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Modelling climate-smart options for the management of nitrogen on agricultural land

Project Description

The grand societal challenge of managing natural resources effectively under environmental change is central to the future management of agricultural land. Sustainable agriculture aims, amongst other things, to grow food whilst also reducing greenhouse gas emissions. There are implicit trade-offs in achieving this aim. For example, if soils are managed solely to maximise productivity, there can be significant losses of reactive nitrogen (N) to the environment through nitrate leaching, emissions of nitrous oxide to the atmosphere and ammonia volatilization.

Climate-smart agriculture (CSA) refers to a set of practices that to some degree or other achieve low emissions and high productivity, through managing the inherent trade-offs. It also seeks to deliver adaptation by increasing resilience to the challenges that farmers face under climate change and thus increasing the capacity of the system to prosper in the face of climate shocks or long-term stresses.

Examining the role of the nitrogen cycle in climate-smart agriculture requires improved treatment of nitrogen in crops models. The student will use the latest version of the General Large Area Model for annual crops (GLAM; Challinor et al., 2004), which operates by solving a system of simultaneous equations using an iterative numerical method. This novel approach enables the tracking of nitrogen content in the crop. The student will therefore parameterise the temporal dynamics of nitrogen content in the various parts of the crop by developing and implementing a novel crop-nutrient-uptake subroutine and nutrient translocation methodology.

Funding Notes

This project is available for funding through the Panorama NERC DTP, please see View Website for funding details and eligibility requirements.

How good is research at University of Leeds in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 79.20

Research output data provided by the Research Excellence Framework (REF)

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