Guelph program stands out to Rajcan. Many of the released cultivars have had wide reach and adaptation and are grown in Ontario, Quebec, and Manitoba, as well as countries like Germany, Austria, Ukraine, and Russia. Research collaboration with Chinese breeders has improved genetic diversity, a key component of any breeding program. Rajcan was able to bring seeds from 50 cultivars from northeast China to Ontario and is excited to make crosses with them and continue the project. He emphasizes that Guelph’s program also yields new plant breeders, which is important for the future of the industry. “I’ve been able to train and graduate 44 graduate students in my time here, and currently have five graduate students in courses,” Rajcan says, noting that all of the graduates are employed and many work in seed industry leadership positions. TECHNOLOGY IS KEY Numerous technological advances have accelerated soybean breeding and Rajcan is excited about further implementation of new technologies. “We want to make sure that every generation is more accurate in terms of the traits we are trying to select for, and we don’t want to waste time and resources on materials that don't hold promise,” he explains. “In the past, just visually looking at the plants was very difficult, but now we are studying genetics by collecting large amounts of data to make selections that are more precise.” Significant advancements have been achieved by using genomic markers to select for traits such as oil, protein, and other seed compositions. A current project in Ontario and Quebec is focused on adapting genomic selection for major Canadian soybean breeding programs and it is expected that breeders will incorporate this technology into their selection strategy in late 2024. Rajcan is hoping to advance phenotyping using drones in the near future. Remote sensing approaches can be used to predict and map yield, maturity, biomass, and other soybean traits. However, breeding for complex traits is complicated and time-consuming as it requires extensive phenotypic, genetic, and environmental information. Although there have been many recent advances, more efforts are needed to increase the speed of the breeding process. The more high-throughput “-omics” approaches are used, the more data is collected. Analyzing big data sets requires intensive computation and the use of modern statistical approaches such as different types of algorithms. Machine learning algorithms for analyzing complex nonlinear and multivariable systems are actively being studied in plant science. Climate change is also top of mind for soybean breeders. Changes in temperature may require Ontario breeders to select genotypes with different maturity ranges as well as modify breeding strategies due to reduced genetic diversity if some genotypes cannot survive in future climate conditions. Yoosefzadeh-Najafabadi and Rajcan’s paper “Six decades of soybean breeding in Ontario, Canada: a tradition of innovation” was published in the Canadian Journal of Plant Science and has received the 2023 editor’s choice award. l ONTARIO GRAIN FARMER 23 NOVEMBER 2023 More than 130 varieties have been released by the University of Guelph since 1970, and many have experienced longevity in the market. ISTVAN RAJCAN AT THE UNIVERSITY OF GUELPH ELORA RESEARCH STATION SOYBEAN RESEARCH PLOTS IN AUGUST, 2023.
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