Modeling leaf maximum net photosynthetic rate of Festuca pallescens , the dominant perennial grass of Patagonian pine-based silvopastoral systems

Author： Caballé Gonzalo

Publisher： Springer Publishing Company

ISSN： 0167-4366

Source： Agroforestry Systems, Vol.83, Iss.1, 2011-09, pp. : 13-24

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

The integrated relationship in a simple mechanistic model between the critical environmental factors controlling leaf photosynthesis of understory species would be a useful tool to optimize the management of the silvopastoral systems. Individual effect of leaf temperature, water stress and light environment over net maximum photosynthetic rate (Pmax) was evaluated on Festuca pallescens leaves grown in a silvopastoral system of two Pinus ponderosa canopy covers (350 and 500 trees ha⁻¹) and natural grassland. The aim was to integrate individual functions for Pmax against these environmental factors into a multiplicative model. We measured pre-dawn water potential (ψ _pd), leaf temperature and net photosynthetic rate (Pn), stomatal conductance (gs) and intercellular CO₂ concentration (Ci) as a function of photosynthetic photon flux density (PPFD). The highest Pmax under non-limiting conditions was 20.4 μmol CO₂ m⁻² s⁻¹ and was defined as standardized dimensionless Pmax _s = 1 for comparison of environmental factors. The leaf temperature function showed an optimum range between 20.2 and 21.8°C where Pmax _s = 1. Then, Pmax _s declined by an average 1 μmol CO₂ m⁻² s⁻¹ C⁻¹ from the optimum to 4.7 and 38.5°C. Pmax _s decreased at a rate of 9.49 μmol CO₂ m⁻² s⁻¹ MPa⁻¹ as water potential reaches −1.9 MPa and showed a lower slope as water potential decreased down to −4.3 MPa. The light environment was estimated from hemispherical photograph analysis. Pmax _s was 20% higher in leaves of open control plants than under the maximum tree canopy cover. The simple multiplicative model accounted for 0.82 of the variation in Pmax. Such a simple mechanistic model is the first step towards a more effective decision support tool.