XVI International Silage Conference, Hämeenlinna, Finland, July 2012
Opportunities for reducing environmental emissions from forage‐based dairy farmsg y
Tom Misselbrook1, Agustin Del Prado2 and David Chadwick1
1Rothamsted Research, North Wyke, Okehampton, Devon UK2BC3, Basque Centre for Climate Change, Bilbao, Spain
Overview Introduction – Environmental impacts of dairy farms
Emissions to the atmosphereE i i t d d f tEmissions to ground and surface water
Potential mitigation methods Farm‐scale modelling Conclusions
IntroductionGro ing global demand for foodGrowing global demand for food
Source: United Nations Population Division, World Population Prospects: The 2010 Revision, medium variant (2011). Reay et al., 2012, Nature Climate Change 2, 410-416
Environmental impacts of food production
ENVIRONMENT PRODUCTION
SUSTAINABLE INTENSIFICATION
Environmental impacts
Pollution to air Pollution to water
Diffuse pollution
Soil degradation Loss of biodiversity Loss of biodiversity Loss of landscape value
Pollution to air
Greenhouse gases – methane, nitrous oxide, carbon dioxide
Ammonia
Non‐methane volatile organic compounds
Greenhouse gasesImpacts:Global warming
Importance:Globally agriculture contributes 10‐12% (IPCC 4th Assessment Report)
K CH N O d CO GWPKey gases CH4, N2O and CO2; GWP values of 25, 297 and 1
S
Stocker and Plattner, Copenhagen Dec 2009, www.ipcc.ch/presentations_and_speeches
Sources:Enteric fermentation by ruminants (CH4)Rice cultivation (CH )Rice cultivation (CH4)Manure management (CH4, N2O)Soils (N2O)( 2 )Fuel use (CO2)
AmmoniaImpacts:EutrophicationA idifi iAcidificationParticulate formationIndirect GHGIndirect GHG
Importance:Globally agriculture contributes c. 80%
Sources:Livestock manuresGra ing
PM2.5 BAU 2020, loss in life expectancyIIASA, 2008 Grazing
Fertiliser applications
Non‐methane volatile organic compoundsImpacts:Photochemical production of ground‐level ozone
Importance:From agriculture – uncertaingIncreasing requirements for accurate reporting
Cutting and drying of grass a potentially important source?
Pollution to water Nitrate Ammonium Phosphorus Sediments Pathogens Organic carbong
Haygarth (2005): “source‐mobilisation‐delivery‐impact” model
Potential mitigation methods
Livestock health
Di t i l ti Dietary manipulation
Crop nutrient management Crop nutrient management
Grazing management
Genetic potential
Examples using a farm‐scale model in the context of a dairy farm
Farm scale dairy model
N and P flows, transformations and losses from the soil‐plant‐animal system
Emissions of CH4
Animal requirements and performance
Farm economics
Oth tt ib t f t i bilit Other attributes of sustainability
Del Prado et al., (2011) Science of the Total EnvironmentDel Prado et al., (2011) Science of the Total Environment
Outputs from SIMSDAIRY
2.0average N
Mil QMilk quality
POLLUTANTS OTHER SUSTAINABILITY ATTRIBUTES
0 8
1.0
1.2
1.4
1.6
1.8
CH4/haaverage P
2.0
2.5
3.0
3.5
4.0Mil Q
Biodivk€/L milk Biodiversity
[Nitrate]
0.0
0.2
0.4
0.6
0.8
[ Phosphorus]0.0
0.5
1.0
1.5
N2O/haNox/haLandscapee LANDSCAPE
Animal Welfare+health
NH3/haGWP/haSoil QSoil quality
Del Prado et al., (2009) J. Agric. Sci.
Potential mitigation methods
Livestock health h l h f l l l l Improved health and fertility gives lower production losses and lower
emission intensity per unit product
25Replacements
Annual methane emission for a 100 cow herd at different fertility levels
20
per h
erd Cows Fertility level
A: CurrentB: 1995 levels
10
15
metha
ne p B: 1995 levels
C: ideal
5
Annu
al t m
0A B C A B C
A
6,000 l cows 9,000 l cows6,000 l cows 9,000 l cows
Garnsworthy, Animal Feed Sci Tech, 2004
Diet manipulation Feed additives
Largely aimed at methane reductionSeveral potential, but effects may be short‐livedp , y
Matching N and P requirementsOften fed in excessOften fed in excessO’Rourke: 43% reduction in P intake gave 63% reduction in excretionLower crude protein content diets, e.g. maize silage
Protecting N from rumen degradationInclusion of tannins or polyphenols in diet
Grazing sward compositionHigh sugar grassesHigh sugar grassesClovers/other legumes (polyphenols and tannins)
Dietary protein level and form
Cattle slurry applied to soils
5
6N
m-2
)
Protein level Protein form
4
sion
(g N
2
3
a em
iss
0
1
Amm
oni
0CP 19% CP 14% Alfalfa Lotus LT Lotus HT
A
Misselbrook et al, J Dairy Sci, 2005
SIMSDAIRY Diet Manipulation: HSG and Low CP Scenarios
• Methane EF per kg DMI modified (recentresults from sheep)
HSG
• Production and N excretion modified(Miller et al., 2001)
• Reseeding frequency same for HSG and g q yconventional
M i f ff t il• Maize grown on farm – offset grass silage• Diet change for dairy cows only• Decreased concentrate CP intake
-CP/+MAIZE
70 %
SIMSDAIRY Diet Manipulation: HSG and Low CP Scenarios
60 %
70 %
HSG – ‘win‐win’Maize – trade‐offs
40 %
50 %
chan
ge Maize – trade‐offs
20 %
30 %
edic
ted
c
HSG
-CP/+maize
10 %
20 %
%pr
e -CP/+maize
-10 %
0 %
-30 %
-20 %
-30 % Output parameter
Crop nutrient management
Fertiliser timing, rate and typeMatching crop requirements
LegumesLess fertiliser N
InhibitorsUreaseUreaseNitrification
Manure management Manure managementMethod, timing and rate of applicationRapid incorporation
Application rate and timing
y = ‐19460+19450*1.00301x25000
30000
35000
40000
‐1yr
‐1Nitrous oxide emissions from f tili li d t l d t
y =‐2780+4720*1.005x
5000
10000
15000
20000
Flux, g N hafertiliser applied to grassland at
different rates
y = 70+474*1.00812x
0
5000
0 100 200 300 400
Fertiliser applied, kg N ha‐1 yr‐1
Cae Banadl Rowden High Mowthorpe
120140160180
ed
grassland
bare fallow
rye cover crop
Cardenas et al., Agric Ecosys Env, 2010
406080100120
kg/ha N leache
Nitrate leaching from cattle
02040
control September October November December January
k
Application timing
slurry applied at different times
Application timing
Smith and Chambers, SUM, 1993
Inhibitors12
8
10
1
UU+7NBPT+U
Urease inhibitor (nBPT) Very effective (50‐80%) in reducing
4
6
g N
-NH
3ha
-1d-
1Very effective (50‐80%) in reducing NH3 emissions from urea fertiliser
0
2
0 2 4 6 8 10 12
kg
160 Days after applicationSanz et al., Atmos Env, 2011
120
140
160
1d-
1 )
ControlUrineUrine + DCD
Nitrification inhibitor (DCD) Very effective (50‐90%) in 60
80
100
ux (g
N2O
-N h
a-
reducing N2O emissions from urine depositions
0
20
40
aily
mea
n N
2O fl
-2006-Mar 26-Mar 15-Apr 05-May 25-May 14-JunD
a
Misselbrook, unpublished
SIMSDAIRY fertiliser use scenarios
120 dairy cows, target milk yield 7120 litres, summer grazing, winter grass and maize silage
Scenarios:Conventional – recommended fertiliser application rates, no clover
Tactical – optimised fertiliser application rates and timings
Organic – no mineral fertiliser, grass‐clover, manure all applied to maize
Del Prado et al., (2011) Science of the Total Environment
SIMSDAIRY fertiliser use scenarios: Outputs
0.8
1N2O
Conv
Tact
0.2
0.4
0.6
CH4NO3
Org
0
Milk quality
GHGNH3
P ll t t1
1.5
Pollutants
0
0.5 BiodiversitySoil quality
Net marginAnimal welfareOther attributes
Grazing management
Extended season or reduced seasonDepends on key objective and location
Potential trade‐offs between impacts
SIMSDAIRY output for extended grazing scenario
Management adaptation to climate change by 2020
Trade‐off air vs. water pollution
Genetic potential
PlantsN uptakep
N partitioning
C sequestration
Plant composition (PUFA)
A i l AnimalsN partitioning to milk
FertilitFertility
Low enteric methane potential
Conclusions
There is an increasing emphasis on maximising production while minimising environmental impacts
Improved efficiency of production can deliver major mitigation benefits:mitigation benefits:Improved livestock health and fertility
Matching diet to requirementsMatching diet to requirements
Attention to the quantity, timing and method of application of nutrients to forage crops
Utilising advances made through genetic improvements
Decision support tools recommended to explore alternatives and identify optimum site‐specific practices
Conclusions
Further research:
Genetic improvement – livestock and plant traitsProduction and environment
Alternative forages
Cost‐effective delivery mechanisms for inhibitors
Better accounting of forage quality (and factors influencing this) in farm scale models
Any questions?