Machine-learning crop meta-models for climate adaptation

In recent years, computational models have become an important tool in climate impact assessments of agricultural productivity such as crop yields. Presently, such models are typically process-based, which means that various plant and environmental processes are simulated jointly. This level of detail is required to consistently estimate and evaluate various target variables in the plant-environment nexus, for example soil attributes and hydrology besides plant growth itself. However, such models are computationally very costly and challenging to setup and process. To obtain just selected results of their simulations, here crop yields, emulators have become a promising alternative. These mimick the actual model while requiring sparser input data and having a substantially shorter runtime. Besides the time saving per se, this also allows for much more complex scenario analyses. Simple emulators have already been developed in the past but had considerable limitations with respect to spatial resolution and the flexibility of crop management for climate adaptation. This project bases itself on the hypothesis that machine learning methods, i.e., algorithms that can learn from massive amounts of data to provide accurate predictions, facilitate a new generation of emulators that dispel the above limitations and can facilitate complex scenario analyses. In a first step, the project team will produce a comprehensive multi-factorial set of training data using a process-based crop growth model. These data will then be used to train several structurally different algorithms and thereby produce an ensemble of emulators or meta-models. These will be evaluated regarding their individual and combined skills to mimick the original model. Finally, the algorithms will be used to conduct complex scenario analyses, including crop yield predictions for a multitude of climate projections to assess their relevance, the role of spatial resolution for the accuracy of crop yield predictions, and studies of adaptation potentials to climate change through cultivar selection and shifts in growing seasons. These scenarios will provide new insights into the complexity and potentials of dynamic adaptation, which is thus far hardly accounted for in the field of agricultural climate impact assessments. This will be paired with comprehensive assessments of robustness and uncertainty in climate impacts relating to climate projections and spatial resolutions. The algorithms themselves developed within the project are expected to be suitable for a wider range of applications such as near real-time scenario evaluations with agricultural stakeholders