modelling the impact on ghg emissions of using underutilized feed resources in dairy goat systems in...
TRANSCRIPT
Modelling the impact on GHG emissions of using underutilized feed resources in dairy goat
systems in Spain
G Pardo1, D Yañez-Ruiz2, I Martin-Garcia2, A Arco2, R Moral3 and A del Prado1 1 Basque Centre For Climate Change (BC3)
2 Estacion Experimental del Zaidín. Consejo Superior de Investigaciones Científicas (CSIC). 3 Dpto Agroquímica y Medio Ambiente, Universidad Miguel Hernández de Elche (UMH)
Summary
I. Introduction
II. Aim of the study
III. Modelling framework
IV. Impact assessment results
V. Conclusions
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Introduction
Dairy goat production:
• Relevant in several areas of Mediterranean European countries
• Traditionally associated to extensive or semi-extensive production systems:
Capacity of this type of livestock to turn low quality forage into products of a high feeding value
Enhancing the use of pastures unsuitable for crop cultivation, such as shrublands, mountainous or semi-desert regions
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Introduction
Dairy goat production:
• High dependence on external concentrates and forages
Rapid intensification during the last years
Severe climatic periods (e.g. droughts) (More likely in the future according to climate change projections)
• Use of unconventional feedstuffs: food industry byproducts
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Objective
Aim of the study: explore through life cycle assessment (LCA) the whole-farm environmental impacts of replacing a typical dairy goat diet in south Spain with two alternative diets including silages made with tomato wastes from horticulture or olive oil by-products.
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Functional unit: 1 kg FPCM of dairy goat
Scenarios:
• Conventional diet
• Alternative diet A: including silage of tomato wastes
• Alternative diet B: including silage of olive cake and leaves
Modelling framework
System boundaries:
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Dairy goat farm
Alternative feedstuffs: -Tomato waste -Olive oil byproducts
Emissions (CH4)
Milk (1kg FPCM)
Manure storage
Feed inputs: -Alfalfa hay (30%) -Oat grain (20%) -Soybean meal (10%) -Sugarbeet pulp (10%) -Maize grain (10%) -Oat hay (20%)
Silage
Goat meat
Emissions (NH3, N2O, CH4)
Crop production
Emissions (NH3, N2O)
Fertilisers
Composting treatment
(NH3, N2O, CH4)
Compost
Avoided emissions
Oat hay (20%)
Avoided emissions
Barley
Modelling framework
Herd modelling: Key biological parameters required for dairy goat
herd modelling: (mortality, fertility, growth rate, replacement rate)
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Modelling framework
Experimental data:
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Diet A: Control
Diet B: Olive by-products
Diet C: Tomato wastes
DMI, g/d 916 1426 1286
Milk production, g/d 1010 1285 1387
Fat, g/kg 58.4 54.6 60.3
CP, g/kg 40.2 36.8 34.4
Lactose, g/kg 46.5 45.3 47.2
Total solids, g/kg 154 145 150
FPCM, kg/d 0.90 1.08 1.22
Methane production, l/kg DMI 31.9 29.3 28.7
Feces
excreted g/day 496 525 543
%N 1.9 1.65 1.71
g N excreted/day 9.4 8.7 9.3
Urine
excreted g/day 625 632 618
%N 0.45 0.43 0.44
g N excreted/day 2.8 2.7 2.7
Modelling framework
Solid waste management model: composting
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GAS EMISSIONS
CO2-C 35.3 Tn C/year N2O-N 0.03 Tn N/year
CH4-C (%) 0.9 Tn C/year NH3-N 0.39 Tn N/year
MIXTURE Total C 36.2 Tn C/year Total N 0.41 Tn N/year
1100 Tn/year VS 65.1 Tn VS/year
200 Tn TS/year COMPOST
173.6 Tn VS/year 225 Tn/year
96.4 Tn C/year 135 Tn TS/year
2.78 Tn TN/year 108 Tn VS/year
2.1 Tn P/year 60 Tn C/year
2.1 Tn K/year 2.4 Tn TN/year
2.1 Tn P/year
RUN-OFF 0.01 Tn TN/year 2.0 Tn K/year
0.04 Tn P/year N 0.1 Tn Ninorg
0.14 Tn K/year 2.2 Tn Norg
Results
Impact assessment:
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Results
Impact assessment: GHG emissions
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Conclusions
Results suggest that the new dietary strategies tested offer promising overall reductions in environmental burdens without productivity being compromised.
GHG reductions estimated are mainly associated to N2O and CO2 emissions (from feed production stages) and enteric CH4 (in the case of tomato wastes)
These findings must be taken with caution as food industry by-products could have alternative and competing uses, other than feed, such as energy production and soil organic amendment. Assumptions made and categories evaluated should be considered carefully in a decision making process.
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G Pardo1, D Yañez-Ruiz2, I Martin-Garcia2, A Arco2, R Moral3 and A del Prado1 1 Basque Centre For Climate Change (BC3)
2 Estacion Experimental del Zaidín. Consejo Superior de Investigaciones Científicas (CSIC). 3 Dpto Agroquímica y Medio Ambiente, Universidad Miguel Hernández de Elche (UMH)
THANKS FOR YOUR ATTENTION
Funded by Spanish National R+D+i Plan (AGL2012-37815-C05-04 and AGL2011-27218) and
European Commission FP7-SOLID (ref. 266367).
This work has been produced within the context of the REMEDIA network
http://redremedia.wordpress.com/