This PhD combines remote sensing, crop models and machine learning in order to produce improved projections of future food production. Historically, studies have used only one of these methods, with recent progress at Leeds and elsewhere focusing on combinations, such as crop models and remote sensing; or machine learning and crop models.
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 acdemics 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.
Machine learning (ML; see Witten et al., 2017) is now a ubiquitous technique and methodology in many fields, including big data, health, environment, robotics etc. There is a huge demand by both academia and industry for PhDs with machine learning expertise. ML is already being tested at Leeds as potential next-generation crop models .
In this studentship, you will draw on the expertise and resources of all supervisors in order to:
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, suitablitity models, remotely-sensed data and ML for projecting realistic future croplands maps
Funded via an exciting new doctoral training centre at Leeds. See View Website
CHALLINOR, A. 2009. Towards the development of adaptation options using climate and crop yield forecasting at seasonal to multi-decadal timescales. Environmental Science & Policy, 12, 453-465. https://doi.org/10.1016/j.envsci.2008.09.008
Monfreda C, Ramankutty N, Foley JA (2008) Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles, 22, GB1022.
Orynbaikyzy, A., U. Gessner and C. Conrad (2019) Crop type classification using a combination of optical and radar remote sensing data: a review, International Journal of Remote Sensing, 40:17, 6553-6595, DOI: 10.1080/01431161.2019.1569791
Rippke U, et al. (2016) Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nat Clim Chang 6:605–609.
WEBBER, H., GAISER, T. & EWERT, F. 2014. What role can crop models play in supporting climate change adaptation decisions to enhance food security in Sub-Saharan Africa? Agricultural Systems, 127, 161-177. http://dx.doi.org/10.1016/j.agsy.2013.12.006
Witten, H., Frank, E. Hall, M. A., and C. J. Pal. Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems). See http://www.cs.waikato.ac.nz/ml/weka/book.html
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