A photothermal model of leaf area index for greenhouse crops

Leaf area index (LAI) is an important variable for modelling canopy photosynthesis and crop water use. In many crop simulation models, prediction of LAI is very sensitive to errors in the value of parameter “specific leaf area” (SLA), which often relies on destructive measurements to determine. In this study, we present a model for predicting LAI of greenhouse crops based on the quantification of easily measured morphological traits as affected by temperature and radiation. Our model predicts LAI based on canopy light interception as a function of node development rate along with specific leaf size and elongation rates characteristics defined on a leaf number basis. Growth studies with five greenhouse crops (cucumber, sweet pepper, chrysanthemum, tulip and lilium) were conducted in different greenhouses and different sites during 2003 to 2009. The model was evaluated, in comparison with two commonly used methods for predicting LAI – the growing degree days (GDD) based model and SLA based model, using independent data from other experiments. The coefficient of determination (r2) and the root mean squared error (RMSE) between the predicted and measured values using our photothermal method are 0.99 and 0.95 (r2, RMSE) for leaf number, 0.98 and 0.01 m for specific leaf length, and 0.98 and 0.13 m2 m-2 for canopy LAI. For the GDD-based model, the r2 and RMSE are 0.93 and 4.23, 0.82 and 0.04 m, 0.87 and 0.48 m2 m-2 for the three traits, respectively. For the SLA-based model, the r2 and RMSE for canopy LAI is 0.81 and 1.24 m2 m-2 when using the estimated SLA data as input or 0.94 and 0.25 m2 m-2 when using the measured SLA data as input. So, our model better predicts LAI for greenhouse crops at different latitudes and a range of planting densities and pruning systems. Although calibrations for specific light regime, pruning practices and cultivars are needed, the fact that production conditions in commercial greenhouse production are often well controlled and production practices are often rather standardized implies a general applicability of our model