Tropical Plant Production and Agricultural Systems Modelling

Mission


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.


Research priorities - Tropical Plant Production and Agricultural Systems Modelling
DivisionConceptFeb2018
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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 n/a-n/a,
DOI:10.1111/jac.12159

Kassie, B.T., et al. (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 risks, adaptation and mitigation

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

model development / improvement and uncertainty analysis

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

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




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