Measuring Greenhouse Gases Emissions from Animal Operations in Environmental Rooms Zifei Liu and Wendy Powers D e p a r t m e n t s o f A n i m a l S c i e n c e a n d B i o s y s t e m s & A g r i c u l t u r e E n g i n e e r i n g
Background Animal operations are important sources of anthropogenic GHG, mostly of CH 4 and N 2 O. Measurements of both CH 4 and N 2 O from animal operations have large uncertainties. Dietary strategies have been studied to reduce air emissions while maintaining animal performance (Powers et al., 2007) . Air emissions from different species were measured in environmental rooms in 17 studies.
Objectives Compare CH 4 measurements from 55C and INNOVA analyzer Summarize the measured GHG emissions and compare with the IPCC values Summarize the effects of various dietary strategies on GHG emissions
Animals and Housing AAQRF at Michigan State University. 12 environmental rooms Each room: H 2.14 m W 3.97 m L 2.59 m
Animals and Housing Cow, heifer or Finishing pigs Turkeys Broiler chickens Laying hens Steer (1) (6) (20) (50) (56-80)
Air sampling and measurement system Incoming air NH 3 , NO, NO 2 Model Room1 17C Room2 Room3 Gas samples Model CH 4 Room4 were Sampling 55C sequentially manifold Room5 monitored Room6 from each BINOS room and Room7 incoming air CO 2 / O 2 Room8 Room9 Room10 CO 2 , CH 4 , N 2 O, Room11 INNOVA NMTHC, NH 3 Room12
Data collection and analysis Software control (LabVIEW v. 8.2) Purge for 9.5 min, data collection for 5.5 min One measurement cycle is 195 min. 7 to 8 daily observations per room Data analyzed using mixed model (SAS v. 9.1) Date was a random variable and room was treated as nested term within diet
Species and diets of the 17 studies Laying Finishing Dairy Broilers Turkeys Steers Heifers hens pigs cows 0, 40%, 60% 0 and 15% DDGS 0 and 20% Reduced DDGS 2*2 DDGS Typical N Western 100%, 110% vs. control High and Midwestern NRC 0, 60% or 60% low rumen or 0,10%,20% 2, 3AA DDGs plus degraded Southeastern added copper DDGS protein U.S. diets and molybdenum 15% DDGS 2*2 with or 0, 20% DDGS 3*2 without organic or Reduced N microbial or Quillaja, inorganic and litter chemical yucca, or no trace minerals amendment additive High and extract Typical PLT low rumen Western degraded Midwestern protein With or or Quillaja, without Southeastern yucca, or no supplemental U.S. diets extract methionine
Air measurements from different instruments BINOS 0.001ppm 100ppm 17C NH 3 CO 2 INNOVA 0.2ppm INNOVA 5.1ppm R 2 =0.95 R 2 =0.96 55C 0.02ppm CH 4 INNOVA 0.1ppm R 2 =0.60
CH 4 measurements from 55C and INNOVA 25 5 y = 1.1692x - 1.3693 CH 4 concentrations by INNOVA (ppm) R² = 0.60 4 20 3 15 2 INNOVA CH 4 10 __________ 1 55C CH 4 5 0 0 5 10 15 20 0 -1 0 5 10 15 20 25 -5 -2 CH 4 concentrations by the Model 55C analyzer CH 4 concentrations by the Model 55C analyzer (ppm) (ppm)
RPD of CH 4 measurements from 55C and INNOVA 2000% 2000% RPD of CH 4 from the two instruments RPD of CH 4 from the two instruments 1800% 1800% 1600% 1600% 1400% 1400% 1200% 1200% 1000% 1000% 800% 800% 600% 600% 400% 400% 200% 200% 0% 0% 400 600 800 1000 1200 1400 1600 5 10 15 20 25 Temperature ( o C) CO 2 concentrations (ppm)
RPD of CH 4 measurements from 55C and INNOVA 1800 Bubble size represent RPD of CH 4 measurements 1600 1400 CO 2 concentration (ppm) 1200 1000 800 600 400 200 0 5 10 15 20 25 30 Temperature ( o C)
Estimating CH 4 emission using the IPCC approaches Enteric Manure CH 4 fermentation Total CH 4 emission CH 4 emission emission =VS·B o ·0.67 ·MCF Swine: 4.2 g head -1 day -1 Steers: 145 g head -1 day -1 Dairy cows: 351 g head -1 day -1 Excreted CH4 conversion volatile solid factor maximum CH4 producing capacity
Comparison of measured CH 4 with the IPCC values 0.35 600 Meaured emissions Meaured emissions 0.3 CH 4 emission rate (g head -1 day -1 ) 500 CH 4 emission rate (g head -1 day -1 ) IPCC estimated IPCC estimated 0.25 400 0.2 300 0.15 200 0.1 100 0.05 0 0 SW0109 SW0209 ST0109 ST0209 ST0110 ST0210 HF0108 HF0208 DY0108 DY0208 BR0108 BR0208 LY0108 LY0109 LY0209 LY0309 TY0108 Codes of studies Codes of studies
Comparison of measured CH 4 with the IPCC values CH 4 emission rate g hd -1 day -1 Species (1)/(2) Measured (1) IPCC (2) Broiler 0.02±0.05 0.036 56% Laying hen 0.03±0.03 0.078 38% Turkey 0.25±0.05 0.25 100% Finishing pig 3.4±2.0 6.9 49% Steer 53±23 151 35% Heifer 220±71 368 60% Dairy cow 399±88 368 108%
Estimating N 2 O emission using the IPCC approaches N N 2 O Direct excretion emission N 2 O rate factor emission 0.31-1.10 0.001-0.02 kg N (1000kg BW) -1 day -1 kg N 2 O-N/kg N
Comparison of measured N excretion rate with the IPCC values 1.2 Measured IPCC 1 kg N (1000kg BW) -1 day -1 0.8 N excretion rate 0.6 0.4 0.2 0 BR0108 BR0208 LY0108 LY0109 LY0209 LY0309 TY0108 SW0109 SW0209 HF0108 HF0208 DY0108 DY0208 Codes of studies
Comparison of measured N 2 O with the IPCC values N 2 O emission rate g kgBW -1 day -1 Species (1)/(2) Measured (1) IPCC (2) Broiler 0.10±0.12 0.0017 59 Laying hen 0.04±0.02 0.0013 31 Turkey 0.05±0.13 0.0012 42 Finishing pig 0.010±0.005 0.0013 8 Steer 0.004±0.006 0.0010 4 Heifer 0.014±0.006 0.0013 11 Dairy cow 0.020±0.006 0.0013 15
GHG emissions in CO 2 e 160 N2O 140 (g kgBW -1 day -1 in CO 2 e) CH4 120 GHG emission CO2 100 80 60 40 20 0 broiler Laying hen Turkey Finishing Steer Heifer Dairy cow pig
Effects of dietary strategies on CH 4 emissions Laying Finishing Dairy Broilers Turkeys Steers Heifers hens pigs cows 0, 40%, 60% 0 and 15% DDGS DDGS 0 and 20% Reduced 2*2 DDGS Typical N Western 100%, 110% vs. control High and Midwestern NRC 0,10%,20% 0, 60% or 60% low rumen or 2, 3AA DDGS DDGs plus degraded Southeastern added copper protein U.S. diets and molybdenum 2*2 15% DDGS 0, 20% DDGS with or 3*2 organic or without Reduced N inorganic microbial or Quillaja, and litter trace minerals chemical yucca, or no amendment additive High and extract Typical PLT low rumen Western degraded Midwestern With or protein or Quillaja, without Southeastern yucca, or no supplemental P<0.10 U.S. diets extract methionine
Effects of diet DDGS on CH 4 emissions 0.020 0.05 CH 4 emmison (g head -1 day -1 ) CH 4 emmison (g head -1 day -1 ) LY0209 LY0108 0.04 (P=0.02) 0.015 (P=0.01) 0.03 0.010 0.02 b b ab a b 0.005 0.01 a 0.00 0.000 0%DDGS 0%DDGS 20%DDGS 20%DDGS 0%DDGS 15%DDGS In Org In Org CH 4 emmison (g head -1 day -1 ) 3 CH 4 emmison (g head -1 day -1 ) 8 SW0109 (P<0.01) SW0209 (P<0.01) 6 2 4 c b 2 b 1 a a a a 0 Control 20%DDGS 20%DDGS 0 In Org A:15%DDGS B: A+Reduced C: B+Microb D: B+Chem N
Effects of diet CP on CH 4 emissions CH4 emission rate CP content in feed 600 18 CH 4 emission (g head -1 day -1 ) DY0108 (P=0.08) DY0208 (P=0.07) 17 CP content in diets (%) 500 16 400 15 300 14 a 13 a 200 b b 12 b b 100 11 // 0 10 MW S W MW S W Diet
Effects of dietary strategies on N 2 O emissions Effects of dietary strategies on N 2 O emissions were not observed in any of the 17 studies at α =0.10 level.
Conclusions Large discrepancy between the INNOVA and the Model 55C CH 4 measurements were observed when air temperatures were relatively high (21 to 25 o C) and CO 2 concentrations were low. The measured CH 4 emission rates were comparable with the IPCC estimated values. The measured N 2 O emission rates were much higher than the IPCC values, especially for poultry (dry manure handling system), indicating an underestimation of the IPCC N 2 O emission factors.
Conclusions Poultry had lower CH 4 emission rates than dairy cows, heifers and steers, but poultry had much higher N 2 O emission rates in g kg BW -1 day -1 . As a result, poultry had similar GWP with dairy cows, heifers and steers, if not higher, as expressed in g kg BW -1 day -1 CO 2 e. Diets with higher content of DDGS resulted in higher CH 4 emissions from laying hens and swine operations. Diets with higher CP content resulted in higher CH 4 emissions from dairy cow operations. The N 2 O emissions were not influenced by the applied dietary strategies.
Implications The results suggested conditions when either instrument for CH 4 measurement is acceptable (temp, concentration) Diet strategies merit further investigation for both ruminant and non-ruminant species.
Acknowledgements This project was supported by National Research Initiative Competitive Grant nos. 2003-35112-17916, 2005-35112- 17912, and 2008-55112-18827 from the USDA Cooperative State Research, Education, and Extension Service Air Quality Program. Funding was provided through USDA Cooperative Agreement Number 2005-35102-15356 and 2005-35112- 17912.
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