Purdue University

Awards Issued Prior to January 20, 2025, Were Funded Under Previous Administrations and May Not Reflect the Priorities and Policies of the Current Administration. in the 1990S, Access to Geo-referencing Tools Launched Precision Agriculture, a Farm Management Concept to Improve Farmer Profits and Environmental Quality by Understanding and Managing Naturally Occurring Variability in Soils Within a Field. Since Then, There Has Been an Explosion of Data Generated on the Farm to Create Layers of Maps Such as Yields and Soil Test Results to Try to Determine Exactly What Amount of Fertilizer Is Needed for Very Small Areas Within Fields. However, Many of the Best Management Practices, Recommendation Frameworks, and Associated Tools for On-farm Decision Making Have Not Been Updated. Indeed, Many Fertilizer Guides Have Proven Ineffective for Precision Agriculture, Which Is Unsurprising Given That These Historic Recommendations Were Developed for Average Conditions Across Fields and Farms and Are Not Finely Tuned Enough to Be Meaningfully Applied to Specific Sub-environments Within Fields. Further, Despite Extensive Public Research Investments in Studies to Optimize Fertilizer Management, the Rigorous Synthesis of These Studies Has Not Been Done; Statistically Sound Synthesis Across Studies Is a Necessary First Step to Making Recommendations That Can Be Tailored to Sites, Soils, and Farm Enterprises as Envisioned for Precision Agriculture. Reasons Why Studies Have Not Been Synthesized Include (i) a Lack of Appreciation, Knowledge and Culture for Synthesis Science, (II) Publication and Communication Systems That Prioritize Novel Results Over Confirmatory or Null Studies, Thereby Skewing the Official Scientific Record as a Foundation for Synthesis; and (III) Lack of Infrastructure and Tools to Facilitate Use and Reuse of Accruing Data Relevant to Management Objectives.long-term Goal and Supporting Objectives: We Hypothesize That Creating Better Infrastructure for Science Synthesis, Coupled With Mechanisms for Using On-farm Data, Will Deliver Recommendations With More Localized Relevance, On-farm Value and Compliance With Emerging Farm-to-fork Sustainability Metrics. Our Overarching Goal Is to Provide Proof-of-concept to a Cyber-enabled Process Linking Public and Private Data and Create a Model Ecosystem and Community for Public-private Research and Clinical Trials for Continuous Improvement of the Evidence for Management Practices. We Will Focus on Phosphorus (P) and Potassium (K) Management Recommendations as Case Studies for the Following Main and Facilitating Objectives:main Obj. 1: Create a Cochrane-style Collaborative (https://www.cochrane.org/) for Research Synthesis That Employs Open-access Cyber Tools for Systematic Reviews With Statistical Meta-analyses as the Foundation for Evidence-based Practice and Incentivizes Open Data in Public Agronomic Research;main Obj. 2: Create a Cyber-framework and Community Ecosystem for Profitable, Evidence-based Decision Making and Resource Stewardship That Ingests On-farm Data, Integrates It With Research Data, and Recommends Locally Relevant Optimization VS Generic Strategies,;facilitating Obj. 3: Develop a 10-CREDIT Certificate Program for Graduate Students and Practicing Professionals Emphasizing Data / Computational Literacy and Rigorous Synthesis of Disparate Data. Main Objectives Will Develop and Implement Best Practices, Policies and Tools for (i) Public-private Partnerships for Data Use in Evidence-based Practice Inclusive of Privacy, Security, Cost and Intellectual Property Concerns (II) Triage and Recovery of High-value Legacy Data and (III) Conversion and Sharing of Grey / Dark Data From Null or Confirmatory Studies in Fair (findable, Accessible, Interoperable Re-usable) Formats. the Facilitating Objective Will Prepare Graduate Students for Conducting Data-intensive Syntheses of Agronomic Research and On-farm Data and Will Equip Practitioners for Site-specific Management Using Their Data. This Proposal Will Create Key Elements for Our Ultimate Vision: a Cyber-framework for Customization That Ingests and Anonymizes User Data, Combines Ingested With Existing Research and On-farm Data, and Uses Artificial Intelligence and Machine Learning to Fine-tune Recommendations With Accruing New Data to Minimize Human Resource Requirements.