Research Scientist
il y a 4 semaines
Fixed-term position (12 months)
Metamodelling: Simplifying Crop Modeling with Machine Learning
Scientific Context
Plant modeling has a rich history, dating back to the 1960s. The field has matured in predicting crop yields under environmental conditions. However, with the increasing importance of agroecology, more research focuses on modeling crops in biodiversified systems, including agroforestry. This involves crops associated with trees, which poses challenges due to the diversity of tree and crop species, management, and long development times. Experimental evidence is scarce to understand agroforestry system functioning, performance, and optimization. Simulation models can greatly aid in this respect.
One such model is Hi-sAFe (Dupraz et al. 2019). It's a daily time-step, 3D, soil-crop-tree model predicting crop and tree growth, yield, and environmental performances based on pedoclimatic conditions and management. The model has been developed over several years and has been the basis for several publications. However, it's difficult to parameterize for a given situation due to the required detailed information on soil, climate, tree, and crop management. Furthermore, one simulation of the 50-year system life typically takes 1-2 hours to run, making the model unsuitable for rapid ex ante evaluation during system design.
Objectives
The objective of this position is to apply machine learning techniques to build a meta-model of the hi-sAFe model, enabling quick evaluations of different systems under various pedoclimatic and management conditions. The first step will be to validate the model in different conditions using existing data on long-term agroforestry system performance. The second step will be to build the training dataset, covering the desired range of cropping systems, tree configurations, soil, climate, management, etc. Then, the full model will be run to produce relevant output variables (crop and tree yield, water and nitrogen balance components). Next, a suitable machine learning technique will be chosen and the meta-model will be trained on the simulated data. Finally, the meta-model will be made available for queries through an API, for coupling with existing or new applications developed by partners of the DigitAF project.
Required Skills and Experience
We are looking for a motivated person able to work with process-based crop models and machine learning algorithms. The successful candidate will have an engineering degree in machine learning, statistics, or applied mathematics with a willingness to learn principles of agronomy and understand crop modeling, or a PhD degree in agriculture, plant sciences, modeling, or related sciences with experience in using and improving crop simulation models, and motivation to acquire new knowledge in machine learning.
Working Conditions
How to Apply
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