Increasing yield prediction accuracy
Researchers find combining multiple types of models improves crop yield predictions, outperforming single models.
Rachel Shekar
138 posts
Rachel (she/her) is a Founding and Managing Editor of in silico Plants. She has a Master’s Degree in Plant Biology from the University of Illinois. She has over 15 years of academic journal editorial
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Weathering the Storm: Understanding Maize Lodging Resistance
A new model identifies the morphological features that determine stalk bending strength.
Introducing the new CPlantBox
Computational model may help unravel the complex processes of drought tolerance.
Using Genomic Prediction to Improve Ecophysiological Crop Models
A guide to combining ecophysiological and genomic models.
Five Root Models Compete in Benchmarking Challenge
Assessing the reliability of root water uptake simulations
SIMPLACE: The Next Generation Platform for Agricultural Systems Analysis
SIMPLACE offers a flexible and transparent approach to developing customized crop models
What drives cambium development?
Using a mathematical model to test a proposed cambial cell division feedback loop.
Unlocking the secret to breeding high yielding protein-rich wheat
Models enable the optimization of multiple trait trade-offs.
Introducing Cropbox: a declarative crop modeling framework
Plant modelers do not also need to be software engineers thanks to Cropbox framework
Is photorespiration necessary for photosynthesis?
Modelling unveils the role of photorespiration in stabilizing the Calvin cycle.
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