A comparison of greenhouse gas (GHG) emissions from dairy farms by four systems models with eight agro-climatic scenarios
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A talk given at Camulodunum Speakers, Colchester 23/5/2016. A Toastmasters International Club. It was for Project 4 of the advanced manual on Technical Presentations. It was a practice run for a speech to be delivered in Potsdam in June at the LiveM meeting of the Macsur2 Knowledge Hub. http://macsur.eu/
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A comparison of greenhouse gas (GHG) emissions from dairy farms by four systems models with eight agro-climatic scenarios
- 1. A comparison of greenhouse gas (GHG) emissions from dairy farms by four systems models with eight agro-climatic scenarios Daniel Sandars ACB ALB, Research Fellow Project 4 Technical Presentations, Camulodunum Speakers, 23 May 2016 Daniel Sandars, Nick Hutchings, Şeyda Özkan, Michel De Haan
- 2. Acknowledgements FACCE-JPI and National Funding Bodies
- 3. The Four Models • SFARMMOD, Cranfield University, UK • DairyWise, Wageningen University, The Netherlands • FarmAC, Aarhus University, Denmark • HolosNor, Norwegian University of Life Sciences
- 4. Eight (23) Agro-climatic scenarios Climate Cool v. Warm Netherlands v. N Spain Soil type Light v. Heavy Sand v. Clay Cropping Grass v. Grass and Forage Maize
- 5. Key Points 1. Overall the models are in good agreement 2. They vary in the detail 3. Experiences and Recommendations
- 7. Total per kg Milk
- 8. From the Cow’s body
- 10. From the Field and farm
- 11. Discussion & experiences • The scenarios only make small differences to the Total • Farm-gate not Life-Cycle GHG emissions – e.g. not the manufacture of fertilisers • No new measurements • Not all management factors can or were controlled – e.g. area of maize, etc. • Hard work – assembling and interpreting data from novel regions – Identifying assumptions and ambiguities between models
- 12. Recommendation • Using all four models together “Ensemble modelling” – Robust average and spread of results – Triangulation effect – The best (and worst) of all models – Need to be able to control more management factors between models – Need to understand the differences and improve the models
- 13. Key points 1. Good general agreement across models 2. The devil is in the ‘smaller’ details 3. Key carbon footprint is: Enteric CH4, Field N2O and Manure CH4 4. Ensemble modelling offers the next step beyond model comparison 5. Challenge the sixth sense when looking at unfamiliar regions, models, and data.