Leaf Nitrogen Research Articles

Crowd-sourced trait data can be used to delimit global biomes

Abstract. Terrestrial biomes and their biogeographic patterns have been derived from a large variety of variables including species distributions and bioclimate or remote sensing products. However, classifying the biosphere into biomes from a functional perspective using biophysical traits has rarely been tested. Such a trait-based biome classification has been limited by data availability. Here, we aimed to exploit crowd-sourced plant observations and trait databases to systematically assess which traits are most suitable for biome classification. We derived global patterns of 33 biophysical traits covering around 50 % of the land surface by combining crowd-sourced species distribution data from the Global Biodiversity Information Facility (GBIF) and trait observations from the TRY database. Using these trait maps as predictors for supervised cluster analyses, we tested to what extent we can reconstruct 31 published biome maps. A sensitivity analysis with randomly sampled combinations of traits was performed to identify the traits that are most appropriate for biome classification. Performance was quantified by comparing modeled biome maps and the respective observation-based biome maps. Finally, spatial gaps in the resulting biome maps were filled using species distribution models to obtain continuous global biome maps. We showed that traits can be used for biome classification and that the most appropriate traits are conduit density; rooting depth; height; and different leaf traits, including specific leaf area and leaf nitrogen content. The best performance of the biome classification was obtained for biome maps based on biogeographic zonation and species distributions, in contrast to biome maps derived from optical reflectance. The availability of crowd-sourced plant observations is heterogeneous, and, despite its exponential growth, large data gaps are prevalent. Nonetheless, it was possible to derive biome classification schemes from these data to predict global biome patterns with good agreement. Therefore, our analysis is a valuable approach towards understanding biome patterns based on biophysical traits and associated ecological strategies.

Morphological and Physiological Characteristics Influencing CO2 Assimilation Rate and Growth in Different Half-Sib Families of Quercus acuta and Q. glauca

Climate change alters vegetation patterns, pushing subtropical forests further north. These forests play a crucial role for carbon neutrality efforts due to their significant CO2 assimilation potential. This study investigated CO2 assimilation rate along with growth, morphological, and physiological traits in 23 half-sib families of Quercus acuta and 26 half-sib families of Q. glauca, two prominent East Asian evergreen broadleaf species. Q. acuta exhibited significantly higher growth rates, with diameter at breast height (DBH) and aboveground biomass exceeding those for Q. glauca by 12.1% and 69.9%, respectively (p < 0.001). Leaf traits, including leaf mass pear area (LMA), leaf nitrogen, and chlorophyll content, were also greater in Q. acuta, showing 24.5%, 45.8%, and 15.6% higher values (p < 0.001). While photosynthetic traits were similar, Q. acuta exhibited a 12.7% higher intrinsic water-use efficiency (iWUE) (p < 0.01). Among half-sib families, marginal differences were observed in growth traits (p < 0.1), and significant differences in leaf morphology and physiological traits (p < 0.05) were observed. A positive correlation was found between growth and physiological traits associated with the CO2 assimilation rate in Q. acuta, but not in Q. glauca. These findings highlight the potential of Q. acuta and Q. glauca for supporting future carbon neutrality efforts and provide traits supporting carbon uptake, valuable for selecting tree species with enhanced carbon sequestration potential.

Testing the contribution of vertebrate predators and leaf traits to mainland–island differences in insect herbivory on oaks

Abstract Ecological theory predicts that herbivory should be weaker on islands than on mainland based on the assumption that islands have lower herbivore abundance and diversity. However, empirical tests of this prediction are rare, especially for insect herbivores, and those few tests often fail to address the mechanisms behind island–mainland divergence in herbivory. In particular, past studies have not addressed the relative contribution of top‐down (i.e. predator‐driven) and bottom‐up (i.e. plant‐driven) factors to these dynamics. To address this, we experimentally excluded insectivorous vertebrate predators (e.g. birds, bats) and measured leaf traits associated with herbivory in 52 populations of 12 oak (Quercus) species in three island–mainland sites: The Channel Islands of California vs. mainland California, Balearic Islands vs. mainland Spain, and the island Bornholm vs. mainland Sweden (N = 204 trees). In each site, at the end of the growing season, we measured leaf damage by insect herbivores on control vs. predator‐excluded branches and measured leaf traits, namely: phenolic compounds, specific leaf area, and nitrogen and phosphorous content. In addition, we obtained climatic and soil data for island and mainland populations using global databases. Specifically, we tested for island–mainland differences in herbivory, and whether differences in vertebrate predator effects or leaf traits between islands and mainland contributed to explaining the observed herbivory patterns. Supporting predictions, herbivory was lower on islands than on mainland, but only in the case of Mediterranean sites (California and Spain). We found no evidence for vertebrate predator effects on herbivory on either islands or mainland in any study site. In addition, while insularity affected leaf traits in some of the study sites (Sweden‐Bornholm and California), these effects were seemingly unrelated to differences in herbivory. Synthesis. Our results suggest that vertebrate predation and the studied leaf traits did not contribute to island–mainland variation patterns in herbivory, calling for more nuanced and comprehensive investigations of predator and plant trait effects, including measurements of other plant traits and assessments of predation by different groups of natural enemies.

Acclimation of Photosynthesis to CO2 Increases Ecosystem Carbon Storage due to Leaf Nitrogen Savings.

Photosynthesis is the largest flux of carbon between the atmosphere and Earth's surface and is driven by enzymes that require nitrogen, namely, ribulose-1,5-bisphosphate (RuBisCO). Thus, photosynthesis is a key link between the terrestrial carbon and nitrogen cycle, and the representation of this link is critical for coupled carbon-nitrogen land surface models. Models and observations suggest that soil nitrogen availability can limit plant productivity increases under elevated CO2. Plants acclimate to elevated CO2 by downregulating RuBisCO and thus nitrogen in leaves, but this acclimation response is not currently included in land surface models. Acclimation of photosynthesis to CO2 can be simulated by the photosynthetic optimality theory in a way that matches observations. Here, we incorporated this theory into the land surface component of the Energy Exascale Earth System Model (ELM). We simulated land surface carbon and nitrogen processes under future elevated CO2 conditions to 2100 using the RCP8.5 high emission scenario. Our simulations showed that when photosynthetic acclimation is considered, photosynthesis increases under future conditions, but maximum RuBisCO carboxylation and thus photosynthetic nitrogen demand decline. We analyzed two simulations that differed as to whether the saved nitrogen could be used in other parts of the plant. The allocation of saved leaf nitrogen to other parts of the plant led to (1) a direct alleviation of plant nitrogen limitation through reduced leaf nitrogen requirements and (2) an indirect reduction in plant nitrogen limitation through an enhancement of root growth that led to increased plant nitrogen uptake. As a result, reallocation of saved leaf nitrogen increased ecosystem carbon stocks by 50.3% in 2100 as compared to a simulation without reallocation of saved leaf nitrogen. These results suggest that land surface models may overestimate future ecosystem nitrogen limitation if they do not incorporate leaf nitrogen savings resulting from photosynthetic acclimation to elevated CO2.

How to combine soil and plant indicators to manage nitrogen fertilisation in vineyards?

ContextAlthough grapevine nitrogen (N) needs are moderate, N fertilisation in vineyards requires carefully management because berry development, aromatic composition and hence winemaking are largely influenced by N nutrition. Our objective was to develop a method for reasoned N fertilisation between and during the seasons. MethodsSpecific sensitivities of soil and plant indicators to N supply and availability were estimated and indicator robustness was quantified using contrasted environmental conditions, crop management and N fertilisation in an experimental network comprising five vineyards in southern France over four successive years. The effects of four N fertilisation treatments combining contrasted amounts, forms and timing on the indicators were tested using linear models. Multiple factorial multiple analysis was used to study the effect of environmental and management factors on indicator sensitivity to mineral N fertilisation at budburst. ResultsYield, soil mineral nitrogen at budburst, chlorophyll concentration (SPAD) at veraison, leaf nitrogen content and yeast assimilable nitrogen (YAN) at harvest, were all sensitive to N fertilisation after four years. Different effects of the N treatments were observed from year three for SPAD and from year one for YAN. Additionally, SPAD and YAN indicators distinguished contrasted strategies based on different timings of N supply or the form of N fertiliser. Lastly, the response of the SPAD and YAN indicators was only slightly influenced by pedoclimatic conditions or cultural practices, at least for the variables tested here. ConclusionTwo N indicators measured from veraison (SPAD) to harvest (YAN), can provide valuable information for tactical and long-term fertilisation decisions. Notably, early SPAD information may improve technical vineyard management of interactions between fertilisation and other practices (e.g. weed control or tillage) that interfere with plant N nutrition and enable later correction of YAN values. Combining these indicators with non-destructive imaging techniques should improve N and mineral monitoring in general.

Plant community composition and traits modulate the impacts of drought intensity on soil microbial community composition and function

Terrestrial ecosystems are increasingly threatened by extreme drought events. Soil microbial communities are central to terrestrial ecosystem function via their role in regulating biogeochemical cycling. Consequently, the impact of increasingly intense drought events on soil microbial communities will have knock-on effects for how ecosystems cope with climate change. In an outdoor grassland mesocosm experiment, we determined how increasing drought intensity affects bacterial and fungal community composition, and functioning, during and after drought. We also tested whether plant community resource acquisition strategy (fast- versus slow-strategy plant communities), plant community composition, and plant functional traits mediate soil microbial responses to increasing drought intensity. We found that increasing drought intensity markedly shifted bacterial and fungal community composition, and these effects persisted until the end of the experiment (two months after re-wetting). Bacterial and fungal communities that experienced severe droughts did not return to baseline composition, while those that experienced a mild drought did. Microbial community functioning (potential extracellular enzyme activity) was reduced at peak drought and shortly after re-wetting. While drought intensity effects on bacterial or fungal communities were insensitive to plant community resource acquisition strategy, functional group abundance (aboveground biomass of grass or forb plant species) composition (grass:forb ratio) and leaf traits (leaf dry matter content and leaf nitrogen concentration) explained significant variation in bacterial and fungal community composition during and after drought. Notably, plant community leaf dry matter content and soil nitrogen were the key factors mediating the effect of increasing drought intensity on microbial indicator taxa (ASVs). We conclude that increasing drought intensity affects grassland soil microbial communities during and after drought, and this impact is influenced by plant community composition and functional traits.

Seagrass Tolerance to Simulated Herbivory Along a Latitudinal Gradient: Predicting the Potential Effects of Tropicalisation

ABSTRACTThe polewards range expansion of tropical herbivorous fish into temperate latitudes is leading to overgrazing of marine habitats and community phase shifts in some regions. Here, we test the potential effects of increased herbivory on the temperate habitat‐forming seagrass Posidonia australis. We used a series of simulated herbivory experiments to predict the potential impacts of climate‐mediated increases in seagrass consumption along P. australis entire latitudinal range (~9° latitude) in eastern Australia (1700 km of coastline). We subjected treatment plots to two levels of simulated herbivory (10% or 80% of leaves clipped) and compared them to unclipped controls. We measured seagrass leaf growth rates and tissue chemical traits: carbohydrates in rhizomes, leaf phenolics, and nutrients (carbon, nitrogen, and C:N ratio) in leaves and rhizomes. At the warmest range‐edge population, we also tested how responses to increased herbivory may vary between summer and winter, or with repeated clipping events. Clipped shoots maintained growth rates similar to unclipped controls despite losing up to 80% of leaf biomass. This was consistent along the full latitudinal range and after repeated simulated herbivory at the northernmost location. One‐off clipping events impacted plant architecture, increasing the number of subdividing shoots. At the species range edge, leaves grew more in winter than in summer, and clipping tended to lower seagrass growth only in winter; however, higher levels of shoot subdivision were produced over summer than in winter. Plant chemical traits could not explain consistently the growth patterns observed despite some traits varying with latitude (e.g., leaf nitrogen content decreased with latitude and C:N ratio increased) and/or simulated herbivory. Synthesis: P. australis growth is not affected by increases in simulated herbivory and may be relatively resilient to future increases in seagrass consumption, suggesting that this species could be a relative ‘winner’ under future climate change conditions that lead to enhanced herbivory.

Optimizing Irrigation and Nitrogen Application to Enhance Millet Yield, Improve Water and Nitrogen Use Efficiency and Reduce Inorganic Nitrogen Accumulation in Northeast China.

Enhancing irrigation and nitrogen fertilizer application has become a vital strategy for ensuring food security in the face of population growth and resource scarcity. A 2-year experiment was conducted to determine to investigate the effects of different irrigation lower limits and nitrogen fertilizer application amounts on millet growth, yield, water use efficiency (WUE), N utilization, and inorganic nitrogen accumulation in the soil in 2021 and 2022. The experiment was designed with four irrigation lower limits, corresponding to 50%, 60%, 70%, and 80% of the field capacity (FC), referred to as I50, I60, I70, and I80. Four nitrogen fertilizer application were also included: 0, 50, 100, and 150 kg·hm-2 (designated as F00, F50, F100, and F150), resulting in a total of 16 treatments. Binary quadratic regression equations were established to optimize the irrigation and nitrogen application. The results demonstrated that the plant height, stem diameter, leaf area index, aboveground biomass, yield, spike diameter, spike length, spike weight, WUE, and nitrogen agronomic efficiency for millet initially increased before subsequently decreasing as the irrigation lower limit and nitrogen fertilizer application increased. Their maximum values were observed in the I70F100. However, the nitrogen partial factor productivity (PFPN) exhibited a gradual decline with increasing nitrogen application, reaching its peak at F50. Additionally, PFPN displayed a pattern of initial increase followed by a decrease with rising irrigation lower limits. The accumulation of NO3--N and NH4+-N in the 0~60 cm soil layer increased with the increase of nitrogen fertilizer application in both years, while they tended to decrease as the irrigation lower limit increased. An optimal irrigation lower limit of 64% FC to 74% FC and nitrogen fertilizer application of 80 to 100 kg ha-1 was recommended for millet based on the regression equation. The findings of this study offer a theoretical foundation and technical guidance for developing a drip irrigation and fertilizer application for millet cultivation in Northeast China.

Prediction of Corn Leaf Nitrogen Content in a Tropical Region Using Vis-NIR-SWIR Spectroscopy

Traditional techniques for measuring leaf nitrogen content (LNC) involve slow and laborious processes, and radiometric data have been used to assist in the nutritional analysis of plants. Therefore, this study aimed to evaluate the performance of LNC predictions in corn plants based on laboratory hyperspectral Vis-NIR-SWIR data. The treatments corresponded to 60, 120, 180, and 240 kg ha−1 of nitrogen, in addition to the control (0 kg ha−1), and they were distributed using a randomized complete block design. At the laboratory, hyperspectral data of the leaves and LNC were obtained. The hyperspectral data were used in the calculation of different vegetation indices (VIs), which were applied in a predictive model—partial least squares regression (PLSR)—and the capacity of the prediction was assessed. The combination of bands and VIs generated a better prediction (0.74 < R2 < 0.87; 1.00 < RMSE < 1.50 kg ha−1) in comparison with the individual prediction by band (0.69 < R2 < 0.85; 1.00 < RMSE < 1.77 kg ha−1) and by VI (0.55 < R2 < 0.68; 1.00 < RMSE < 1.78 kg ha−1). Hyperspectral data offer a new opportunity to monitor the LNC in corn plants, especially in the region comprising the bands from 450 to 750 nm, since these were the bands that were most sensitive to the LNC.

Mechanized Transplanting Improves Yield and Reduces Pyricularia oryzae Incidence of Paddies in Calasparra Rice of Origin in Spain

The rice variety Bomba is grown in Calasparra—a rice of origin in southeast Spain—resulting in a product with excellent cooking quality, although its profitability has declined in recent years due to low grain yields and susceptibility to rice blast disease (Pyricularia oryzae Cavara). An innovation project to test the efficacy of mechanized transplanting against traditional direct seed sowing was conducted in 2022 and 2023 at four locations for the first time. A lower plant density (67–82 plants m−2) and shorter plants with higher leaf nitrogen content were observed in transplanted plots compared with seed sowing (130–137 plants m−2) in the first year. The optimal climatic conditions for P. oryzae symptom appearance were determined as temperatures of 25–29 °C and a 50–77% relative humidity. The most-affected sowing plots presented 3–20% leaf area damage and a reduction in yield to values of 1.5 t ha−1 in the first year and 2.12 t ha−1 in the second year. In transplanted plots, there was generally less humidity at the plant level and therefore, disease incidence was low in both seasons. Grain yields did not significantly differ among the treatments studied; however, there were differences in the yield components of panicle density and the number of grains for panicles. Principal component analysis revealed two principal components that explained 81% of the variability. Variables related to yield contributed positively to the first component, while plant biomass variables contributed to the second component. Plant density, tiller density, and panicle density were found to be positively correlated (r > 0.81 ***). Overall, transplanting (frame of 30 × 15–18 cm2) resulted in uniform crop growth with less rice blast disease, as well as higher grain yields (2.92–3.89 t ha−1), in comparison with the average for the whole D.O. Calasparra (2.3–2.5 t ha−1) in both seasons and a good percentage of whole grains at milling. This is novel knowledge which can be considered useful for farmers operating in the region.

Leaf nutrient basis for the differentiation of photosynthetic traits between subtropical evergreen and deciduous trees.

Compared to evergreens, deciduous tree species usually have higher photosynthetic efficiency to complete vegetative and reproductive growth in a shorter growing season. However, the nutrient basis for the differentiation of photosynthesis functional traits between evergreen and deciduous tree species has not yet been clarified. Thirty evergreen and twenty deciduous angiosperm tree species from a subtropical common garden were compared in terms of photosynthetic traits and leaf nutrients. Generally, their differences in area-based photosynthetic capacity were uncorrelated with area-based leaf nutrient content but were caused by the fraction of nitrogen allocated to photosynthetic components. By comparison, the differences in mass-based photosynthetic capacity were more correlated with leaf nitrogen content than leaf phosphorus and potassium content. Convergence in phosphorus and potassium constraints to photosynthesis occurred in deciduous tree species but not in evergreen tree species. Furthermore, leaf C/N ratio played a more significant role than leaf mass per area in determining the differentiation of photosynthetic traits between evergreen and deciduous groups. Our findings provide insight into the nutrient basis for photosynthetic carbon gain and functional strategies across trees species.

Optimizing Growth of Melon (Cucumis melo L. cv. Madesta) in Nutrient Film Technique and Drip Irrigation Hydroponics with Varied Substrates

Hydroponic systems offer a promising solution for urban farming and the utilization of unproductive land. Successful implementation, however, requires careful optimization to select the most effective hydroponic system tailored to specific plants and environmental conditions. This study aims to compare the growth and physiological responses of Madesta melons (Cucumis melo L. cv. Madesta) cultivated using the nutrient film technique (NFT) and drip irrigation system (DIS) with variations in growth media. The Madesta melon seeds underwent a two-week germination phase in coco peat media, followed by transplanting into NFT and DIS setups utilizing diverse growth media, including rice husk, rice husk mixed with compost, and compost only. Over four weeks post-cultivation, assessments were conducted on key growth metrics such as leaf count, leaf diameter, plant height, and stem diameter. Plant physiological responses were also analyzed, encompassing chlorophyll and nitrogen levels, along with the mineral composition within leaves and fruits. Results revealed that the DIS cultivation outperformed the NFT in terms of growth outcomes. Among the varied media combinations, the rice husk and compost blend supported growth most effectively. Notably, no significant differences were observed in leaf and fruit nitrogen content between the DIS and NFT systems, and the overall mineral content of the media remained relatively stable before and after the cultivation period. Mineral content analysis revealed calcium as the predominant element in the leaves, while potassium emerged as the most abundant mineral in the fruits. This research sheds light on the potential of hydroponic systems, specifically the DIS method, for enhancing melon cultivation, emphasizing the importance of selecting appropriate growth media to maximize plant growth.

Unraveling key environmental drivers of spatial variation in plant functional traits: Insights from Dacrydium pectinatum de Laub. in natural communities on Hainan Island, China

Accurate predictions of species distribution and coexistence in response to environmental changes depend on a thorough understanding of how functional traits relate to environmental conditions. However, there is still limited evidence regarding the sources of functional trait variation and the environmental mechanisms that regulate these traits in tropical forests. This study focuses on Dacrydium pectinatum de Laub., a crucial and endangered species native to the tropical mountain forests of Hainan Island, China. We gathered functional trait data from 68 permanent plots of D. pectinatum situated in three regions: Bawangling (163 tree species), Diaoluoshan (127 tree species), and Jianfengling (175 tree species). Nine functional traits were measured at the species level, and their variation patterns and sources were analyzed using linear mixed-effects models and variance decomposition methods. Our findings reveal that environmental pressures cause variations in trait relationships across different site conditions at the species level. Typically, inter-specific trait variation exceeds intra-specific variation, particularly for leaf area (LA) and specific leaf area (SLA). This indicates that trait variability is influenced by how species adapt to and balance specific environmental conditions. Site conditions significantly impact trait variation, especially for SLA, leaf dry matter content (LDMC), and wood density (WD). Additionally, intraspecific variation is notable for certain traits, reflecting individual responses to environmental heterogeneity. At the community level, eight out of nine traits exhibit significant changes in community-weighted mean (CWM) across various site conditions and environmental gradients. For instance, LA's CWM increases with soil fertility, whereas SLA, leaf nitrogen content (LNC), and leaf phosphorus content (LPC) show differences between site conditions. LDMC and WD decrease significantly in high-fertility environments, suggesting a transition from resource conservation to resource acquisition strategies within communities as soil fertility rises. Geographic variables play a crucial role in trait distributions across different community levels, with elevation and soil factors also contributing significantly to trait variation. Overall, our study provides direct evidence of how environmental filtering influences plant functional trait distributions and community assembly. Future research should explore the mechanisms driving these trait-environment relationships and their responses across various ecological contexts.

Identifying optimal ground feature classification and assessing leaf nitrogen status based on UAV multispectral images in an apple orchard

Identifying optimal ground feature classification and assessing leaf nitrogen status based on UAV multispectral images in an apple orchard

Nutrient Stoichiometry and Tree Development: Insights from a 5-Year Study on Catalpa bungei Fertilization

Short-term fertilization may provide limited improvements in tree growth and demonstrate suboptimal fertilizer efficiency; however, its benefits often fall short of expectations. Unfortunately, research addressing the sustained impacts of prolonged fertilization (e.g., beyond five years) on trees’ developmental dynamics and productivity remains relatively scarce. This study focused on a 7-year-old Catalpa bungei plantation located in Jinan City, Shandong Province, China. The study employed two fertilization techniques: hole fertilization (HF) and integrated water and fertilizer application (WF), with a no-fertilization treatment serving as the control (CK). The findings revealed that the WF significantly enhanced stand productivity. When comparing the different treatments, the productivity of WF stands demonstrated a remarkable increase of 39.7% compared to HF stands and 55.1% compared to CK stands. After five years of fertilization, the stands treated with WF exhibited a significant increase in volume accumulation, reaching 112.36 m3·hm−2. Additionally, the productivity of these WF-fertilized stands achieved an impressive 41.75 m3·hm−2·a−1. Fertilization notably enhanced the nitrogen content in the leaves and fine roots of C. bungei, as well as the potassium content in the coarse roots. These nutrients were found to be more concentrated in the corresponding organs within the WF stands. Over the entire growth cycle, there was a substantial consumption of key nutrients, with leaf nitrogen, phosphorus, and potassium contents decreasing by 30.5%, 18.8%, and 47.3%, respectively. Similarly, the coarse root potassium and fine root phosphorus content decreased by 24.7% and 24.4%, respectively. The enhancement in leaf nitrogen content following fertilization significantly contributed to increases in tree height, breast height diameter (DBH), and individual tree volume. Similarly, the enrichment of potassium in the branches and coarse roots was associated with improvements in DBH and tree volume. To maximize forest stand productivity, the WF fertilization method demonstrated superior results compared to HF. Therefore, WF should be prioritized in future fertilization experiments for C. bungei.

Inoculation with Azospirillum brasilense as a Strategy to Reduce Nitrogen Fertilization in Cultivating Purple Maize (Zea mays L.) in the Inter-Andean Valleys of Peru.

Purple maize has gained global significance due to its numerous nutraceutical benefits. However, sustaining its production typically requires high doses of nitrogen fertilizers, which, when applied in excess, can contaminate vital resources such as soil and water. Inoculation with nitrogen-fixing microorganisms, such as those from the Azospirillum genus, has emerged as an alternative to partially or fully replace nitrogen fertilizers. This study aimed to evaluate the inoculation effect with A. brasilense and varying nitrogen fertilization levels on the yield and quality of purple maize. The experiment was carried out using a randomized complete block design (RCBD) with a 2 × 5 factorial arrangement and five replications. Treatments comprised two inoculation levels (control without inoculation and inoculation with A. brasilense) under five nitrogen doses (0, 30, 60, 90, and 120 kg∙ha-1, applied as urea). Inoculation with A. brasilense resulted in a 10.5% increase in plant height, a 16.7% increase in root length, a 21.3% increase in aboveground fresh biomass, a 30.1% increase in root fresh biomass, and a 27.7% increase in leaf nitrogen concentration compared to the non-inoculated control. Regarding yield, the inoculated plants surpassed the control in both purple maize yield (kg∙ha-1) and cob weight by 21.8% and 11.6%, respectively. Across all fertilization levels and parameters assessed, the inoculated treatments outperformed the control. Furthermore, for parameters, namely plant height, leaf nitrogen content, and cob dimensions (length, diameter, and weight), the A. brasilense inoculation treatment with 90 kg N∙ha-1 was statistically equivalent or superior to the non-inoculated control with 120 kg N∙ha-1. These results indicate that inoculation with A. brasilense positively impacted purple maize at all nitrogen levels tested and improved nitrogen use efficiency, enabling a reduction of 30 kg N∙ha-1 without compromising performance in key parameters.

Foliar Nourishment with Different Potassium Sources to Maximize Yield Through Improving Nutrient Uptake in Citrus Aurantifolia Trees Grown in Potassium-Deficient Soil

AbstractPurpose: Based on its crucial regulatory role in several biochemical processes, potassium (K) is considered to greatly influence fruit yield and quality. Methods: Two field experiments were carried out in two seasons (2021 and 2022) to explore the response of lemon (Baladi cv.) trees grown in K-deficient soil to four different K fertilizer sources applied individually as a foliar spray. K citrate (KC1 = 1.3 vs. KC2 = 2.6 g L− 1), K nitrate (KN1 = 1.5 vs. KN2 = 3.0 g L− 1), K tartrate (KT1 = 1.5 vs. KT2 = 3.0 g L− 1), and K thiosulfate (KS1 = 1.25 vs. KS2 = 2.5 g L− 1) were applied three times, and the treated trees were compared with untreated trees. This study was established with a randomized complete block design (RCBD) using four replicates. Results: The findings revealed that the KC treatment, regardless of dosage, showed clear superiority in terms of the values for the leaf phosphorus (%), manganese, and zinc contents (mg kg− 1), as well as fruit firmness, fruit dry matter, and total soluble solids in both seasons. In second place was the KT treatment, which yielded the best values for fruit length and width in 2021, the best values for leaf nitrogen and iron contents and fruit weight and volume in 2022, and the best values regarding variable fluorescence by maximum fluorescence, relative chlorophyll content (SPAD readings), the photosynthetic performance index, total acidity, and vitamin C in both seasons. Furthermore, the highest total lemon yield and leaf potassium contents were achieved with the KN treatment in both seasons. The heat map illustrating that most of the studied characteristics were positively and significantly correlated with total lemon yield (TLY). Conclusions: The KN treatment, closely followed by the KC treatment, is regarded to be the best treatment for most of the studied trails.

Implications of soil waterlogging for crop quality: A meta-analysis

Soil waterlogging in many arable regions of the world challenge the quantum and quality of crop production. While previous studies have assessed the impact of waterlogging on crop yields, understanding of how waterlogging implicates with crop quality remains in its infancy. Here, we conduct a systematic literature review and meta-analysis to assess how waterlogging influences grain quality. We also explore the role of engineering and agronomic strategies for alleviating adverse effects of waterlogging. We reveal that soil waterlogging has less impacts on grain quality than on yield; the latter decreasing by an average of 23 %, while average grain protein and starch content of waterlogged crops reduced by 6.7 % and 7.3 %, respectively. We attribute these differences to underlying mechanics of yield and grain quality formation, as well as biological processes conferring adaptation, plasticity and recovery. Reduced grain quality under waterlogging is associated with decreased activity of enzymes involved in leaf nitrogen and carbon metabolism. Unlike yields however, grain quality suffers less deterioration with prolonged waterlogging, with ultimate effects realized being a function of species-specific tolerance, timing and duration of waterlogging relative to crop stage, soil type and growing season weather. We underscore the potential in engineering and/or agronomic interventions for alleviating detrimental effects of waterlogging.

Advancing lettuce physiological state recognition in IoT aeroponic systems: A meta-learning-driven data fusion approach

Automatically identifying key physiological factors in plants, such as leaf relative humidity (LRH), chlorophyll content (Chl), and nitrogen levels (N), is vital for effective aeroponic management and improving growth, yield, quality, and sustainability. Meta-learning (MetaL) solutions utilize data fusion and intelligent processing, ensuring fast and consistent outcomes. This paper aims to develop a novel MetaL framework that leverages multimodal data sources—including spectral, thermal, and IoT environmental data—to enable real-time, non-invasive identification of LRH, Chl, and N content in aeroponically grown lettuce. The research examined various spectral reflectance indices (SRIs) and thermal indicators from plant characteristics. Model-based feature selection was implemented using back-propagation neural networks (BPNN), decision trees (DT), and gradient boosting machines (GBM) to identify key attributes and optimize hyperparameters. The experimental findings indicated that deploying GBM-based top variables as the foundational model, combined with BPNN as the meta-model, significantly improved the accuracy of analyzing the assigned factors. The prediction scores (R²) for LRH, Chl, and N increased to 0.875 (RMSE=0.879), 0.886 (RMSE=0.694), and 0.930 (RMSE=0.184), respectively, compared to applying BPNN-based features alone as a standalone model. Overall, the designed methodology contributes to more accurate predictions of plant physiological states, enabling proactive steps toward sustainable aeroponic agriculture.

Effect of Paenibacillus favisporus CHP14 inoculation on selenium accumulation and tolerance of Pakchoi (Brassica chinensis L.) under exogenous selenite treatments

The effects of Paenibacillus favisporus CHP14 inoculation on selenium (Se) accumulation and Se tolerance of Pakchoi were studied by a pot experiment conducted in greenhouse. The results revealed that the growth traits such as plant height, root length, and biomass were significantly elevated during CHP14 treatment at 0 ∼ 8.0 mg·kg−1 Se(IV) levels. CHP14-inoculated plants accumulated more Se in root and shoot, which were 24.1%∼57.3% and 7.5%∼50.9% higher than those of non-inoculated plants. The contents of leaf nitrogen (N), phosphorus (P), magnesium (Mg), and iron (Fe), as well as the ratio of indoleacetic acid and abscisic acid contents (IAA/ABA) were increased by CHP14 inoculation, and positively associated with photosynthetic pigment contents (p < 0.05). At ≥ 4.0 mg·kg−1 Se(IV) levels, superoxide dismutase, peroxidase, and glutathione peroxidase activities of Pakchoi roots were increased with CHP14 inoculation, by 9.9%∼17.1%, 28.4%∼40.7%, and 7.4%∼15.3%, respectively. Moreover, CHP14 inoculation enhanced ascorbate-glutathione (AsA-GSH) metabolism in roots by upregulating the related enzymes activities and antioxidant contents under excess Se(IV) stress. These findings suggest that CHP14 is beneficial to improve plant growth and enhance Se(IV) resistance of Pakchoi, and can be exploited as potential inoculants for phytoremediation process in Se contaminated soil.