- Agriculture arose independently in many regions of the world, and that pattern may hold clues to identifying the resilient agriculture of the near-future, in a rapidly changing world.
- Climate change will radically shift crop distributions in the coming century and in order to adapt to this future we must learn from the past.
- Identifying agrobiodiversity hotspots and conserving them as libraries of crop diversity is key to delivering resilient and sustainable food security.
Around 12,000 years ago 99% of the human population were hunter gatherers, but by 5000 years ago 99% were farmers (Weisdorf, 2005). This characterises one of the most fundamental transitions in our species’ history, dramatically altering our relationship with nature. After 300,000 years of modern Homo sapiens’, agriculture arose rapidly and independently in as many as 11 separate regions – termed the Neolithic Revolution (Harlan, 1971; Meyer et al., 2012). What precipitated this radical change? Was it driven by growing population, increasing cultural and technological sophistication, over exploitation of natural resources or environmental change? In the coming century of climate change, global agriculture will be forced to undergo another rapid change. Crop distributions will shift to keep pace with suitable climate envelopes and new or underutilised species may form an increasingly important part of our food system (Borrell et al., 2020; Rampersad et al., 2023). What can early agriculture teach us about the potential to adapt?
This project will investigate the rapid, but asynchronous origins of agriculture in a number of discrete regions at the end of the last ice age, and the highly heterogeneous distribution of ‘centres of domestication’ that do not necessarily align with broader global biodiversity gradients (Pironon et al., 2020). This is important, because we face a twin biodiversity crisis, whereby the decline of wild biodiversity is mirrored by a decline in the diversity of crops we use. Indeed, over recent decades our global food system has become increasingly uniform (Khoury et al., 2014). Despite evidence that humans have consumed more than 7000 species of plants, more than half of global calories are derived from just three species – rice, wheat and maize (SOTWPF, 2020). Understanding what drove and maintained hotspots of agrobiodiversity will be the key to conserving them and their associated indigenous knowledge for a sustainable and resilient future food system.
This project supports our broader goal of area-based conservation for agrobiodiversity. By identifying agrobiodiversity hotspots we can support countries to meet the Global Biodiversity Framework 30×30 target, and avoid loss of the biodiversity on which we are most dependent.
Figure 1: A) The global distribution of biodiversity and agrobiodiversity hotspots. Biodiversity and agrobiodiversity hotspots do not always overlap, suggesting they may be driven by different processes. B) A smallholder farm in the Ethiopian Highlands, an area of exceptionally high biodiversity where Kew is working to support crop conservation. C) Fruits of enset, a crop endemic to Ethiopia that provides the staple food for 20 million people.
HostUniversity of Leicester
- Climate and Environmental Sustainability
- Organisms and Ecosystems
Prof Mark Williams (Leicester)
Dr James Borrell (Kew)
Prof Robin Allaby (Warwick)
Dr Juan Carlos Berrio (Leicester)
This project is timely because of multiple newly available datasets and increasingly accessible analysis techniques employing Google Earth Engine. In the first phase, the student will collate archaeobotanical and socio-cultural data on the origins of global crop species. We will then integrate high-resolution global palaeoclimate layers (e.g. http://www.paleoclim.org/) across the Greenlandian, Northgrippian and Meghalayan data to understand the environmental conditions and sociocultural context associated with asynchronous adoption of agriculture and domestication of diverse crops. The multiple independent origins of agriculture over a several thousand-year period, together with heterogeneous global climate trends provide a means to examine and test hypotheses for the drivers of agriculture.
A major feature of this proposal is the use of interdisciplinary methods. The student will be trained to integrate diverse archaeological evidence of past land-use change, to generate alternative lines of evidence with analytical modelling approaches including redundancy and geographically weighted path analysis.
Training and skills
Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.
The student will benefit from training in a wide range of techniques across the broad expertise of the supervisory panel integrating traditional and emerging techniques. This includes large-scale spatial analysis and multivariate statistics, including path analysis and machine learning. The student will learn best practice in open and reproducible coding (e.g. GitHub, Markdown), including working in R, Google Earth Engine and on Kew’s high performance computing facility as required. Additional training to analyse historic land-use change will support additional lines of evidence. Integration into our diverse research groups will support skills development for a range of future careers.
Partners and collaboration
This PhD project benefits from collaboration across Dr Borrell’s Agrobiodiversity Conservation group at the Royal Botanic Gardens Kew, Prof Allaby’s archaeobotany and domestication team at the University of Warwick, Prof Williams’ Anthropocene research group at the University of Leicester and Dr Juan Carlos Berrio’s extensive expertise in Holocene palaeoecology. The student will also benefit from the Institute for Environmental Futures at Leicester, which brings together interdisciplinary research teams to address problems of major societal importance. Collectively, these research groups employ complementary approaches to tackling this proposal’s core research questions, generating a diverse, dynamic and welcoming environment for the student.
Further details on how to contact the supervisor for this project and how to apply for this project can be found here:
To apply to this project:
- You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2024.
- You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
- Please submit your application and complete the host institution application process via: https://le.ac.uk/study/research-degrees/funded-opportunities/centa-phd-studentships. Please scroll to the bottom of the page and click on the “Apply for NERC CENTA Studentship” button. Your CV can uploaded to the Experience section of the online form, the CENTA application form 2024 can be uploaded to the Personal Statement section of the online form. Please quote CENTA 2024-L13-CENTA2-SGGE5-WILL when completing the application form.
Applications must be submitted by 23:59 GMT on Wednesday 10th January 2024.
Building a spatial dataset of crop origins and domestication centres through time, using published data as a starting point (Meyer et al., 2012; Milla, 2020). This will comprise dated molecular or archaeobotanical evidence of domestication or use, georeferenced and combined to understand the relative intensities of domestication and adoption of agriculture. Additionally, the student will develop a critical understanding of testable hypotheses for drivers of agriculture, and collate relevant predictors at a global scale. The student will be supported to develop skills in relevant software such as Google Earth Engine and statistical tools through training and support from our wider teams.
Outputs 1: A database of spatiotemporal crop origins published as an actively updated dataset (e.g. Zenodo) with a citable DOI.
Developing an ensemble of models to understand spatiotemporal drivers of domestication. Core to this approach will be mixed effects models integrated through geographically weighted path analysis, enabling the importance of drivers to vary through time and space (Pacheco Coelho et al., 2019). We will evaluate modelling choices such as variable selection through bootstrapping across parameter space. Alternative lines of evidence including historic land use changes will be explored to test and validate models.
Outputs 2: Manuscript submitted on evaluating drivers of agrobiodiversity hotspots through space and time.
Building on Y1-2, the student will be supported to apply their findings across three outputs. First, we will project our model globally to test whether our drivers support poorly known or as yet unidentified domestication centres (for example, Eastern North America if debated as a possible domestication centre; Smith, 2006). This will provide an important opportunity to cross reference our findings with archaeological evidence of land use change. Second, we will compare empirical and modelled patterns of agrobiodiversity with contemporary global biodiversity patterns, to understand whether they are the result of different processes. Finally, modelled outputs will support wider collaborative efforts to prioritise area-based agrobiodiversity conservation efforts as Other Effective Area-based Conservation Measures (OECMs), supporting high biodiversity countries towards 30×30 targets.
Outputs 3: Finalising output 2, and integrating evidence to identify poorly known domestication centres. In addition, a manuscript comparing the distribution of agrobiodiversity and wild biodiversity hotpots. The student will contribute to a policy brief, collaborating with Bioversity International, outlining the potential for agrobiodiversity conservation through OECMs aimed at high agrobiodiversity countries where we have active programmes, including Peru and Ethiopia. These outputs will be timely with significant impact potential as the student completes during the culmination of the current Global Biodiversity Framework in 2030.
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Milla, R., 2020. Crop Origins and Phylo Food: A database and a phylogenetic tree to stimulate comparative analyses on the origins of food crops. Global Ecol Biogeogr 29, 606–614. https://doi.org/10.1111/geb.13057
Pacheco Coelho, M.T., Pereira, E.B., Haynie, H.J., Rangel, T.F., Kavanagh, P., Kirby, K.R., Greenhill, S.J., Bowern, C., Gray, R.D., Colwell, R.K., Evans, N., Gavin, M.C., 2019. Drivers of geographical patterns of North American language diversity. Proc. R. Soc. B. 286, 20190242. https://doi.org/10.1098/rspb.2019.0242
Pironon, S., Borrell, J.S., Ondo, I., Douglas, R., Phillips, C., Khoury, C.K., Kantar, M.B., Fumia, N., Soto Gomez, M., Viruel, J., Govaerts, R., Forest, F., Antonelli, A., 2020. Toward Unifying Global Hotspots of Wild and Domesticated Biodiversity. Plants 9, 1128. https://doi.org/10.3390/plants9091128
Rampersad, C., Geto, T., Samuel, T., Abebe, M., Gomez, M.S., Pironon, S., Büchi, L., Haggar, J., Stocks, J., Ryan, P., Buggs, R.J.A., Demissew, S., Wilkin, P., Abebe, W.M., Borrell, J.S., 2023. Indigenous crop diversity maintained despite the introduction of major global crops in an African centre of agrobiodiversity. Plants People Planet ppp3.10407. https://doi.org/10.1002/ppp3.10407
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