (2012). Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties. Glob. Chang. Biol., 18, 2899-2914.
- Ruan, G., Schmidhalter, U., Yuan, F., Cammarano, D., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2023). Exploring the transferability of wheat nitrogen status estimation with multisource data and Evolutionary Algorithm-Deep Learning (EA-DL) framework. Eur. J. Agron., 143, 126727.
- Giraldo, P., Benavente, E., Manzano-Agugliaro, F., Gimenez, E. (2019). Worldwide research trends on wheat and barley: A bibliometric comparative analysis. Agronomy, 9, 352.
- Ashfaq, M., Khan, I., Alzahrani, A., Tariq, M.U., Khan, H., & Ghani, A. (2024). Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion. IEEE Access, 12, 40947-40961.
- Sinwar, D., Dhaka, V.S., Sharma, M.K., & Rani, G. (2020). AI-based yield prediction and smart irrigation. In Internet of Things and Analytics for Agriculture; Springer: Singapore, 2, 155-180.
- Iqbal, N., Shahzad, M. U., Sherif, E. S. M., Tariq, M. U., Rashid, J., Le, T. V., & Ghani, A. (2024). Analysis of wheat-yield prediction using machine learning models under climate change scenarios. Sustainability, 16(16), 6976.
- Zhang, D., Liu, J., Li, D., Batchelor, W. D., Wu, D., Zhen, X., & Ju, H. (2023). Future climate change impacts on wheat grain yield and protein in the North China Region. Science of the Total Environment, 902, 166147.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
- Haykin, S. (2009). Neural networks and learning machines (3rd ed.). Pearson Education.
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
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