Main research question/goal
The productivity and quality of crops depends on the complex interaction between environmental conditions in which the crop is growing and its genetic background. In a mechanistic crop model, scientists can relate the most important environmental drivers (including management) to crop variables by means of mathematical equations. This research project develops such a model for several crucial ILVO crops like perennial ryegrass and soybean. The aim is not only to integrate eco-physiological and genetic elements to predictable physiological mechanisms, but also to enable projections of relevant characteristics like productivity and quality under climate change.Research approach
First, we collect relevant historical and new data on crop production, quality and environmental variables for perennial ryegrass and soybean. Then we look for model parameters that can be estimated based on the available data, and investigate which additional data would be added value for parameter estimation. This phase is called ‘identifiability analysis’ and is crucial in building robust models. Based on experimental data, the parameter values can be estimated. Finally, we conduct virtual experiments where factors (e.g. planting density) are altered to predict variations in productivity and quality.Relevance/Valorisation
Crop growth models are powerful tools to (i) understand growth and quality of crops in interaction with their environment, (ii) to identify genetic sources that are best adapted to growth in specific environments and (iii) to predict crop performance under future climate change scenarios. Such crop growth models allow us to better understand the integrated response of plants on variable environmental conditions. Moreover, we achieve better predictions of the value of certain genotypes or selections in different conditions. Finally, we will be able to calculate the effect of current and future climate scenario’s on crop productivity and quality.