Short Description:
This book provides an introduction to the genetic concepts of reproductive systems, recombination, mutation, segregation and linkage analysis, inbreeding, quantitative inheritance, fertility regulation, population genetics and polyploidy.

Long Description:
This book provides an introduction to the genetic concepts of reproductive systems, recombination, mutation, segregation and linkage analysis, inbreeding, quantitative inheritance, fertility regulation, population genetics and polyploidy.

Word Count: 61452

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Short Description:
This book covers basic principles in the genetic improvement of crop plants. Emphasis is on methods of cultivar development in self-pollinating, cross-pollinating and asexually propagating crops. Relevant examples of crop improvement research in Africa are utilized to cover factors affecting cultivar release, multiplication, and distribution of high-quality seed.

Long Description:
This book covers basic principles in the genetic improvement of crop plants. Emphasis is on methods of cultivar development in self-pollinating, cross-pollinating and asexually propagating crops. Relevant examples of crop improvement research in Africa are utilized to cover factors affecting cultivar release, multiplication, and distribution of high-quality seed.

Word Count: 82321

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

This open textbook places emphasis on the design of a process pipeline for continuous development of new improved cultivars as a means to implement the cycle of crop improvement. Essential topics in New Line Development and New Line Evaluation are addressed, such as choice of parents, creation of progeny, and evaluation and selection of progeny. Students learn to design a process pipeline to produce improved cultivars that meet a specific product target which represents stakeholders’ needs.
Each of the books in the PBEA series comes with a section in its back matter titled "Applied Learning Activities" which includes additional content aligned to each chapter such as handouts and worksheets, csv files, code for statistical analysis in R, and recommended readings.

Short Description:
This book focuses on genomics and plant biotechnology approaches used in plant breeding, the analysis of genomic data, and their application in quality control measures. The application of alternative genomic technologies to enhance conventional breeding strategies is also presented.

Long Description:
This book focuses on genomics and plant biotechnology approaches used in plant breeding, the analysis of genomic data, and their application in quality control measures. The application of alternative genomic technologies to enhance conventional breeding strategies is also presented.

Word Count: 78625

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

This open textbook is intended to expose students to the application of quantitative genetic models to plant breeding populations. Specific topics include conducting and interpreting multi-environment trials, resource allocation using engineering principles, genetic modeling of quantitative traits, simulation modeling, variance, covariance and heritability, prediction, selection, and genetic gain.

This open textbook covers common statistics used in agriculture research, including experimental design in plant breeding and genetics, as well as the analysis of variance, regression, and correlation.

Each of the books in the PBEA series comes with a section in its back matter titled "Applied Learning Activities" which includes additional content aligned to each chapter such as handouts and worksheets, csv files, code for statistical analysis in R, and recommended readings.