Key Publications
Technological innovations
Hoffmann, M.P., et al. (2016). Assessing the Potential for Zone-Specific Management of Cereals in Low-Rainfall South-Eastern Australia: Combining On-Farm Results and Simulation Analysis
Journal of Agronomy and Crop Science 203, 14–28.
DOI:10.1111/jac.12159
Kassie, B.T., Van Ittersum, M.K., Hengsdijk, H., Asseng, S., Wolf, J. & Rötter, R.P. (2014). Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia
Field Crops Research 160, 41-53.
DOI:10.1016/j.fcr.2014.02.010
Climate risk, adaptation and mitigation
Bracho-Mujica, G., et al. (2024). Effects of Changes in Climatic Means and Variability on Future Wheat and Maize Yields and the Role of Adaptive Agro-Technologies in Reducing Negative Impacts.
Agricultural and Forest Meteorology Volume 346,2024,109887.
https://doi.org/10.1016/j.agrformet.2024.109887
Appiah, M., et al. (2023).
Projected impacts of sowing date and cultivar choice on the timing of heat and drought stress
in spring barley grown along a European transect.
Field Crops Research 291, 108768.
DOI: 10.1016/j.fcr.2022.108768
Asseng, S., et al. (2015). Rising temperatures reduce global wheat production
Nature Climate Change 5, 143-147.
DOI:10.1038/nclimate2470
Hoffmann, M.P., et al. (2018). Exploring adaptations of groundnut cropping to prevailing climate variability and extremes in Limpopo Province, South Africa
Field Crops Research 219, 1-13.
DOI: 10.1016/j.fcr.2018.01.019
Rötter, R.P., et al. (2018). Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes - A review
Field Crops Research 221, 142–156.
DOI: 10.1016/j.fcr.2018.02.023
Kahiluoto, H., et al. (2014). Cultivating resilience by empirically revealing response diversity
Global Environmental Change 25, 186-193.
DOI:10.1016/j.gloenvcha.2014.02.002
Rötter, R.P., et al. (2015). Use of crop simulation modelling to aid ideotype design of future cereal cultivars
Journal of Experimental Botany 66, 3463-3476.
DOI:10.1093/jxb/erv098
Utilizing and managing genetic diversity
Rötter, R.P., Tao, F., Höhn, J.G., Palosuo, T. (2015) Use of crop simulation modelling to aid ideotype design of future cereal cultivars
Journal of Experimental Botany 66 (12), 3463-3476
DOI: 10.1093/jxb/erv098erv098
Tao, F., Rötter, R.P., Palosuo, T., et al. (2016) Designing future barley ideotypes using a crop model ensemble
European Journal of Agronomy 82(A), 144-162
DOI: 10.1016/j.eja.2016.10.012
Model development, evaluation and improvement
Liu, K. et al., (2023).Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates.
Nat Commun 14, 765
DOI: 10.1038/s41467-023-36129-4
de Wit, A., et al. (2015). WOFOST developer's response to article by Stella et al.
Environmental Modelling & Software 59, 44-58.
DOI:10.1016/j.envsoft.2015.07.005
Hoffmann, M.P., et al. (2014). Simulating potential growth and yield in oil palm with PALMSIM: Model description, evaluation and application
Agricultural Systems 131, 1-10.
DOI:10.1016/j.agsy.2014.07.006
Rötter, R.P., et al. (2011). Crop–climate models need an overhaul
Nature Climate Change 1, 175-177.
DOI:10.1038/nclimate1152
Rötter, R.P., et al. (2014). Robust uncertainty
Nature Climate Change 4, 251-252.
DOI:10.1038/nclimate2181
Wallach, D., et al. (2016). Estimating model prediction error: Should you treat predictions as fixed or random?
Environmental Modelling & Software 84, 529-539.
DOI:10.1016/j.envsoft.2016.07.010
Integrated analysis of agricultural systems and land use
Ewert, F., et al. (2015). Crop modelling for integrated assessment of risk to food production from climate change
Environmental Modelling & Software 72, 287-303.
DOI:10.1016/j.envsoft.2014.12.003
Liu, X., et al. (2016). Dynamic economic modelling of crop rotations with farm management practices under future pest pressure
Agricultural Systems 144, 65-76.
DOI:10.1016/j.agsy.2015.12.003