Integrating High-throughput Phenotyping, Genomic Selection, and Spatial Analysis for Plant Breeding and Management - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Integrating High-throughput Phenotyping, Genomic Selection, and Spatial Analysis for Plant Breeding and Management write by Margaret Rose Krause. This book was released on 2019. Integrating High-throughput Phenotyping, Genomic Selection, and Spatial Analysis for Plant Breeding and Management available in PDF, EPUB and Kindle. Recent advances in high-throughput phenotyping, genomics, and precision agriculture have provided plant breeders and farmers with a wealth of information on the growth and development of crop plants. Methods for effectively leveraging these data resources are needed in order to drive genetic gain in breeding programs and to increase efficiency in farming systems. Three novel approaches for the development and management of high yielding, adapted crop varieties are presented. First, aerial hyperspectral reflectance phenotypes of bread wheat (Triticum aestivum L.) were used to develop relationship matrices for the prediction of grain yield within and across environments with genomic selection. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phenotypes gave the highest accuracies overall; however, improvements in accuracy over single-kernel marker- and pedigree-based models were reduced when correcting for days to heading. Second, aerial phenotypes collected on small, unreplicated plots representing the seed limited stage of wheat breeding programs were evaluated for their potential use as selection criteria for improving grain yield. The aerial phenotypes were shown to be heritable and positively correlated with grain yield measurements evaluated in replicated yield trials. Results also suggest that selection on aerial phenotypes at the seed-limited stage would cause a directional response in phenology due to confounding of those traits. Lastly, on-farm trials were conducted in collaboration with the New York Corn and Soybean Growers Association to identify optimal planting rates for corn (Zea mays L.) and soybean (Glycine max L.) given the underlying spatial variability of the soil and topographical characteristics of the fields. A random forest regression-based approach was created to develop variable rate planting designs for maximizing yields.
High-Throughput Crop Phenotyping
High-Throughput Crop Phenotyping - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook High-Throughput Crop Phenotyping write by Jianfeng Zhou. This book was released on 2021-07-17. High-Throughput Crop Phenotyping available in PDF, EPUB and Kindle. This book provides an overview of the innovations in crop phenotyping using emerging technologies, i.e., high-throughput crop phenotyping technology, including its concept, importance, breakthrough and applications in different crops and environments. Emerging technologies in sensing, machine vision and high-performance computing are changing the world beyond our imagination. They are also becoming the most powerful driver of the innovation in agriculture technology, including crop breeding, genetics and management. It includes the state of the art of technologies in high-throughput phenotyping, including advanced sensors, automation systems, ground-based or aerial robotic systems. It also discusses the emerging technologies of big data processing and analytics, such as advanced machine learning and deep learning technologies based on high-performance computing infrastructure. The applications cover different organ levels (root, shoot and seed) of different crops (grains, soybean, maize, potato) at different growth environments (open field and controlled environments). With the contribution of more than 20 world-leading researchers in high-throughput crop phenotyping, the authors hope this book provides readers the needed information to understand the concept, gain the insides and create the innovation of high-throughput phenotyping technology.
Introducing Sparsity Into Selection Index Methodology with Applications to High-throughput Phenotyping and Genomic Prediction
Introducing Sparsity Into Selection Index Methodology with Applications to High-throughput Phenotyping and Genomic Prediction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introducing Sparsity Into Selection Index Methodology with Applications to High-throughput Phenotyping and Genomic Prediction write by Marco Antonio Lopez Cruz. This book was released on 2020. Introducing Sparsity Into Selection Index Methodology with Applications to High-throughput Phenotyping and Genomic Prediction available in PDF, EPUB and Kindle. Research in plant and animal breeding has been largely focused on the development of methods for a more efficient selection by altering the factors that affect genetic progress: selection intensity, selection accuracy, genetic variance, and length of the breeding cycle. Most of the breeding efforts have been primarily towards increasing selection accuracy and reducing the breeding cycle.Genomic selection has been successfully adopted by many public and private breeding organizations. Over years, these institutions have developed and accumulated large volumes of genomic data linked to phenotypes from multiple populations and multiple generations. This data abundance offers the opportunity to revolutionize genetic research. However, these data sets are also increasingly heterogeneous, with many subpopulations and multiple generations represented in the data. This translates into potentially heterogeneous allele frequencies and different LD patterns, thus leading to SNP-effect heterogeneity.Genomic selection methods were developed with reference to homogeneous populations in which SNP-effects are assumed constant across the whole population. These methods are not necessarily optimal for the contemporary available data sets for model training. Therefore, a first focus of this dissertation is on developing novel methods that can leverage the large-scale of modern data sets while coping with the heterogeneity and complexity of this type of data.In recent years, there have also been important advances in high-throughput phenotyping (HTP) technologies that can generate large volumes of data at multiple time-points of a crop. Examples of this include hyper-spectral imaging technologies that can capture the reflectance of electromagnetic power by crops at potentially thousands of wavelengths. The integration of HTP in genetic evaluations represents a great opportunity to further advance plant breeding; however, the high-dimensional nature of HTP data poses important challenges. Therefore, a second focus of this dissertation is on the development of a novel approach to efficiently incorporate HTP data for breeding values prediction.Thus, this dissertation aims to contribute novel methods that can improve the accuracy of genomic prediction by optimizing the use of large, potentially heterogeneous, genomic data sets and by enabling the integration of HTP data. We present a novel statistical approach that combines the standard selection index methodology with variable-selection methods commonly used in machine learning and statistics, and developed software to implement the method. Our approach offers solutions to both genomic selection with potentially highly heterogeneous genomic data sets, and the integration of HTP in genetic evaluations.
Molecular Plant Breeding
Molecular Plant Breeding - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Molecular Plant Breeding write by Yunbi Xu. This book was released on 2010. Molecular Plant Breeding available in PDF, EPUB and Kindle. Recent advances in plant genomics and molecular biology have revolutionized our understanding of plant genetics, providing new opportunities for more efficient and controllable plant breeding. Successful techniques require a solid understanding of the underlying molecular biology as well as experience in applied plant breeding. Bridging the gap between developments in biotechnology and its applications in plant improvement, Molecular Plant Breeding provides an integrative overview of issues from basic theories to their applications to crop improvement including molecular marker technology, gene mapping, genetic transformation, quantitative genetics, and breeding methodology.
Stability of Traits Across Environments Using Image Phenotyping and Genotyping
Stability of Traits Across Environments Using Image Phenotyping and Genotyping - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stability of Traits Across Environments Using Image Phenotyping and Genotyping write by Nicolas Morales. This book was released on 2022. Stability of Traits Across Environments Using Image Phenotyping and Genotyping available in PDF, EPUB and Kindle. Genetic gain for important agronomic traits can be accelerated in plant breeding by better understanding the environmental effects on field experimental plots over the growing season. Today, aerial image remote sensing via unoccupied aerial vehicles (UAVs) offers cost-effective collection of high throughput phenotypes (HTPs) with high temporal and spatial resolution. However, utilizing HTP to evaluate the genetic merit of tested accessions in a modern breeding program requires effective integration of the captured imagery with genome-wide marker data, experimental design and geographic information, agronomic phenotypic data, and soil and weather data. Furthermore, plant breeders require timely predictions of genetic effects for selecting accessions to advance; therefore, the data should be readily integrated into a quantitative genetics statistical framework. Hence, this dissertation presents four chapters: (1) a database schema for storing genome-wide marker data, (2) a web-database platform for managing plant breeding programs and their experiments, (3) a web-database tool for reliably processing aerial imagery into HTP, and (4) a statistical approach integrating HTP with genomic data to better resolve genetic effects over spatio-temporal environmental effects. In (1), a NoSQL data model is presented within the Chado database schema, utilizing the NoSQL and relational capabilities of PostgreSQL to link the genome-wide marker data and the plant breeding experimental data, respectively. Benchmarking demonstrates computation of a genomic relationship matrix (GRM) and a genome wide association study (GWAS) for datasets involving 1,325 diploid Zea mays L. (maize), 314 triploid Musa acuminata (banana), and 924 diploid Manihot esculenta (cassava) samples genotyped with 955,690, 142,119, and 287,952 genotype-by-sequencing (GBS) markers, respectively. In (2), Breedbase illustrates a web-database platform enabling plant breeders around the world to manage their breeding program data in a standardized process. Importantly, the Breeding API (BrAPI) allows open access and interoperability to the data. Then (3) focuses on ImageBreed as a web-database tool for processing aerial image phenotypes into HTP. Multi-spectral or color imagery from UAVs or from fixed camera systems can be uploaded, processed into orthophotomosaics if required, designated into geospatially referenced plot-polygons, and then summarized into vegetation indices (VIs) or convolutional neural network (CNN) HTP. In (4), the normalized difference vegetation index (NDVI) collected on several years of Genomes-to-Fields (G2F) hybrid maize (Zea mays L.) field experiments is used to improve genomic prediction for grain yield, grain moisture, and ear height. The proposed approach enables greater understanding of spatial heterogeneity in the field and improves the estimation of genetic effects. To conclude, continued aggregation of genomic and image data, coupled with statistical approaches, will enable plant breeders to better understand the stability of genetic effects across space and time. Future research into latent genetic spaces embedded in ground rover lidar point clouds and aerial imagery is an exciting avenue to understanding the permanent environment and genetic stability of accessions.
