Terminal and rotaterminal crossbreeding systems for pork producers (1995)

Crossbreeding is a widely established management practice among commercial pork producers. Over the years, the industry has used rotational crossbreeding programs extensively. Rotational programs are relatively easy to operate, enable pork producers to develop their own females and exploit most of the possible heterosis.Reprint 6/95/5M

Purchasing a herd boar for commercial swine production

"Success in commercial swine production is determined by a pork producer's ability to use resources efficiently. However, the level of efficiency that can be obtained is a set by the genetic merit of the breeding herd. The genetic merit of the breeding herd is established by the crossbreeding program used and the introduction of new seedstock either by purchasing females or boars."--First page.Ronald O. Bates (Department of Animal Science, College of Agriculture)New 6/90/5

Purchasing a herd boar for commercial swine production

AGRICULTURALMU GuidePUBLISHED BY UNIVERSITY EXTENSION, UNIVERSITY OF MISSOURI-COLUMBIASwine BreedingSuccess in commercial swine production is determined by a pork producer's ability to use resources efficiently. However, the level of efficiency that can be obtained is set by the genetic merit of the breeding herd. The genetic merit of the breeding herd is established by the crossbreeding program used and the introduction of new seedstock either by purchasing females or boars. The most popular crossbreeding programs have been rotational programs that produce replacement females so that only boars need to be brought into the herd. Farmers can use other more specialized crossbreeding programs, only purchase boars as well. For more information, see MU publication G 2311, Terminal and Rotaterminal Crossbreeding Systems for Pork Producers.Ronald O. Bates (Department of Animal Sciences)Reviewed November 2018 -- websit

Terminal and rotaterminal crossbreeding systems for pork producers

"Crossbreeding is a widely established management practice among commercial pork producers. Over the years, the industry has extensively used rotational crossbreeding programs. Rotational programs are relatively easy to operate, enable pork producers to develop their own females and exploit most of the possible heterosis."--First page.Ronald O. Bates (Animal Science Department College of Agriculture)New 5/90/5

Rotational crossbreeding systems for pork producers (1993)

More than 90 percent of hogs marketed are of mixed breeding, an indication that crossbreeding is a well accepted management program. Yet crossbreeding alone does not guarantee efficient, profitable production. Choosing the system and breeds that fit your management and environment and operating the system correctly should result in profitable production

A Comparison Study of Project-Based-Learning in Upper-Division Engineering Education

A new model for engineering education was launched in January 2010 in northeastern Minnesota. The Iron Range Engineering (IRE) model is a project-based-learning (PBL) methodology that focuses on producing graduates with integrated technical and professional knowledge and competencies. A unique and important element of the IRE model has 100% of IRE student learning taking place in the context of industry projects. Students at IRE are upper-division engineering students who transferred from Minnesota community college lower-division engineering programs. To understand the impact that IRE methodology may have on preparing engineers with the competencies needed for the future workplace, a comparison study has been developed to investigate the extent to which students in integrated applied models are affected. The curriculum model and comparison study are described within this paper, along with preliminary results on student development and engagement

Swine management check sheet (1993)

This publication highlights some of the major swine management techniques producers should periodically check

Relationships of Reproductive Traits among Littermates in Swine

Animal Scienc

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

Background: F2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios. Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90. Conclusions: Combining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.Fil: Gualdron Duarte, Jose Luis. Michigan State University; Estados Unidos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, Ronald O.. Michigan State University; Estados UnidosFil: Ernst, Catherine W.. Michigan State University; Estados UnidosFil: Raney, Nancy E.. Michigan State University; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Steibel, Juan P.. Michigan State University; Estados Unido