Simulation crop models are increasingly being used in agribusiness and policy making for facing the challenge of climate change and other applications. In response to a growing demand and new applications, the University of Copenhagen has designed a series of courses in which students will gain a comprehensive basis for understanding and working with simulation crop models in their many applications and in particular in climate change assessments.

This course is the first one of a series. Students will acquire a working, state-of-the-art knowledge of the concepts and methods upon which the remaining courses in the series will build. From leading scientists in their field, students will learn about:

The crop physiological and soil processes described in dynamic crop models on growth and development

>System analysis as the basis for dynamic crop models
>Simulation methods; calibration, uncertainty, sensitivity analysis and evaluation techniques
>Construction of climate change scenarios based on a weather generator
>Model-based climate change impact assessment
>Future prospects in crop modelling: crop ideotype design, ensemble modelling and next generation models.

After the course, you will acquire:

Knowledge:

>Understand the principles of system models
>Describe qualitatively and mathematically the main physiological and soil processes
>Understand statistical concepts applied to dynamic crop models
>Recognize the aspects affecting prediction performance and uncertainties.

Skills:

>Code physiological processes using the R programming language
>Apply calibration and evaluation methods on dynamic crop model
>Carry out simulations for crop-climate impact assessment, including the development of climate scenarios using a weather generator.

Competences:

>Discuss the limitations in crop modelling
>Decide the most convenient evaluation criteria
>Assess critically the results of climate change impact assessments

Books:
Wallach, D., Makowski, D., Jones, J.W., & Brun, F. (2013) Working with Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment. Academic Press.

Hay, R.K., & Porter, J.R. (2006) The physiology of crop yield. Blackwell Publishing, 2006.

Papers:
Porter J.R. and M.A. Semenov, 2005. Crop responses to climatic variation. Phil. Trans. R. Soc. B 360:2021–2035.

Wallach, D. (2011). Crop model calibration: a statistical perspective. Agronomy Journal, 103(4), 1144-1151.

Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Kharabarov, N., Neuman, K., Piontel, F., Pugh, T. A. M., Scmid, E., Stehfest, E., Tang, H., Jones, J.W. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9), 3268-3273.

Daniel Wallach, Chargé de mission
UMR 1248 Agrosystèmes et Développement Territorial,
Institut National de la Recherche Agronomique, 31326 Castanet-Tolosan Cedex, France

Mikhail Semenov, PhD
Rothamsted Research, Harpenden, AL5 2JQ, Great Britain

Jørgen Olesen
Department of Agroecology - Water and Climate,
Blichers Allé 20, building PV20, 8820, 2009, 8830 Tjele, Denmark

Efstathios Diamantopoulos
Department of Plant and Environmental Science,
Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark

Please note that international students are themselves responsible to apply and get their own VISAs in advance

Course fee: 4.000 DKK

Signing up will be possible from 3rd April - 29th June 2018
(1)Register online clicking on “Apply” and

(2)Send an email to Lisa Mølgaard Lehmann  (lmle@plen.ku.dk) describing your motivations and background. Indicate whether you have experience in using R and RStudio.