Using deep machine learning for adaptation of food production to climate change

Climate change presents numerous challenges to society. When the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC) was released in March 2014, food production and food security was highlighted as a key area of concern. Adaptation options for crop production systems are usually assessed using crop simulation models together with climate models.

Process-based crop models have been used to develop options to adapt to climate change. Statistical models of yield responses to weather and climate have also been used to develop adaptation options; but it is not yet clear which models are best, even for specific applications. Crop-climate indices are a broader set of tools that focus on specific biophysical processes, rather than attempting to reproduce yields. Again, evaluation is difficult; however the technique is clearly powerful, especially when used in conjunction with process-based modelling to answer specific research questions on adaptation.

Machine Learning techniques (ML) present a more powerful set of tools than the narrower and simpler statistical models and crop-climate indices. This PhD use ML techniques as an integrative tool for multiple methods of assessing climate impacts on crops.

The work will be conducted collaboratively with the industry partner, Unilever, and will use their extensive crop productivity data. The ultimate aim is to use those methods to identify adaptation options for the industry partner, including both domestic climate change resilience and international sourcing.