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A comparison of greenhouse gas (GHG) emissions
from dairy farms by four systems models
with eight agro-climatic scenarios
...
The Four Models
• SFARMMOD, Cranfield University, UK
optimised management; emission factors
• DairyWise, Wageningen Univer...
Eight (23)
Agro-climatic scenarios
Results 100 year CO2 Equivalents
Climate
1. Cool:
Netherlands
2. Warm:
Northern
Spain
S...
Scenario key data
Cool climate grazing 5 months
Warm climate grazing 10 months
16 hours/day grazing
Minimum use of concent...
Key Points of this talk
1. Overall the models are in good agreement
2. They vary in the detail
3. There are wider experien...
Key to Results charts
Total per kg Milk
Enteric
Manure management
Field and indirect N2O
Discussion & experiences
• The scenarios only make small differences to the Total
– Farm-gate not Life-Cycle GHG emissions...
Take-away points
1. Good general agreement across models, but
differences in detail
2. Key carbon footprint is: Enteric CH...
Acknowledgements
FACCE-JPI and National Funding Bodies
Aside 1: Area based comparison
Aside 2: Strange data and
intuition
Aside 3: A recommendation
• Using all four models together “Ensemble
modelling”
– Robust average and spread of results
– T...
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A comparison of greenhouse gases emmisions from dairy farms using four systems models and eight agroclimatic scenarios

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Given on the 16th June at FACCE-JPI Macsur LiveM conference "Modelling Grassland-Livestock Systems under Climate Change" 15-16th June, Potsdam, Germany
Farm systems modelling is an important tool to quantify and understanding the impacts of management decisions on farm productivity and environmental burdens, such as greenhouse gas emissions.

Predicted emission intensity varied little between models, from 0.98 to 1.02 carbon dioxide equivalents (kg milk)-1, corresponding to a variation of about ±5%. This similarity disguised much larger variations in the underlying sources. For the two largest sources (enteric methane and soil nitrous oxide), which accounted for on average 55% and 26% of the total GHG emissions respectively. The differences and limitations of the inter-comparison are discussed and ways forward are suggested.

Published in: Environment
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A comparison of greenhouse gases emmisions from dairy farms using four systems models and eight agroclimatic scenarios

  1. 1. A comparison of greenhouse gas (GHG) emissions from dairy farms by four systems models with eight agro-climatic scenarios Daniel Sandars, Research Fellow MACSUR2: LiveM Potsdam,16 June 2016 Daniel Sandars, Nick Hutchings, Şeyda Özkan, Michel De Haan
  2. 2. The Four Models • SFARMMOD, Cranfield University, UK optimised management; emission factors • DairyWise, Wageningen University, The Netherlands optimised feeding; empirical emission factors • FarmAC, Aarhus University, Denmark user inputs management; emission factors (except dynamic soil model) • HolosNor, Norwegian University of Life Sciences user inputs management; emission factors
  3. 3. Eight (23) Agro-climatic scenarios Results 100 year CO2 Equivalents Climate 1. Cool: Netherlands 2. Warm: Northern Spain Soil type 1. Light: Sandy 2. Heavy: Clayey Cropping 1. Grass 2. Grass and Forage Maize
  4. 4. Scenario key data Cool climate grazing 5 months Warm climate grazing 10 months 16 hours/day grazing Minimum use of concentrates No manure import/export 600 kg LW & 7000 kg ECM/cow/yr Dairy cows + followers (1:1) Plant-available N: Grass 275 kg/ha/yr Maize 150 kg/ha/yr (Manure broadcast) For each scenario, adjust cow numbers to match feed supply
  5. 5. Key Points of this talk 1. Overall the models are in good agreement 2. They vary in the detail 3. There are wider experiences and recommendations to share
  6. 6. Key to Results charts
  7. 7. Total per kg Milk
  8. 8. Enteric
  9. 9. Manure management
  10. 10. Field and indirect N2O
  11. 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, etc • No new comparisons to measurements • Not all management factors can or were controlled e.g. area of maize, etc (See aside 1). • Hard work e.g. assumptions and ambiguities and novel regions and data (See aside 2) • Ensemble Modelling? (See aside 3)
  12. 12. Take-away points 1. Good general agreement across models, but differences in detail 2. Key carbon footprint is: Enteric CH4, Field N2O and Manure CH4 3. Ensemble modelling offers the next step beyond model comparison 4. Challenge the sixth sense when looking at unfamiliar regions and data. 5. Communication is key and takes work
  13. 13. Acknowledgements FACCE-JPI and National Funding Bodies
  14. 14. Aside 1: Area based comparison
  15. 15. Aside 2: Strange data and intuition
  16. 16. Aside 3: A 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 control management factors between models – Need to understand the differences and improve the models

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