Phytate is the major storage form of phosphorus in crop seeds, but is not well digested by humans and non-ruminant animals. In addition, phytate chelates several essential micronutrients which are also excreted contributing to phosphorus pollution in the environment. The present study is aimed at biochemical and molecular characterization of two low phytate pea mutant lines, 1-150-81 and 1-2347-144 developed at the Crop Development Centre, University of Saskatchewan in collaboration with Dr. Victor Raboy, USDA, Idaho.
Ascochyta blight caused by Mycosphaerella pinodes (MP) is the most important pea disease in Canada and most pea growing regions in the world, often causing serious yield losses. Genetic resistance to ascochyta blight accumulated through two decades of breeding reduces disease severity, however, under cool, wet conditions, the resistance is not sufficient to prevent economic losses. Some accessions of Pisum fulvum, a wild relative of field pea, possess a high level of resistance to ascochyta blight. This project was designed to initiate a long-term strategy for enhancement of ascochyta blight resistance in pea using an integrated genetic improvement approach through interspecific hybridization, careful phenotyping and molecular genotyping.
The first objective is to improve the nitrogen contribution of pulses to the rotation by assessing the nitrogen budget of faba bean, a crop likely to have greater nitrogen fixation and growth than pea and lentil. The second is to measure the biomass and nitrogen content of a range of faba genotypes and cultivars. The third objective is to assess the nitrogen fixation ability of faba genotypes by shoot N metabolism under typical dryland prairie conditions and controlled stress conditions, and develop a specific amino-acid screening method to screen for high N fixation. We intend to use the results to screen a wider range of germplasm for improving future varieties.
Lentil seed is a good source of phenolic compounds, which can have health benefits. This project will try to find how different seed coat colours in lentil can be related to the phenolics profile. A fast extraction method and an optimized LC-MS analysis were applied to compare green, gray, tan, and brown seed coat colour lentils. Also, the so called zero-tannin genotypes were compared with the normal ones based upon their phenolic profile. The effect of storage on phenolic profile of lentil seeds was investigated, as well.
The objective of this study is to determine the genetic control of several traits in field pea including mycosphaerella blight resistance, lodging resistance and micronutrient concentration by genotyping and phenotyping a recombinant inbred line population which is segregating for these traits.
The nutritional value of pea, lentil, chickpea and dry bean grains are highly important for human health. Biofortification, enriching the nutritional contribution of staple crops through plant breeding, is one option that is now widely discussed in the fields of nutrition and public health at the national and international levels.
In this research, we plan to investigate how lentil indeterminacy can be managed by four strategies. The first is to test if lentil maturity can be controlled by soil N supply in zero tillage and conventional tillage soils. The second is to test if a desiccant at low concentrations can trigger senescence and maturity under high N conditions. The third strategy is to test if nine lentil genotypes that are commercially grown vary in N uptake, N fixation, or N redistribution within the plant during reproductive growth. Finally, the fourth strategy is to identify earlier establishing and earlier senescing rhizobia strains so N fixation will only occur up to mid-reproductive growth. Together, all these strategies will give us a detailed understanding of how N is partitioned in lentil in order to have satisfactory crop maturity.
Preparation of EST data: Sequences were extracted from dbEST and were subjected to quality control screening (vector, E. coli, polyA, T, or CT removal, minimum length = 100 bp, < 3% N). Preparation of transcript (ET) database: All sequences from the appropriate divisions of GenBank (including RefSeq) were extracted. Non-coding sequences were discarded and cDNAs and coding sequences from genomic entries were saved. Sequences and related information (e.g. PubMed links) are stored in the qcGene database (qcGene). Assembly: Cleaned EST sequences and non-redundant transcript (ET) sequences were combined. Using the Paracel Transcript Assembler Program, sequences were assembled into contigs. TCs are consensus sequences based on two or more ESTs (and possibly an ET) that overlap for at least 40 bases with at least 94% sequence identity. These strict criteria help minimize the creation of chimeric contigs. These contigs are assigned a TC (Tentative Consensus) number. TCs may comprise ESTs derived from different tissues. The best hits for TC's were assigned by searching the TC set against a non-redundant amino acid database(nraa) using BLAT. The top five hits based on score were selected and displayed for each TC. Caveats: TCs are only as good as the ESTs underlying them; there may be unspliced or chimeric ESTs and thus TCs. There is still redundancy in the TC set because sequences must match end to end and at a certain percent identity to be combined. Directionality of the TCs should not be assumed. Not all TCs contain protein-coding regions.
An Illumina Golden Gate array was developed using SNPs identified as part of the Lentil 454 Sequencing & Genotyping Project. Loci where chosen such that the SNPs should be distributed evenly across the genome based on comparison to Medicago truncatula.
A detailed analysis of pea, spring wheat and canola and other praririe crops is part of a current collaboration (2009-2011) with Dr Lynn Seymour, Department of Statistics, University of Georgia, USA. The aim of this project is to explore and relate the variability in yields for Saskatchewan, Alberta and Manitoba crop districts to 30 years of weather. The objectives are to identify the effect of changed weather on crop adaptation, identify the threshold temperatures and rainfall requirement for stable yield, and develop a strategy for improving future cultivars to keep pace with climate change. At the cropping district level, researchers and growers will be able to connect how much change in yield for wheat, pea or canola will result when certain weather measurements deviate from monthly averages or extremes for the actual months within a cropping season. When we know how yield performs when several weather factors change together, we can change crop management accordingly, and we can provide future varieties that can tolerate a shifting climate.