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Poor grain filling in inferior spikelets (IS), which is influenced by the remobilization of nonstructural carbohydrates (NSC) stored in the sheath and internode of rice plants, limits the expected high yield of large‐panicle rice. NSC remobilization from the sheath to the panicle is regulated by the T6P/SnRK1 pathway. However, in large‐panicle rice, it is unclear whether IS grain filling is related to the NSC remobilization mediated by T6P/SnRK1 signaling. In this study, two large‐panicle cultivars—W1844 and CJ03—with distinct differences in IS grain filling were used to explore the physiological mechanism mediating IS development. Compared to W1844, CJ03 IS showed lower expression of the genes related to sucrose uploading, later sucrose peaking, and delayed starch accumulation. In the CJ03, low OsSUTs expression and NSC output, transport rate, and contribution rate were detected in the sheaths and internodes. These results suggest that poor NSC remobilization results in insufficient assimilate supply for the IS, and consequently, poor IS grain filling. Furthermore, poor NSC remobilization coincided with the increased T6P content and decreased SnRK1 activity during grain filling in CJ03 IS. The expression levels of genes related to T6P metabolism and those encoding the catalytic subunit of SnRK1 were consistent with the observed T6P content and SnRK1 activity in the sheaths and internodes. Therefore, IS grain filling is potentially affected by T6P/SnRK1 signaling‐mediated NSC remobilization in large‐panicle rice. [ABSTRACT FROM AUTHOR]

Slow-controlled release fertilizers are experiencing a popularity in rice cultivation due to their effectiveness in yield and quality with low environmental costs. However, the underlying mechanism by which these fertilizers regulate grain quality remains inadequately understood. This study investigated the effects of five fertilizer management practices on rice yield and quality in a two-year field experiment: CK, conventional fertilization, and four applications of slow-controlled release fertilizer (UF, urea formaldehyde; SCU, sulfur-coated urea; PCU, polymer-coated urea; BBF, controlled-release bulk blending fertilizer). In 2020 and 2021, the yields of UF and SCU groups showed significant decreases when compared to conventional fertilization, accompanied by a decline in nutritional quality. Additionally, PCU group exhibited poorer cooking and eating qualities. However, BBF group achieved increases in both yield (10.8 t hm−2 and 11.0 t hm−2) and grain quality reaching the level of CK group. The adequate nitrogen supply in PCU group during the grain-filling stage led to a greater capacity for the accumulation of proteins and amino acids in the PCU group compared to starch accumulation. Intriguingly, BBF group showed better carbon–nitrogen metabolism than that of PCU group. The optimal nitrogen supply present in BBF group suitable boosted the synthesis of amino acids involved in the glycolysis/ tricarboxylic acid cycle, thereby effectively coordinating carbon–nitrogen metabolism. The application of the new slow-controlled release fertilizer, BBF, is advantageous in regulating the carbon flow in the carbon–nitrogen metabolism to enhance rice quality. [ABSTRACT FROM AUTHOR]

Sucrose non-fermenting-1-related protein kinase 1 (SnRK1) and AMP-activated protein kinase (AMPK) are highly conserved. Compound 991 is an AMPK activator in mammals. However, whether 991 also activates SnRK1 remains unknown. The addition of 991 significantly increased SnRK1 activity in desalted extracts from germinating rice seeds in vitro. To determine whether 991 has biological activity, rice seeds were treated with different concentrations of 991. Germination was promoted at low concentrations but inhibited at high concentrations. The effects of 991 on germination were similar to those of OsSnRK1a overexpression. To explore whether 991 affects germination by specifically affecting SnRK1, germination of an snrk1a mutant and the wild type under 1 μM 991 treatment was compared. The snrk1a mutant was insensitive to 991. Phosphoproteomic analysis showed that the differential phosphopeptides induced by 991 and OsSnRK1a overexpression largely overlapped. Furthermore, SnRK1 might regulate rice germination in a dosage-dependent manner by regulating the phosphorylation of three phosphosites, namely S285-PIP2;4, S1013-SOS1, and S110-ABI5. These results indicate that 991 is a specific SnRK1 activator in rice. The promotion and inhibition of germination by 991 also occurred in wheat seeds. Thus, 991 is useful for exploring SnRK1 function and the chemical regulation of growth and development in crops. [ABSTRACT FROM AUTHOR]

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perspectivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorized according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future.