Developing a Climate-Smart Practice Optimization Tool for Sustainable Agriculture in the US

Goals

This VIP aims to develop a climate-smart practices optimization tool for enhancing the climate resilience of agriculture in the US.

What are climate-smart agricultural practices?

Practices that could intensify crop yields by:

  • Optimizing nutrient use efficiency (NUE)
  • Optimizing water use efficiency (WUE)
  • Minimizing greenhouse gasses (GHG) emissions

We will integrate observations with artificial intelligence (AI) and process-based models to investigate the question:

How can we optimize agricultural management practices (e.g., biochar addition, fertilization, irrigation, planting, and harvest) to achieve the “Climate-smart” objective?

This project involves two types of activities for undergraduate students:

  • Type 1 (Data-dominated work) focuses on agricultural practice-related data mining, statistical analysis, optimization, and visualization.
  • Type 2 (model-dominated work) focuses on model parameters optimization, and AI model training and testing.

As a student, you will be able to choose which path you want to follow based on what you enjoy, and what you're studying. If you're into data, experiments, and creating visuals, Type 1 might be your thing. If you love working with computers, software, and using AI to solve problems, then Type 2 may be where you'd thrive. 

With either type of activity, you'll be learning about agricultural practices while making a real impact on future climate-smart agriculture. 

Issues Involved or Addressed

  • Climate resilience assessment and optimization
  • Climate-smart agriculture and ecosystem management
  • Water cycle, energy balance, and carbon cycle
  • Soil carbon sequestration and nutrient availability
  • Water quantity and quality
  • Model parameter optimization
  • AI model training and testing

Methods and Tech

  • Data mining, collection, and organization (Microsoft Excel, etc.)
  • Statistical data analysis, interpretation, and visualization (Python/MATLAB/R)
  • Spatial data analysis and visualization
  • Artificial intelligence and modeling (Python/Fortran)
  • Science and interdisciplinary communication

Academic Majors of Interest

Our VIP team is open to all majors, with particular interest in the students with any of the following backgrounds:

  • Hydrology and Atmospheric Sciences
  • Ecology & Evolutionary Biology
  • Environmental Science
  • Natural Resources
  • Computer Science
  • Mathematics and/or Statistics & Data Sciences
  • Crop Sciences
  • Geosciences

Preferred Interests and Preparation

Skills:

  • Basic ability to conduct literature reviews, gather relevant research materials and synthesize information
  • Basic ability to use statistical analysis tools, such as Excel, R, MATLAB, or Python
  • Preferred: Computer programming (e.g., Python/Fortran) or AI/ML model experience
  • Preferred: Spatial data analysis and mapping experiences, such as GIS applications, NETCDF data analysis and visualization

Attributes:

  • A genuine interest in addressing climate change and its impact on agriculture
  • Enjoys interdisciplinary research and collaborative work environments
  • Reliability and accountability in handing research tasks and data
  • Commitment to research excellence and the ability to meet project deadlines
  • Careful and meticulous approach to data analysis and research tasks

Application Process

If you are excited about our VIP research in developing a climate-smart agriculture practices tool, we encourage you to apply.Join us in our mission to create a more climate-resilient future for agriculture in the United States. Together, we can make a difference!

If you are interested in joining this VIP team for class credit, contact Dr. Song by completing a VIP Interest Form and selecting the team "Developing a Climate-Smart Practice Optimization Tool for Sustainable Agriculture in the US."

If you are interested in a Federal Work-Study position with this team, please submit 1) your resume/CV, and 2) a one-page cover letter detailing your interest and/or relevant experience to Dr. Yang Song (chopinsong@arizona.edu(link sends e-mail)).

Team Advisor

Yang Song, PhD