Plant Nitrogen Research Articles

Impact of Plant Spacing and Fertilization Dose on the Growth and Yield Traits of Okra (Abelmoschus esculentus (L.) Moench) in Sudan

This study examines the impact of plant spacing and nitrogen fertilization doses on the growth and yield traits of okra (Abelmoschus esculentus (L.) Moench) in Sudan. Using a factorial experiment within a completely randomized block design, the research explored four spacing levels within raw, S1, S2, S3, and S4 (20, 25, 30, and 35 cm, respectively) and four nitrogen fertilizer doses, Ur1, Ur2, Ur3 and Ur4 (60, 80, 100, and 120 kg.ha-1 , respectively). Key metrics were measured: plant height, number of leaves, number of nodes, pod length, fresh weight, and seed count per pod. The results, analyzed via OPSTAT statistical software and ANOVA, indicated that a 60 cm × 30 cm spacing combined with a nitrogen dose of 60 kg.ha-1 significantly optimized growth parameters and yield components. Specifically, this combination resulted in the tallest plants (15.50 cm), the highest number of leaves (7.30), nodes (7.30), and greater fresh pod weight (7.69 g) and length (5.50 cm). Significant interaction effects between spacing and fertilizer doses were observed across all parameters at a 5% confidence level. The study recommends a 60 cm × 30 cm spacing with a nitrogen dose of 60 kg.ha-1 to maximize okra growth, yield, and quality in Sudan's agroecological zones, supporting sustainable agricultural practices and enhanced productivity. These findings can be directly applied to optimize okra production in Sudan, contributing to the country's agricultural sustainability.

Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions

The use of remote sensing technologies to monitor the nitrogen nutrient status of crops is gradually becoming a more sensible choice, as traditional methods are time-consuming, labor-intensive, and destructive. However, most predictive models utilizing remote sensing data are statistical rather than mechanistic, making them difficult to extend at interannual and regional scales. This study explored the interannual and regional transferability of the potato plant nitrogen content (PNC) prediction models, which combined environmental variables (EVs, e.g. temperature, precipitation, etc.) with proximal hyperspectral vegetation indices (VIs). Two methodologies were implemented to fuse EVs and VIs. The first involved a multiple regression analysis utilizing a multivariate linear model and a random forest approach, with VIs and EVs treated as independent variables, respectively. The second, a hierarchical linear model (HLM), employed EVs to dynamically adjust the relationship between VIs and PNC for different experimental sites. The predictive outcomes demonstrated that (i) the conventional method relying solely on optical VIs exhibited limited accuracy and stability in interannual and regional PNC forecasting; (ii) albeit the multivariate regression approach significantly enhanced model accuracy within the calibration set, its scalability across years and regions remained suboptimal; (iii) the HLM method exhibited high precision and scalability across years and regions, with R2, RMSE, and NRMSE values of 0.68, 0.50 %, and 19.68 % in the validation set, respectively. Those findings corroborate that using a two-tier HLM method can automatically adjust for discrepancies in VIs response to PNC through EVs, thereby enhancing the model's stability. Provided that remote sensing data and EVs are sustainably acquired over the potato growth cycle, it will provide a particularly promising approach to potato nitrogen diagnostics as a decision-making tool for regional application of nitrogen fertilizer.

Inoculation with arbuscular mycorrhizal fungi improves plant biomass and nitrogen and phosphorus nutrients: a meta-analysis

Arbuscular mycorrhizal fungi (AMF) have profound effects on plant growth and nitrogen (N) and phosphorus (P) nutrition. However, a comprehensive evaluation of how plant N and P respond to AMF inoculation is still unavailable. Here, we complied data from 187 original researches and carried out a meta-analysis to assess the effects of AMF inoculation on plant growth and N and P nutrition. We observe overall positive effects of AMF inoculation on plant performance. The mean increases of plant biomass, N concentration, P concentration, N and P uptake of whole plant are 47%, 16%, 27%, 67%, and 105%, respectively. AMF inoculation induces more increases in plant concentrations and storage of P than N. Plant responses to AMF inoculation are substantially higher with single AMF species than with mixed AMF species, in laboratory experiments than in field experiments, and in legumes than in non-legumes. The response ratios of plant N and P nutrition are positively correlated with AMF colonization rate, N addition, P addition, and water condition, while unvaried with experiment duration. The biggest and smallest effect sizes of AMF inoculation on plant performance are observed in the application of nitrate and ammonium, respectively. Accordingly, this meta-analysis study clearly suggests that AMF inoculation improves both plant N and P nutrients and systematically clarifies the variation patterns in AMF effects with various biotic and abiotic factors. These findings highlight the important role of AMF inoculation in enhancing plant N and P resource acquisitions and provide useful references for evaluating the AMF functions under the future global changes.

Potential of novel Rhizobium strains as biofertilizer for improved groundnut (Arachis hypogaea L.) cultivation in Eastern India

ABSTRACT Comprehensive assessments were conducted on eight fast growing, novel Rhizobium isolates, short listed from a preliminary selection of 68 isolates obtained from root nodules of groundnut plants grown in diverse agro-climatic regions of eastern India. Additionally, two conventional rhizobia strains, viz. IGR6 and GN2, (collected from the Nodule Research Center, BCKV), were utilized for comparative analysis. Cluster analysis, based on carbon utilization and biochemical parameters, grouped all the test isolates with an average similarity level of 73%. The ability of the isolates to utilize a wide range of carbon sources, including hexoses, pentoses, and disaccharides, confirms their fast-growing nature. Further analysis through 16S rDNA extraction and nucleotide sequencing revealed that the promising fresh isolates exhibited 95–99% similarity to known Rhizobium species in public databases. When evaluated in pot culture experiments using new alluvial soil from the Gangetic Plains in eastern India, the native fresh isolate NRA1(PP355674) outperformed other isolates and conventional strains in terms of nodulation efficiency, plant dry matter production, and plant nitrogen uptake. This study highlights the significant potential of native Rhizobium isolates to surpass conventionally used strains in biological nitrogen fixation, supporting the development of sustainable, environmentally-friendly, and cost-effective agricultural practices.

Coupling Image-Fusion Techniques with Machine Learning to Enhance Dynamic Monitoring of Nitrogen Content in Winter Wheat from UAV Multi-Source

Plant nitrogen concentration (PNC) is a key indicator reflecting the growth and development status of plants. The timely and accurate monitoring of plant PNC is of great significance for the refined management of crop nutrition in the field. The rapidly developing sensor technology provides a powerful means for monitoring crop PNC. Although RGB images have rich spatial information, they lack the spectral information of the red edge and near infrared bands, which are more sensitive to vegetation. Conversely, multispectral images offer superior spectral resolution but typically lag in spatial detail compared to RGB images. Therefore, the purpose of this study is to improve the accuracy and efficiency of crop PNC monitoring by combining the advantages of RGB images and multispectral images through image-fusion technology. This study was based on the booting, heading, and early-filling stages of winter wheat, synchronously acquiring UAV RGB and MS data, using Gram–Schmidt (GS) and principal component (PC) image-fusion methods to generate fused images and evaluate them with multiple image-quality indicators. Subsequently, models for predicting wheat PNC were constructed using machine-selection algorithms such as RF, GPR, and XGB. The results show that the RGB_B1 image contains richer image information and more image details compared to other bands. The GS image-fusion method is superior to the PC method, and the performance of fusing high-resolution RGB_B1 band images with MS images using the GS method is optimal. After image fusion, the correlation between vegetation indices (VIs) and wheat PNC has been enhanced to varying degrees in different growth periods, significantly enhancing the response ability of spectral information to wheat PNC. To comprehensively assess the potential of fused images in estimating wheat PNC, this study fully compared the performance of PNC models before and after fusion using machine learning algorithms such as Random Forest (RF), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB). The results show that the model established by the fusion image has high stability and accuracy in a single growth period, multiple growth periods, different varieties, and different nitrogen treatments, making it significantly better than the MS image. The most significant enhancements were during the booting to early-filling stages, particularly with the RF algorithm, which achieved an 18.8% increase in R2, a 26.5% increase in RPD, and a 19.7% decrease in RMSE. This study provides an effective technical means for the dynamic monitoring of crop nutritional status and provides strong technical support for the precise management of crop nutrition.

Stoichiometric and Accumulation Characteristics of Nitrogen and Phosphorus in Artificial and Natural Herbaceous Plants along Lakeshore Buffer Zone

Lakeshore buffer zones serve as transitional areas between terrestrial and aquatic ecosystems, playing a crucial role in intercepting non-point source pollutants, purifying river and lake water, and maintaining ecological system dynamics. This study focuses on the vegetation of the lakeshore buffer zone in Songhua Lake, the largest artificial lake in Northeast China. The nitrogen (N) and phosphorus (P) pollutant contents and accumulation characteristics of herbaceous plants were investigated and analyzed in different regions and with different species. The study results indicate that there were no significant differences in N and P content, N: P ratio, and average accumulation among vegetation in the near-water, middle, and far lakeshores of the buffer area. The herbaceous plants with the highest N and P content and N: P ratios were Artemisia sieversiana, Sium suave, and Artemisia sieversiana, respectively. Purus frumentum planted in the middle lakeshore accumulated the highest amounts of N and P, reaching 87.97 g plant−1 and 3.86 g plant−1, respectively. The aboveground parts of plants showed significantly higher accumulation of N and P compared to the underground parts. The average enrichment coefficient for aboveground parts and underground parts for N were 4.83 and 4.35, respectively, all exceeding 1. However, their capability to enrich P was relatively weak, with only the aboveground parts of F − 3 showing some enrichment ability. Among herbaceous plants, Artemisia sieversiana and Sium suave exhibited the strongest capability for transporting N and P from underground to aboveground. Overall, harvesting aboveground biomass for the ecological removal of N and P in the study area appears feasible. Biomass is a critical factor influencing the nutrient interception capacity of vegetation, with Purus frumentum identified as an optimal restoration plant for sustainable management practices, and Artemisia sieversiana and Sium suave have the potential for rapid remediation.

Diversity and Bioactivity of Endophytic Actinobacteria Associated with the Roots of Artemisia herba-alba Asso from Algeria.

The isolation of endophytic actinobacteria from the roots of wild populations of Artemisia herba-alba Asso, a medicinal plant collected from the arid lands of Algeria, is reported for the first time. Forty-five actinobacterial isolates were identified by molecular analysis and in vitro evaluated for antimicrobial activity and plant growth-promoting (PGP) abilities (1-Aminocyclopropane-1-carboxylic acid (ACC) deaminase activity, nitrogen fixation, phosphate and potassium solubilization, ammonia, and siderophores production). The phylogenetic relationships based on 16S rRNA gene sequences show that the genus Nocardioides (n = 23) was dominant in the sampled localities. The remaining actinobacterial isolates were identified as Promicromonospora (n = 11), Streptomyces (n = 6), Micromonopora (n = 3), and Saccharothrix (n = 2). Only six (13.33%) strains (five Streptomyces and one Saccharothrix species) were antagonistic in vitro against at least one or more indicator microorganisms. The antimicrobial activity of actinobacterial strains targeted mainly Gram-positive bacteria. The results demonstrate that more than 73% of the isolated strains had ACC deaminase activity, could fix atmospheric nitrogen and were producers of ammonia and siderophores. However, only one (2.22%) strain named Saccharothrix sp. BT79 could solubilize phosphorus and potassium. Overall, many strains exhibited a broad spectrum of PGP abilities. Thus, A. herba-alba provides a source of endophytic actinobacteria that should be explored for their potential biological activities.

Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of Winter Wheat

The accurate estimation of nitrogen content in crop plants is the basis of precise nitrogen fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate the nitrogen in crop plants, but the accuracy will still be affected by the variety, the growth stage, and other factors. We aimed to (1) analyze the correlation between the plant nitrogen content of winter wheat and spectral, texture, and structural information; (2) compare the accuracy of nitrogen estimation at single versus multiple growth stages; (3) assess the consistency of UAV multispectral images in estimating nitrogen content across different wheat varieties; (4) identify the best model for estimating plant nitrogen content (PNC) by comparing five machine learning algorithms. The results indicated that for the estimation of PNC across all varieties and growth stages, the random forest regression (RFR) model performed best among the five models, obtaining R2, RMSE, MAE, and MAPE values of 0.90, 0.10%, 0.08, and 0.06%, respectively. Additionally, the RFR estimation model achieved commendable accuracy in estimating PNC in three different varieties, with R2 values of 0.91, 0.93, and 0.72. For the dataset of the single growth stage, Gaussian process regression (GPR) performed best among the five regression models, with R2 values ranging from 0.66 to 0.81. Due to the varying nitrogen sensitivities, the accuracy of UAV multispectral nitrogen estimation was also different among the three varieties. Among the three varieties, the estimation accuracy of SL02-1 PNC was the worst. This study is helpful for the rapid diagnosis of crop nitrogen nutrition through UAV multispectral imaging technology.

Strip‐cropping legacy enhances potato plant defence responses to aphids via soil‐mediated mechanisms

Abstract Intensive agriculture often comes at the expense of soil health. A shift towards practices that foster soil health will support yield and defences against pests and diseases. Growing crops in monoculture is the standard in modern agriculture, though strip‐cropping, in which different crops are planted in strips, is a promising strategy in the transition towards sustainable agriculture. Increasing crop diversity is hypothesized to positively influence arbuscular mycorrhizal fungi (AMF), thereby enhancing soil health, but the mechanisms by which AMF‐plant symbioses contribute to higher yields and reduced pest populations in strip‐cropping systems remain unclear. We used a green‐house experiment with potato plants to explore the soil legacy effects of mono‐cropping versus strip‐cropping systems, AMF inoculation and aphid infestation on AMF root colonization, the induction of plant defences (untargeted LCMS‐based metabolomics), aphid population size and potato yield. We found that potato plants grown in strip‐cropping soil had higher AMF colonization than plants grown in mono‐cropping soil. Potato plants grown in strip‐cropping soil also had higher shoot nitrogen content, increased solamargine levels, and reduced aphid populations. AMF root colonization was only enhanced by the addition of commercial AMF in mono‐cropping soils. Potato plant metabolites were affected by strip‐cropping soil, including jasmonic acid (JA) derivatives. Structural equation models revealed that strip‐cropping soil directly reduced aphid populations and also had a negative direct effect on the JA precursor OPC‐8, and hydroxyJA‐glucosides, indicating complex effects of strip‐cropping soils on JA‐inducible plant defences. Indirect benefits of strip‐cropping soil and AMF inoculation on tuber yield were mediated by their direct positive effects on plant nitrogen content. Our results emphasize the potential of strip‐cropping to enhance AMF root colonization in the field. We show that soil legacy effects of strip‐cropping alter the plant metabolome in ways that suppress of aphid populations. Strip‐cropping legacy effects are the result of crop diversity, crop neighbour and edge effects resulting from crop management practices. While the mechanisms by which soil from strip‐cropping supresses pest populations still need to be identified, our study underscores the potential for strip‐cropping to enhance pest control and yield via soil mediated processes. Read the free Plain Language Summary for this article on the Journal blog.

Belowground diversity drives multifunctionality in grazing pastures on the eastern Tibetan Plateau

Livestock grazing can alter ecosystem structure, functions, and services across diverse biomes, with grazing intensity being a key factor affecting grassland function. Although the effects of grazing on plant and soil properties have been extensively studied, the effects of grazing intensity on biodiversity and ecosystem multifunctionality (EMF) remain largely unexplored. Therefore, this study addresses this gap using 28 indicator variables from a well-controlled yak grazing intensity experiment in alpine meadows on the eastern Tibetan Plateau. The results showed that aboveground diversity (calculated using plant species richness and insect diversity) exhibited a hump-shaped and significant response to increasing grazing intensity, multidiversity (whole-ecosystem biodiversity) and belowground diversity (calculated using nematode richness and microbial diversity) showed no significant response, and EMF significantly declined. Grazing decreased carbon and nitrogen cycling indices (calculated by carbon and nitrogen in plants and soils), but did not affect phosphorus cycling. Structural equation modelling indicated that EMF was directly affected by grazing intensity and belowground diversity (i.e., nematode and fungal diversity), rather than by multidiversity, aboveground diversity, and plant pathogens. Grazing-induced decreases in plant pathogens showed no direct or indirect effects on EMF but increased multidiversity and aboveground diversity. Overall, our results highlight the critical role of conserving belowground diversity in promoting and maintaining multifunctionality in grazing pastures on the Tibetan Plateau.

Carbon‐to‐nitrogen stoichiometry of organic amendments regulates microbial biomass growth and nitrogen mineralization in soil

AbstractThe carbon‐to‐nitrogen (C:N) ratio of organic amendments regulates nutrient cycling in soil through its influence on microbial activity. Where an organic amendment has a C:N ratio sparingly higher than that of soil microbes (C:N ratio 6.7—8.7), meeting microbial requirements of carbon and nitrogen may promote microbial biomass growth and nitrogen mineralization. Here we sought to understand the microbial response to the addition of organic amendments with varying C:N ratios and subsequent changes in nitrogen mineralization and plant nitrogen uptake. Dried insect larvae, beef feedlot manure and mixtures of beef feedlot manure with insect larvae or sugarcane residue were used as soil amendments to create a gradient of molar C:N ratios (Cm:Nm) ranging from 6 to 28. An untreated control treatment with a C:N ratio of 17 was also included. Sorghum (Sorghum bicolor L.) was grown in control and organic matter amended soils for 20 days after germination. The mixture of beef feedlot manure and insect larvae treatment (Cm:Nm = 9.7) had on average 68% higher microbial biomass compared with the other treatments. Beef feedlot manure with Cm:Nm ratio of 11 did not influence microbial biomass growth. The concentration of mineral nitrogen was highest in the insect larvae (Cm:Nm = 6) treatment, where 23% of the plant nitrogen was derived from the insect larvae as indicated by the δ15N of plant dry matter. We observed both carbon and nitrogen contents in the organic amendment determine microbial biomass growth and nitrogen mineralization in soil. The results presented here demonstrate that microbial response to organic amendments determines the availability of mineral nitrogen in soil.

Optimizing growth: The role of plant density and nitrogen fertilization on dual-purpose yellow sorghum (PYPS-2)

Optimizing growth: The role of plant density and nitrogen fertilization on dual-purpose yellow sorghum (PYPS-2)

Optimizing an Ultisol of marginal land for soybean cultivation through inorganic fertilizer substitution with Tithonia compost and biochar ameliorant

Soybean has a strategic role as a food commodity in Jambi, Indonesia, but until now, it has still not been self-sufficient. There is still an opportunity to increase soybean productivity in Jambi by 35-49.6% by optimizing the use of marginal, dominated by Ultisol, which has low inherent soil fertility. One of the efforts that can be made to improve soil fertility for cultivating soybean plants in Jambi is to utilize compost and biochar as soil ameliorants to substitute for inorganic fertilizer. This study aimed to assess the effect of compost and biochar ameliorant as substitutes for inorganic fertilizer on the growth and yield of soybean plants grown on an Ultisol of Jambi. The experiment was arranged in a simple randomized block design with the combination of the percentage of inorganic fertilizer with compost and biochar ameliorant (100+0, 75+25, 50+50, 25+75, and 0+100) as the treatment factors. The observed variables were plant height, number of branches, plant nitrogen content, rate of symbiotically fixed nitrogen, weight of 100 seeds, and yield. The results showed that providing compost and biochar ameliorants can replace the role of inorganic fertilizer, and providing 100% compost and biochar ameliorant and a combination of 25% inorganic fertilizer and 75% compost biochar ameliorant can provide the highest growth and yield in soybean plants planted in Ultisol dry land.

Response to Addition of Lighting Duration and Electrical Induction in Lettuce (Lactuca sativa L.) Plant on the Uptake of Nitrogen, Phosphors, Potassium and Yield

Background: The impact of excessive use of chemical fertilizers and pesticides causes problems of land degradation, low fertility levels and reduced cation exchange capacity. To increase nutrient uptake and yield of lettuce plants, cultivation techniques using induction and additional lighting are needed. The aim is to increase nutrient absorption and plant yields. Methods: The research design used a split plot with Factorial Randomized Groups. Factor 1 Lighting duration (P) for 4 and 8 hours per day and Factor 2, induction with DC (Direct Current) current (S). Induction 1 time per day (S1); The treatment in this study was repeated 4 times. Induction 2 times per day (S2) and Induction 3 times per day (S3). Result: The combination P2S2 showed good results regarding the uptake of Nitrogen (20.1 Kg.Ha-1), Phosphorus (2.71 Kg.Ha-1) and Potassium (67.23 Kg.Ha-1) in plant leaves. The P1S2 treatment showed a leaf area of (1652.19 cm2). The adding 4 hours of light and an induction interval of 2 times per day gave good results on the uptake of Nitrogen, Phosphorus and Potassium in the leaf of lettuce plants.

Deciphering cleaner and sustainable frontiers in scientific cow waste valorization: a review.

The forecasted global population growthis poised to create a greater exigency for livestock-derived food production,leading to a significant wastegeneration from the industrial-scalelivestockoperations, which necessitates to develop sustainable waste management solutions. The heightened demand for livestock and dairy products has driven a surge in cow waste (CW) production. While CW is typically used as organic fertilizer or solid fuel, improper disposal poses potential environmental hazards. Anaerobic digestion and composting transform CW into valuable products, such as biofuels and organic fertilizers, with the potential for electricity and heat generation, biochar production, and advanced friction materials. The CW contains essential inorganic and organic compounds vital for plant functions, including lignin, cellulose, hemicellulose, nitrogen, and minerals such as potassium, sulfur, iron, magnesium, copper, cobalt, and manganese. Additionally, the rich microbial diversity in cow dung drives the production of bioenergy carriers like biomethane and biohydrogen, promoting cost-effective energy generation and environmental sustainability. This review employs bibliometric analysis to explore the latest trends in CW applications, with a particular focus on innovative applications such as cellulose extraction, biochar production, microbial fuel cells, and nanoparticle synthesis. Itfurther evaluates the environmental impacts of these technologies and assesses their potential to advance sustainable and cleaner frontiers in the valorizationof CW.

Arbuscular mycorrhizal fungi affected soft wheat quality properties and nutritional index by regulating the composition and secondary structure of protein

Arbuscular mycorrhizal fungi (AMF) inoculation has important regulatory influence on plant growth and nitrogen uptake, which are intimately associated with soft wheat yield and quality. The study aimed to investigate the impact of AMF inoculation on soft wheat grain yield, protein compositions, protein quality, secondary structure of gluten, dough properties and biscuits baking quality. In this study, AMF inoculation significantly increased grain yield. AMF inoculation decreased grain protein content by 5.1%, glutenin content by 8.5%, GMP by 14.3%, and HMW-GS by 8.8% compared with CK. Conversely, AMF inoculation increased the ratio of gliadin/glutenin. Further analysis showed AMF treatment had a looser gluten network than CK, significantly reduced β-sheet and β-turn content, while α-helix content increased. The nutritional index of grain protein was slightly reduced under AMF inoculation, yet the biological value was improved. Reduced wet gluten content, gluten performance index and dough development time under AMF treatment through regulation of soft wheat protein composition and gluten protein structure. Therefore, biscuits in AMF treatment showed reduced thickness, hardness, significantly greater spread ratio and sensory score compared with CK. These indicate that AMF application would be a beneficial agronomic practice to synergistically optimize yield and grain quality in soft wheat.

MEDICAGO sativa-mediated green synthesis of Zinc nano-complex: Impacts on the phytochemical properties of Hyssopus officinalis under Alkaline stress

The purpose of this study was to investigate the interaction between sodium bicarbonate stress (SBCS) (0, 10, 20, and 40 mM) and zinc nanocomplex (Zn NC) (0, 1, 2, and 4 mM) on the growth and bioactive compounds of Hyssopus officinalis L. plants at the growth stage. The application of Zn NC increased essential oil (EO) production and the biosynthesis of phenolic compounds (PCs) and flavonoids in the plant. The accumulation of these compounds amplified the plant's antioxidant capacity compared to control. Treatment with Zn NC increased these compounds significantly more than alkalinity. 2 mM and 4 mM Zn NCs increased average APX activity substantially by 37 % and 55 %, respectively. The oxidative damage and depletion of mineral nutrients in the leaves were caused by SBCS. However, Zn NC-treated plants significantly increased nitrogen, phosphorus, potassium, zinc, and iron concentrations in the bicarbonate-stressed plants. A 44 % increase in proline content with 20 mM SBCS and 4 mM Zn NCs compared to control was revealed. In alkaline soils, Zn NC can be a beneficial and environmentally acceptable zinc supply that increases plant growth and bioactive compound production. Totally, applying 2 and 4 mM Zn NC treatment can counteract the negative effects of SBCS on plant growth and nutrient uptake.

A Nitrogen-specific Interactome Analysis Sheds Light on the Role of the SnRK1 and TOR Kinases in Plant Nitrogen Signaling

Nitrogen (N) is of utmost importance for plant growth and development. Multiple studies have shown that N signaling is tightly coupled with carbon (C) levels, but the interplay between C/N metabolism and growth remains largely an enigma. Nonetheless, the protein kinases Sucrose Non-fermenting 1 (SNF1)-Related Kinase 1 (SnRK1) and Target Of Rapamycin (TOR), two ancient central metabolic regulators, are emerging as key integrators that link C/N status with growth. Despite their pivotal importance, the exact mechanisms behind the sensing of N status and its integration with C availability to drive metabolic decisions are largely unknown. Especially for SnRK1, it is not clear how this kinase responds to altered N levels. Therefore, we first monitored N-dependent SnRK1 kinase activity with an invivo Separation of Phase-based Activity Reporter of Kinase (SPARK) sensor, revealing a contrasting N-dependency in Arabidopsis thaliana (Arabidopsis) shoot and root tissues. Next, using affinity purification (AP) and proximity labeling (PL) coupled to mass spectrometry (MS) experiments, we constructed a comprehensive SnRK1 and TOR interactome in Arabidopsis cell cultures during N-starved and N-repleted growth conditions. To broaden our understanding of the N-specificity of the TOR/SnRK1 signaling events, the resulting network was compared to corresponding C-related networks, identifying a large number of novel, N-specific interactors. Moreover, through integration of N-dependent transcriptome and phosphoproteome data, we were able to pinpoint additional N-dependent network components, highlighting for instance SnRK1 regulatory proteins that might function at the crosstalk of C/N signaling. Finally, confirmation of known and identification of novel SnRK1 interactors, such as Inositol-Requiring 1 (IRE1A) and the RAB GTPase RAB18, indicate that SnRK1, present at the ER, is involved in N signaling and autophagy induction.

Effects of liquid urea rates on nitrogen dynamics, growth, and yield of corn (Zea mays L.)

Arbitrary use of urea fertilizer reduces nitrogen use efficiency (NUE) and increases the risk of environmental pollution. An experiment was conducted at the Universiti Putra Malaysia from November 2019 to March 2020 to evaluate the application methods and rates of liquid urea (LU) on the yield performance of corn. The treatments were, U0 = control, GU100 = Granular urea (GU) 100%, LU100 = LU 100%, LU50 = LU 50% and LU33 = LU 33%, in two equal splits at 10th and 28th days after sowing (DAS) in randomized completely block design, replicates four. Results showed that plant height (206.99 cm, 216.92 cm, 214.61 cm), ear height (88.13 cm, 88.63 cm, 86.00 cm), days of maturity (88.75, 89.00, 86.75 days), number of grains per kernel row (32.25, 34.50, 33.75), fresh cob weight (10,886.60 kg ha−1, 10,946.60 kg ha−1, 10,927.93 kg ha−1) and 100-grain weight (20.51 g, 22.50 g, 21.39 g) of corn were not different significantly (p ≤ 0.05) in GU100, LU100 and LU50 treatments, respectively. The highest yield of corn was found with LU100 (6249.03 kg ha−1) treatment whereas the yield in LU50 (5666.50 kg ha−1) and GU100 (5746.64 kg ha−1) were not different significantly. Nitrogen (%) in plants was the highest in LU100 followed by LU50 treatment which was significantly higher than GU100 treatment. The total N content was also the highest in LU100 (102.83 kg ha−1) though the total N content was not different significantly in LU50 (77.62 kg ha−1) and GU100 (83.84 kg ha−1) treatments. The NUE was the highest in LU50 (66.92%) treatment followed by LU100 (51.47%) treatment. The results of the study suggested that LU was superior to GU and the LU100 was the best application rate while LU50 treatment was comparable to GU100 in corn cultivation.

Responses of plant carbon, nitrogen, and phosphorus content in terrestrial ecosystems to warming: A Meta-analysis.

We conducted a Meta-analysis with 264 datasets from 55 publications to investigate the effects of warming duration and intensity on plant carbon, nitrogen and phosphorus contents. The results showed that warming significantly reduced shoot carbon (effect value of -1.7%), root carbon (-4.0%), litter carbon (-3.7%), shoot nitrogen (-7.0%) and litter nitrogen contents (-6.4%). For different ecosystem types, warming significantly decreased shoot carbon (-0.8%), shoot nitrogen (-5.9%), root carbon (-7.4%), litter carbon (-2.1%), and litter nitrogen content (-13.4%) in grasslands, while significantly increased shoot carbon (2.7%) in scrublands and litter phosphorus content (42.4%) in forests. Short-term warming (<5 years) decreased shoot carbon (-0.4%), shoot phosphorus (-0.4%) and litter nitrogen (-13.4%) contents, while medium- to long-term warming (5-10 years) increased shoot carbon (0.6%), shoot phosphorus (20.2%) and litter nitrogen (6.2%) contents. The 0-2 ℃ warming intensity increased shoot phosphorus (10.1%) and root phosphorus (27.4%) contents of plants, while the >2 ℃ warming intensity decreased shoot phosphorus (-3.7%) and root phosphorus (-6.5%) content. The effect values of plant shoot carbon and shoot nitrogen were significantly and positively correlated with humidity index. Warming showed negative effects on plant carbon, nitrogen and phosphorus contents in terrestrial ecosystems, and such effects were moderated by the duration and intensity of warming.