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Battisti, R., Sentelhas, P. C., & Boote, K. J. (2018). Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil. Int J Biometeorol, 62(5), 823–832.
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Durand, J. - L., Delusca, K., Boote, K., Lizaso, J., Manderscheid, R., Weigel, H. J., et al. (2018). How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield? Europ. J. Agron., 100, 67–75.
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Kirtman, B. P., Misra, V., Burgman, R. J., Infanti, J., & Obeysekera, J. (2017). Florida climate variability and prediction. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 511–532). Gainesville, FL: Florida Climate Institute.
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Maiorano, A., Martre, P., Asseng, S., Ewert, F., Müller, C., Rötter, R. P., et al. (2017). Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles. Field Crops Research, 202, 5–20.
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Rahman, M. H. ur, Ahmad, A., Wang, X., Wajid, A., Nasim, W., Hussain, M., et al. (2018). Multi-model projections of future climate and climate change impacts uncertainty assessment for cotton production in Pakistan. Agricultural and Forest Meteorology, 253-254, 94–113.
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Ruane, A. C., Hudson, N. I., Asseng, S., Camarrano, D., Ewert, F., Martre, P., et al. (2016). Multi-wheat-model ensemble responses to interannual climate variability. Environmental Modelling & Software, 81, 86–101.
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Tegegne G, Melesse AM, & Worqlul, A. W. (2020). Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes. Sci Total Environ, 704.
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Wallach, D., Nissanka, S. P., Karunaratne, A. S., Weerakoon, W. M. W., Thorburn, P. J., Boote, K. J., et al. (2017). Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice. European Journal of Agronomy, 88, 53–62.
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