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

Prof. Andy Challinor (SEE), Dr. Marcelo Galdos (SEE) and Caroline Orfila (SFN)

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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. 

Much of the focus of CSA to date has been on low-carbon technologies. Nitrogen is a feature of existing CSA analyses in that the application of commercially produced N fertiliser directly implies increased emissions from the industrial processes involved. This project takes a nitrogen-centric view of CSA by bringing together a set of emerging tools to look at nitrogen cycles and associated trade-offs, such as crop residue retention vs fodder question. 

The nitrogen cycle, along with the partitioning of other compounds, is also important in determining the nutritional content of food. Whilst the modelling of climate and its impacts on crops is well developed, the processes that determine nutritional content have only just begun to be incorporated into models. For example, whilst N limitation is known to be an important issue for grain yields under climate change (nitrogen is needed in order for crops to benefit from the CO2 fertilisation effect), only recently has it been shown to have an effect on protein yields (Asseng et al., in press).

Methods and data

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.

The student will use the newly-developed nitrogen module in GLAM in order to simulate key processes in the nitrogen cycle in the soil module (e.g. hydrolysis, mineralisation, fixation, volatilisation, nitrification and denitrification in root zone; Li et al., 2015) The developed model will numerically solve one-dimensional advection-dispersion-reaction (ADR) equation in variably saturated soil column. 

Extensive data exist for the project. There will be field trial data sets available to this studentship, including: 1) analytical data from the Africap project on N content of soil and a nutrient content of maize kernels grown under controlled CA regimes; 2) nutrient analytical data from FACE experiments in wheat kernels grown under various stresses (in collaboration with DTU, Denmark). 3) Data from the Agricultural Model Inter-comparison and improvement Project, especially the Low Input study, which uses four sites across Africa with contrasting ago-ecologies and soil conditions.

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Related undergraduate subjects:

  • Agriculture
  • Applied mathematics
  • Atmospheric science
  • Earth system science
  • Environmental biology
  • Environmental science
  • Natural resource management
  • Natural sciences
  • Physics
  • Plant science
  • Soil science
  • Sustainability and environmental management