We are seeking an individual to pursue an M.Sc. in modeling complex-covers used in regenerative cropping and livestock agricultural systems to evaluate changes to key soil metrics (e.g., carbon) and animal performance (e.g., gain, milk production). Regenerative agricultural systems provide evidence of improvements to water-soil-plant-and-animal components but depend on geographic location, management, and production goals. Therefore, developing a mathematical model capable of simulating complex-covers for different agronomic and livestock systems is essential to inform management decisions and forecast potential changes. In collaboration with the United States Department of Agriculture-ARS and Cornell University, the student will participate in data sampling/gathering and refinement, mathematical modeling, and participatory modeling with industry stakeholders. The student will learn to develop dynamic models to evaluate different complex-cover scenarios for ruminant systems (dairy and beef) that can inform producer management decisions regarding key performance metrics at a systems level.
Competitive candidates will be highly motivated and possess a B.S. in Animal Science, Soil Science, Agronomy, Natural Resources, Computational Biology, or a related field. Experience with the Python programming language is preferred but not required. Qualified candidates will also demonstrate experience or interest in applying quantitative methods to complex-cover and livestock systems. We particularly welcome applications from under-represented groups in Animal Science. A minimum GPA of 3.0. GRE is not required. TOEFL of 550 (84). To apply: Send a single PDF that includes a brief cover letter outlining experience and research interests, curriculum vitae, unofficial university transcripts (a list of relevant coursework and grades is acceptable), and contact information for three references to Dr. Hector Menendez at firstname.lastname@example.org. Applications will be accepted until May 31, 2021.