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Session 10 14.00_adel_prado_whole-farm models
- 1. Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems Agustin del Prado, Basque Centre for Climate Change (BC3), Spain Paul Crosson, Teagasc, Ireland Jørgen Olesen, Aarhus University, Denmark Alan Rotz, USDA-ARS, University Park, USA
- 2. Agustin Del Prado Jørgen Olesen Paul Crosson Alan Rotz
- 3. Outline 1. General overview 2. Basic principles of farm models 3. Whole-farm models for quantification and mitigation of GHG 4. Linking mitigation and adaptation to climate change 5. Final Recommendations
- 4. Models reviewed LANDDAIRY +NGAUGE Casey & Holden Foley et al. Lovett et al. O’Brien et al. SIMSDAIRY FARMSIM DAIRYWISE FARMGHG FASSET Holos-NOR Holos DAIRYGEM IFSM OVERSEER, HoofPrint Ecomod-suite DAIRYNZ
- 5. Main components of a ruminant livestock system grass
- 6. Sources of GHG (from the cradle to farm gate) Enteric CH4 manure CH4 Animal soils N2O Feed Production manure N2O Manure Fuel combustion Secondary emissions Different levels Data from del Prado et al. (2013), Animal (this issue)
- 7. Component 1: the animal How much do they eat or meat/milk produce? • • • • Energy and nutrient requirements (e.g. protein) Feed on offer (e.g. fiber, energy, protein) Genetics Structure of the herd mechanistic CH4 CO2 empirical Farm models prefer empirical How much do they excrete?
- 8. Component 2: manure handling How much excreta? How much and how is it mixed & collected? How much and how is it stored? Is manure treated? How much and how is applied?
- 9. Component 3: feed production vs Soil N2O Emission Factor Bouwman (1998) mechanistic and dynamic Empirical and static Affected by soil type, weather and management • Soil environment • Soil inorganic N availability • Soil Organic Matter • Competing processes (plant, denitrification, leaching…)
- 10. Component 3: feed production Indirect emissions Indirect N2O: NH3, NO3Important to account for pollution swapping or synergetic effects of measurements targeting reduction of GHG emissions • Many models use Emission Factors (but not all) • Mechanistic NH3 requires wind, pH, etc…info • Mechanistic NO3- soil water transport modelling (complex)
- 11. Component 3: feed production grazing Bryant and Snow (2012) • How much herbage is produced? • Digestibility, protein? • How much N fixation?
- 12. Component 3: feed production grazing Specialized models e.g. ECOMOD+DairyMod (Johnson et al., 2008) • Grazing patterns • Spatial variability (urine, dung patches) vs “Other” models e.g. SIMSDAIRY (based on Brown et al., 2005 and Scholefield et al., 1996) • Semi-empirical • More uniform grasslands
- 13. Soil Carbon Respired CO2 ↓ ↓ ↓ Saturated flow ↓ Fixed CO2 ↓ ↓ Radiant energy Runoff C Stored C ↓ Leached C ↓ IFSM (Integrated Farm System Model)
- 14. But C field-scale modelling and experiments… Modelled with LANDDAIRY farm model+RothC Long-term effect on soil C stocks of applying slurry vs digestate vs compost 3 pools of SOC with different decomposition rate After RAMIRAN 2013 presentation (del Prado A. and Pardo G.)
- 15. Uncertainty-model structure • Complex model structure •More reliable results •More mitigation options • BUT – model parameterisation requirements much greater
- 16. Uncertainty-emission factors Foley et al. (2011) Clarke et al., (2013) Sensitivity analysis MC simulation • Emission factors •Considerable source of uncertainty •Soil N2O and carbon cycling
- 17. Interactions among farm components-key to mitigation
- 18. milk+meat concentrates CATTLE CATTLE purchased/sold forages silage grazed NH3 grazing N fixation DUNG URINE PLANT housing MANURE MANURE CO2 purchased/sold roots + stubbles SOIL Manipulation 1 (Animal)-Crude protein concentration N2O
- 19. higher energy lower protein milk+meat milk+meat concentrates CH4 CATTLE CATTLE purchased/sold forages silage grazed CH4 grazing N fixation DUNG URINE PLANT housing MANURE MANURE ∆volatile solids (VS) B0 CO2 purchased/sold roots + stubbles SOIL Manipulation 1 (Animal)-Crude protein concentration
- 20. milk+meat concentrates CATTLE CATTLE purchased/sold forages silage grazed grazing N fixation DUNG URINE PLANT CO2 housing MANURE MANURE NH3 roots + stubbles SOIL SOIL Manipulation 1 (Animal)-Crude protein concentration purchased/sold
- 21. milk+meat concentrates CATTLE CATTLE purchased/sold forages silage grazed grazing N fixation DUNG URINE PLANT housing MANURE MANURE CO2 purchased/sold roots + stubbles N2O SOIL SOIL ∆inorganic N NO3 Manipulation 1 (Animal)-Crude protein concentration
- 22. milk+meat concentrates CATTLE CATTLE purchased/sold forages silage grazed grazing N fixation DUNG URINE PLANT housing MANURE MANURE CO2 purchased/sold roots + stubbles SOIL SOIL ∆inorganic N Manipulation 1 (Animal)-Crude protein concentration
- 23. CH4 milk+meat concentrates ∆ crude protein concentration ∆ urine: dung ratio … CATTLE CATTLE purchased/sold forages silage grazed grazing N fixation DUNG URINE PLANT housing MANURE MANURE CO2 purchased/sold roots + stubbles SOIL SOIL Manipulation 1 (Animal)-Crude protein concentration
- 24. Confinement vs grazing 1200 Conc. Purchased 1000 ton DM / yr 800 Grain produced 600 Grazed forage 400 Hay & silage produced 200 0 Confined, High Confined, Moderate Confined with pasture Outdoors, all grass Rotz et al. (2009)
- 25. Confinement vs grazing 1.0 C-footprint: kg CO2e / kg ECM Secondary emissions Engine emissions Manure handling Net animal/feed 0.8 0.6 1000 800 Carbon dioxide Methane Nitrous oxide 600 400 200 0 0.4 0.2 0.0 Confined, High Confined, Moderate Confined with pasture Outdoors, all grass Rotz et al. (2009)
- 26. Confinement vs grazing C-footprint: kg CO2e / kg ECM 1.0 0.8 Secondary emissions Manure handling Engine emissions Net animal/feed 0.6 0.4 0.2 0.0 Confined, High Confined, Moderate Confined with pasture Rotz et al. (2009) (US) 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 Outdoors, all grass DEFRA-AC0209 (Anon, 2010) (UK) O’Brien et al. 2012 (IRL) • Results will be biased on specific definition of farm system and model • In US and UK cases GHG results are better for medium-grazing scenario
- 27. Modelling mitigation measures • • • • • • • • Ba: baseline Man: manure changes FQ: frequency of reseeding N-Fert: optimisation of mineral fertiliser N Fertilit: improving animal fertility Diet: optimising N intake LIP: adding lipid supplements DCD: applying nitrification inhibitors -Measures applied in combination may have interactions amongst each other -The reduction of GHG when we combine measures is not equal to adding the reduction effects from single measures. Del Prado et al.(2010)
- 28. Farm economics is an important factor for mitigation • Level of adoption will largely depend on economics • A number of models permit economic evaluation of mitigation strategies • A few models evaluate economics and GHG impacts together e.g. MACCs Foley et al. (2011)
- 29. Should we try to account for non-market values? PROVISION Milk Q quality Mil PROVISION 4.0 BAseline 1-Baseline MARKET VALUE 3.5 2 €/L leche 3.0 £/ milk 2.5 Biodiversity 2.0 4 Biodiv 1.5 3 (SOIL, FARM) 1.0 0.5 5 0.0 6 7 N2O/ha 8 Anim. Welfare 9 10 11 SUSTAINABLE Sustainable Animal Welfare +health Landscape LANDSCAPE Soil Q CULTURAL/ Soil quality ETICS Soil protection (structure, fertility) Ecosystem Services 11 farm scenarios showing results for different Ecosystem services Example taken from Del Prado et al. (2009) using the SIMSDAIRY model
- 30. Farm models should be able to be used for mitigation +adaptation to climate change impacts Start day of grass growing season SW YH WA 18 SC 110 100 90 80 70 YH WA SC 14 12 10 8 6 4 60 2 50 a SW 16 120 annual grass growth (t DM ha-1yr-1) average start day (Since 1st Jan) of grazing season 130 Grass productivity 0 baseline 2020 2050 2080 b baseline 2020 2050 2080 framework -Farm-models may be integrated in frameworks. -For most regions in the UK grass productivity and growing season will increase (about a month in 2020) but grass digestibility will decrease. -Adaptation may be increasing grazing for one month. Del Prado et al.(in prep.)
- 31. Farm models should be able to be used for mitigation +adaptation to climate change impacts South West England (example) C-footprint NH3 8.0 1700 g CO2-eq/l milk 7.5 gNH3/Lmilk 1600 1500 1400 1300 1200 7.0 6.5 6.0 5.5 1100 baseline scenario 2020 2020 (ADAPT) 25 20 15 10 5 5.0 1000 NO3- 30 g NO3-N/L milk NO3 1800 0 baseline 2020 scenario 2020 (ADAPT) baseline 2020 scenario 2020 (ADAPT) -More variable results for C-footprint and N leachate in 2020. -C-footprint decreases and NH3 and NO3- increase. -One month extra grazing (adaptation) has no effect on C-footprint but positive for NH3 and negative for NO3-. Del Prado et al.(in prep.)
- 32. Recommendations to improve farm modelling for quantification of GHG, mitigation and adaptation • We need to balance complexity in farm models • Quantifying uncertainties is essential (linkage of components and in relation to parameterisation). • We need better datasets against which to test farm scale models. • We need to improve simulation of soil C fluxes and N2O emissions.
- 33. Recommendations to improve farm modelling for quantification of GHG, mitigation and adaptation • Future farm models for mitigation and adaptation must be sufficiently sensitive to weather conditions and incorporate economics. • We need to test and compare farm scale simulation models for their sensitivity to climate change (temperature, precipitation and CO2). • Wider environmental and socio-economic impacts need to be considered when developing tailored recommendations. • Farm modelers should collaborate together.
- 34. Acknowledgements grant no. CGL2009-10176 grant no. PC2010-33A grant no. 266018 Also thanks to the Guest Editor (Nick Holden) and 2 anonymous reviewers