Shade Avoidance 3 Mediates Crosstalk Between Shade and Nitrogen in Arabidopsis Leaf Development

After nitrogen treatments, plant leaves become narrower and thicker, and the chlorophyll content increases. However, the molecular mechanisms behind these regulations remain unknown. Here, we found that the changes in leaf width and thickness were largely compromised in the shade avoidance 3 (sav3) mutant. The SAV3 gene encodes an amino-transferase in the auxin biosynthesis pathway. Thus, the crosstalk between shade and nitrogen in Arabidopsis leaf development was investigated. Both hypocotyl elongation and leaf expansion promoted by the shade treatment were reduced by the high-N treatment; high-N-induced leaf narrowing and thickening were reduced by the shade treatment; and all of these developmental changes were largely compromised in the sav3 mutant. Shade treatment promoted SAV3 expression, while high-N treatment repressed SAV3 expression, which then increased or decreased auxin accumulation in cotyledons/leaves, respectively. SAV3 also regulates chlorophyll accumulation and nitrogen assimilation and thus may function as a master switch responsive to multiple environmental stimuli.

Variation for Nitrogen Use Efficiency Traits in Wheat Under Contrasting Nitrogen Treatments in South-Eastern Europe

Wheat cultivars differ in their response to nitrogen (N) fertilizer, both in terms of its uptake and utilization. Characterizing this variation is an important step in improving the N use efficiency (NUE) of future cultivars while maximizing production (yield) potential. In this study, we compared the agronomic performance of 48 diverse wheat cultivars released between 1936 and 2016 at low and high N input levels in field conditions to assess the relationship between NUE and its components. Agronomic trait values were significantly lower in the low N treatment, and the cultivars tested showed a significant variation for all traits (apart from the N remobilization efficiency), indicating that response is genotype-dependent, although significant genotype × environment effects were also observed. Overall, we show a varietal improvement in NUE over time of 0.33 and 0.30% year–1 at low and high N, respectively, and propose that this is driven predominantly by varietal selection for increased yield. More complete understanding of the components of these improvements will inform future targeted breeding and selection strategies to support a reduction in fertilizer use while maintaining productivity.

Optimisation of the amount of nitrogen enhances quality and yield of pepper

The goals of this study were to explore the characteristics of nitrogen (N) absorption and utilisation of chilli peppers (Capsicum annuum L.), improve the utilisation rate of nitrogen, and provide a theoretical basis for scientific fertilisation. In this experiment, pepper cv. Huoyanjiaowang was used as the material, and potted sand cultures and field randomised block experiments were conducted to study the effects of fertilisation of different forms of nitrogen on the photosynthetic characteristics, chlorophyll, nitrate nitrogen, alkaline nitrogen, capsaicin, dihydrocapsaicin and yield. In the pot experiment, the nitrogen application rates were 0, 10, 100, 320 and 600 mg/L, a level of nitrogen of 100 mg/L significantly inhibited the growth of pepper. With the increase in the application of nitrogen, the photosynthetic capacity gradually decreased, and 10 mg/L was the optimal nitrogen level. Under 0 and 10 mg N/L nitrogen levels in the field experiment, the content of chlorophyll of this group was significantly lower than those of other treatment groups, indicating that the plot lacked nitrogen. With the increase in the level of application of nitrogen, the contents of nitrate nitrogen and alkaline hydrolysis nitrogen in the soil increased. The yield of 153.18 kg/ha and 230 kg/ha nitrogen treatments was relatively high. Therefore, among the five nitrogen treatment levels, treatment with 153.18–230 kg N/ha was the most effective at stimulating the growth and yield of pepper.

Physiological and Comparative Transcriptomic Analysis Provide Insight Into Cotton (Gossypium hirsutum L.) Root Senescence in Response

Nitrogen (N) deficiency is one of the pivotal environmental factors that induce leaf senescence. However, little is known regarding the impact of low N on root senescence in cotton. Thus, the objective of this study was to investigate the effect of low nitrogen on root senescence. In this study, the molecular mechanism of cotton root senescence in response to nitrogen deficiency was investigated by combing physiological and transcriptomic analysis when no nitrogen and normal nitrogen (138mg N·kg−1 soil). The results showed that: (1) nitrogen starvation induced the premature senescence of leaf, while delaying root senescence. (2) The increase in catalase (CAT) activity at 60, 80, and 100days after emergence (DAE), combined with decrease of malonaldehyde content at 60, 80, and 100 DAE, and the content of abscisic acid (ABA), all of these contributed to the delay of root senescence by low nitrogen treatment. (3) To study the molecular mechanisms underlying root senescence, the gene expression profiling between low nitrogen and normal nitrogen treatments were compared pairwise at 20, 40, 60, 80, and 100 DAE. A total of 14,607 genes were identified to be differentially expressed at these five points. (5) Most genes involved in glutathione (GSH) and ascorbate peroxidase (APX) synthesis were upregulated, while ABA, apoptosis, caspase, and cell cycle-related differentially expressed genes (DEGs) were downregulated. Coupled with the physiology data, these results provide new insights into the effect of nitrogen starvation on root senescence.

The combined effect of sowing methods, and nitrogen application rates on photosynthetic characteristics, and soil water consumption of wheat

Sustainability of winter wheat yield under dryland conditions depends on Improvements in crop photosynthetic characteristics and, crop yield. Study the effects of sowing method and N-nitrogen rates on yield, selected sowing, and soil water storage, nitrogen translocation. Experiment comprised of three sowing methods: wide-space sowing (WSS), furrow sowing (FS), and drill sowing (DS) and seven nitrogen treatments: 0 kg ha− 1, 90 kg ha− 1, 180 kg ha− 1, 210 kg ha− 1, 240 kg ha− 1, 270 kg ha− 1 and 300 kg ha− 1.The results indicated that the sowing methods significantly affected the yield, and grain. The increase in grain yield was 25%, respectively. The photosynthetic traits, and leaf area index were highest under WS followed by FS. The plant height was highest under DS. I (WSS), and (II) (DS). Sowing method WSS with N level N240 significantly enhanced the Photosynthesis Rate, intercellular CO2, and transpiration rate .Our results indicated that implication of a proper sowing method coupled with enhanced nitrogen doses resulted in an increase in yield. WSS 240 kg ha− 1 enhances photosynthetic characteristics of flag leaves, and promotes to achieve high yield. The plants were improved, which ware beneficial to the improvement of sugar content.

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships

Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.

Effects of water and nitrogen coupling on the photosynthetic characteristics, yield, and quality of Isatis indigotica

Isatis indigotica is a commercial medicinal crop that is widely cultivated with high water and nutrient application, in the arid areas of northwest China. Rational irrigation and nitrogen application are key factors for successful crop management. The objective of this study was to determine the effect of water and nitrogen coupling on the photosynthetic characteristics, yield, and quality of Isatis indigotica produced in northwestern China. Field trials were conducted for 2 consecutive years at an irrigation test station. Data on photosynthetic parameters, yield, and quality were collected from individual Isatis indigotica for each treatment during 2018–2019. The application of nitrogen significantly increased photosynthetic rates and yield under the same irrigation conditions. However, the yields were reduced in the excess water treatments (W3N1 and W3N2) and in the excess nitrogen treatments (W1N3, W2N3, and W3N3) in contrast to the optimum W2N2 treatment. Moreover, the quality indicators of the W2N2 treatment decreased compared with CK, which was due to water stress and more photoassimilates being available to the roots, but the effective quality index value could be effectively improved by greatly increasing the yield.

Effects of Simulated Nitrogen Deposition on Leaf-Litter Decomposition and Nutrient Release of A Cold Temperate Coniferous Forest in Jiaozi Snow Mountain National Nature Reserve, Southwest China

Purpose Litter decomposition is a key process of nutrient cycling in terrestrial ecosystems, an important part of the global carbon budget, and deeply affected by global atmospheric nitrogen deposition. However, the effects of different forms of N addition on litter decomposition and nutrient release are unclear in a cold temperate coniferous forest in a subtropical Chinese plateau. Methods Three N sources (NH4)2SO4, NaNO3, and NH4NO3 were used in the gradient N deposition method. Each N source was divided into four treatments, from low to high, they were CK (control 0 kg N·hm− 2·a− 1), low N (low-N 5 kg N·hm− 2·a− 1), medium n (medium-N 15 kg N·hm− 2·a− 1), high N (high-30 kg N·hm− 2·a− 1), and each treatment repeated three times. Results After two years, the litter decomposition rates of low and medium ammonium nitrate treatments were the fastest as compared to the control, while high and low ammonium nitrate treatments were the slowest. Under the same nitrogen deposition conditions, the litter decomposition rates of low nitrogen treatments were higher than high nitrogen treatments. The order of litter decomposition rates was ammonium nitrate > ammonium sulfate > sodium nitrate. Nitrogen deposition decreased the amount of C in litter leaves but increased N and P levels slightly. Phosphorus changes over time were more complex than C and N over time. Conclusions These results showed that high nitrogen deposition in the future could increase litter decomposition rates and delay the nutrient release, which may be beneficial to improve soil carbon sequestration.

Large-scale field phenotyping using backpack LiDAR and GUI-based CropQuant-3D to measure structural responses to different nitrogen treatments in wheat

Plant phenomics is widely recognised as a key area to bridge the gap between traits of agricultural importance and genomic information. A wide range of field-based phenotyping solutions have been developed. Nevertheless, disadvantages of these current systems have been identified concerning mobility, affordability, accuracy, scalability, and the ability to analyse big data collected. Here, we present a novel solution that combines a commercial backpack LiDAR device and graphical user interface (GUI) based software called CropQuant-3D, which has been applied to analyse 3D morphological traits in wheat. To our knowledge, this is the first use of backpack LiDAR in field-based plant research, acquiring millions of 3D points to represent spatial features of crops. A key part of the innovation is the GUI-based software that can extract plot-based traits from large and complex point clouds with limited computing time. We describe how we developed and used the combined system to quantify canopy structural changes, impossible to measure previously. Also, we demonstrate the biological relevance of our work through a case study that examined wheat varieties to three different levels of nitrogen fertilisation in field experiments. The results indicate that the solution can differentiate significant genotype and treatment effects on key traits, with strong correlations with manual measurements. Hence, we believe that the solution presented here could consistently and speedily quantify traits at a larger scale, indicating the system could be used as a reliable research tool in large-scale and multi-location field phenotyping to contribute to the resolution of the phenotyping bottleneck.