JULY 2026 SEEDWORLD.COM/CANADA 11 Scholarships | CPBI ‘26 AT 25, Anirup Sengupta is already think ing in decades. Not because he is moving slowly. Quite the opposite. The University of Saskatchewan plant scientist has pub lished papers, presented internationally, won awards, and has now earned a 2026 CPBI Scholarship. But Sengupta’s work is aimed at a much longer horizon: the future of crop improvement in a world where agricul ture has to move faster, become more precise, and adapt to climate pressure, emerging disease and the constant demand for more sustainable food systems. His PhD research brings together three powerful tools in modern plant breeding: reference genome development, genetic diversity analysis and genomic selection. The crop at the centre of that work is cicer milkvetch, a perennial forage legume valued for its nutritional quality and its ability to avoid the frothy bloat problems that can affect ruminants. Its promise is significant. Its challenges are equally clear: low germination, poor seedling establishment and limited genomic resources. For Sengupta, the starting point is simple: before breeders can improve a crop quickly, they need a map. “Without a reference genome,” he says, “breeding is a bit like trying to assemble a massive puzzle without seeing the picture on the box.” That reference genome — the crop’s first complete genetic blueprint — would allow researchers to identify genes linked to key traits such as seedling vigour, forage yield, disease resistance and stress That change is being driven by genomics, bioinformatics, artificial intel ligence, machine learning, drone-based phenotyping and environmental data. The result is not a replacement for breed ers. It is an upgrade to their decision- making. That distinction matters. In agricul ture, a model is only useful if it con nects back to the field. Sengupta’s own training reflects that. His resume spans plant breeding, genetics, plant pathology, genomic data analysis, GIS, statistics, R, Python and field and greenhouse experimentation. His master’s work at the University of Manitoba focused on genome-wide association studies and genomic selection models for leaf rust resistance in winter wheat. His career through-line has been mentorship. Sengupta repeatedly points to supervisors, collaborators and senior scientists as a major reason for his suc cess. “Good mentors can accelerate our growth a lot,” he says. He came to Canada from India in 2022 working in wheat breeding with Curt McCartney and collaborators including Bill McCallum and Colin Hiebert. After that, he moved into his PhD work with Bill Biligetu and Andrew Sharpe, connected to the Crop Development Centre in Saskatchewan. McCallum, a research scientist with Agriculture and Agri-Food Canada at the Morden Research and Development Centre, says Sengupta is “a strong stu dent, a diligent and dedicated researcher who will make significant contributions to this field.” HE GREW UP AROUND FARMING. NOW HE’S USING BIG DATA TO RETHINK CROP BREEDING Anirup Sengupta’s journey from India to Canadian plant genomics reveals how the next generation of breeders is combining field experience, AI and DNA-level insight. Anirup Sengupta’s work is aimed at a much longer horizon: the future of crop improvement in a world where agriculture has to move faster. tolerance. From there, genetic diversity analysis helps breeders understand which plant populations are truly different from one another. The third piece is genomic selection, one of the tools Sengupta sees as most transformative. Instead of waiting several growing seasons to evaluate which plants perform best in the field, breeders can use DNA marker information and computational models to predict future performance much earlier. Together, the three approaches create what Sengupta calls a pipeline for faster, smarter crop improvement. From Observation to Prediction He believes the biggest shift underway is that plant breeding is becoming more predictive.
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