Leptosphaeria maculans is the causal agent of blackleg, a serious disease on canola/rapeseed in western Canada, Australia and Europe. Genetic resistance and extended crop rotation provided effective disease control in western Canada for years but the emergence of new pathogen races has reduced the effectiveness of current management strategies. The objective of this study was to analyse L. maculans isolates derived from canola stubble in commercial fields collected in 2010 and 2011 across western Canada for the presence and frequency of avirulence (Avr) genes. A total of 674 isolates were examined for the presence of Avr alleles AvrLm1, AvrLm2, AvrLm3, AvrLm4, AvrLm6, AvrLm7, AvrLm9, AvrLepR1, AvrLepR2 and AvrLmS using a set of differential host genotypes carrying known resistance genes or PCR amplification of AvrLm1, AvrLm6 and AvrLm4-Lm7. Certain alleles were more prevalent in the pathogen population, with AvrLm6 and AvrLm7 present in >85% of isolates, while AvrLm3, AvrLm9 and AvrLepR2 were present in <10% of isolates. A total of 55 races (different combinations of Avr alleles) were detected, with the two most common ones being AvrLm2-Lm4-Lm6-Lm7 and AvrLm2-Lm4-Lm6-Lm7-LmS. Races carrying as many as seven and as few as one known Avr allele were detected. Selection pressure from the race-specific resistance genes carried in canola cultivars has probably played a significant role in the current Avr profile, which may have also contributed to the recent increase in blackleg observed in western Canada.

Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root count and mass using imagery taken during emergence and canopy closure, respectively. We created augmented plots, composed of random combinations of the study plots, to further exploit the importance of early canopy growth area. We achieved a R2 = 0.70 and root mean squared error (RMSE) of 84 roots (~24%) for root count, using 2018 emergence imagery. The same model resulted in a RMSE of 127 roots (~35%) when tested on the unseen 2019 data. Harvested root mass was best modeled with canopy closing imagery, with a R2 = 0.89 and RMSE = 6700 kg/ha using 2018 data. We applied the model to the 2019 full-field imagery and found an average yield of 41,000 kg/ha (~40,000 kg/ha average for upstate New York). This study demonstrates the potential for table beet yield models using a combination of radiometric and canopy structure data obtained at early growth stages. Additional imagery of these early growth stages is vital to develop a robust and generalized model of table beet root yield that can handle imagery captured at slightly different growth stages between seasons.

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