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Author: Benjamin Bruce Stewart-Brown
Publisher:
ISBN:
Category :
Languages : en
Pages : 654
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Book Description
PI 416937 is a Japanese plant introduction which has been utilized in the development of many high yielding lines over the past ~20 years. Nine genomic regions were identified from this PI under positive selection while 17 genomic regions were identified under negative selection. These genomic regions were not significantly associated with yield across replicated yield trials, but a methodology was illustrated for identifying regions under selection for yield and utilizing these regions for incorporation of beneficial diversity. Genomic selection is a strategy for modeling allelic effects across an entire genome to increase the rate of genetic gain for quantitative traits. Implementation of genomic selection for prediction of yield as well as higher heritability traits such as protein and oil content was investigated in soybean. There appeared to be an inflation in predictive ability due to population structure when performing cross-validation. Larger training sets, higher heritability traits, and closer genetic relationships between training and validation sets improved prediction while marker density had little effect. Light-tawny pubescence has been hypothesized to be related to improving yield as this phenotype has been hypothesized to increases light reflectance in the leaf canopy which reduces canopy temperature and plant stress, thus increasing yield potential. QTL mapping and GWAS were used to map and pinpoint the Td locus, but yield trials failed to validate a significant yield advantage associated with the light-tawny phenotype. G13-6299 is a recently released germplasm line from the UGA Soybean Breeding Program which contains 19% exotic pedigree, possesses nematode resistance and desirable agronomic characteristics, and is high yielding. This line was developed for utilization by breeders in order to increase grain yield via the incorporation of beneficial exotic yield alleles.
Author: Benjamin Bruce Stewart-Brown
Publisher:
ISBN:
Category :
Languages : en
Pages : 654
Get Book Here
Book Description
PI 416937 is a Japanese plant introduction which has been utilized in the development of many high yielding lines over the past ~20 years. Nine genomic regions were identified from this PI under positive selection while 17 genomic regions were identified under negative selection. These genomic regions were not significantly associated with yield across replicated yield trials, but a methodology was illustrated for identifying regions under selection for yield and utilizing these regions for incorporation of beneficial diversity. Genomic selection is a strategy for modeling allelic effects across an entire genome to increase the rate of genetic gain for quantitative traits. Implementation of genomic selection for prediction of yield as well as higher heritability traits such as protein and oil content was investigated in soybean. There appeared to be an inflation in predictive ability due to population structure when performing cross-validation. Larger training sets, higher heritability traits, and closer genetic relationships between training and validation sets improved prediction while marker density had little effect. Light-tawny pubescence has been hypothesized to be related to improving yield as this phenotype has been hypothesized to increases light reflectance in the leaf canopy which reduces canopy temperature and plant stress, thus increasing yield potential. QTL mapping and GWAS were used to map and pinpoint the Td locus, but yield trials failed to validate a significant yield advantage associated with the light-tawny phenotype. G13-6299 is a recently released germplasm line from the UGA Soybean Breeding Program which contains 19% exotic pedigree, possesses nematode resistance and desirable agronomic characteristics, and is high yielding. This line was developed for utilization by breeders in order to increase grain yield via the incorporation of beneficial exotic yield alleles.
Author: Felipe Lopes da Silva
Publisher: Springer
ISBN: 3319574337
Category : Science
Languages : en
Pages : 439
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Book Description
This book was written by soybean experts to cluster in a single publication the most relevant and modern topics in soybean breeding. It is geared mainly to students and soybean breeders around the world. It is unique since it presents the challenges and opportunities faced by soybean breeders outside the temperate world.
Author: Henry T. Nguyen
Publisher: Springer
ISBN: 3319641980
Category : Science
Languages : en
Pages : 216
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Book Description
This book examines the application of soybean genome sequences to comparative, structural, and functional genomics. Since the availability of the soybean genome sequence has revolutionized molecular research on this important crop species, the book also describes how the genome sequence has shaped research on transposon biology and applications for gene identification, tilling and positional gene cloning. Further, the book shows how the genome sequence influences research in the areas of genetic mapping, marker development, and genome-wide association mapping for identifying important trait genes and soybean breeding. In closing, the economic and botanical aspects of the soybean are also addressed.
Author: Kristin Bilyeu
Publisher: CRC Press
ISBN: 1439844666
Category : Science
Languages : en
Pages : 388
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Book Description
The soybean is an economically important leguminous seed crop for feed and food products that is rich in seed protein (about 40 percent) and oil (about 20 percent); it enriches the soil by fixing nitrogen in symbiosis with bacteria. Soybean was domesticated in northeastern China about 2500 BC and subsequently spread to other countries. The enormous
Author: Gary Stacey
Publisher: Springer Science & Business Media
ISBN: 0387722998
Category : Science
Languages : en
Pages : 405
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Book Description
Soybean genomics is of great interest as one of the most economically important crops and a major food source. This book covers recent advances in soybean genome research, including classical, RFLP, SSR, and SNP markers; genomic and cDNA libraries; functional genomics platforms; genetic and physical maps; and gene expression profiles. The book is for researchers and students in plant genetics and genomics, plant biology and pathology, agronomy, and food sciences.
Author: Mohsen Yoosefzadeh Najafabadi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Improving yield potential in major food-grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Selections for high-yielding cultivars have been mainly focused on the yield performance per se but not necessarily on secondary related-traits associated with yield. Recent substantial advances in proximal sensing have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits, including yield, at early growth stages. Nevertheless, the implementation of large datasets generated by proximal sensing such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. Therefore, this thesis was aimed to: (1) investigate the potential use of soybean hyperspectral reflectance, hyperspectral reflectance indices (HVI), and yield components such as number of nodes (NP), number of non-reproductive nodes (NRNP), number of reproductive nodes (RNP), and number of pods (PP) per plant for predicting the final seed yield using different machine learning (ML) algorithms, (2) select the top-ranking hyperspectral reflectance and HVI in predicting soybean yield and fresh biomass (FBIO) using recursive feature elimination (RFE) strategy, (3) implement genetic optimization algorithm and the improved version of the strength Pareto evolutionary algorithm 2 (SPEA2) to optimize yield components and HVI for maximizing soybean seed yield and FBIO, and (4) study the genetics of soybean yield and its secondary related-traits in order to discover genomic regions underlying the traits by using genome-wide association study (GWAS). In this study, different ML algorithms such as ensemble stacking (E-S), ensemble bagging (EB), and deep neural network (DNN) were tested to evaluate their efficiency in predicting soybean yield and FBIO production using a panel of 250 genotypes evaluated in four environments. Also, for the first time, we implemented ML algorithms in GWAS to detect the associated QTL with soybean yield components. The results of this study may provide a perspective for geneticists and breeders regarding the use of ML algorithms in phenomics and genomics that will result in the selection of superior soybean genotypes.
Author: Shabir Hussain Wani
Publisher: Springer Nature
ISBN: 3031122321
Category : Technology & Engineering
Languages : en
Pages : 278
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Book Description
Soybean (Glycine max L. (Merr)) is one of the most important crops worldwide. Soybean seeds are vital for both protein meal and vegetable oil. Soybean was domesticated in China, and since last 4-5 decades it has become one of the most widely grown crops around the globe. The crop is grown on an anticipated 6% of the world’s arable land, and since the 1970s, the area in soybean production has the highest percentage increase compared to any other major crop. It is a major crop in the United States, Brazil, China and Argentina and important in many other countries. The cultivated soybean has one wild annual relative, G. soja, and 23 wild perennial relatives. Soybean has spread to many Asian countries two to three thousand years ago, but was not known in the West until the 18th century. Among the various constraints responsible for decrease in soybean yields are the biotic and abiotic stresses which have recently increased as a result of changing climatic scenarios at global level. A lot of work has been done for cultivar development and germplasm enhancement through conventional plant breeding. This has resulted in development of numerous high yielding and climate resilient soybean varieties. Despite of this development, plant breeding is long-term by nature, resource dependent and climate dependent. Due to the advancement in genomics and phenomics, significant insights have been gained in the identification of genes for yield improvement, tolerance to biotic and abiotic stress and increased quality parameters in soybean. Molecular breeding has become routine and with the advent of next generation sequencing technologies resulting in SNP based molecular markers, soybean improvement has taken a new dimension and resulted in mapping of genes for various traits that include disease resistance, insect resistance, high oil content and improved yield. This book includes chapters from renowned potential soybean scientists to discuss the latest updates on soybean molecular and genetic perspectives to elucidate the complex mechanisms to develop biotic and abiotic stress resilience in soybean. Recent studies on the improvement of oil quality and yield in soybean have also been incorporated.
Author: Billy E. Caldwell
Publisher:
ISBN:
Category : Soybean
Languages : en
Pages : 712
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Book Description
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309437385
Category : Science
Languages : en
Pages : 607
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Book Description
Genetically engineered (GE) crops were first introduced commercially in the 1990s. After two decades of production, some groups and individuals remain critical of the technology based on their concerns about possible adverse effects on human health, the environment, and ethical considerations. At the same time, others are concerned that the technology is not reaching its potential to improve human health and the environment because of stringent regulations and reduced public funding to develop products offering more benefits to society. While the debate about these and other questions related to the genetic engineering techniques of the first 20 years goes on, emerging genetic-engineering technologies are adding new complexities to the conversation. Genetically Engineered Crops builds on previous related Academies reports published between 1987 and 2010 by undertaking a retrospective examination of the purported positive and adverse effects of GE crops and to anticipate what emerging genetic-engineering technologies hold for the future. This report indicates where there are uncertainties about the economic, agronomic, health, safety, or other impacts of GE crops and food, and makes recommendations to fill gaps in safety assessments, increase regulatory clarity, and improve innovations in and access to GE technology.
Author: Fikret Isik
Publisher: Springer
ISBN: 3319551779
Category : Science
Languages : en
Pages : 409
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Book Description
This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
