In the face of climate uncertainty, growers wonder which grape varieties will flourish in their regions in the future, or if any will grow there at all. Joel Harms, Ph.D. student in the Department of Bioresource Engineering at McGill University in Australia is using artificial intelligence to simulate the potential to grow pinot noire in different regions of the world that are currently considered too cool. The project mapped 1,300 varieties to 16 different points of climate data including temperature, precipitation, and growing degree days. The findings could play a crucial role in identifying the winegrowing regions of tomorrow. Resources: 207: Managing Catastrophic Loss in Vineyards: Lessons from Cyclone Gabrielle in New ZealandCal-AdaptDevelopment of a generative AI-based model for guiding grape variety selection under contemporary climate dynamics Generative AI for Climate-Adaptive Viticulture Development Joel Harms Google Scholar Page Mapping Global of the Potential for Pinot Noir Cultivation under Climate Uncertainty using Generative AI University of Adelaide Wine Economics Research Center Vineyard Team Programs: Juan Nevarez Memorial Scholarship - Donate SIP Certified – Show your care for the people and planet Sustainable Ag Expo – The premiere winegrowing event of the year Vineyard Team – Become a Member Get More Subscribe wherever you listen so you never miss an episode on the latest science and research with the Sustainable Winegrowing Podcast. Since 1994, Vineyard Team has been your resource for workshops and field demonstrations, research, and events dedicated to the stewardship of our natural resources. Learn more at www.vineyardteam.org. Transcript [00:00:00] Beth Vukmanic: In the face of climate uncertainty, growers wonder which grape varieties will flourish in their regions in the future, or if any, will grow there at all. [00:00:13] Welcome to Sustainable Wine Growing with the Vineyard Team, where we bring you the latest in science and research for the wine industry. I'm Beth Vukmanic, Executive Director. [00:00:23] In today's podcast, Craig McMillan, Critical Resource Manager at Niner Wine Estates, with longtime SIP certified vineyard and the first ever SIP certified winery. Speaks with Joel Harms, PhD student in the Department of Bioresource Engineering at McGill University in Australia. [00:00:42] Joel is using artificial intelligence to simulate the potential to grow Pinot Noir in different regions of the world that are currently considered too cool. [00:00:52] The project mapped 1, 300 varieties to 16 different points of climate data. including temperature, precipitation, and growing degree days. The findings could play a critical role in identifying the wine growing regions of tomorrow. [00:01:07] Want to be more connected with the viticulture industry but don't know where to start? Become a member of the Vineyard Team. Get access to the latest science based practices, experts, growers, and wine industry tools through both infield and online education so that you can grow your business. Visit vineyardteam. org and choose grower or business to join the community today. Now let's listen in. [00:01:34] Craig Macmillan: Our guest today is Joel Harms. He's a PhD student in the Department of Bioresources Engineering at McGill University. And today we're going to talk about mapping global future potential for Pinot Noir cultivation under climate uncertainty using generative AI. [00:01:51] Bye. Bye. This is a really interesting topic. I came across an abstract from a recent ASEV meeting and I was like, I just have to know more about this. This just sounds too interesting. But welcome to the podcast, Joel. [00:02:04] Joel Harms: Okay. Thank you very much. Thank you for having me. [00:02:06] Craig Macmillan: What got you interested in this topic in terms of this wine grape region? Stuff. [00:02:12] Joel Harms: I think it was more about I wanted to build models that are useful, I guess, broadly useful in vineyard management and like establishing new vineyards and like kind of covering some of the base problems. Initially, my thought was, how can we. see which grape varieties are alike. [00:02:32] How can we like make a representation of them in like a latent space. But then I found out , if I do that, that's, you know, somewhat useful, but if I take that just a step further, I could just connect it with climate data already. And then we would have a model that could, be used for prediction and it would be so I guess. How do I say like broad or general enough so that you could apply it in any environment. So like any climate can be used to predict any grape suitability matrix, which is quite nice. And so then I thought, no, let's do it. Let's try that. [00:03:11] Craig Macmillan: So your colleagues and yourself did some simulations, as we just mentioned specifically around Pinot Noir and the potential to grow it in different parts of the world that currently are...
