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Nitrous oxide (N2O) is one of the most important climate-forcing gases, and a large portion of global anthropogenic N2O emissions come from agricultural soils. Yet, how contrasting global change factors and agricultural management can interact to drive N2O emissions remains poorly understood. Here, conducted within a rice–wheat cropping system, we combined a two-year field experiment with two pot experiments to investigate the influences of elevated atmospheric carbon dioxide (eCO2) and crop straw addition to soil in altering N2O emissions under wheat cropping. Our analyses identified consistent and significant interactions between eCO2 and straw addition, whereby eCO2 increased N2O emissions (+ 19.9%) only when straw was added, and independent of different N fertilizer gradients and wheat varieties. Compared with the control (i.e., ambient CO2 without straw addition), eCO2 + straw addition increased N2O emission by 44.7% and dissolved organic carbon to total dissolved nitrogen (DOC/TDN) ratio by 115.3%. Similarly, eCO2 and straw addition significantly impacted soil N2O-related microbial activity. For instance, the ratio of the abundance of N2O production genes (i.e., nirK and nirS) to the abundance of the N2O reduction gene (i.e., nosZ) with straw addition was 26.0% higher than that without straw under eCO2. This indicates an increased denitrification potential and suggests a change in the stoichiometry of denitrification products, affecting the balance between N2O production and reduction, leading to an increase in N2O emissions. Taken together, our results emphasize the critical role of the interaction between the specific agronomic practice of straw addition and eCO2 in shaping greenhouse gas emissions in the wheat production system studied, and underline the need to test the efficacy of greenhouse gas mitigation measures under various management practices and global change scenarios. [ABSTRACT FROM AUTHOR]

The yield potential of large-panicle rice is often limited by grain-filling barriers caused by the development of inferior spikelets (IS). Photoassimilates, which are the main source of rice grain filling, mainly enter the caryopsis through the dorsal vascular bundle. The distribution of assimilates between superior spikelets (SS) and IS is influenced by auxin-mediated apical dominance; however, the mechanism involved is still unclear. In this study, the effect of auxin signaling on the grain filling of SS and IS was investigated in two large-panicle japonica rice varieties, W1844 and CJ03. Compared to SS, IS displayed delayed initiation of filling and a significantly lower grain weight. Furthermore, the endosperm development in IS remained stagnant at the coenocytic stage. The development of the dorsal vascular bundle in the IS was also slow, and poor sucrose-unloading was observed during the initial grain filling stage. However, the endosperm development in IS immediately started after the improvement of dorsal vascular bundle development. GUS activity staining further revealed that indole-3-acetic (IAA) was localized in the dorsal vascular bundle and surrounding areas, suggesting that the low IAA content observed in the IS during the initial grain filling stage may have delayed the development of the dorsal vascular bundle. Therefore, these results demonstrate that IAA may control sugar transport and unloading by regulating dorsal vascular bundle development, consequently affecting endosperm development in IS. [ABSTRACT FROM AUTHOR]

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]

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.