Tropical Plant Production and Agricultural Systems Modelling
>
Our goal is to conduct research and research-oriented training to further the understanding of the functioning of major tropical plant production systems in a changing environment. Environmental changes comprise the big challenges agricultural systems are increasingly facing in the future in the different regions in the tropics and sub-tropics: water scarcity, soil nutrient depletion, soil loss, more severe adverse weather events, enhanced ozone concentrations and climate change. Last, but not least, in collaboration with other disciplines we conduct quantitative research on the various dimensions of food security at different scales.
Key Publications
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
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
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
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
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
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
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
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
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
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