東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Prospects for Improving Alfalfa Yiel...

University of California, Davis., Horticulture and Agronomy.

FindBook

Google Book

Amazon

博客來

Prospects for Improving Alfalfa Yield Using Genomic- and Phenomic-Based Breeding /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prospects for Improving Alfalfa Yield Using Genomic- and Phenomic-Based Breeding // Matt Francis.
作者:	Francis, Matt,
面頁冊數:	1 electronic resource (90 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Plant sciences. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30997450
ISBN:	9798382605159

Prospects for Improving Alfalfa Yield Using Genomic- and Phenomic-Based Breeding /
Francis, Matt,

Prospects for Improving Alfalfa Yield Using Genomic- and Phenomic-Based Breeding /Matt Francis. - 1 electronic resource (90 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

The primary goal of the research presented in this dissertation is to assess the prospects of utilizing modern plant breeding methodology to address the lack of biomass yield improvement in alfalfa. Biomass yield improvement in alfalfa has remained stagnant for the last ~30 years and cultivated areas are in steady decline. Significant advancements have been made in genomics and phenomics in recent years with applications in plant breeding. Chapter 1 reviews relevant literature including the role alfalfa plays in western agriculture, history, biology, breeding, and an introduction to genomic selection and high throughput phenotyping. Chapter 2 seeks to empirically test genomic selection for forage dry matter yield in alfalfa. Genomic selection has proven successful for increasing the rate of genetic gain in other crops, and models have been developed for a variety of traits in alfalfa. However, no practical testing has occurred to date. Chapter 3 builds on the findings from the previous chapter by addressing some limitations in the predictive model. Namely, building a model based on family bulks and half-sib family performance, rather than on an individual plant basis, to better reflect the commercial environment alfalfa is grown in. The results from chapter 2 and 3 provide encouraging prospects with prediction accuracies around 0.3. However, one of the greatest limitations for a successful genomic selection breeding program is the size of the training population, particularly in crops with limited resources such as alfalfa. Chapter 4 explores the potential to greatly increase the size of breeding trials without a comparable increase in costs. We utilized drone based multispectral imagery to estimate biomass of alfalfa and a range of forage grasses across a range of plot types typically used in forage breeding trials. Remote sensing and high throughput phenotyping massively reduce the labor component of data collection while simultaneously providing accurate estimates of forage biomass.

English

ISBN: 9798382605159Subjects--Topical Terms:

3173832
Plant sciences.
Subjects--Index Terms:

Alfalfa

Prospects for Improving Alfalfa Yield Using Genomic- and Phenomic-Based Breeding /
LDR:03507nmm a22004573i 4500 001 2428528
005 20250414021847.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798382605159
035 $a (MiAaPQD)AAI30997450
035 $a AAI30997450
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Francis, Matt, $e author. $3 3815802
245 1 0 $a Prospects for Improving Alfalfa Yield Using Genomic- and Phenomic-Based Breeding / $c Matt Francis.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (90 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisors: Brummer, E. Charles Committee members: Knapp, Steven J.; Van Deynze, Allen.
502 $b Ph.D. $c University of California, Davis $d 2024.
520 $a The primary goal of the research presented in this dissertation is to assess the prospects of utilizing modern plant breeding methodology to address the lack of biomass yield improvement in alfalfa. Biomass yield improvement in alfalfa has remained stagnant for the last ~30 years and cultivated areas are in steady decline. Significant advancements have been made in genomics and phenomics in recent years with applications in plant breeding. Chapter 1 reviews relevant literature including the role alfalfa plays in western agriculture, history, biology, breeding, and an introduction to genomic selection and high throughput phenotyping. Chapter 2 seeks to empirically test genomic selection for forage dry matter yield in alfalfa. Genomic selection has proven successful for increasing the rate of genetic gain in other crops, and models have been developed for a variety of traits in alfalfa. However, no practical testing has occurred to date. Chapter 3 builds on the findings from the previous chapter by addressing some limitations in the predictive model. Namely, building a model based on family bulks and half-sib family performance, rather than on an individual plant basis, to better reflect the commercial environment alfalfa is grown in. The results from chapter 2 and 3 provide encouraging prospects with prediction accuracies around 0.3. However, one of the greatest limitations for a successful genomic selection breeding program is the size of the training population, particularly in crops with limited resources such as alfalfa. Chapter 4 explores the potential to greatly increase the size of breeding trials without a comparable increase in costs. We utilized drone based multispectral imagery to estimate biomass of alfalfa and a range of forage grasses across a range of plot types typically used in forage breeding trials. Remote sensing and high throughput phenotyping massively reduce the labor component of data collection while simultaneously providing accurate estimates of forage biomass.
546 $a English
590 $a School code: 0029
650 4 $a Plant sciences. $3 3173832
650 4 $a Genetics. $3 530508
650 4 $a Remote sensing. $3 535394
650 4 $a Botany. $3 516217
650 4 $a Environmental science. $3 677245
653 $a Alfalfa
653 $a Genomic selection
653 $a Plant breeding
653 $a Forage breeding
653 $a Prediction accuracies
690 $a 0479
690 $a 0369
690 $a 0799
690 $a 0768
690 $a 0309
710 2 $a University of California, Davis. $b Horticulture and Agronomy. $e degree granting institution. $3 3815803
720 1 $a Brummer, E. Charles $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0029
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30997450