 Hi, my name is Rich Horsley. I'm the head of the Department of Plant Sciences and one of 11 plant breeders in the department. I'm the department's barley breeder. I've been asked to speak with you today about breeding new crop varieties. Development of new varieties takes 10 to 12 years from the time of crossing until it's released to the farmer. Barley crosses that I make this spring in the greenhouse won't be available to the farmer to produce for commodity until 2033. During this 10 to 12 year period, we're evaluating experimental varieties for agronomic performance, disease resistance and end use quality. If you're working with wheat, that end use quality would be milling and baking quality. If you're working with barley, that end use quality would be molting and brewing quality. Development of new varieties is a collaborative effort between the breeder, pathologist, entomologist and use quality chemist and the NDSU research extension centers located across the state. Additionally, input from farmers and end users is critical in development of our new varieties because it's these individuals that really dictate what those new varieties will look like. Traditionally, our breeding efforts have been focused on development of varieties for large commodity markets, both international and domestic. But with the growth and desire for locally produced crops and production of craft and artisan food products made from these local crops, we have expanded our breeding efforts to include these markets as well. One of the most important advances in plant breeding the past 10 to 15 years has come from our ability to manage and utilize big data to predict how varieties will perform before we even make the cross. The data we're using in this case is the DNA data. And from those experimental varieties develop from those crosses. Breeders, we can predict how these varieties will perform before they're ever grown in the field. We can predict their yield, their disease resistance and their end use quality. Now these big data can be put into three different categories. The first would be the performance data that we collect from the evaluation of these experimental varieties in the field, greenhouse and laboratory. The second category would be the DNA data that we collect on each line. And the last group of data would be those data collected from high throughput phenotyping, a big fancy word, but basically it's data collected using drones or remote sensing. These files that contain the DNA and drone data are huge. For example, when we genotype wheat we use 90,000 DNA markers. And if you think about how you would portray this data in an Excel file, this would be an Excel file with over 90,000 columns wide. If you were to genotype 300 different parents that you're using for crossing with this DNA chip, you would generate 27 million data points. And so using a program like Excel to manage these data and to extract information out of it, it just isn't possible. When it comes to that high throughput phenotyping data, if you've ever tried to copy, edit, or manage video files on your own, you've learned how big and how unwieldy these files are. We were fortunate in 2015 to receive new funding from the state legislature that allowed us to hire two bioinformaticists to help all of our breeders and our scientists at the experiment station to manage and utilize their breeding data more efficiently. But if we're gonna go the next step in utilizing the DNA drone and remote sensing data to its fullest and developing new varieties, we're gonna need to expand our ability to manage and utilize these big data. I wanna thank you for your time for my presentations today, thank you. My name's Joel Caten and I'm an animal scientist. And I work with a team of animal scientists across the NDSU system. We're focused on investigating nutrition and developmental outcomes in offspring. And we're using livestock as models to understand that. We're doing this because it's relevant to agriculture and to broader societal issues. It's relevant to agriculture because developmental programming impacts efficiency, sustainability, and profitability of animal agriculture and consequently, agro-ecosystems. Not only that, but it addresses one of the major challenges facing agriculture in the next two or three decades. And that's feeding the world population. In addition, work in this area tamps down on chronic disease and fosters human health through understanding nutritional principles and through providing nutritious products for human diets. When we approach this, we have to do it in a balanced, sustainable way with a one health concept in mind. With a one health concept, we're talking about the idea that animal health, human health, and the health of the agro-ecosystems are completely entwined. So the implications of our work are that maternal nutrition matters. In fact, small changes in maternal nutrition can impact offspring outcomes. We know some specifics and mechanisms, but we're interested in learning more about those. Can we manage developmental programming to mitigate the negative and foster the positive outcomes? We think so, but we need to learn a lot more. We're interested in strategic nutrient supply, demand, and use because these are the foundations of targeted supplementation programs. And data from research in this area are expanding our understanding of biology, impacting animal agriculture, and have broad implications on major societal issues.