ALFADVISOR: A WEB-BASED CYBER-PLATFORM TO ESTIMATE ALFALFA YIELD AND QUALITY TO SUPPORT HARVEST SCHEDULING

Alfalfa is a valuable and intensively produced perennial forage crop in the United States that plays a vital role in the food supply chain as a feedstock for animals. As a forage crop, alfalfa producers are concerned with both the yield and quality of the harvested production, and timing of alfalfa cutting is a critical management practice to maximize yield potential, quality, and profitability of an alfalfa crop. To maximize alfalfa production, quality and total economic return, the overall goal of this project is to deliver a free public cyber-platform as a service that combines satellite remote sensing, machine learning, and economic modeling to (i) provide timely estimations of alfalfa yield and quality at the field scale, and (ii) optimize harvest scheduling by integrating the most important factors, including the trade-off between alfalfa yield and quality, drying rate, and weather risks (e.g. rain on cut forage). This tool will use a web-interface to allow users to visualize yield and quality maps within their fields to assess the riskiness of different harvest management practices and options and to develop plans that best satisfy their risk preferences. The proposed project fully integrates novel satellite data, machine learning, economic modeling and cyberinfrastructure. Also, as a research and extension integrated project, the research outcomes will be disseminated through multiple extension and outreach approaches. We plan to attain the overall research goal by pursuing the following specific objectives:Objective 1: Develop models to estimate alfalfa yield, quality and drying rate in real-time by combining satellite imagery and environmental factors to inform harvesting time.Objective 2: Develop a decision-making model to provide economically optimum harvest scheduling during the growing season.Objective 3: Create a web-based cyber-platform to disseminate tools for free public use.