Claim · model-written summary
Crop-model ensemble explained more yield variability than regression models for maize.
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice.”
Title
Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications
Christoph Müller; Joshua Elliott; James Chryssanthacopoulos; et al. Geoscientific model development 2017. doi:10.5194/gmd-10-1403-2017
Other claims from this paper (10)
- Method / resource Models reproduced temporal yield variability for maize with correlation up to 0.888.
- Method / resource Models reproduced temporal yield variability for wheat with correlation up to 0.673.
- Method / resource Models reproduced temporal yield variability for soybean with correlation up to 0.643.
- Method / resource Most models reproduced rice yield variability poorly.
- Method / resource Many models reproduced yield variability for most major producing countries, and some did for all countries.
- Method / resource Crop-model ensemble explained more yield variability than regression models for soybean.
- Method / resource Crop-model ensemble did not explain more yield variability than regression models for wheat.
- Method / resource Crop-model ensemble did not explain more yield variability than regression models for rice.
- Recommendation Authors propose the best performing model by crop and region as a benchmark.
- Method / resource The model evaluation system was made accessible for comparison with reference data and the GGCMI benchmark.