Optimal temporal-spatial fluorescence techniques for phenotyping nitrogen status in oilseed rape

Optimized nitrogen application ameliorates the photosynthetic performance and yield potential in peanuts as revealed by OJIP chlorophyll fluorescence kinetics

BACKGROUND

Nitrogen (N) is a crucial element for increasing photosynthesis and crop yields. The study aims to evaluate the photosynthetic regulation and yield formation mechanisms of different nodulating peanut varieties with N fertilizer application.

METHOD

The present work explored the effect of N fertilizer application rates (N0, N45, N105, and N165) on the photosynthetic characteristics, chlorophyll fluorescence characteristics, dry matter, N accumulation, and yield of four peanut varieties.

RESULTS

The results showed that N application increased the photosynthetic capacity, dry matter, N accumulation, and yield of peanuts. The measurement of chlorophyll a fluorescence revealed that the K-phase, J-phase, and I-phase from the OJIP curve decreased under N105 treatment compared with N0, and WOI, ET0/CSM, RE0/CSM, ET0/RC, RE0/RC, φPo, φEo, φRo, and Ψ0 increased, whereas VJ, VI, WK, ABS/RC, TR0/RC, DI0/RC, and φDo decreased. Meanwhile, the photosystem activity and electron transfer efficiency of nodulating peanut varieties decreased with an increase in N (N165). However, the photosynthetic capacity and yield of the non-nodulating peanut variety, which highly depended on N fertilizer, increased with an increase in N.

CONCLUSION

Optimized N application (N105) increased the activity of the photosystem II (PSII) reaction center, improved the electron and energy transfer performance in the photosynthetic electron transport chain, and reduced the energy dissipation of leaves in nodulating peanut varieties, which is conducive to improving the yield. Nevertheless, high N (N165) had a positive effect on the photosystem and yield of non-nodulating peanut. The results provide highly valuable guidance for optimizing peanut N management and cultivation measures.

Decoupling of nitrogen allocation and energy partitioning in rice after flowering

Estimation of energy partitioning at leaf scale, such as fluorescence yield (ΦF) and photochemical yield (ΦP), is crucial to tracking vegetation gross primary productivity (GPP) at global scale. Nitrogen is an important participant in the process of light capture, electron transfer, and carboxylation in vegetation photosynthesis. However, the quantitative relationship between leaf nitrogen allocation and leaf energy partitioning remains unexplored. Here, a field experiment was established to explore growth stage variations in energy partitioning and nitrogen allocation at leaf scale using active fluorescence detection and photosynthetic gas exchange method in rice in the subtropical region of China. We observed a strongly positive correlation between the investment proportion of leaf nitrogen in photosynthetic system and ΦF during the vegetative growth stage. There were significant differences in leaf energy partitioning, leaf nitrogen allocation, and the relationship between ΦF and ΦP before and after flowering. Furthermore, flowering weakened the correlation between the investment proportion of leaf nitrogen in photosynthetic system and ΦF. These findings highlight the crucial role of phenological factors in exploring seasonal photosynthetic dynamics and carbon fixation of ecosystems.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles

Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.