Single award · grant

IN the Recent Past Especially the Last Half of the Twentieth Century Advances in Irrigation Fertilization…

Award ID: 20206702132799

FY2020 · complete year · data as of 2026-07-12

A single federal award, with both ends of the trail linked.

$20M
Total award value

Award 20206702132799 is a grant from the Department of Agriculture to University of Illinois, $20M obligated, FY2020 to FY2027, for IN the Recent Past Especially the Last Half of the Twentieth Century Advances in Irrigation…. The work is performed in Illinois. Source

Description

IN the Recent Past Especially the Last Half of the Twentieth Century Advances in Irrigation Fertilization Mechanization and Breeding Have Helped Agriculture Keep Pace with the Growth in World Population. This Success However Has Come at Enormous Costs to the Environment and Human Health: Due to Overuse of Chemicals and Antibiotics Soil Degradation and Erosion Increased Herbicide Resistance Fertilizer Runoff Etc. Today We Are Faced with the Challenge of Feeding an Additional 2-3 Billion People by 2050 Despite Labor Availability and Arable Land Decreasing and Environmental Constraints Increasing. Conventional AS-Practiced Technologies Cannot Solve These Looming Fundamental Challenges in a Sustainable Manner.advances in the Theory and Practice of Artificial Intelligence (Ai) Play an Important Role in Addressing These Challenges in Two Key Ways. First Improvements in Autonomy and Human-Augmented Systems Can Increase Agricultural Output While Also Reducing Harmful Environmental Impact with Fixed or Even Slightly Reduced Labor Requirements E.g. By Allowing Far More Detailed Monitoring and Decision-Making in Large-Scale Production of Both Crops and Livestock. Second Ai Research Can Yield New Techniques for Decision-Making Based on Agricultural Data Which Comes from Highly Diverse Sources and Which Spans Increasingly Wide Spatial Scales from Individual Plants to Whole Regions and Widely Varying Time Scales from Seconds to Entire Seasons.the Aifarms Institute Will Develop and Demonstrate the Advances in Ai Necessary to Address the Major Challenges Facing World Agriculture by Combining Basic Research Novel Experimental Facilities State-Of-The-Art Data Analytics and Eventually Evaluation in Production Settings. Farmers Will Be Involved in All Stages of the Work from Identifying Practical Constraints on Technological Solutions to Evaluating Them in the Field and Providing Feedback on the Incentives and Barriers to Their Adoption. The Institute Will Work with Technology and Agriculture Companies to Transfer the Outcomes of the Research Into Production Use. Multidisciplinary Educational Programs Spanning Computer Science and Agriculture with Increased Ai Content Will Develop the Skilled Workforce Needed to Develop Deploy and Support These Advanced Technologies in Complex Modern Agricultural Operations. Education and Farmer Training Programs Will Be Carefully Designed to Increase Participation of Underrepresented and Disadvantaged Groups in Both Agriculture and Relevant Stem Disciplines.if Aifarms Is Successful IT Will Lead to Technological Advances That Enable Farmers to Increase Food Production and Profitability in the Long Term to Do So Without Worsening Environmental Harms to Be Much More Resilient to Climate Change and to Absorb Steadily Declining Agricultural Labor. IT Will Lead to Foundational Advances in Ai That Will Have Broad Benefits in Many Other Areas. IT Will Also Lead to a More Diverse Agriculture and Technologyworkforce.

Award details

Award typeGrant
FAIN20206702132799
Action date2020
Period84 mo
Place of performanceIllinois →
Category—
Competed?Not recordedSource
Total (exact)$19,998,042

More awards to University of Illinois

More from Department of Agriculture

awarded 2023$3.2B
awarded 2022$3.1B
awarded 2021$2.9B
awarded 2018$2.7B
awarded 2024$2.7B
Why the labels? The graph is only worth anything if the links are trusted. Facts taken straight from a federal filing are Source; figures we calculate are Computed; links we infer (corporate parents, districts, similar contractors, connections between records) are Inferred, worded carefully, never asserted as fact and never a claim that one thing caused another. Spot an error? Tell us.