Proteomics analysis of round and wrinkled pea (Pisum sativum L.) seeds during different development periods.

Seed development is complex, influenced by genetic and environmental factors. Understanding proteome profiles at different seed developmental stages is key to improving seed composition and quality. We used label-free quantitative proteomics to analyze round and wrinkled pea seeds at five growth stages: 4, 7, 12, 15, and days after anthesis (DAA), and at maturity.

Association study of crude seed protein and fat concentration in a USDA pea diversity panel.

Pea (Pisum sativum L.) is a key rotational crop and is increasingly important in the food processing sector for its protein. This study focused on identifying diverse high seed protein concentration (SPC) lines in pea plant genetic resources.

Effective population size in field pea.

Background: Effective population size (N) is a pivotal parameter in population genetics as it can provide information on the rate of inbreeding and the contemporary status of genetic diversity in breeding populations. The population with smaller N can lead to faster inbreeding, with little potential for genetic gain making selections ineffective. The importance of N has become increasingly recognized in plant breeding, which can help breeders monitor and enhance the genetic variability or redesign their selection protocols.

A pulse crop dataset of agronomic traits and multispectral images from multiple environments.

Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials.

Selecting High-Performing and Stable Pea Genotypes in Multi-Environmental Trial (MET): Applying AMMI, GGE-Biplot, and BLUP Procedures.

A large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over ten years (2012-2021) to analyze and test key aspects fundamental to pea breeding. Six balanced datasets were used to test the predictive success of the BLUP and AMMI family models.

Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics.

Introduction: Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs.

Prediction of Protein Concentration in Pea ( L.) Using Near-Infrared Spectroscopy (NIRS) Systems.

Breeding for increased protein concentration is a priority in field peas. Having a quick, accurate, and non-destructive protein quantification method is critical for screening breeding materials, which the near-infrared spectroscopy (NIRS) system can provide. Partial least square regression (PLSR) models to predict protein concentration were developed and compared for DA7250 and FT9700 NIRS systems.

Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea ( L.).

Aphanomyces root rot (ARR) is a devastating disease that affects the production of pea. The plants are prone to infection at any growth stage, and there are no chemical or cultural controls. Thus, the development of resistant pea cultivars is important.

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.

Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection.

Phenotypic and Genetic Characterization of the Lentil Single Plant-Derived Core Collection for Resistance to Root Rot Caused by .

Lentil () is a pulse crop grown for its amino acid profile, moderate drought tolerance, and ability to fix nitrogen. As the global demand for lentils expands and new production regions emerge so too have the complement of diseases that reduce yield, including the root rot complex. Although the predominant causal pathogen varies based on growing region, is often found to be an important contributor to disease.

Organic dry pea (Pisum sativum L.) biofortification for better human health.

A primary criticism of organic agriculture is its lower yield and nutritional quality compared to conventional systems. Nutritionally, dry pea (Pisum sativum L.) is a rich source of low digestible carbohydrates, protein, and micronutrients.

Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction.

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea ( L.

A Case for the Use of Artificial Intelligence in Glaucoma Assessment.

We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4, 2020. The Collaborative Community on Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology.

Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data.

The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.

Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil.

Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping approaches. The utilization of such technologies has enabled the generation of multidimensional plant traits creating big datasets. However, to harness the power of phenomics technologies, more sophisticated data analysis methods are required.

Chickpea Seed Rot and Damping-Off Caused by Metalaxyl-Resistant and Its Management with Ethaboxam.

Metalaxyl and its isomer mefenoxam have been the primary fungicides used as seed treatments in managing Pythium seed rot and damping-off of chickpea (). However, outbreaks of seed rot and damping-off of metalaxyl-treated chickpea seeds were found in the dryland agriculture regions of southeastern Washington and northern Idaho. spp.

Dissecting the Genetic Architecture of Aphanomyces Root Rot Resistance in Lentil by QTL Mapping and Genome-Wide Association Study.

Lentil ( Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait.

Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops.

The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method.

Pulse Crop Effects on Gut Microbial Populations, Intestinal Function, and Adiposity in a Mouse Model of Diet-Induced Obesity.

The dietary fiber gap that is present in many countries co-exists with a low intake of grain legumes (pulses) that have 2-3 times more dietary fiber than cereal grains that are commonly recommended to increase fiber intake. Given the relationships among dietary fiber, gut health and chronic disease risk, a study was undertaken in a preclinical mouse model for obesity to examine how commonly consumed pulses, i.e.

Comprehensive epigenome characterization reveals diverse transcriptional regulation across human vascular endothelial cells.

Background: Endothelial cells (ECs) make up the innermost layer throughout the entire vasculature. Their phenotypes and physiological functions are initially regulated by developmental signals and extracellular stimuli. The underlying molecular mechanisms responsible for the diverse phenotypes of ECs from different organs are not well understood.

Genome-Wide Association Mapping for Agronomic and Seed Quality Traits of Field Pea ( L.).

Genome-wide association study (GWAS) was conducted to identify loci associated with agronomic (days to flowering, days to maturity, plant height, seed yield and seed weight), seed morphology (shape and dimpling), and seed quality (protein, starch, and fiber concentrations) traits of field pea ( L.). A collection of 135 pea accessions from 23 different breeding programs in Africa (Ethiopia), Asia (India), Australia, Europe (Belarus, Czech Republic, Denmark, France, Lithuania, Netherlands, Russia, Sweden, Ukraine and United Kingdom), and North America (Canada and USA), was used for the GWAS.

Author Correction: Arsenic accumulation in lentil (Lens culinaris) genotypes and risk associated with the consumption of grains.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A reference genome for pea provides insight into legume genome evolution.

We report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel's original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes.

Effect of cover crops on the yield and nutrient concentration of organic kale (Brassica oleracea L. var. acephala).

Kale is a leafy green vegetable regularly grown using non-organic agricultural systems. In recent years, organic kale demand has increased at near-doubling rates in the USA due to its perceived nutritional value. The objective of this study was to determine the effect of organic cover cropping systems on subsequent kale biomass production and nutrient composition (protein, mineral, and prebiotic carbohydrate concentrations) and to assess organic kale as a potential whole food source of daily essential mineral micronutrients and prebiotic carbohydrates.