A multivariate approach for assessing leaf photo-assimilation performance using the IPL index

The detection of leaf functionality is of pivotal importance for plant scientists from both theoretical and practical point of view. Leaves are the sources of dry matter and food, and they sequester CO<inf>2</inf> as well. Under the perspective of climate change and primary resource scarcity (i.e. water, fertilizers and soil), assessing leaf photo-assimilation in a rapid but comprehensive way can be helpful for understanding plant behavior under different environmental conditions and for managing the agricultural practices properly. Several approaches have been proposed for this goal, however, some of them resulted very efficient but little reliable. On the other hand, the high reliability and exhaustive information of some models used for estimating net photosynthesis are at the expense of time and ease of measurement. The present study employs a multivariate statistical approach to assess a model aiming at estimating leaf photo-assimilation performance, using few and easy-to-measure variables. The model, parameterized for apple and pear and subjected to internal and external cross validation, involves chlorophyll fluorescence, carboxylative activity of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCo), air and leaf temperature. Results prove that this is a fair-predictive model allowing reliable variable assessment. The dependent variable, called I<inf>PL</inf> index, was found strongly and linearly correlated to net photosynthesis. I<inf>PL</inf> and the model behind it seem to be (1) reliable, (2) easy and fast to measure and (3) usable in vivo and in the field for such cases where high amount of data is required (e.g. precision agriculture and phenotyping studies).