|Title||Remote agronomy (@ Droevendaal Experimental Farm)|
|Group||Farming Systems Ecology|
|Supervisor(s)||Dirk van Apeldoorn|
Most productive modern agro-ecosystems are characterised by an increased homogenisation in space and time. It is hypothesised that as a result of this homogenisation agricultural systems will be increasingly sensitivity to unexpected shocks. Climate change will most likely increase the number of unexpected shocks through an increase of extreme events.
Diverse ecosystems tend to be more stable over time and recent research in resilience theory suggests that stability through diversity is also applicable for social-ecological system. A prudent strategy to deal with this increasing uncertainty is therefore system diversification. In agro-ecosystems diversification can take place across time and space at and at multiple scales. Intercropping, crop rotation, mixed farming and pest-suppressive landscapes are examples of diversification strategies at different scales.
An open question is at what scale a cropping system should diversify. Therefore in 2014 a strip cropping experiment was started. In this experiment we wish to sort out how diversity influences system performance with respect to crop yield, soil fertility and suppression of pest and diseases. The experiment features in many of the FSE-taught course since it integrates the different elements of ecological intensification. Results show that more diversity creates more edge-effects, improved nutrient use and healthier crops. In 2016 we will be looking at ways to improve yields, improve and conserve soil life and explore novel ways of crop monitoring with drones, moreover the experiment will be enlarged to have strips of 3 meter and 6 meter wide so comparisons on spatial diversity can be made.
In this research you will integrate remotely sensed data acquired by drones with field observations. The work will be done in close collaboration with the Geo-information Science and Remote Sensing group (GSR).
Investigate the use of hyperspectral and high resolution remote sensing data for improving crop management
• crop monitoring
• spatial explicit sampling
• linking remote sensing images with crop performance
|Used skills||Experiences gained
• preparation of a research proposal and field sampling protocol
• working as part of an interdisciplinary project team within a systems experiment
• monitoring of a systems experiment
• hands on experimental work on the sampling of diverse agroecosystems
• knowledge on options of diverse agroecosystem
• writing a research report and contribute to a journal manuscript
|Requirements||Assumed knowledge on:
• Basic knowledge on remote Sensing/GIS