The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: Benefits for field phenotyping

Improving photosynthesis is considered a promising way to increase crop yield to feed a growing population. Realizing this goal requires non-destructive techniques to quantify photosynthetic variation among crop cultivars. Despite existing remote sensing-based approaches, it remains a question whether solar-induced fluorescence (SIF) can facilitate screening crop cultivars of improved photosynthetic capacity in plant breeding trials. Here we tested a hypothesis that SIF yield rather than SIF had a better relationship with the maximum electron transport rate (Jmax). Time-synchronized hyperspectral images and irradiance spectra of sunlight under clear-sky conditions were combined to estimate SIF and SIF yield, which were then correlated with ground-truth Vcmax and Jmax. With observations binned over time (i.e. group 1: 6, 7, and 12 July 2017; group 2: 31 July and 18 August 2017; and group 3: 24 and 25 July 2018), SIF yield showed a stronger negative relationship, compared with SIF, with photosynthetic variables. Using SIF yield for Jmax (Vcmax) predictions, the regression analysis exhibited an R2 of 0.62 (0.71) and root mean square error (RMSE) of 11.88 (46.86) μmol m-2 s-1 for group 1, an R2 of 0.85 (0.72) and RMSE of 13.51 (49.32) μmol m-2 s-1 for group 2, and an R2 of 0.92 (0.87) and RMSE of 15.23 (30.29) μmol m-2 s-1 for group 3. The combined use of hyperspectral images and irradiance measurements provides an alternative yet promising approach to characterization of photosynthetic parameters at plot level

Effects of Interaction Between Red Rice and Two Rice Cultivars on Morphological, Physiological and Ecological Characteristics.

Germination, emergence, and early seedling development patterns were analyzed for red rice (Oryza sativa L.) and the rice (Oryza sativa L.) cultivars Mars, Saturn, Lemont and Bellemont. Red rice had the highest germination percentage, fastest emergence rate, and earliest development of both shoots and roots. These results suggest that the competitive ability of red rice is based on these characteristics which enable it to preempt more resources at early stages of stand development. Interaction between red rice and Lemont or Mars was evaluated by comparing morphological and physiological characteristics of these plants when grown in pure stands and in 50:50 mixtures. Plant height, top dry weight, tiller and leaf number, leaf area index, and leaf area duration of the cultivars were reduced significantly in the presence of red rice in the mixture. The effects of the interaction were detected as early as 28 days after emergence in the cultivars, first as a reduction in leaf area index and then as reduced top dry weight. When red rice was grown in mixture it produced more tillers and leaves and greater top dry weight than when grown in monoculture. These growth attributes may also be responsible for its competitive ability. Effects of end-of-day light quality on early growth and development of red rice, Lemont and Mars were examined in a controlled environment. Exposure of the base of the plants to red light at the end of the day promoted an increase in the number of tillers per plant. The magnitude of the increase was greater for red rice than for the cultivars. The results support the hypothesis that tillering is controlled by a shift in spectral quality of the light reaching the bottom of the canopy. Root interaction between red rice and Lemont or Mars reduced nitrogen and phosphorus content of shoots for Lemont and the phosphorus content of shoots for Mars. The high root cation exchange capacity exhibited by red rice could be the root property associated with its greater nutrient uptake and below ground competitive ability

Breeding for increased nitrogen-use efficiency: a review for wheat (T. aestivum L.)

Nitrogen fertilizer is the most used nutrient source in modern agriculture and represents significant environmental and production costs. In the meantime, the demand for grain increases and production per area has to increase as new cultivated areas are scarce. In this context, breeding for an efficient use of nitrogen became a major objective. In wheat, nitrogen is required to maintain a photosynthetically active canopy ensuring grain yield and to produce grain storage proteins that are generally needed to maintain a high end-use quality. This review presents current knowledge of physiological, metabolic and genetic factors influencing nitrogen uptake and utilization in the context of different nitrogen management systems. This includes the role of root system and its interactions with microorganisms, nitrate assimilation and its relationship with photosynthesis as postanthesis remobilization and nitrogen partitioning. Regarding nitrogen-use efficiency complexity, several physiological avenues for increasing it were discussed and their phenotyping methods were reviewed. Phenotypic and molecular breeding strategies were also reviewed and discussed regarding nitrogen regimes and genetic diversity

Advanced phenotyping offers opportunities for improved breeding of forage and turf species

ISSN:1095-8290ISSN:0305-736

Advances in field-based high-throughput photosynthetic phenotyping

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis

Advanced phenotyping offers opportunities for improved breeding of forage and turf species

Background and Aims Advanced phenotyping, i.e. the application of automated, high-throughput methods to characterize plant architecture and performance, has the potential to accelerate breeding progress but is far from being routinely used in current breeding approaches. In forage and turf improvement programmes, in particular, where breeding populations and cultivars are characterized by high genetic diversity and substantial genotype × environment interactions, precise and efficient phenotyping is essential to meet future challenges imposed by climate change, growing demand and declining resources. Scope This review highlights recent achievements in the establishment of phenotyping tools and platforms. Some of these tools have originally been established in remote sensing, some in precision agriculture, while others are laboratory-based imaging procedures. They quantify plant colour, spectral reflection, chlorophyll-fluorescence, temperature and other properties, from which traits such as biomass, architecture, photosynthetic efficiency, stomatal aperture or stress resistance can be derived. Applications of these methods in the context of forage and turf breeding are discussed. Conclusions Progress in cutting-edge molecular breeding tools is beginning to be matched by progress in automated non-destructive imaging methods. Joint application of precise phenotyping machinery and molecular tools in optimized breeding schemes will improve forage and turf breeding in the near future and will thereby contribute to amended performance of managed grassland agroecosystem

Comparative studies of compatible and incompatible pepper–Tobamovirus interactions and the evaluation of effects of 24-epibrassinolide

The aim of study was to gain a deeper knowledge about local and systemic changes in photosynthetic processes and sugar production of pepper infected by Obuda pepper virus (ObPV) and Pepper mild mottle virus (PMMoV). PSII efficiency, reflectance, and gas exchange were measured 48 and/or 72 h after inoculation (hpi). Sugar accumulation was checked 72 hpi and 20 d after inoculation (as a systemic response). Inoculation of leaves with ObPV led to appearance of hypersensitive necrotic lesions (incompatible interaction), while PMMoV caused no visible symptoms (compatible interaction). ObPV (but not PMMoV) lowered F v /F m (from 0.827 to 0.148 at 72 hpi). Net photosynthesis decreased in ObPV-infected leaves. In ObPV-inoculated leaves, the accumulation of glucose, fructose, and glucose-6-phosphate was accompanied with lowered sucrose, malt oheptose, nystose, and trehalose cont ents. PMMoV inoculation increased the contents of glucose, maltose, and raffi nose in the inoculated leaves, while glucose-6-phos phate accummulated in upper leaves

Investigation on data fusion of sun-induced chlorophyll fluorescence and reflectance for photosynthetic capacity of rice

Studying crop photosynthesis is crucial for improving yield, but current methods are labor-intensive. This research aims to enhance accuracy by combining leaf reflectance and sun-induced chlorophyll fluorescence (SIF) signals to estimate key photosynthetic traits in rice. The study analyzes 149 leaf samples from two rice cultivars, considering reflectance, SIF, chlorophyll, carotenoids, and CO2 response curves. After noise removal, SIF and reflectance spectra are used for data fusion at different levels (raw, feature, and decision). Competitive adaptive reweighted sampling (CARS) extracts features, and partial least squares regression (PLSR) builds regression models. Results indicate that using either reflectance or SIF alone provides modest estimations for photosynthetic traits. However, combining these data sources through measurement-level data fusion significantly improves accuracy, with mid-level and decision-level fusion also showing positive outcomes. In particular, decision-level fusion enhances predictive capabilities, suggesting the potential for efficient crop phenotyping. Overall, sun-induced chlorophyll fluorescence spectra effectively predict rice's photosynthetic capacity, and data fusion methods contribute to increased accuracy, paving the way for high-throughput crop phenotyping