Biochar induced improvement in root system architecture enhances nutrient assimilation by cotton plant seedlings

Abstract Background Raising nitrogen use efficiency of crops by improving root system architecture is highly essential not only to reduce costs of agricultural production but also to mitigate climate change. The physiological mechanisms of how biochar affects nitrogen assimilation by crop seedlings have not been well elucidated. Results Here, we report changes in root system architecture, activities of the key enzymes involved in nitrogen assimilation, and cytokinin (CTK) at the seedling stage of cotton with reduced urea usage and biochar application at different soil layers (0–10 cm and 10–20 cm). Active root absorption area, fresh weight, and nitrogen agronomic efficiency increased significantly when urea usage was reduced by 25% and biochar was applied in the surface soil layer. Glutamine oxoglutarate amino transferase (GOGAT) activity was closely related to the application depth of urea/biochar, and it increased when urea/biochar was applied in the 0–10 cm layer. Glutamic-pyruvic transaminase activity (GPT) increased significantly as well. Nitrate reductase (NR) activity was stimulated by CTK in the very fine roots but inhibited in the fine roots. In addition, AMT1;1, gdh3, and gdh2 were significantly up-regulated in the very fine roots when urea usage was reduced by 25% and biochar was applied. Conclusion Nitrogen assimilation efficiency was significantly affected when urea usage was reduced by 25% and biochar was applied in the surface soil layer at the seedling stage of cotton. The co-expression of gdh3 and gdh2 in the fine roots increased nitrogen agronomic efficiency. The synergistic expression of the ammonium transporter gene and gdh3 suggests that biochar may be beneficial to amino acid metabolism

Restoration via the aggregate spray-seeding technique affected the soil proteobacteria on an Uninhabited Island: Community structure, metabolic function, nutritional type, and life strategy

This study aimed to explore the impact of aggregate spray-seeding (ASS) restoration measures on the soil proteobacterial community. Using environmental DNA sequencing, we analyzed the proteobacterial communities in the soils of 3 natural vegetation (NV) plots, 3 traditional afforestation (TA) plots, and 12 spray-seeding restoration (SR) plots located on Triangle Island in Zhuhai city, China, during both the summer and winter seasons. We estimated the metabolic function, nutritional type, and life strategy of Proteobacteria through the FAPROTAX and rrnDB databases. Our findings demonstrated that Proteobacteria was the predominant phylum (relative abundance = 40.1–48.4%) in the soil bacterial communities across all three treatments. The relative abundance of Alphaproteobacteria ranged from 28.5% to 38.1%, which was significantly greater (2.6–10.3 times, p < 0.05) than that of Betaproteobacteria or Gammaproteobacteria. Most (90%) proteobacterial genera and all rhizobial genera found in the NV and TA soils were also present in the SR soil, but there were distinct differences in the proteobacterial community structures between the SR soil and NV/TA soil. Across all seasons and treatments, the proteobacterial communities were related to functions such as ureolysis, nitrogen fixation, nitrate reduction, and hydrocarbon degradation. The relative abundance of Proteobacteria associated with chitinolysis was greater in the SR soil than in the NV and TA soils. Among the overall proteobacterial community, the chemoheterotrophic, chemoautotrophic, and phototrophic bacteria accounted for 65–77%, 19–31%, and less than 5%, respectively. Alphaproteobacteria tended to be K strategic, while Betaproteobacteria and Gammaproteobacteria tended to be r strategic. The soil pH, organic carbon content, and nitrogen content were significantly correlated with the metabolic function and nutritional type of the soil Proteobacteria according to the Mantel test results. In conclusion, the application of the ASS technique can effectively restore the biodiversity, metabolic function, and nutritional-type structure of the soil proteobacterial community. Additionally, this study highlights that certain metabolic functions of the proteobacterial community in SR soil undergo changes in response to the use of certain restoration materials. These findings suggest that the targeted addition of specific repair materials can modulate soil microorganism functionality and provide a valuable theoretical foundation for ecological restoration engineering practices

Influences of Shifted Vegetation Phenology on Runoff Across a Hydroclimatic Gradient

Climate warming has changed vegetation phenology, and the phenology-associated impacts on terrestrial water fluxes remain largely unquantified. The impacts are linked to plant adjustments and responses to climate change and can be different in different hydroclimatic regions. Based on remote sensing data and observed river runoff of hydrological station from six river basins across a hydroclimatic gradient from northeast to southwest in China, the relative contributions of the vegetation (including spring and autumn phenology, growing season length (GSL), and gross primary productivity) and climatic factors affecting the river runoffs over 1982–2015 were investigated by applying gray relational analysis (GRA). We found that the average GSLs in humid regions (190–241 days) were longer than that in semi-humid regions (186–192 days), and the average GSLs were consistently extended by 4.8–13.9 days in 1982–2015 period in six river basins. The extensions were mainly linked to the delayed autumn phenology in the humid regions and to advanced spring phenology in the semi-humid regions. Across all river basins, the GRA results showed that precipitation (r = 0.74) and soil moisture (r = 0.73) determine the river runoffs, and the vegetation factors (VFs) especially the vegetation phenology also affected the river runoffs (spring phenology: r = 0.66; GSL: r = 0.61; autumn phenology: r = 0.59), even larger than the contribution from temperature (r = 0.57), but its relative importance is climatic region-dependent. Interestingly, the spring phenology is the main VF in the humid region for runoffs reduction, while both spring and autumn growth phenology are the main VFs in the semi-humid region, because large autumn phenology delay and less water supply capacity in spring amplify the effect of advanced spring phenology. This article reveals diverse linkages between climatic and VFs, and runoff in different hydroclimatic regions, and provides insights that vegetation phenology influences the ecohydrology process largely depending on the local hydroclimatic conditions, which improve our understanding of terrestrial hydrological responses to climate change

Informing the SWAT model with remote sensing detected vegetation phenology for improved modeling of ecohydrological processes

The Soil and Water Assessment Tool (SWAT) model has been widely applied for simulating the water cycle and quantifying the influence of climate change and anthropogenic activities on hydrological processes. A major uncertainty of SWAT stems from the poor representation of vegetation dynamics due to the use of a simplistic vegetation growth and development module. Using long-term remote sensing-based phenological data, the SWAT model's vegetation module was improved by adding a dynamic growth start date and the dynamic heat requirement for vegetation growth rather than using constant values. The new SWAT model was verified in the Han River basin, China, and found its performance was much improved in comparison with that of the original SWAT model. Specifically, the accuracy of the leaf area index (LAI) simulation improved notably (coefficient of determination (R2) increased by 0.193, Nash–Sutcliffe Efficiency (NSE) increased by 0.846, and percent bias decreased by 42.18 %), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE was similar). Additionally, it is found that the original SWAT model substantially underestimated evapotranspiration (Penman-Monteith method) in comparison with the new SWAT model (65.09 mm (or 22.17 %) for forests, 92.27 mm (or 32 %) for orchards, and 96.16 mm (or 36.4 %) for farmland), primarily due to the inaccurate representation of LAI dynamics. Our results suggest that an accurate representation of phenological dates in the vegetation growth module is important for improving the SWAT model performance in terms of estimating terrestrial water and energy balance