Using Leaf Mass per Area as predictor of light interception and absorption in crop/weed monoculture or mixed stands.

摘要

The partitioning of light absorption is difficult to assess in natural or cultivated mixed vegetation canopies. Previous research has shown that it is possible to estimate light absorption in monospecific canopies from the Leaf Mass per Area (M_A), which is closely related to the prevailing light conditions experienced by the leaf during its development. The effectiveness of this approach in assessing light partitioning at individual plant level was tested on monospecific and mixed crop/weed plots. Canopy absorbed PAR (APAR) was estimated in monocultures or 1:1 mixed stands of Abutilon theophrasti Medicus and Glycine max L. (soybean) using a relationship between intercepted PAR (IPAR) and M_A, calibrated at individual leaf level against incident light measurements. The accuracy of estimates was evaluated through comparison with APAR calculated from incident light measurements taken independently at various heights at whole canopy level. The use of the experimentally assessed relationship of IPAR vs. M_A provided acceptable absorption estimates. The average departure of estimates from measurements, expressed as Root Mean Squared Error (RMSE), was 10.2%. By comparison, a numerically optimized version of the model that excluded the effects of experimental errors in single-leaf PAR measurements, yielded estimates with an average RMSE of 4.8%. The lower accuracy of the estimates based on the experimental IPAR-M_A relationship was due to experimental error but also to a high sensitivity of the M_A-based estimates to the model coefficients. M_A variability in monoculture or mixed canopies was confirmed to be highly dependent on the PAR distribution, so M_A is potentially suitable for use as a predictor of light absorption. However, due to the sensitivity of the model, a high experimental accuracy in IPAR-M_A assessment as well as in M_A and LAI distribution has to be guaranteed in order to perform reliable estimates by this method.Digital Object Identifier http://dx.doi.org/10.1016/j.agrformet.2011.01.004