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Biomass at anthesis is an important trait in predicting yield of Brassica oilseeds in environments where seed filling occurs in dry and warm conditions. This study aimed to compare the ability of non-destructive canopy cover measurements (Sunfleck ceptometer and digital photography) with direct biomass sampling at anthesis to predict the yield of canola-quality B. juncea (juncea canola) hybrids. Field experiments were conducted in the Victorian Mallee (226-248 mm annual rainfall) and the Wimmera (266-407 mm annual rainfall) regions from 2012 to 2014. Nineteen juncea canola genotypes were sown in the first year and 10 to 11 genotypes in the subsequent two years of field experiments. The experimental plots were laid out in a randomized complete block design with three replications. Days to 50% flowering, canopy cover and biomass at 50% flowering and seed yield were recorded. The study concluded that at low rainfall sites (<250 mm annual rainfall), the canopy cover measurements had consistent and significant positive relationships with biomass at anthesis (r 2 =0.43-0.61 in 2012 and r 2 =0.72 in 2013) and seed yield (r 2 =0.25-0.41 in 2012 and r 2 =0.51 in 2013). Canopy cover also showed a positive and significant relationship with early flowering (r 2 =0.52 in 2012 and r 2 =0.60 in 2013) at the relatively low rainfall site. These results suggest that non-destructive canopy cover measurement could replace direct biomass sampling at anthesis in prediction of yield of juncea canola hybrids in low rainfall environments.

Two glasshouse and two field experiments were conducted in 2013 and 2014 to compare the relative importance of four physiological traits: osmotic adjustment (OA), leaf proline concentration, canopy temperature depression (CTD) and root depth on drought performance of canola quality B. juncea (juncea canola). Glasshouse experiments were conducted at The University of Melbourne, Parkville, and field experiments were conducted at Horsham, Victoria. The experiments used juncea canola hybrids and their parental lines and were laid out in a randomised complete block design with three replications. The glasshouse experiments consisted of two treatments, well watered and water deficit from first open flower to maturity, whereas the field experiments were sown at a site that received 266 mm annual rainfall in 2014. In the glasshouse, canopy temperature depression was the only trait to show a positive and consistent association with drought performance of juncea canola. Cooler canopy temperature was also associated with improved yield in field experiments. Root depth was positively correlated with CTD in 2014 in glasshouse,whereas no correlation of root depth with OA and leaf proline was observed. The results indicated that CTD was the only reliable trait among those tested to screen juncea canola for drought tolerance. Root depth of juncea canola hybrids was a constitutive trait and probably was a result of hybrid vigour.

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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