Agricultural soils are vital for food production. They are also a source of greenhouse gas emissions, as a result of the microbial processes of decomposition, nitrification, and denitrification. Globally, soils are the biggest terrestrial store of carbon – small changes in the rates of greenhouse gas emission from soils, acting over large areas, can have major consequences for a country’s agricultural greenhouse gas budget.
Ireland currently faces the challenge of balancing an increasing demand for food production with reducing agricultural greenhouse gas emissions. Emissions from agriculture account for around one third of Ireland’s total greenhouse gas emissions. With agriculture covering about 60% of the land area, there is potential to make big changes in the agricultural greenhouse gas budget, by changing management practices to reduce soil greenhouse gas emissions.
To predict how different management practices might alter soil greenhouse gas emissions, we can use models to simulate them. In this project at Trinity College Dublin, I’m testing three process-based biogeochemical cycling models, to see if they can reliably simulate greenhouse gas emissions from Irish agricultural soils. By using statistics to compare the model simulations against measured greenhouse gas data at a variety of arable and grassland sites with differing management practices, I can identify which model is likely to be the most reliable for simulating greenhouse gas emissions from each combination of agricultural system and management.
Once I’ve identified the best model for simulating each kind of agricultural system, I can use the model to simulate emissions for each cell on a 5 x 5 km grid map of Ireland. This will form a tool that can be used to experiment with different scenarios of agricultural land-use change – how will, for example, a 10% increase in the area of pasture affect the overall agricultural greenhouse gas emissions budget?