Adapting 3-PG foliar variables to deciduous trees in response to water restriction: poplar short rotation plantations under Mediterranean conditions

Abstract

Poplar plantations growing in short rotation are a crucial biomass source of raw material for bioenergy and/or bioproducts, making an important contribution towards achieving a low-carbon bioeconomy. To optimize yield predictions of poplar plantations, this study aims to adapt the foliar variables of the process-based model 3-PG (Physiological Principles Predicting Growth) to a deciduous species like poplar. A total of 138 trees were sampled from a poplar plantation of the highly productive hybrid P. x canadensis (‘AF2’) over a first rotation at a planting density of 10000 trees ha−1. Two irrigation scenarios, full soil field capacity (FC) and 50% FC, were considered to take into account the impact of climate change in the context of irrigation restrictions. Based on this information, the objectives were not only to determine the required species-specific foliar parameters but also to adapt the 3-PG model architecture to a pattern of variation along each growing season and identify the foliar parameters which present a significant response to restrictive irrigation. For this, specific leaf area (SLA) changes were modelled and the litterfall rate (γF), and maximum canopy quantum efficiency (αCx)were calculated. SLA follows a similar dynamic in terms of water availability and year,with SLA for mature leaves being 19.9 m2 kg−2 and the SLA at the beginning of the growing season 10.4 m2 kg2.Leaf litter season begins in late August and lasts until early December,with 26 per cent litterfall by October. Additionally, the highly sensitive parameter αCx was calibrated and a proposed value of 0.093 molC mol PAR−1 was used. The validation of the proposed parameterization showed realistic estimates of the changes of leaf biomass and LAI during the growing season. These results will enable improved 3-PG-based estimations of the real variation along the growing season of variables such as Net Primary Productivity, leaf litterfall or analysis of the soil–plant nutrient cycle.