Prasad Bandaru Research Plant Physiologist US Arid-Land Agricultural Research Center 21881 N. Cardon Ln. Maricopa, Arizona, 85138 520-316-6357 520-316-6330 (FAX)

Education:

Ph.D. Plant and soil sciences, University of Delaware, Newark, DE (2009). Research conducted at USDA-ARS, hydrology and remote sensing lab, and crop system and global change lab, Beltsville, MD

M.S. Plant, soil and environmental sciences, West Texas A&M University, Canyon, TX (2005). Research conducted at USDA-ARS, conservation and production research lab, Bushland, TX

B.S. Agriculture, Andhra Pradesh Agricultural University, Hyderabad, India (2000).

Professional Experience

Research Plant Physiologist, Plant Physiology & Genetics Unit, USDA-ARS, Maricopa, AZ (2022-present)

Associate Research Professor, Dep. Geographical Sciences, University of Maryland, College Park, MD (2015-2022).

Visiting Associate Professor, Dep. Civil Engineering, IIT-Bombay, Mumbai, India (2019-2021).

Project Scientist, Energy Institute, University of California-Davis, Davis, CA (2013-2015).

Research Scientist, PNNL, Joint Global Change Research Institute, College Park, MD (2010-2013)

Postdoctoral Researcher, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN (2008-2010).

Research Interests

My research broadly focuses on improving modeling capabilities to reliably simulate crop growth, water, carbon, and nitrogen interactions under different climatic, soil and management conditions at various spatial scales, and developing operational decision support tools using combination of process-based crop models, and remote sensing. The ultimate goal of my research program is to help different stakeholders involved in agriculture to optimize agricultural practices to reduce agricultural production risks and promote sustainable agriculture. My modeling research uses a three-tier approach: Evaluation, Improvement, and Implementation (EII). Current and planned studies in my lab include

• Evaluating sensitivity of the crop model sub-routines to different cultivars, extreme weather events (e.g., drought, frost) and various cropping practices (e.g., cover crops).
• Evaluating and developing algorithms to estimate regional scale crop modeling inputs and parameters (e.g., crop LAI, crop rotations, tillage practices) using satellite remote sensing, and machine learning.
• Improving model parameterization and configuration using various optimization techniques.
• Developing robust computation tools to implement crop models at various spatial scales ranging from field to global scales and produce operational monitoring and forecasting systems and data products.

 Page

 1. Biography (Current Page)

2. Publications (ARIS)

3. Research Projects