1. Will Genomic Selection for Increased Feed Efficiency Improve the Profitability and Sustainability of Dairy Farms?
David Worden* & Getu Hailu
Food, Agricultural & Resource Economics
University of Guelph *Contact:

2. 58% Increase in Global Dairy Demand by 2050 (FAO)
What will the environmental impact be?
Technical progress –productivity increases: international competitiveness, food security.

3. Contribution
Growing body of research on developing genomic selection to breed for increased feed efficiency and reduced methane emissions in dairy cattle
Few studies on the economic impact of adopting genomic technology at farm level in livestock
No study examined the net economic and environmental benefits from adopting genomic selection for increased feed efficiency and reduced methane emissions on dairy farms

4. Summary of Results
We find positive net returns from investment ($19.1 – $25.61 per cow per year) in genomic selection for increased feed efficiency but the results are sensitive to the reliability of the technology and the percentage increase in feed efficiency
Estimated methane reductions range between 3.76 and tonnes in total over 25-year period
Incentive to increase scale of operation under adoption

5. Objectives
To provide ex ante estimate of the net financial and environmental benefit from the adoption of genomic selection for feed efficiency.
To identify potential constraints to adoption or factors that limit the net benefit.

6. Technology Adoption
Farmers reduce the amount of feed needed and produce the same amount of milk
Farmers both reduce feed use and increase milk output
Farmers use the same amount of feed but produce more milk

7. Genomics and Genomic Selection
Establishing the link between genotype (genetic makeup) and phenotype (expressed physical traits of the animal)
Genotyping can be used in young animals (such as dairy heifers) to establish whether they are likely to possess certain traits (such as feed efficiency) in their adult life
Livestock producers have been selectively breeding for millennia, genomics provides more information and speeds up the process
Two methods of adoption: artificial insemination (from genotyped bulls) or genotyping of heifers and keeping those with desired trait

8. Feed Efficiency & Reduced Methane Emissions
Feed is the largest variable cost for most dairy operations
Feed efficiency
the genetic trait that allows cows to produce the same amount of milk while consuming less feed relative to the average
Selecting for feed efficiency has not been economically feasible in the past because tracking phenotypic outcomes is prohibitively expensive
Genomic technology has provided the first real opportunity to select for feed efficiency traits

9. Lagged Technology Adoption for Genomic Selection

10. Literature Pryce et al., (2015) De Haas et al., (2011)
genomics makes it possible to select for feed efficiency for the first time (lower cost to track genotypes rather than measure phenotypic outcomes)
De Haas et al., (2011)
positive correlation between feed intake and methane emissions, selection for feed efficiency may hold benefit as an environmental management practice as well

11. Literature (cont.) Hailu et al., (2016) - Contingent valuation
established Canadian dairy producers are willing to pay to genotype for novel traits (chronic mastitis in this case)
Goddard et al., (2016) – Capital budgeting
net benefit from adoption of genomic selection for feed efficiency in Canadian beef industry
adoption unlikely due to asymmetry of incentives through the supply-chain
did not consider the role that predictive accuracy/reliability (uncertainty) plays in adoption

12. Discounted Cash Flow Model
where ∆𝑁𝑃𝑉 is the change in the net present value, equal to the difference in the sum of discounted profits between the adopter farm (g=1) and non-adopter farm (g=0) over a 25-year period, and r is the discount rate

13. Empirical Model
Monte Carlo simulation with stochastic optimization
Random variables: feed intake, prices, genomic prediction accuracy, milk yield, land prices, feed yield per hectare, methane conversion ratio, and calf sex ratio
We model a 3% reduction in daily feed intake for cows and heifers selected for feed efficiency
Most values parameterized from survey of Ontario dairy farms
Constant scale, Expansion, and Contraction scenarios
Simplified estimate of value from the real option to delay adoption

14. Scenario Output Growth AI Genotype Year AI Year Genotype
Feed Reduction (t)
CH4 Reduction (t)
ΔNPV
per Cow
Prob [Neg Return]
Base 0%
Yes
522
10.43
$51,433
$23.55
44.0% A* No -
349
7.00
$52,509
$24.04
40.9% B 5%
1,059
21.17
$105,530
$24.58
44.1% C
698
13.98
$98,069
$22.84
44.2% D* 2
1,050
20.99
$109,962
$25.61 E
-5%
282
5.61
$23,626
$19.10
45.1% F*
190
3.758
$27,153
$21.95
41.1%
Asterisks to denote optimization results

15. Results (cont.)
Base model – $23.55 net benefit per cow from immediate and full adoption of both artificial insemination and genotyping of heifers
5% expansion – delay genotyping by 2 years, $25.61 per cow
5% contraction – only adoption of artificial insemination is optimal, $21.95 per cow
Methane reduction between 3.76 to tonnes in total over 25 years

16. Implications
Positive return from genomic selection for feed efficiency
High probability of negative returns (approx. 44%) and high degree of uncertainty about key parameters (feed intake values) and accuracy of genomic technologies – further study needed
Adoption of both artificial insemination and genotyping of heifers is only optimal under expansion scenarios

17. Implications (cont.)
Returns on investment in genomic selection is affected by:
Changes in demand and prices of inputs (e.g., feed)
Structure of production (e.g., farm size)
Accuracy of genomic prediction
Positive value from delaying adoption only present for genotyping heifers under expansion
These results do not include the social benefit from reduced methane emissions. Might there be grounds for subsidizing adoption?

18. Thank you I am happy to answer any Questions

19. References
De Haas, Y., J.J. Windig, M.P.L. Calus, J. Dijkstra, M. de Haan, A. Bannink, and R.F. Veerkamp. (2011). Genetic Parameters for Predicted Methane Production and Potential for Reducing Enteric Emissions through Genomic Selection. Journal of Dairy Science, 94,
FAO (Food and Agriculture Organization of the United Nations). (2011). World Livestock 2011: Livestock in Food Security. FAO, Rome, Italy.
Goddard, E., A. Boaitey, G. Hailu, and K. Poon. (2016). Improving Sustainability of Beef Industry Supply Chains. British Food Journal, 118(6),
Hailu, G., Y. Cao, and X. Yu. (2016). Risk Attitudes, Social Interactions, and the Willingness to Pay for Genotyping in Dairy Production. Canadian Journal of Agricultural Economics, 00, 1-25.
Pryce, J.E., O. Gonzalez-Recio, G. Nieuwhof, W.J. Wales, M.P. Coffey, B.J. Hayes and M.E. Goddard. (2015). Hot Topic: Definition and Implementation of a Breeding Value for Feed Efficiency in Dairy Cows. Journal of Dairy Science, 98, 7340–7350.

20. Acknowledgements