Agriculture and Food Systems - Theses
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ItemAdvancing Genomics Resources and Phenotyping Methods to Improve Salt Tolerance in Lentil( 2021)Pulses, also known as grain legumes, are members of the family Leguminosae and are grown for their edible seeds, containing high amounts of proteins and fibre. The work performed in this thesis is focused on lentil (Lens sp.), which is a self-pollinating, diploid, cool-season grain legume. Lentil production is constrained by multiple biotic and abiotic stresses that reduce growth and grain yield. The development of lentil varieties/cultivars with improved characteristics, including better yield, adaptation, and resistance to biotic and abiotic stresses, is a priority for international breeding programs. Therefore, the thesis investigated advanced genomic and phenomic approaches to characterize lentil germplasm for breeding purposes. Cultivated lentil (Lens culinaris Medik.) has a relatively narrow genetic base. Therefore, characterization of genetic diversity and genomic differentiation of wild gene pools is essential to identify any favorable alleles/genes that can be introduced into elite germplasm. A total of 467 wild and cultivated lentil accessions originating from multiple geographical locations were assessed for understanding genetic and allelic variations using transcriptome sequencing. An enriched single nucleotide polymorphic (SNP) resource (c. 422,101) has been delivered to lentil breeders for mining diverse genotypes for hybridization in future research and breeding. Understanding the relationship between lentil accessions and their geographical origins is also vital for identifying favorable alleles/traits that can be introgressed into the lentil germplasm. However, a weak correlation was observed between the lentil accessions, except for some accessions belong to L. culinaris and L. ervoides. Therefore, the study proposed that identifying lentil accessions with wide genetic distance variations within the same gene pool is more promising for selecting lentil accessions for breeding purposes, which also avoids crossing barriers between different gene pools. Lentil accessions that belong to L. culinaris, L. ervoides and L. nigricans were shown broad genetic distance boundaries. Therefore, these accessions with specific agronomic traits can be used to widen the lentil germplasm for breeding purposes. The genomic differentiation in each lentil species/subspecies was also analyzed using the allele-frequency-based analysis. The major genomic differentiation was observed on Chromosome 1 (Chr1; c. 1.0 Mbp), and results implied that L. nigricans was distantly related to other lentil species/subspecies. A total of five candidate genes were identified on c. 1.0 Mbp physical distance; however, the functionality of these genes in relation to wild and cultivated lentil species/subspecies still needs to be understood. One of the major abiotic stresses affecting gross profit and yield stability in Australian lentil cultivation is soil salinity. Identification of salt-tolerant varieties is the most viable and long-term option to maintain lentil productivity. However, this requires reliable and efficient screening methods. Salt tolerance assessment in lentil is currently based on morpho-physiological characterization and visual score ratings, which are often time-consuming, labor-intense and error-prone. Therefore, a novel high-throughput phenotyping (HTP) approach based on an image-based screen was developed using the LemnaTec 3D scanalyzer system to circumvent the limitations faced by current methods and accelerate the identification of the salt-tolerant varieties. The optimal salt concentration (100 mmol) and growth stages that distinguish salt tolerance levels were identified. Among the multiple phenotypic traits measured, area and color parameters were identified as the most informative traits for salt tolerance in lentil. The significant correlation observed between traditional and image-based screens (r = 0.55; p < 0.0001) demonstrated the accuracy of the developed protocol for salt tolerance in lentil, thereby can replace the conventional phenotyping approach. In addition to the phenotypic approaches, the understanding of the genetic basis of salt tolerance in lentil is important to develop salt-tolerant varieties. Recently, genome-wide association studies (GWAS) have been identified as a powerful tool to dissect the genetic basis of many phenotypic traits in diverse germplasm. Advances in resequencing approaches such as genotyping-by-sequencing (GBS) methods have also enabled the generation of a panel of SNP markers for large genome species like lentil. Two GBS approaches, targeted-capture (tGBS) and transcriptome-based sequencing (GBS-t), were tested to generate high-confidence SNP markers for association study. Among them, tGBS delivered the highest number of SNP markers with uniform distribution across the genome. Genomic regions for salt tolerance in lentil were identified on Chromosome 2 as well as on Chromosome 4. A high-affinity potassium transporter (HKT) gene was identified as the most possible candidate gene for salt tolerance in lentil. Mineral composition analysis performed on salt-treated and control lentil accessions has also been identified; Na+ ions absorbed by tolerant lentil accessions actively re-translocated them into roots or hold within the roots, supporting the candidate gene identified through GWAS. Pedigree analysis performed on salt-tolerant lentil genotypes identified two lentil accessions, ILL7685 and ILL1719, that could have been potential sources of allele contribution to salt tolerance in the lentil population. Overall, the study enriched the genomic and phenomic resources associated with lentil, thereby assisting future lentil research and breeding.
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ItemDevelopment of Advanced Phenomics Tools for Molecular Breeding of Biomass Yield Improvement in Perennial Ryegrass (Lolium perenne. L)( 2020)Increasing the biomass yield of perennial forage crops remains a crucial factor underpinning the profitability of grazing industries, and therefore is a priority for breeding programs. The rate of genetic gain for dry matter yield (DMY) in forage crops is likely to be increased with the development and application of genomic selection (GS) strategies. However, realising the full potential of GS will require an increase in the amount of phenotypic data and the rate at which it is collected. Phenotyping remains a critical bottleneck in the implementation of GS in forage species. Current assessment of DMY in forage crop breeding includes visual scores, sample clipping and mowing of plots, which are often costly and time-consuming. New ground and aerial-based platforms equipped with advanced sensors offer opportunities for fast, non-destructive and low-cost, high-throughput phenotyping (HTP) of plant growth, development and yield in a field environment. This thesis aimed to develop, validate and deploy sensor-based non-destructive aerial, and ground-based HTP platforms to estimate DMY of perennial ryegrass plants in-field. Firstly, calibration and validation of aerial and ground based HTP platforms were developed to provide a non-destructive method to accurately estimate perennial ryegrass height and DMY. Secondly, the thesis compared a range of traits alone and in combination to demonstrate sensor based DMY estimation. Thirdly, this study demonstrated the application of combining normalised difference vegetative index (NDVI) from multispectral imaging and ultrasonic sonar estimates of plant height to estimate the seasonal distribution of DMY of 48,000 perennial ryegrass plants. Fourthly, the study discussed the application of developed platforms and data processing workflow which allows for an increase in the accuracy of genomic breeding values prediction in GS. Calibration and validation results indicated that plant height measurements from ultrasonic sonar and light detection and ranging (LiDAR) sensors showed the potential to measure plant height with consistent repeatability under controlled conditions and in field trials. NDVI demonstrated the capability for non-destructive DMY estimation of individual ryegrass spaced planted and sward plots. However, DMY estimation from NDVI saturates as the biomass increases. The combination of plant height and NDVI was found to improve the DMY prediction accuracy by up to 10%. This was achieved by combining NDVI and plant height with a simple multiplication combination (NDVI multiply by plant height), with the possibility to further improve the accuracy by combining the parameters in different models. This thesis assesses further the feasibility of combining NDVI from multispectral imaging and ultrasonic sonar estimates of plant height to estimate DMY of single plants in a large perennial ryegrass breeding program. Results demonstrated that the best prediction models of DMY of spaced-planted perennial ryegrass plants come from the multiplicative combination of NDVI and plant height (NDVIsq_PH). The K-fold and random split cross-validation findings imply that the combination of NDVI and plant height improved prediction accuracy over the use of NDVI and plant height alone. This yielded an accuracy of the coefficient determination of DMY estimation of more than 0.63 and root mean square error (RMSE) for fresh herbage yield, and dry herbage yield was less than 33 gram per plant and 8 gram per plant, respectively across multiple growing seasons. Therefore, to assess the application of combining NDVI and plant height for accurate DMY, a computational workflow was developed for image acquisition, data processing and analysis of spaced-planted perennial ryegrass plants. Fifty advanced breeding lines and commercial cultivars represented by a total of 48,000 individual plants were used to develop and validate the computation workflow for DMY prediction across three growing seasons. Combining NDVI and plant height of individual plants was a robust method to enable HTP of DMY estimation in a large population of ryegrass breeding. Similarly, the plot-level model indicated good to high-correlation between the predicted and measured DMY across three seasons with coefficient determination between 0.19- 0.81 and root RMSE values ranging from 0.09-0.21 kilogram per plot. The model was further validated using a combined regression of the three seasonal harvests. This study will have a significant contribution to the wide application of sensor technologies in forage research and plant breeding.