By Kimberly Mann Bruch, ACCESS, and Ken Chiacchia, PSC
Farming requires extensive planning. A team from the University of Maryland hopes that DAWN will be a useful crop management tool. Adobe Stock 523624107.
Maryland Researchers Advance Agriculture Dashboard Using ACCESS Resources
Every season, farmers face an age-old problem: How can they plant, fertilize, and water their crops for the best possible yield? A team led from the University of Maryland has used two supercomputers — PSC’s Bridges-2 and Johns Hopkins University’s Rockfish — to create and run DAWN, an artificial intelligence (AI)-enhanced, interactive program allowing farmers to explore different crop management options given the weather expected from national climate predictions.
WHY IT’S IMPORTANT
Farming is one of the most important, and hardest, jobs on the planet. Our modern world would come crashing to a halt if we all couldn’t depend on people who specialize in growing food for the rest of us.
But nature doesn’t make farming easy. From the beginning of agriculture about 10,000 years ago, farmers have used every trick they could think of to decide what crops to plant, when to plant, and how to plant. Making the right decisions — growing the right food crops at the right time, with the right fertilization and water management — has long made the difference between plenty and starvation. It can be a little bit better today, when sophisticated meteorology makes the weather less of a question mark, and global trade can help make up shortfalls locally. But as we know, supply-chain problems can make it hard to transport food, and in any case, a bad year can put farmers out of business and cripple our ability to grow food next year.
“DAWN aims to empower stakeholders with the ability to access and utilize its capabilities to enhance land, water, and fertilizer management across various agricultural systems and scales … Our dashboard tools are co-produced with farmers so that they are tailored to their needs and effective for agricultural decision making.”
— Xin-Zhong Liang, UMD
A team led by Professor of Atmospheric Science Xin-Zhong Liang at the University of Maryland (UMD) decided that modern farmers could succeed better if they had a computerized “dashboard” that integrates different types of weather and climate prediction — such as the National Oceanic and Atmospheric Administration’s operational seasonal climate forecasts — in a simple tool that helps them make growing decisions. To build their tool, Dashboard for Agricultural Water Use and Nutrient Management (DAWN), they turned to two supercomputers in the NSF ACCESS system — Rockfish at Johns Hopkins University and Bridges-2 at PSC, a leading member of ACCESS.
HOW PSC HELPED
DAWN would need to couple climate-crop simulations with emerging AI technology. Assistant research scientist Chao Sun, a member of Liang’s team, aimed to provide farmers with multidisciplinary insight on their specific farming area’s climate and crop prediction with a six-month lead time. He and Liang’s team worked with farmers to ensure that DAWN’s interface would offer them the information they need in a user-friendly way.
The scientists’ dashboard tool would start with regional high-resolution climate-crop coupled numerical model predictions for the entire continental U.S. as well as the Gulf of Mexico. By pairing these data with complex AI algorithms, they could improve on the predictive power of climate-only forecasts. DAWN’s AI algorithm would look at past crop progress and compare it with predictive future situations based on precipitation, temperature, irrigation, fertilizer, and more.
“The complex algorithms and large memory required to run DAWN wouldn’t have been possible on our local machines; hence, we reached out to the NSF for ACCESS allocations … Bridges-2 and Rockfish provided us with the power needed for our work.”
— Chao Sun, UMD
The Anton simulations of amyloid fibril growth revealed that, in addition to sticking to the end of a fibril (Bulk-docking), amyloid peptides (left, blue) could first attach to the length of the fibril (right, gray) and then slide to the end to stick (Surface-docking). This in effect increases the size of the target and makes fibril growth accelerate with time.
The Anton simulations revealed an unexpected behavior by the amyloid peptides. Scientists had known that fibrils grew by new peptides attaching to the end of the fibril. What happened in addition in the simulations, though, was that the amyloid peptides would first stick to the side of the fibril, then slide to the end to attach. In effect, this gave the peptides a much larger target to aim for, explaining how the initial, slow growth of the fibril (when it was short, and so didn’t offer a large target) after a time lag gave way to more explosive growth (once the fibril was longer and easier for the amyloids to “find”).
The discovery challenges previous theoretical “dock-and-lock” models for how amyloid peptides attach to form fibrils and then plaques. The scientists used their new discoveries to create a new model for amyloid growth, which both explains the previous data better and predicts new behaviors by the amyloid peptides and fibrils that can be tested in the laboratory. Ultimately, the scientists hope their work will lead to therapies to treat or prevent Alzheimer’s and many other human disorders. The team, including former PhD students Ruoyao Zhang and Sharareh Jalali, reported their results in the U.S. National Academy of Science’s journal PNAS Nexus in February 2024.