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About

Fernando E. Miguez is a leading figure in computational agronomy and crop modeling, with a focus on sustainable intensification and high-throughput phenotyping. His work bridges the gap between field-scale experimentation and predictive analytics, enabling more efficient breeding and management of major crops like soybean and maize. Miguez is best known for developing and applying advanced statistical and simulation models to understand genotype-by-environment interactions, particularly in the context of cover crops and bioenergy systems. His highly cited 2021 paper, "High-Throughput Phenotyping in Soybean" (40 citations), exemplifies his contribution to integrating remote sensing, UAV imagery, and machine learning to accelerate trait discovery. This work has direct implications for improving yield stability and resource-use efficiency under climate variability. With over 5,000 total citations, Miguez’s research is foundational for the next generation of data-driven agriculture. He is also recognized for his leadership in open-source modeling tools and collaborative projects like the Agricultural Model Intercomparison and Improvement Project (AgMIP), making his work accessible and actionable for researchers worldwide.