Novel techniques for the remote sensing of photosynthetic processes

Remote sensing (RS) techniques have evolved into an important instrument to investigate forest function. New methods based on the remote detection of leaf biochemistry and photosynthesis are being developed and applied in pilot studies from airborne and satellite platforms (PRI, solar-induced fluorescence; N and chlorophyll content). Non-destructive monitoring methods, a direct application of RS studies, are also proving increasingly attractive for the determination of stress conditions or nutrient deficiencies not only in research but also in agronomy, horticulture and urban forestry (proximal RS). In this work I will focus on some novel techniques recently developed for the estimation of photochemistry and photosynthetic rates based (i) on the proximal measurement of steady-state chlorophyll fluorescence yield, or (ii) the remote sensing of changes in hyperspectral leaf reflectance, associated to xanthophyll de-epoxydation and energy partitioning, which is closely coupled to leaf photochemistry and photosynthesis. I will also present and describe a mathematical model of leaf steady-state fluorescence and photosynthesis recently developed in our group. Two different species were used in the experiments: Arbutus unedo, a schlerophyllous Mediterranean species, and Populus euroamericana, a broad leaf deciduous tree widely used in plantation forestry. Results show that ambient fluorescence could provide a useful tool for testing photosynthetic processes from a distance. These results confirm also the photosynthetic reflectance index (PRI) as an efficient remote sensing reflectance index estimating short-term changes in photochemical efficiency as well as long-term changes in leaf biochemistry. The study also demonstrated that RS techniques could provide a fast and reliable method to estimate photosynthetic pigment content and total nitrogen, beside assessing the state of photochemical process in our plants’ leaves in the field. This could have important practical applications for the management of plant cultivation systems, for the estimation of the nutrient requirements of our plants for optimal growth.

Dissecting the Heat-Stress Tolerance QTLome in Durum Wheat

Heat stress negatively affects wheat performance during its entire cycle, particularly during the reproductive stage. In view of the climate change and the prediction of a continued increase in temperature in the new future, it is urgent to concentrate efforts to discover novel genetic sources able to improve the resilience of wheat to heat stress. In this direction, this study addressed two different experiments in durum wheat to identify novel QTLs suitable to be applied in marker-assisted selection for heat tolerance. Chlorophyll fluorescence (ChlF) is a valuable indicator of plant response to environmental changes allowing a detailed assessment of PSII activity in view of its non-invasive measurement and high-throughput phenotyping. In the first study (Chapter 2), the Light-Induced Fluorescence Transient (LIFT) method was used to access ChlF data to map QTLs for ChlF-related traits during the vegetative growth stage in durum wheat under heat stress condition. Our results provide evidence that LIFT consistently measures ChlF at the level of high-throughput phenotyping combined with high accuracy which is required for Genome-Wide Association Study (GWAS) aimed at identifying genomic regions affecting PSII activity. The 50 QTLs identified for ChlF-related traits under heat stress mostly clustered into five chromosomes hotspots unrelated to phenology, a feature that makes these QTLs a valuable asset for marker-assisted breeding programs across different latitudes. In the second study (Chapter 3), a set of 183 accessions suitable for GWAS, was exposed to optimal and high temperature during two crop seasons under field conditions. Important agronomic traits were evaluated in order to identify valuable QTLs for GY and its components. The GWAS analysis identified several QTLs in the single years as well as in the joint analysis. From the total QTLs identified, 13 QTL clusters can be highlighted to be affecting heat tolerance across different years and/or different traits.