1. Faculty of Environment
  2. School of Earth and Environment
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Samuel Bancroft

Samuel Bancroft

  • Email: eesjb@leeds.ac.uk
  • Thesis title: Developing a new methodology for assessing future food production based on machine learning, remote sensing, and crop models.
  • Supervisor: Professor Andy Challinor, Professor Netta Cohen, Professor Anthony G Cohn

Profile

Remotely-sensed croplands data are routinely used to map crop cultivation patterns and distinguish crop types from each other. These maps are in turn used by academics and by industry for a range of uses, from commodity and supply chain monitoring to projecting the future impacts of climate change. Global croplands maps include (e.g. Monfreda et al., 2008). The resources involved in putting together such datasets mean that they are not frequently updated. Whilst, the underpinning methodologies are constantly being improved (Orynbaikyzy et al., 2019), much remains to be done. Distinguishing annual crops from perennials, for example, is one key issue identified by Unilever.

Crop models are regularly used to develop options to adapt to climate change (e.g. Webber et al., 2014, Challinor, 2009), by simulating first the impacts of weather and climate on yields. However, it is only when the areas under crop cultivation are also known or estimated that any kind of assessment of total crop production can be made. Knowing these future cropped areas is not trivial, since they will be determined to some extent by changes in crop suitability driven by climate change. Models of crop suitability can be used to see how suitable future climates are for particular crops (e.g. Rippke et al., 2016). However, these models are not generally used alongside process-based crop models. Rather, assessments of future crop production either assume that cropped areas equal that of some historical dataset, or else they use an existing assessment of future land use (e.g. LUH2 ), which generally do not account for crop suitability.

For this project I will:

1. Evaluate and use remote sensing data for identification of crop specific locations and production intensities.
2. Conduct a scoping study to determine the most promising crops, regions, datasets and models to use in the PhD for predicting future crop locations and intensities.
3. Use and develop ML methods to improve croplands datasets, by drawing on ongoing progress in this area (e.g. Orynbaikyzy et al., 2019).
4. Explore the combined use of crop yield models, suitability models, remotely-sensed data and machine learning for projecting realistic future croplands maps.

Qualifications

  • Integrated MPhys Physics, Durham University 2014-2018