The prediction of leaf area from LR and the prediction of biomass require future studies.
Where VPD effects are found to be transient, it is anticipated that models relating leaf growth only with temperature (Clifton-Brown and Jones, [1997]) are adequate for the prediction of leaf area development of Miscanthus × giganteus in the range of temperature conditions between 6°C and 20°C.
In this paper, we report on the development and testing of a model that links a mechanistic phenological model that depends on the prediction of leaf appearance (Sirius) with a simplification of a detailed canopy model that calculates the numbers and areas of leaves on mainstems and tillers (ARCWHEAT1).
Under-prediction of leaf area would be expected to have detrimental effects on biomass and grain yield prediction.
In practice over prediction of leaf area tends to be disconnected from the prediction of biomass and grain as light interception does not increase linearly with canopy size.
For example, over-prediction of leaf area index from four to five increases fractional interception by only 5% and therefore has little effect on predicted crop production.
In order to make global, spatially continuous predictions for leaf water stable isotope ratios, we implemented these mechanistic models of leaf water enrichment in ArcGIS software (ESRI Corporation, Redlands, CA).
Spatially explicit model predictions of the isotopic composition of leaf water and other biosphere and atmosphere pools have been made for some time using various platforms and approaches [5], [6], [7].
In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.
The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies.
