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Estimating the impact of clinical mastitis in dairy cows on greenhouse gas emissions using a dynamic stochastic simulation model: a case study

Published online by Cambridge University Press:  18 June 2019

P. F. Mostert ,
E. A. M. Bokkers ,
I. J. M. de Boer  and
C. E. van Middelaar
P. F. Mostert*
Affiliation:
Animal Production Systems Group, Wageningen University and Research, PO Box 338, 6700 AH Wageningen, The Netherlands
E. A. M. Bokkers
Affiliation:
Animal Production Systems Group, Wageningen University and Research, PO Box 338, 6700 AH Wageningen, The Netherlands
I. J. M. de Boer
Affiliation:
Animal Production Systems Group, Wageningen University and Research, PO Box 338, 6700 AH Wageningen, The Netherlands
C. E. van Middelaar
Affiliation:
Animal Production Systems Group, Wageningen University and Research, PO Box 338, 6700 AH Wageningen, The Netherlands
*
†E-mail: pimmostert@hotmail.com
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Abstract

The increasing attention for global warming is likely to contribute to the introduction of policies or other incentives to reduce greenhouse gas (GHG) emissions related to livestock production, including dairy. The dairy sector is an important contributor to GHG emissions. Clinical mastitis (CM), an intramammary infection, results in reduced milk production and fertility, increases culling and mortality of cows and, therefore, has a negative impact on the efficiency (output/input) of milk production. This may increase GHG emissions per unit of product. Our objective was to estimate the impact of CM in dairy cows on GHG emissions of milk production for the Dutch situation. A dynamic stochastic simulation model was developed to simulate the dynamics and losses of CM for individual lactations. Cows receive a parity (1 to 5+), a milk production and a calving interval (CI). Based on the parity, cows have a risk of CM, with a maximum of three cases in a lactation. Pathogens causing CM were classified as gram-positive bacteria, gram-negative bacteria, or other. Based on the parity and pathogen combinations, cows had a reduced milk production, discarded milk, prolonged CI and a risk of removal (culling and mortality) that reduce productivity of dairy cows and therefore increase GHG emissions per unit of product. Using life cycle assessment, emissions of GHGs were estimated from cradle to farm gate for processes along the milk production chain that are affected by CM. Processes included were feed production, enteric fermentation, and manure management. Emissions of GHGs were expressed as kg CO2 equivalents per ton of fat-and-protein-corrected milk (kg CO2e/t FPCM). Emissions of cows with CM increased on average by 57.5 (6.2%) kg CO2e/t FPCM compared with cows without CM. This increase was caused by removal (39%), discarded milk (38%), reduced milk production (17%) and prolonged CI (6%). The GHG emissions increased by 48 kg CO2e/t FPCM for cows with one case of CM, by 69 kg CO2e/t FPCM for cows with two cases of CM and by 92 kg CO2e/t FPCM for cows with three cases of CM compared with cows without CM. Preventing CM can be an effective strategy for farmers to reduce GHG emissions and can contribute to sustainable development of the dairy sector, because this also can improve the income of farmers and the welfare of cows. The impact of CM on GHG emissions, however, will vary between farms due to environmental conditions and management practices.

Keywords

diseasehealthenvironmental impactcarbon footprintmodeling

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Type
Research Article
Information
animal , Volume 13 , Issue 12 , December 2019 , pp. 2913 - 2921
Copyright
© The Animal Consortium 2019 

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