Handbook of Quantitative Forest Genetics

. Ed(s): Fins, Lauren; Friedman, Sharon; Brotschol, Janet V. (USDA Forest Service, Olympia, USA)

ISBN 10: 0792315685 / ISBN 13: 9780792315681

Edité par Kluwer Academic Publishers, 1992

A propos de cet article

Contains chapters focusing on the decision points faced by quantitative geneticists and breeders in designing programs and analyzing data. This handbook was designed as a tool for forest geneticists, tree breeders and other tree improvement personnel, as well as a textbook for university courses at the graduate level in quantitative genetics. Editor(s): Fins, Lauren; Friedman, Sharon; Brotschol, Janet V. (USDA Forest Service, Olympia, USA). Series: Forestry Sciences. Num Pages: 423 pages, biography. BIC Classification: PSAK. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 235 x 155 x 23. Weight in Grams: 774. . 1992. 1992nd Edition. hardcover. . . . .

N° de réf. du vendeur V9780792315681

Détails bibliographiques

• Titre: Handbook of Quantitative Forest Genetics
• Éditeur: Kluwer Academic Publishers
• Date de parution: 1992
• Langue: anglais
• ISBN à 10 chiffres: 0792315685
• ISBN à 13 chiffres: 9780792315681
• Reliure: Couverture rigide
• État de l'article: New

A propos de ce titre

Synopsis

This handbook was designed as a reference tool for forest geneticists, tree breeders and other tree improvement personnel, as well as a textbook for university courses and short-courses at the graduate level in quantitative genetics.
The chapters focus on the decision points faced by quantitative geneticists and breeders in designing programs and analyzing data. Beginning with a justification for the use of quantitative genetics in decision making in tree improvement programs, the book continues with a brief presentation of fundamental principles, followed by discussions and evaluations of mating designs and field test designs, the use of best linear predictors to estimate breeding values, the use of computer programs in the analysis of variance for genetic information, the deployment of genetically improved stock for capturing gains, the use of economic models for program justification, and the development of seed transfer guidelines.