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Integrated Analyses to Promote Sustainable Soil Health (SUSTAINable soils)

Prof Mat Goddard, University of Lincoln; Dr Nicolas Rubido, University of Aberdeen; Prof Simon Parsons, University of Lincoln; Dr Dan Magnone, University of Lincoln

Soil Sample_3.JPG

Interview date

TBD


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See our Application Page.

Research Aims

Soils provide critical ecosystem services as soils: produce 97% of the world’s food calories; provide key water regulation processes for flood management; harbouring the highest levels of biodiversity on Earth; and, contain the largest dynamic reservoir of carbon on Earth (larger than that stored in the atmosphere and vegetation combined). The relationship between biological, physical, and chemical soil properties is the key mechanism to managing these services; for example, soil microbes are capable of both releasing and retaining carbon and nitrogen that play significant roles in the flux of atmospheric greenhouse gasses. However, there is virtually no integrated holistic knowledge of soil properties, and more importantly how these are affected by land use and management change. Appropriate land use and management are key ways to mitigate and possibly reverse greenhouse gas levels, protect biodiversity, and produce food more sustainably. The novel proposition is to initiate an understanding of nature of the integrated response of multiple soil properties to land management and change. This will contribute to an evidence base that will support land-use change decisions, like the ELM scheme, to help mitigate climate change by increasing carbon sequestration and reduced GHG emissions and reduce flood risks and biodiversity loss both locally and globally. 

Methodology: 
To achieve this holistic knowledge, we will develop data-driven methods to analyse the available biological, physical, and chemical soil properties, including clustering analysis for data exploration and mining, empirical decompositions for feature extraction and signal processing, and similarity analysis for statistical modelling and network inference.

 

Training: 
This will provide an opportunity to work across a broad range of computer science, applied mathematics, and environmental and soil science areas, with work that relates to environmental and agricultural sustainability and evidence-based policy making. Training on soils and land uses will be accompanied by training on data and relevantcomputing Science areas. 

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