Leaf Nitrogen Research Articles

Exploring the adaptive mechanisms and strategies of various populations of Sporobolus ioclados in response to arid conditions in Cholistan desert

This study explored the drought resistance mechanisms of different populations of Sporobolus ioclados (Poaceae), locally known as “Sawri,” “Drabhri” and “Dhrbholi” native to Africa and the Indian Subcontinent. These populations were grown in conventional nursery practices at Khawaja Fareed Government College in Rahim Yar Khan, Pakistan, and subsequently subjected to four distinct levels of drought within carefully monitored experimental settings. The experiment was conducted in a two-factorial design involving populations and drought treatments and was repeated three times. The physiological and morphological responses of S. ioclados, including plant height, number of roots, root length, flag leaf area, stomatal features, proline concentration and nitrogen content, displayed significant variability in response to the imposed drought stress. Drought resulted in increases in proline concentration and nitrogen content. The number of roots decreased, while the length and width of the stomata increased in various populations. A combination of advanced statistical techniques, such as ANOVA, PCA, HCA, and DFA, provided a comprehensive understanding of the mechanism of plant adaptation and the extent of population diversity within the species. The Yazman and Nwab Wala populations exhibited the highest rates of photosynthesis and stomatal conductance, while S. ioclados demonstrated notable drought tolerance at the T4 level of drought stress. A negative correlation was found between proline levels, nitrogen contents, and photosynthesis, suggesting that proline has a protective role in drought. The diverse adaptation strategies indicated by S. ioclados populations have revealed the potential of this species for afforestation and climate change mitigation in dry environments.

Contrasting Life-Form Influences Guam Ficus Foliar Nutrient Dynamics

Tropical trees that remain evergreen and exhibit leaf litterfall that is gradual over time coexist with trees that are seasonally deciduous and exhibit pulsed litterfall. The manner in which these trees acquire, store, and contribute nutrients to the biogeochemical cycle may differ. Green and senesced leaves from deciduous Ficus prolixa trees were compared with those from Ficus tinctoria on the island of Guam. The results enabled stoichiometry and resorption calculations. F. prolixa’s young green leaf nitrogen (N) and potassium (K) concentrations were double, and the phosphorus (P) concentration was triple, those of F. tinctoria. Concentrations converged as the leaves aged such that no differences in concentration occurred for senesced leaves, indicating that nutrient resorption proficiency did not differ between the two species. In contrast, the resorption efficiency was greater for F. prolixa than F. tinctoria for all three nutrients. The N:P values of 6–11 and K:P values of 5–7 were greater for young F. tinctoria leaves than young F. prolixa leaves. The N:K values were 1.1–1.6 and did not differ between the two species. No differences in pairwise stoichiometry occurred for senesced leaves for any of the nutrients. These Guam results conformed to global trends indicating that seasonally deciduous plants are more acquisitive and exhibit greater nutrient resorption efficiency. The differences in how these two native trees influence the community food web and nutrient cycling lies mostly in the volume and synchronicity of pulsed F. prolixa litter inputs, and not in differences in litter quality. These novel findings inform strategic foresight about sustaining ecosystem health in Guam’s heavily threatened forests.

Predicting leaf nitrogen content of coffee trees using the canopy hyperspectral reflectance feature bands, vegetation index and machine learning

ABSTRACT Leaf nitrogen content (LNC) is an essential indicator of crop nitrogen status. To rapidly and correctly estimate the LNC using hyperspectral remote sensing, the canopy hyperspectral reflectance of coffee trees treated with five levels of nitrogen fertilization in a greenhouse was obtained in this study. Five methods were used for hyperspectral data preprocessing, namely, Savitzky–Golay (SG) smoothing, a combination of SG and standard normal variate transformation (SG-SNV), a combination of SG and first-order derivative (SG-FD), a combination of SG and second-order derivative (SG-SD), and a combination of SG and multiplicative scatter correction (SG-MSC). Feature wavelengths were extracted using variables combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and the combination (CARS-SPA) of CARS and successive projections algorithm (SPA). Vegetation indexes (VIs) were constructed and subjected to correlation analysis and variance inflation factor (VIF) analysis. Linear and nonlinear models including partial least squares regression (PLSR), back propagation neural network (BPNN), extreme learning machine (ELM), random forest regression (RFR), and support vector regression (SVR), were adopted to construct LNC retrieval models for coffee trees. The results indicated that SG-MSC could increase the signal-to-noise ratio of hyperspectral data well. The wavelengths selected by CARS-SPA were more relevant to LNC, and combined with ELM resulted in the best performance of LNC prediction (R2 P = 0.901, RMSEP = 0.825 g·kg−1, RPD = 3.229). Ten VIs were obtained through correlation analysis and VIF, and the VIs-based ELM prediction model also performed moderately well (R2 P = 0.814, RMSEP = 1.131 g·kg−1, RPD = 2.354). By comparing the coffee LNC prediction models established by different methods, two coffee LNC inversion models with better prediction accuracy were obtained, which provide a scientific basis for accurate diagnosis of coffee trees LNC, and are of great significance for optimizing the field management.

Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images

As a pivotal role in crop development, the application of nitrogen fertilizers enhances yields, while excessive use poses environmental risks. Precision nitrogen management requires rapid crop nitrogen assessment in field conditions and mobile phone images can be a promising approach. However, few studies have investigated how arbitrary white balance and exposure value settings affect crop nitrogen diagnosis using phone photos. Therefore, a new deep learning framework: self-correcting leaf nitrogen concentration estimation network (SCLNC-Net), was proposed to explore the potential of mobile phone images for non-destructive nitrogen status monitoring. Imperfect camera metering and incorrect user operations were addressed through attention U-Net for automatic white balance and exposure correction. Then a lightweight deep learning algorithm MobileNetV3 was utilized to estimate rice leaf nitrogen concentration based on the self-corrected images. Compared with most deep learning models, SCLNC-Net has fewer parameters and higher computational efficiency, which is better suited for deployment on mobile platforms. The results revealed that SCLNC-Net enhanced image quality and acquired high accuracy in the estimation of rice leaf nitrogen concentration (R2 = 0.84, RMSE = 0.39 %, RRMSE = 13 %). Moreover, the robustness and effectiveness of the proposed SCLNC-Net remained excellent across various shooting angles, mobile phone types, and rice varieties. This study provides insights into rapid and cost-efficient nutrient assessment and precision nitrogen management.

PREDICTING THE DECOMPOSITION RATE, MASS LOSS, AND NUTRIENT RELEASE OF SINGLE AND MIXED LEAF LITTER TYPES USING DECOMPOSITION MODELS IN THE NORTHERN GUINEA SAVANNAH OF NIGERIA

<p><strong>Background</strong>: This study presented a non-linear model to biologically describe the decomposition pattern, mass loss and nutrient release of four leaf litter species: <em>Khaya senegalensis </em>(African mahogany), <em>Mangifera indica </em>(Mango)<em>, Gmelina arborea</em> (Beechwood), <em>Eucalyptus camaldulensis</em> (River red gum) and a mixture of the leaf litters using the standard litter bag technique. <strong>Objective</strong>: To explore different mathematical decomposition decay models in evaluating the decomposition rate and the relationship between mass loss and chemical parameters of some selected trees in Nigeria's northern Guinea savannah. <strong>Methodology</strong>: The experiment was a Completely Randomized Design with three replications. Fifteen litter bags were randomly placed in the field and retrieved at intervals of 0, 14, 28, 42, 56, 84, and 112 days (16 weeks). Three non-linear models were used to estimate the decomposition rate of the litter. Pearson correlation analysis was used to determine the relationship between mass loss and chemical composition. <strong>Results</strong>: Decomposition pattern gradually increased from 7 % up to 78.5 % by week 0 to 16 weeks. The leaf litter of <em>Mangifera indica </em>had<em> </em>the highest mass loss (62.9 %), followed by the litter mixture (44.0 %), <em>Eucalyptus camaldulensis </em>(43.6 %), <em>Gmelina arborea</em> (40.5 %) and <em>Khaya senegalensis </em>(39.3 %). Single exponential model (<em>Adj R<sup>2</sup></em>=93.25-98.59%), double exponential model (<em>Adj R<sup>2</sup></em>=87.93-98.98%), and three parameters asymptotic negative exponential model (<em>Adj R<sup>2</sup>=</em>93.82-98.84%)<em>,</em> described the decomposition process efficiently. Correlation analysis of mass loss and chemical composition was highly significant (p ≤ 0.05), among all the leaf litter chemical properties, organic carbon, phosphorus, and nitrogen were the restraining factors. <strong>Implication</strong>: The mass loss was closely linked to the chemical properties of all the litter types. Among these properties, organic carbon and phosphorus were the limiting factors. <strong>Conclusion</strong>: We conclude that the single-leaf litter of <em>Mangifera indica</em> and <em>Khaya senegalensis</em> were superior in chemical composition, and decomposition than the mixed-leaf litter therefore they have the potential to enhancing soil fertility in the study area.</p>

Long-Term Alpine Plant Responses to Global Change Drivers Depend on Functional Traits.

Forecasting plant responses under global change is a critical but challenging endeavour. Despite seemingly idiosyncratic responses of species to global change, greater generalisation of 'winners' and 'losers' may emerge from considering how species functional traits influence responses and how these responses scale to the community level. Here, we synthesised six long-term global change experiments combined with locally measured functional traits. We quantified the change in abundance and probability of establishment through time for 70 alpine plant species and then assessed if leaf and stature traits were predictive of species and community responses across nitrogen addition, snow addition and warming treatments. Overall, we found that plants with more resource-acquisitive trait strategies increased in abundance but each global change factor was related to different functional strategies. Nitrogen addition favoured species with lower leaf nitrogen, snow addition favoured species with cheaply constructed leaves and warming showed few consistent trends. Community-weighted mean changes in trait values in response to nitrogen addition, snow addition and warming were often different from species-specific trait effects on abundance and establishment, reflecting in part the responses and traits of dominant species. Together, these results highlight that the effects of traits can differ by scale and response of interest.

Nutrition for commercial flower production of Longiflorum asiatica (LA) hybrid under northern plains

The study was conducted during the winter seasons, comprising the various combinations of inorganic fertilizers (N, P2O5, and K2O) and organic manure (FYM). Treatment T24 [(25 t FYM+160 kg N+120 kg P2O5+100 kg K2O)/ha] emerged as the statistically significant treatment, outperforming others in respect of tallest plant (122.43 cm), leaf count per plant (79.33), leaf length (7.90 cm), and early flower bud initiation (28.01 days), bud initiation to colour shown (47.11 days), and colour shown to flower opening (6.10 days). This treatment also tended to show the longest flower retention on plant (15.30 days), number of flower buds per plant (4.20), bud length (8.96 cm), stalk length (110.10 cm), stalk diameter (0.96 cm), flower diameter (22.98 cm), total chlorophyll content (6.50 mg/g), leaf nitrogen (4.70%), potassium (4.10%), and net assimilation ratio (NAR) (0.0065 mg/cm2/ day). Treatment T23 [(25 t FYM+120 kg N+90 kg P2O5+75 kg K2O)/ha] proved next best treatment. The lowest value was recorded in T25 (Control). Overall, treatment T24 proved to be the most effective treatment statistically for the commercial flower production of LA hybrid lilium for northern Indian plains.

Foliar methane and nitrous oxide fluxes in tropical tree species

Methane (CH₄) and nitrous oxide (N₂O) are critical biogenic greenhouse gases (GHGs) with global warming potentials substantially greater than that of carbon dioxide (CO₂). The exchange of these gases in tropical forests, particularly via foliar processes, remains poorly understood. We quantified foliar CH₄ and N₂O fluxes among tropical tree species and examined their potential association with the leaf economics spectrum (LES) traits. Sampling within Lawachara National Park, Bangladesh, we used in-situ measurements of foliar CH₄ and N₂O fluxes employing off-axis integrated cavity output spectroscopy (CH₄, CO₂ and H₂O) and optical feedback–cavity enhanced absorption spectroscopy (N₂O) analyzers. Leaves were measured under dark, low, and high (0, 100, and 1000 μmol·m−2·s−1) light conditions. Surveyed tree species exhibited both net foliar uptake and efflux of CH₄, with a mean flux not different from zero, suggesting negligible net foliar emissions at the stand level. Plant families showed differences in CH₄, but not N₂O fluxes. Consistent efflux was observed for N₂O, with a mean of 0.562 ± 0.060 pmol·m−2·s−1. Pioneer species exhibited a higher mean N₂O flux (0.81 ± 0.17 pmol·m−2·s−1) compared to late-successional species (0.37 ± 0.05 pmol·m−2·s−1). Pioneer species also showed a trend toward a higher mean CH₄ flux (0.24 ± 0.21 nmol·m−2·s−1) compared to mid-successional (−0.01 ± 0.26 nmol·m−2·s−1) and late-successional species (−0.05 ± 0.28 nmol·m−2·s−1). Moreover, among all leaf traits within the leaf economic spectrum, a significant positive relationship was observed between leaf N₂O flux and total leaf nitrogen. Our results suggest that pioneer tree species significantly contribute to net CH₄ and N₂O emissions, potentially counteracting the carbon sequestration benefits in regenerating tropical forests. These findings indicate that accurate GHG budgeting should include direct measurements of foliar CH₄ and N₂O fluxes. Moreover, the results suggest that forest conservation and management strategies that prioritize late successional species will better mitigate GHG emissions.

Strong conservatism in leaf anatomical traits and their multidimensional relationships with leaf economic traits in grasslands under different stressful environments

BackgroundPlant traits and plant adaptive strategies have been affected by the increasing intensity and severity of environmental changes. Given the uncertainty surrounding future environmental conditions, investigating plant trait variations under various stresses is crucial for unraveling plant survival strategies. Leaf anatomical traits are closely responsible for plants’ photosynthesis, respiration and transpiration. However, knowledge of how the multi-species leaf anatomical traits varied in extremely and moderately stressful environments is limited. Our objective was to compare the variation of leaf anatomic traits and adaptation strategies in two different stressful regions of the Qinghai-Tibet Plateau (TP) and Mongolian Plateau (MP) of China.MethodsWe sampled ten sites in each of the two regions (MP and TP) along an environmental gradient. Seven leaf anatomical traits and two leaf economic traits were measured for all leaf samples. Leaf anatomical traits include the traits related to leaf physiological processes (mesophyll thickness (MT), palisade tissue thickness (PT), spongy tissue thickness (ST), palisade-spongy tissue thickness ratio (PST) and epidermal thickness (ET)) and the traits related to trait construction investment (epiderm-leaf thickness ratio (ET/LT) and mesophyll-leaf thickness ratio (MT/LT)). Leaf economic traits include specific leaf area (SLA) and leaf nitrogen content (LN).ResultsThe results revealed that leaf anatomical traits in the TP exhibited greater phylogenetic conservation with thicker structures, being less susceptible to environmental impacts than those in the MP. Additionally, the leaf anatomical and economic traits decoupled both in the MP and TP.ConclusionThese findings highlight that plants adopt diverse strategies to cope with extremely and moderately environmental stresses, but multidimensional trait patterns are generally favored in stressful environments.

Water controls the divergent responses of terrestrial plant photosynthesis under nitrogen enrichment

Abstract Quantifying leaf photosynthetic response to nitrogen (N) deposition under contrasting water conditions is important for reliably modelling terrestrial carbon and water cycles, a topic that has not been well understood. Here, we analysed 737‐paired observations from 102 publications to assess the response of 11 leaf photosynthesis‐related properties to N addition under different water conditions. Our research includes global experiments, with 19 conducted in the field and 83 in greenhouses. Treatments without water reduction were classified as ‘no water change’, while those with reduced water or precipitation causing physiological drought were categorized as ‘drought’. We found that, compared to the control group, N addition significantly increased leaf photosynthetic rate (Pn; 20.9%), leaf transpiration (E; 8.3%) and stomatal conductance (gs; 14.1%). However, the decrease in Pn (−11.6%), E (−24.7%) and gs (−23.9%) under the combination of N addition and drought indicated that N addition could not offset the negative effects of drought. Furthermore, N addition significantly enhanced water‐use efficiency (WUE) by 19.8% under no water change conditions and by 21.1% under drought conditions. Within plant functional groups, herbaceous species exhibited greater susceptibility to N addition than woody species, especially under drought conditions. The observed patterns of increase in Pn with longer experimental duration and WUE with higher N rate under drought conditions showed that plants would gradually adapt to long‐term water stress in the context of N deposition. Furthermore, our results showed that drought could strengthen the correlations between leaf photosynthetic properties. Lastly, our study demonstrated that N addition and drought significantly impacted leaf nitrogen content and SPAD, respectively, and further affected gs, Pn and WUE. Synthesis. Our results emphasize the crucial role of water conditions in shaping the response of leaf photosynthesis to nitrogen (N) enrichment and also acknowledge the significance of leaf functional traits in regulating the dynamics of leaf photosynthetic processes.

Quantifying chlorophyll content index for efficient nitrogen management in rice (Oryza sativa L.)

Applying nitrogen fertilizer strategically enhances rice productivity in nitrogen-deficient soils. Splitting fertilizer doses across growth stages optimizes nutrient use efficiency. Monitoring chlorophyll levels correlates closely with leaf nitrogen content, crucial for assessing crop health. Non-destructive chlorophyll content meter techniques, such as the Chlorophyll Content Index (CCI), allow for precise nitrogen management adjustments in the field. However, its critical value may differ depending on crops and varieties. Hence, to find out the critical CCI value for rice cv. ADT 43, observational trials were conducted at the Experimental Farm of Annamalai University during two seasons Kuruvai (June-Oct 2023) and Navarai (Dec-March 2024). The pooled data of the present study indicated that the average LNC (maximum tillering, panicle initiation and booting stages) has a linear relationship (R2=0.8545) with the yield of rice crop. The corresponding higher yield ( greater than 7t/ha) coincided with LNC of 3.1%. Likewise, the average CCI has a linear relationship (R2=0.9031) with the LNC of rice plants. The corresponding LNC ( greater than 7t/ha) was matched with the CCI value of 29. This CCI value of 29 is considered as the critical CCI value. CCI can be used as a decision-support tool for N-fertilization of short duration rice crop, and when CCI reduced below this critical value the farmers can fertilise the field with adequate N.

Strangler fig–host tree associations: Insights into the ecology and management of tropical urban green spaces

Societal Impact StatementThe strangler fig is known for its hemiepiphytic growth form and conspicuous strangling behavior in the tropics worldwide. It also plays an important role in providing ecological functions in tropical urban ecosystems. This study reveals strangler figs tend to colonize large trees with suitable microsites in a large tropical botanical garden and cause some negative effects on their hosts. We advocate balanced management strategies considering ecological functions, potential risks, and overall values of stranglers and their hosts. These results provide a scientific basis for us to develop better practices for plant management in urban green spaces (especially botanical gardens with high plant biodiversity) in tropical urban ecosystems.Summary Strangler figs colonize trees in tropical cities, which contribute to a unique urban ecology and enrich local ecological functions. Understanding ecological associations between strangler figs and their host trees can improve green space management in tropical urban ecosystems. We investigated 9282 trees growing in the Xishuangbanna Tropical Botanical Garden and then analyzed the diversity, characteristics, and network of strangler figs and their host trees. We found 13 strangler fig species (319 individuals) widely colonized 67 host species, with palm hosts bearing 52% of all strangler individuals. Strangler figs had a high colonization rate in large trees with appropriate microsites (e.g., persistent palm petioles and the fork of mature trees with rough trunks). Leaf nitrogen and phosphorus content of hosts decreased significantly after strangler figs' aerial roots had entered the ground. The strangler–host network was characterized by relatively high specialization and low nestedness, and simulated management of strangler figs on large hosts and palm hosts could simplify the strangler–host network. Strangler fig colonization can be managed. Planting trees with large diameters at breast height and rough bark can increase the colonization of stranglers, while cutting off aerial roots can inhibit their establishment. The epiphytic stage is the best time to manage strangler figs. We recommend taking into consideration the trade‐offs among ecological functioning, human safety, and the multifaceted value of strangler figs and their host trees and thereby implementing comprehensive management strategies tailored to different contexts for improving green space management in the tropics.

Optimal agronomic measures combined with biochar increased rice yield through enhancing nitrogen use efficiency in soda saline-alkali fields

Soda saline-alkali stress severely hampers rice growth, nitrogen use efficiency, and yield formation. Biochar addition has been recognized as a potential solution to mitigate the adverse effects of saline-alkali stress on crops. Similarly, optimal agronomic measures are known to optimize crop growth conditions and enhance yield formation and resource utilization efficiency. Despite this knowledge, there is limited understanding of the combined effects of optimizing agronomic measures and biochar addition on ionic accumulation, nitrogen use efficiency, and rice yield in soda saline-alkali paddy fields. In this study, a 3-year field experiment was undertaken to evaluate the combined effects of optimal agronomic measures and biochar application on rice physiological properties, nitrogen use efficiency, and yield under five agronomic treatments: zero-fertilizer control (CK), farmers’ practice (FP), high-yield and high-efficiency management (OPT1), super-high-yield management (OPT2), high-yield and high-efficiency management combined with biochar (OPT1+B), and super-high-yield management combined with biochar (OPT2+B). The results demonstrate that treatments of optimal agronomic practices combined with biochar application (OPT2+B and OPT1+B) effectively reduced leaf Na+ concentration, Na+/K+ ratio, abscisic acid (ABA) concentration, and malondialdehyde (MDA) concentration, while enhancing leaf K+ concentration, improving leaf water status, and reducing relative electrical leakage over three years. Furthermore, these combined treatments positively influenced the enzyme activities of nitrate reductase (NR), glutamine synthetase (GS), glutamate synthase (GOGAT), and leaf nitrogen content (LN), as well as SPAD values. Additionally, the average nitrogen agronomic use efficiency (AEn) increased by 154.71 %, 109.81 %, 50.67 %, and 32.60 % in OPT1+B, OPT2+B, OPT1, and OPT2, respectively, compared to FP, while nitrogen physiological use efficiency (PEn) decreased by 64.03 %, 58.56 %, 29.46 %, and 21.81 %. The average grain yield (GY) increased by 311.42 %, 302.86 %, 196.57 %, 178.86 %, and 133.72 % in OPT2+B, OPT1+B, OPT2, and OPT1, respectively, compared to FP. AEn exhibited positive correlations with K+, leaf water status (LWS), NR, GS, GOGAT, LN, SPAD, and GY. These findings will offer new insights into the sustainable development and utilization of soda saline-alkali lands.

Optimal Irrigation and Fertilization Enhanced Tomato Yield and Water and Nitrogen Productivities by Increasing Rhizosphere Microbial Nitrogen Fixation

Irrigation and nitrogen application rates have significant effects on greenhouse tomato yields, as well as water and nitrogen use efficiencies, but little is known regarding how these rates affect plant–microbiome interactions and how the associated changes might impact tomato yields. In this greenhouse study conducted over two years, the effects of three irrigation levels (moderate deficit with 65–75% water holding capacity threshold, slight deficit with 75–85%, and sufficient irrigation with 85–95%) and four nitrogen application levels (60, 120, 240, and 360 kg ha−1) on tomato growth, yield, water and nitrogen productivities, and rhizosphere microbial diversities and functions were investigated. The results demonstrated that the highest tomato leaf area, dry biomass, yield, and water and nitrogen productivities were obtained under the treatment with sufficient irrigation. With increasing nitrogen application, the tomato leaf area, dry biomass, yield, and water and nitrogen productivities showed a trend of first increasing and then decreasing. Overall, the treatment (N2W3) with sufficient irrigation and 240 kg ha−1 N was associated with the highest tomato growth, yield, and water and nitrogen productivities. Moreover, optimal irrigation and nitrogen application obviously altered the structures of rhizosphere bacterial and fungal communities, particularly recruiting microbiota conferring benefits to tomato growth and nitrogen fixation—namely, Lysobacter and Bradyrhizobium. Ultimately, optimal irrigation and nitrogen application significantly increased the relative abundances of functions related to carbon, sulfur, and nitrogen metabolism, especially nitrogen fixation. In summary, optimal irrigation and fertilization enhanced tomato yield, as well as water and nitrogen productivities by increasing the nitrogen fixation functions of the rhizosphere microbiome. Our results provide significant implications for tomato cultivation in greenhouses, in terms of optimized irrigation and fertilization.

Global patterns of plant functional traits and their relationships to climate

Plant functional traits (FTs) determine growth, reproduction and survival strategies of plants adapted to their growth environment. Exploring global geographic patterns of FTs, their covariation and their relationships to climate are necessary steps towards better-founded predictions of how global environmental change will affect ecosystem composition. We compile an extensive global dataset for 16 FTs and characterise trait-trait and trait-climate relationships separately within non-woody, woody deciduous and woody evergreen plant groups, using multivariate analysis and generalised additive models (GAMs). Among the six major FTs considered, two dominant trait dimensions—representing plant size and the leaf economics spectrum (LES) respectively—are identified within all three groups. Size traits (plant height, diaspore mass) however are generally higher in warmer climates, while LES traits (leaf mass and nitrogen per area) are higher in drier climates. Larger leaves are associated principally with warmer winters in woody evergreens, but with wetter climates in non-woody plants. GAM-simulated global patterns for all 16 FTs explain up to three-quarters of global trait variation. Global maps obtained by upscaling GAMs are broadly in agreement with iNaturalist citizen-science FT data. This analysis contributes to the foundations for global trait-based ecosystem modelling by demonstrating universal relationships between FTs and climate.

Physiological Basis of Plant Growth Promotion in Rice by Rhizosphere and Endosphere Associated Streptomyces Isolates from India

This study demonstrated the plant growth-promoting capabilities of native actinobacterial strains obtained from different regions of the rice plant, including the rhizosphere (FT1, FTSA2, FB2, and FH7) and endosphere (EB6). We delved into the molecular mechanisms underlying the beneficial effects of these plant-microbe interactions by conducting a transcriptional analysis of a select group of key genes involved in phytohormone pathways. Through in vitro screening for various plant growth-promoting (PGP) traits, all tested isolates exhibited positive traits for indole-3-acetic acid synthesis and siderophore production, with FT1 being the sole producer of hydrogen cyanide (HCN). All isolates were identified as members of the Streptomyces genus through 16S rRNA amplification. In pot culture experiments, rice seeds inoculated with strains FB2 and FTSA2 exhibited significant increases in shoot dry mass by 7% and 34%, respectively, and total biomass by 8% and 30%, respectively. All strains led to increased leaf nitrogen levels, with FTSA2 demonstrating the highest increase (4.3%). On the contrary, strains FB2 and FT1 increased root length, root weight ratio, root volume, and root surface area, leading to higher root nitrogen content. All isolates, except for FB2, enhanced total chlorophyll and carotenoid levels. Additionally, qRT-PCR analysis supported these findings, revealing differential gene expression in auxin (OsAUX1, OsIAA1, OsYUCCA1, OsYUCCA3), gibberellin (OsGID1, OsGA20ox-1), and cytokinin (OsIPT3, OsIPT5) pathways in response to specific actinobacterial treatments. These actinobacterial strains, which enhance both aboveground and belowground crop characteristics, warrant further evaluation in field trials, either as individual strains or in consortia. This could lead to the development of commercial bioinoculants for use in integrated nutrient management practices.

An enhanced chlorophyll estimation model with a canopy structural trait in maize crops: Use of multi-spectral UAV images and machine learning algorithm

Leaf chlorophyll concentration (LCC) is a key indicator of leaf nitrogen (N) and changes in canopy structure, particularly the leaf area index (LAI), play a significant role in estimating LCC. Spectral prediction models for chlorophyll are a useful tool for timely nutritional management, particularly in precision agriculture. However, the accuracy of LCC estimation is influenced by the LAI. Considering LAI data as input in the spectral prediction model is still inadequate for improving LCC estimation. This study tested the hypothesis that LCC estimate accuracy could be enhanced by using LAI as an input using high-resolution (5 cm) multi-spectral images from an unmanned aerial vehicle (UAV). For this, maize was grown as a test crop under different nutrient management in the hilly ecosystem of Meghalaya. LCC was measured using laboratory destruction methods from ground sampling that coincided with UAV flights. Machine learning algorithms such as random forest (RF), support vector machine (SVM), and kernel ridge regression (KKR) were employed to develop the LCC estimation model, utilizing band reflectance, vegetation indexes, and measured chlorophyll. The model was assessed for its sensitivity to LCC estimation using LAI data. KKR outperformed other two algorithms (RF and SVM) in accuracy of LCC estimation by >11.0 to 19.0 %. The KKR-derived LCC estimation model was significantly improved by the inclusion of LAI (R2 increased from 0.785 to 0.928 and RMSE decreased from 0.065 to 0.053 mg g−1). The model's reliability was proven on multiple UAV flights for maize crops that are healthy and nutrient-stressed. Thus, LCC models derived from multispectral UAV images using KKR algorithms could benefit the adoption of precision agriculture at field scale in mountain ecosystems.

Determination of leaf nitrogen content in apple and jujube by near-infrared spectroscopy

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R2), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients.

Change in functional trait diversity mediates the effects of nutrient addition on grassland stability

Abstract Nutrient enrichment impacts grassland plant diversity such as species richness, functional trait composition and diversity, but whether and how these changes affect ecosystem stability in the face of increasing climate extremes remains largely unknown. We quantified the direct and diversity‐mediated effects of nutrient addition (by nitrogen, phosphorus, and potassium) on the stability of above‐ground biomass production in 10 long‐term grassland experimental sites. We measured five facets of stability as the temporal invariability, resistance during and recovery after extreme dry and wet growing seasons. Leaf traits (leaf carbon, nitrogen, phosphorus, potassium, and specific leaf area) were measured under ambient and nutrient addition conditions in the field and were used to construct the leaf economic spectrum (LES). We calculated functional trait composition and diversity of LES and of single leaf traits. We quantified the contribution of intraspecific trait shifts and species replacement to change in functional trait composition as responses to nutrient addition and its implications for ecosystem stability. Nutrient addition decreased functional trait diversity and drove grassland communities to the faster end of the LES primarily through intraspecific trait shifts, suggesting that intraspecific trait shifts should be included for accurately predicting ecosystem stability. Moreover, the change in functional trait diversity of the LES in turn influenced different facets of stability. That said, these diversity‐mediated effects were overall weak and/or overwhelmed by the direct effects of nutrient addition on stability. As a result, nutrient addition did not strongly impact any of the stability facets. These results were generally consistent using individual leaf traits but the dominant pathways differed. Importantly, major influencing pathways differed using average trait values extracted from global trait databases (e.g. TRY). Synthesis. Investigating changes in multiple facets of plant diversity and their impacts on multidimensional stability under global changes such as nutrient enrichment can improve our understanding of the processes and mechanisms maintaining ecosystem stability.

Leaf stomatal configuration and photosynthetic traits jointly affect leaf water use efficiency in forests along climate gradients.

Water use efficiency (WUE) represents the trade-off between carbon assimilation and water loss in plants. It remains unclear how leaf stomatal and photosynthetic traits regulate the spatial variation of leaf WUE in different natural forest ecosystems. We investigated 43 broad-leaf tree species spanning from cold-temperate to tropical forests in China. We quantified leaf WUE using leaf δ13C and measured stomatal traits, photosynthetic traits as well as maximum stomatal conductance ( ) and maximum carboxylation capacity ( ). We found that leaves in cold-temperate forests displayed 'fast' carbon economics, characterized by higher leaf nitrogen, Chl, specific leaf area, and , as an adaptation to the shorter growing season. However, these leaves exhibited 'slow' hydraulic traits, with larger but fewer stomata and similar , resulting in higher leaf WUE. By contrast, leaves in tropical forests had smaller and denser stomata, enabling swift response to heterogeneous light conditions. However, this stomatal configuration increased potential water loss, and coupled with their low photosynthetic capacity, led to lower WUE. Our findings contribute to understanding how plant photosynthetic and stomatal traits regulate carbon-water trade-offs across climatic gradients, advancing our ability to predict the impacts of climate changes on forest carbon and water cycles.