Claim · model-written summary
The Leaf N mechanism with both SLN dilution and GAI reduction was the best in predicting crop growth and yield.
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“compared with measurements from 47 maize crops grown on 16 sites receiving different N fertiliser treatments”
e6“The simulation using the Leaf N mechanism with both SLN dilution and GAI reduction for quantifying N-deficit effects was the best in predicting crop growth and yield”
Title
F. Y. Li; P. D. Jamieson; P. R. Johnstone; et al. Crop and Pasture Science 2009. doi:10.1071/cp08412
Other claims from this paper (8)
- Method / resource Model-predicted maize biomass, grain yield and N uptake were insensitive to the approaches used for quantifying N-deficit effects in the Leaf N mechanism.
- Method / resource Model-predicted maize biomass, grain yield and N uptake were significantly related to measurements.
- Method / resource Model-predicted biomass fit to measurements had r2 of 0.66–0.69.
- Method / resource Model-predicted grain yield fit to measurements had r2 of 0.50–0.54.
- Method / resource Model-predicted N uptake fit to measurements had r2 of 0.17–0.33.
- Method / resource Linear fits of predicted against measured values showed no significant difference among the three N approaches.
- Method / resource The Leaf N mechanism predicted smaller deviation than the Critical N mechanism.
- Method / resource The Critical N mechanism was better than the Leaf N mechanism in simulating plant growth dynamics in early growth stages.