Wheat Improvement

This open-access textbook provides a comprehensive, up-to-date guide for students and practitioners wishing to access in a single volume the key disciplines and principles of wheat breeding. Wheat is a cornerstone of food security: it is the most widely grown of any crop and provides 20% of all human calories and protein. The authorship of this book includes world class researchers and breeders whose expertise spans cutting-edge academic science all the way to impacts in farmers’ fields. The book’s themes and authors were selected to provide a didactic work that considers the background to wheat improvement, current mainstream breeding approaches, and translational research and avant garde technologies that enable new breakthroughs in science to impact productivity. While the volume provides an overview for professionals interested in wheat, many of the ideas and methods presented are equally relevant to small grain cereals and crop improvement in general. The book is affordable, and because it is open access, can be readily shared and translated -- in whole or in part -- to university classes, members of breeding teams (from directors to technicians), conference participants, extension agents and farmers. Given the challenges currently faced by academia, industry and national wheat programs to produce higher crop yields --- often with less inputs and under increasingly harsher climates -- this volume is a timely addition to their toolkit

Maize Production in a Changing Climate

Plant breeding and improved management options have made remarkable progress in increasing crop yields during the past century. However, climate change projections suggest that large yield losses will be occurring in many regions, particularly within sub-Saharan Africa. The development of climate-ready germplasm to offset these losses is of the upmost importance. Given the time lag between the development of improved germplasm and adoption in farmers’ fields, the development of improved breeding pipelines needs to be a high priority. Recent advances in molecular breeding provide powerful tools to accelerate breeding gains and dissect stress adaptation. This review focuses on achievements in stress tolerance breeding and physiology and presents future tools for quick and efficient germplasm development. Sustainable agronomic and resource management practices can effectively contribute to climate change mitigation. Management options to increase maize system resilience to climate-related stresses and mitigate the effects of future climate change are also discussed

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques, which is now a critical component of mainstream cultivar development pipelines. However, advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs. Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements. Random forest, a machine learning (ML) algorithm, was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation (CV) scenarios consistent with breeding challenges. To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives, feature importance in tandem with a genetic algorithm (GA) technique allowed selection of a subset of phenotypic traits, specifically optimal wavebands. The results illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping (e.g., seed yield harvest). While we illustrate with soybean, this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective

Visual analytics for relationships in scientific data

Domain scientists hope to address grand scientific challenges by exploring the abundance of data generated and made available through modern high-throughput techniques. Typical scientific investigations can make use of novel visualization tools that enable dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These general tools should be applicable to many disciplines: allowing biologists to develop an intuitive understanding of the structure of coexpression networks and discover genes that reside in critical positions of biological pathways, intelligence analysts to decompose social networks, and climate scientists to model extrapolate future climate conditions. By using a graph as a universal data representation of correlation, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool integrates techniques such as graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized B-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using real-world workflows from several large-scale studies. Parallel coordinates has proven to be a scalable visualization and navigation framework for multivariate data. However, when data with thousands of variables are at hand, we do not have a comprehensive solution to select the right set of variables and order them to uncover important or potentially insightful patterns. We present algorithms to rank axes based upon the importance of bivariate relationships among the variables and showcase the efficacy of the proposed system by demonstrating autonomous detection of patterns in a modern large-scale dataset of time-varying climate simulation

Screening and Breeding Soybean for Flood Tolerance

Waterlogging can be detrimental to soybean [Glycine max (L.) Merr.] growth and development, with effects ranging from chlorosis and stunting to yield loss and plant death. Soybean responses to, and the effects of, waterlogging are dependent on the growth stage of the plant at the initiation of waterlogging. The objectives of this study were: (1) to assess the effectiveness of Genomic Selection (GS), Marker Assisted Selection (MAS) and Phenotypic Selection for flood tolerance at the progeny row stage as compared to random selection, for the development of high-yielding flood-tolerant lines; and (2) to compare field-screening and hydroponic greenhouse screening methodologies for hypoxia tolerance. For the first objective, 391 individuals from four populations at the F4:5 generation were either: 1) screened for waterlogging tolerance at the R1 growth stage in observation or first-year yield trial stages; 2) subjected to genomic selection using two different training approaches; 3) underwent marker-assisted selection; or 4) were advanced purely based on agronomic adaptation under non-flooded condition. Subsequently, the tagged selections together with the base populations (control) were entered in a multi-location trial where flood tolerance and yield were assessed, and the responses were compared across the different selection methods. Results from this experiment indicated significant differences between visual selection and the base population, and between genomic selection and base population when long-rows experiment was used in the training set. Random selection and base population were also significantly different on the identification of flood tolerant lines, assessed as tolerance index and probability of discard. Random selection method resulted in the lowest tolerance index and highest probability of discard. We also observed that visual or genomic selection derived from hill plots did not outperform the control in terms of flood tolerance. In addition, all six methods and base populations had similar performance in terms of mean yield. This suggests that breeders must focus on selecting for flood tolerance early in the breeding stages, without major risk of reducing yield potential. For the second objective of this study, a total of 17 soybean genotypes were screened for waterlogging tolerance at the V2 growth stage and under a hydroponic system. Plots of responses by cultivar and test method were analyzed. We observed consistency in results between field and hydroponic system for most of the cultivars, enabling us to discard based on flood susceptibility. Identification of the most efficient selection method for flood tolerance, and the development of a greenhouse screening methodology, will aid plant breeders in developing new flood-tolerant cultivars

Development of phenomic-assisted breeding methodologies for prescriptive plant breeding, efficient cultivar testing, and genomic studies

Plant scientists are beginning to harness the capabilities of high dimensional ‘omic tools (e.g., genomic, phenomic) to usher in the era of digital agriculture to allow the usage of predictive analytics. While genomic tools have been developed to exploit high-density genetic markers for breeding decision making, a gap persists in the availability of phenomic-assisted breeding methodologies. Here we develop frameworks malleable to crop species and breeding objective to leverage complex high-dimension phenomic data using machine learning (ML) and optimization techniques for the development of data driven solutions designed to empower plant scientists to; develop prescriptive breeding solutions, improve the operation efficiency of breeding programs, and to expand the capacity of current phenotyping efforts through the use of a fine-tuned package of sensors assembled for a specific breeding objective. In this consortium of work, we show that phenomic predictors can be deployed for ML assisted prescriptive-breeding techniques for precision product placement and in turn these same phenomic predictors can be used for efficient cultivar testing (e.g., seed yield) to optimize breeding program operational efficiencies. Furthermore, phenomic sensors provided a wealth of data making this work ripe for genomic studies revealing the underlying genomic regions controlling yield predicting phenomic traits and rapid scanning of genotyped germplasm using genomic prediction. This work will allow breeders to continually optimize their breeding programs to begin fusing widely available genomic data with the upcoming capabilities of high throughput phenotyping techniques to streamline cultivar development pipelines

Computational and Experimental Approaches to Reveal the Effects of Single Nucleotide Polymorphisms with Respect to Disease Diagnostics

DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules