TY - JOUR
T1 - Effect of feed-related farm characteristics on relative values of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain
AU - van Middelaar, C.E.
AU - Berentsen, P.B.M.
AU - Dijkstra, J.
AU - van Arendonk, J.A.M.
AU - de Boer, I.J.M.
PY - 2015
Y1 - 2015
N2 - Breeding has the potential to reduce greenhouse gas (GHG) emissions from dairy farming. Evaluating
the effect of a 1-unit change (i.e., 1 genetic standard deviation improvement) in genetic traits on GHG emissions
along the chain provides insight into the relative importance of genetic traits to reduce GHG emissions.
Relative GHG values of genetic traits, however, might depend on feed-related farm characteristics. The
objective of this study was to evaluate the effect of feed-related farm characteristics on GHG values by
comparing the values of milk yield and longevity for an efficient farm and a less efficient farm. The less efficient
farm did not apply precision feeding and had lower feed production per hectare than the efficient
farm. Greenhouse gas values of milk yield and longevity were calculated by using a whole-farm model and
2 different optimization methods. Method 1 optimized farm management before and after a change in genetic
trait by maximizing labor income; the effect on GHG emissions (i.e., from production of farm inputs up to
the farm gate) was considered a side effect. Method 2 optimized farm management after a change in genetic
trait by minimizing GHG emissions per kilogram of milk while maintaining labor income and milk production
at least at the level before the change in trait; the effect on labor income was considered a side effect.
Based on maximizing labor income (method 1), GHG values of milk yield and longevity were, respectively,
279 and 143 kg of CO2 equivalents (CO2e)/unit change per cow per year on the less efficient farm, and 247
and 210 kg of CO2e/unit change per cow per year on the efficient farm. Based on minimizing GHG emissions
(method 2), GHG values of milk yield and longevity were, respectively, 538 and 563 kg of CO2e/unit change
per cow per year on the less efficient farm, and 453 and 441 kg of CO2e/unit change per cow per year on
the efficient farm. Sensitivity analysis showed that, for both methods, the absolute effect of a change in genetic
trait depends on model inputs, including prices and emission factors. Substantial changes in relative importance between traits due to a change in model inputs occurred only in case of maximizing labor income. We concluded that assumptions regarding feed-related farm characteristics affect the absolute level of GHG values, as well as the relative importance of traits to reduce emissions when using a method based on maximizing labor income. This is because optimizing farm management based on maximizing labor income does not give any incentive for lowering GHG emissions. When using a method based on minimizing GHG emissions, feedrelated farm characteristics affected the absolute level of the GHG values, but the relative importance of the traits scarcely changed: at each level of efficiency, milk yield and longevity were equally important. Key words: breeding, milk yield, longevity, economic value.
AB - Breeding has the potential to reduce greenhouse gas (GHG) emissions from dairy farming. Evaluating
the effect of a 1-unit change (i.e., 1 genetic standard deviation improvement) in genetic traits on GHG emissions
along the chain provides insight into the relative importance of genetic traits to reduce GHG emissions.
Relative GHG values of genetic traits, however, might depend on feed-related farm characteristics. The
objective of this study was to evaluate the effect of feed-related farm characteristics on GHG values by
comparing the values of milk yield and longevity for an efficient farm and a less efficient farm. The less efficient
farm did not apply precision feeding and had lower feed production per hectare than the efficient
farm. Greenhouse gas values of milk yield and longevity were calculated by using a whole-farm model and
2 different optimization methods. Method 1 optimized farm management before and after a change in genetic
trait by maximizing labor income; the effect on GHG emissions (i.e., from production of farm inputs up to
the farm gate) was considered a side effect. Method 2 optimized farm management after a change in genetic
trait by minimizing GHG emissions per kilogram of milk while maintaining labor income and milk production
at least at the level before the change in trait; the effect on labor income was considered a side effect.
Based on maximizing labor income (method 1), GHG values of milk yield and longevity were, respectively,
279 and 143 kg of CO2 equivalents (CO2e)/unit change per cow per year on the less efficient farm, and 247
and 210 kg of CO2e/unit change per cow per year on the efficient farm. Based on minimizing GHG emissions
(method 2), GHG values of milk yield and longevity were, respectively, 538 and 563 kg of CO2e/unit change
per cow per year on the less efficient farm, and 453 and 441 kg of CO2e/unit change per cow per year on
the efficient farm. Sensitivity analysis showed that, for both methods, the absolute effect of a change in genetic
trait depends on model inputs, including prices and emission factors. Substantial changes in relative importance between traits due to a change in model inputs occurred only in case of maximizing labor income. We concluded that assumptions regarding feed-related farm characteristics affect the absolute level of GHG values, as well as the relative importance of traits to reduce emissions when using a method based on maximizing labor income. This is because optimizing farm management based on maximizing labor income does not give any incentive for lowering GHG emissions. When using a method based on minimizing GHG emissions, feedrelated farm characteristics affected the absolute level of the GHG values, but the relative importance of the traits scarcely changed: at each level of efficiency, milk yield and longevity were equally important. Key words: breeding, milk yield, longevity, economic value.
KW - life-cycle assessment
KW - genomic selection
KW - economic values
KW - milk-production
KW - methane
KW - cattle
KW - mitigation
KW - impact
KW - level
KW - model
U2 - 10.3168/jds.2014-8310
DO - 10.3168/jds.2014-8310
M3 - Article
SN - 0022-0302
VL - 98
SP - 4889
EP - 4903
JO - Journal of Dairy Science
JF - Journal of Dairy Science
IS - 7
ER -