New Hybrid-Maize Improves Corn Yield Predictions Under Drought Stress
New Hybrid-Maize Improves Corn Yield Predictions Under Drought Stress February 27, 2017
Crop simulation models are powerful tools to guide research, education, and extension as well as to inform policy making. Such models generally perform better under non-water stress conditions and it has been a scientific challenge to simulate crop yield under severe drought stress.
After 10 years of extensive use of the Hybrid-Maize model in research and extension, the model developers, including Haishun Yang, Patricio Grassini and Ken Cassman of the UNL Department of Agronomy and Horticulture, have integrated changes to better simulate drought stress. The authors revised the model’s mathematical sub-routines describing root growth and distribution, impacts of water deficit on leaf expansion and senescence, and kernel setting. They also incorporated new functions to account for soil evaporation and runoff as influenced by soil surface conditions including crop residue coverage and field slope.
This revised model has proven to be more robust in simulating corn yield across a wide range of environments, from fully irrigated high-yield corn reaching 300 bu/ac to dryland corn in years with very little in-season rainfall leading to complete crop failure.
Corn Yield Forecasts
The Hybrid-Maize model is one of the tools used to make the Corn Yield Forecasts featured in CropWatch. Look forward to tracking more corn yield forecasts this summer.
These revisions make the new version of the Hybrid-Maize model (Version 2016) a more powerful tool for evaluating hybrid-specific traits and crop management practices in harsh rainfed environments, such as those found in the western US Corn Belt and Sub-Saharan Africa.
The research on which the new model was based was published in the peer-reviewed March 2017 Field Crops Research journal (2017, Vol 204, pp180–190) in "Improvements to the Hybrid-Maize model for simulating maize yields in harsh rainfed environments."
More information about the modeling software is available at http://hybridmaize.unl.edu/. For further information or questions, please contact two of its developers: Haishun Yang, crop modeler and associate professor of agronomy, at firstname.lastname@example.org or Patricio Grassini, Nebraska extension cropping systems specialist, at email@example.com.