Claim · model-written summary
Authors propose the best performing model by crop and region as a benchmark.
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier
Verbatim
e0“we propose that the best performing crop model per crop and region establishes the benchmark for all others”
t0“crop”
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 maize.
- 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.
- Method / resource The model evaluation system was made accessible for comparison with reference data and the GGCMI benchmark.