|Teaching method||Contact hours|
|Course coordinator(s)||ing. H Drenth|
|Lecturer(s)||dr. JB Evers|
|dr. ir. L Bastiaans|
|prof. dr. NPR Anten|
|dr. ir. W van der Werf|
|ing. H Drenth|
|Examiner(s)||dr. JB Evers|
Language of instruction:
Assumed knowledge on:
HPP-21306 Crop Ecology is advised.
The last century agriculture has been characterised by control and specialization: monocrops being grown with intensive use of fertilizers and pesticides. But environmental concerns, disease and pest resistance to pesticides and finite availability of resources call for a change in strategy. Diversity both in terms of utilizing different crop species and in combining different ecology-based management practices may provide an alternative. Yet, the mechanisms that drive the relationship between species diversity and functioning ecosystem functioning and how this in combination with diversity in crop management can be used to create productive, resource-efficient and resilient crop systems remains poorly understood. This course focusses on this knowledge gap.
The first part of the course deals with what we can learn from natural systems, and analyzes the mechanisms that drive relationship between diversity and ecosystem functioning. The focus will be on resource sharing between neighbouring plants, and this will be addressed in an assignment containing both modelling and measurements on plants in the greenhouse.
The second part addresses how this knowledge about effects of species diversity can be utilized in agricultural systems, looking at resource sharing between different species in a mixed cropping situation. Also here an assignment will be done that contains both modelling and plant measurements, aiming at the exploration of option to optimize mixed crop performance.
Finally, the third part focuses on diversity in management options in the suppression of weed growth, taking into account the interaction between crop and weed plants and how this plays a role in integrated weed management (weed control through a combination of methods extending beyond herbicide use). Similarly, this will include both modelling and plant measurements.
The complexity of the questions addressed above cannot be answered by experiments alone and expertise with computer simulations models has become an indispensable tool for researchers in this field. This course therefore has a heavy modelling component. Simulation models will be used for the analysis of plant-plant interaction and the consequences of management decisions. Therefore, during the course, you will be taught the principles of both functional-structural plant modelling using the modelling platform GroIMP, as well as mechanistic modelling of crop-weed interactions, using the modelling platform FST.
After successful completion of this course students are expected to be able to:
- understand key concepts in community ecology, ecological and physiological aspects of plant-plant interactions and population dynamics;
- have introductory knowledge of two advanced crop modelling techniques;
- measure and interpret plant traits in simple greenhouse experiments in the context of light sharing and functional diversity in crops;
- integrate knowledge using novel plant growth modelling techniques, interpret model output, and address questions on functional diversity in crops by combining model output and experimental data;
- link research on natural and agricultural systems and thus bridge the gap between environmental and production oriented research.
This course features a number of assignments, which will be carried out in groups of 4 students. Participation in these assignments is compulsory and group deliverables will be graded. For each assignment, the criteria for quality of the deliverable will be clearly communicated on blackboard.
- a written test with open and multiple choice questions (60%, minimum grade 5.5);
- assessment of the assignments (40%).
The literature consists of the contents of the lectures and assignments, plus reading material posted to Blackboard which is clearly marked as exam material. Any other reading material can be regarded as background information for further reading.