Veterinary Science Collected Works - Research Publications
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ItemRoots' Drought Adaptive Traits in Crop Improvement.(MDPI AG, 2022-08-30)Drought is one of the biggest concerns in agriculture due to the projected reduction of global freshwater supply with a concurrent increase in global food demand. Roots can significantly contribute to improving drought adaptation and productivity. Plants increase water uptake by adjusting root architecture and cooperating with symbiotic soil microbes. Thus, emphasis has been given to root architectural responses and root-microbe relationships in drought-resilient crop development. However, root responses to drought adaptation are continuous and complex processes and involve additional root traits and interactions among themselves. This review comprehensively compiles and discusses several of these root traits such as structural, physiological, molecular, hydraulic, anatomical, and plasticity, which are important to consider together, with architectural changes, when developing drought resilient crop varieties. In addition, it describes the significance of root contribution in improving soil structure and water holding capacity and its implication on long-term resilience to drought. In addition, various drought adaptive root ideotypes of monocot and dicot crops are compared and proposed for given agroclimatic conditions. Overall, this review provides a broader perspective of understanding root structural, physiological, and molecular regulators, and describes the considerations for simultaneously integrating multiple traits for drought tolerance and crop improvement, under specific growing environments.
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ItemCombining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea.(Frontiers Media SA, 2022)Field pea is the most commonly grown temperate pulse crop, with close to 15 million tons produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines' advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial high-throughput phenotyping (normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low-to-moderate broad-sense heritability (0.31-0.71) and narrow-sense heritability (0.13-0.71) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0-S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6-50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.
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ItemAutomated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping.(Wiley, 2022-03)Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple two-band indices that limit the net performance and often do not generalise well for traits other than those for which they were originally designed. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel two- to six-band trait-specific indices in a streamlined process covering model selection, optimisation and evaluation, driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results showed that AutoVI can rapidly generate complex novel VIs (at least a four-band index) that correlated strongly (R2 > 0.8) with measured chlorophyll and sugar contents in wheat. Automated hyperspectral vegetation index-derived indices were used as features in simple and stepwise multiple linear regressions for chlorophyll and sugar content estimation, and outperformed the results achieved with the existing 47 VIs and those provided using partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable to high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface for the AutoVI is provided here.
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ItemGenome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.).(Springer Science and Business Media LLC, 2022-04)UNLABELLED: Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11032-022-01295-8.