Literature DB >> 24881792

Methods to determine the relative value of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain.

C E van Middelaar1, P B M Berentsen2, J Dijkstra3, J A M van Arendonk4, I J M de Boer5.   

Abstract

Current decisions on breeding in dairy farming are mainly based on economic values of heritable traits, as earning an income is a primary objective of farmers. Recent literature, however, shows that breeding also has potential to reduce greenhouse gas (GHG) emissions. The objective of this paper was to compare 2 methods to determine GHG values of genetic traits. Method 1 calculates GHG values using the current strategy (i.e., maximizing labor income), whereas method 2 is based on minimizing GHG per kilogram of milk and shows what can be achieved if the breeding results are fully directed at minimizing GHG emissions. A whole-farm optimization model was used to determine results before and after 1 genetic standard deviation improvement (i.e., unit change) of milk yield and longevity. The objective function of the model differed between method 1 and 2. Method 1 maximizes labor income; method 2 minimizes GHG emissions per kilogram of milk while maintaining labor income and total milk production at least at the level before the change in trait. Results show that the full potential of the traits to reduce GHG emissions given the boundaries that were set for income and milk production (453 and 441kg of CO2 equivalents/unit change per cow per year for milk yield and longevity, respectively) is about twice as high as the reduction based on maximizing labor income (247 and 210kg of CO2 equivalents/unit change per cow per year for milk yield and longevity, respectively). The GHG value of milk yield is higher than that of longevity, especially when the focus is on maximizing labor income. Based on a sensitivity analysis, it was shown that including emissions from land use change and using different methods for handling the interaction between milk and meat production can change results, generally in favor of milk yield. Results can be used by breeding organizations that want to include GHG values in their breeding goal. To verify GHG values, the effect of prices and emissions factors should be considered, as well as the potential effect of variation between farm types.
Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  economic value; life cycle assessment; longevity; milk yield

Mesh:

Year:  2014        PMID: 24881792     DOI: 10.3168/jds.2013-7413

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  5 in total

1.  Derivation of economic values for production traits in aquaculture species.

Authors:  Kasper Janssen; Paul Berentsen; Mathieu Besson; Hans Komen
Journal:  Genet Sel Evol       Date:  2017-01-05       Impact factor: 4.297

2.  Response to a selection index including environmental costs and risk preferences of producers.

Authors:  Beshir M Ali; John W M Bastiaansen; Yann de Mey; Alfons G J M Oude Lansink
Journal:  J Anim Sci       Date:  2019-01-01       Impact factor: 3.159

3.  Effect of production quotas on economic and environmental values of growth rate and feed efficiency in sea cage fish farming.

Authors:  M Besson; I J M de Boer; M Vandeputte; J A M van Arendonk; E Quillet; H Komen; J Aubin
Journal:  PLoS One       Date:  2017-03-13       Impact factor: 3.240

4.  The genetic correlation between feed conversion ratio and growth rate affects the design of a breeding program for more sustainable fish production.

Authors:  Mathieu Besson; Hans Komen; Gus Rose; Marc Vandeputte
Journal:  Genet Sel Evol       Date:  2020-02-07       Impact factor: 4.297

5.  Effects of dry period length on production, cash flows and greenhouse gas emissions of the dairy herd: A dynamic stochastic simulation model.

Authors:  Akke Kok; Corina E van Middelaar; Pim F Mostert; Ariëtte T M van Knegsel; Bas Kemp; Imke J M de Boer; Henk Hogeveen
Journal:  PLoS One       Date:  2017-10-27       Impact factor: 3.240

  5 in total

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