Bull efficiency using dairy genetic traits


Autoři: Christine E. Whitt aff001;  Loren W. Tauer aff002;  Heather Huson aff003
Působiště autorů: United States Department of Agriculture, Economic Research Services, Washington D.C., United States of America aff001;  Charles H. Dyson School of Applied Economics and Management, Cornell SC Johnson College of Business, Cornell University, Ithaca, New York, United States of America aff002;  Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223436

Souhrn

Dairy bulls are evaluated using progeny data and genomic testing to determine the quantity of specific traits that they will pass to their daughters. Some bulls excel in some traits but not others. Specifying these various traits as outputs, with the single input of insemination, technical, revenue, allocative, and profit efficiency of bulls available for artificial insemination are estimated using Free Disposal Hull. Although bulls generally are highly technically efficient, because only high performing bull semen is offered for sale, bulls are less revenue, allocative and profit efficient. These efficiencies are relative to peer bulls and can be updated as new bulls become available.

Klíčová slova:

Crop genetics – Economics – Fats – Insemination – Milk – Pregnancy – Semen – Allocative efficiency


Zdroje

1. Mareth T, Thome AM T, Scavarda LF, Oliveira FLC. Technical efficiency in dairy farms; research framework, literature classification and research agenda. International Journal of Productivity and Performance Management. 2017; 66: 380–404.

2. Jaenicke EC, Lengnick LL. A soil-quality index and its relationship to efficiency and productivity growth measures: two decompositions. American Journal of Agricultural Economics. 1999; 81: 881–893.

3. Nalley LL, Barkley AP, Featherstone AM. The genetic and economic impact of the CIMMYT wheat breeding program on local producers in the Yaqui Valley, Sonora, Mexico. Agricultural Economics. 2010; 41: 453–462.

4. Babcock BA, Foster WE. Measuring the potential contribution of plant breeding to crop yields: Flue-cured Tobacco, 1954–87. American Journal of Agricultural Economics. 1991; 73: 850–859.

5. VanRaden PM. Net merit as a measure of lifetime profit: 2017 revision. USDA. https://www.aipl.arsusda.gov/reference/nmcalc-2017.htm# Derivation of economicvalues.

6. Pryce JE, Hayes BJ, Goddard ME. 2012. Novel Strategies to Minimize Progeny Inbreeding While Maximizing Genetic Gain Using Genomic Information. Journal of Dairy Science 95(1): 377–88. doi: 10.3168/jds.2011-4254 22192217

7. Bogetoft P, Otto L. Benchmarking with DEA, SFR, and R. New York: Springer, 2011.

8. Richards TJ, Jeffrey SR. Establishing indices of genetic merit using hedonic pricing: An application to dairy bulls in Alberta. Canadian Journal of Agricultural Economics. 1996; 44: 1–26.

9. Amer PR, Fox GC. Imputing input characteristic values from optimal commercial breed or variety choice decisions: Comment. American Journal of Agricultural Economics. 1994; 77: 1054–58.

10. Amer PR, Fox GC, Smith C. Economic weights from profit equations: Appraising their accuracy in the long fun. Animal Production. 1994; 58: 11–18.

11. Kerr AW. Selective breeding, heritable characteristics and genetic-based technological change in the Canadian beef cattle industry. Western Journal of Agricultural Economics. 1984; 9: 14–28.

12. Shanks RD, Freeman AE. Choosing progeny-tested Holstein sires that meet genetic goals at minimum semen costs. Journal of Dairy Science. 1979; 62: 1429–1434

13. Amer PR, Fox GC. Estimation of economic weights in genetic improvement using neoclassical production theory: An alternative to rescaling. Animal Production. 1992; 54: 341–50.

14. Atsbeha DM, Kristofersson D, Rickertsen K. “Broad breeding goals and production costs in dairy farming.” Journal of Productivity Analysis. 2015; 43: 403–415.

15. Atsbeha DM, Kristofersson D, Rickertsen K. Animal breeding and productivity growth on dairy farms. American Journal of Agricultural Economics. 2012; 94: 996–1012.

16. Weigel K. Understanding genomics and its applications on a commercial dairy farm. High Plains Dairy Conference.2010 http://highplainsdairy.org/2010/21_Weigel_Understanding%20Genomics_FINAL.pdf

17. Holstein Foundation. Understanding Genetics.2017 http://www.holsteinfoundation.org/pdf_doc/workbooks/Gen_Sire_WKBK.pdf.

18. Cole J, Wiggans G, VanRaden P, Miller R. Stillbirth (co)variance components for a sire-maternal grandsire threshold model and development of a calving ability index for sire selection. Journal of Dairy Science. 2007; 90: 2489–2496. doi: 10.3168/jds.2006-436 17430953

19. Brown G, Patterson T, Cain N. The devil in the details: non-convexities in ecosystem service provision. Resource and Energy Economics. 2001; 33: 355–365.

20. Chavas JP. On the productive value of biodiversity. Environmental and Resource Economics. 2019; 42: 109–131.

21. Simar L, Wilson P. Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science. 1998; 44: 49–61.

22. Tauer LW. Input aggregation and computed technical efficiency. Applied Economics Letters. 2001; 8: 295–207.


Článek vyšel v časopise

PLOS One


2019 Číslo 11