Christopher J. Heffernan
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Crop, Soil and Environmental Sciences
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Christopher J. Heffernan's research focuses on utilizing machine learning to analyze environmental factors influencing crop yields. His work investigates the relationships between weather patterns, soil conditions, and the productivity of maize and soybean crops in the United States. Heffernan employs predictive learning models to gain insights into these complex interactions, aiming to understand the drivers behind yield variations. His scholarship includes a 2026 publication titled "Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield." Heffernan has one publication with Lawton Lanier Nalley, Jason A. Tullis, and Harrison Smith, all from the University of Arkansas at Fayetteville.
Metrics
- h-index: 1
- Publications: 1
- Citations: 4
Selected Publications
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Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield (2026)
Collaboration Network
Top Collaborators
- Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield
- Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield
- Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield
- Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield
- Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield
- Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield
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