Search Results (1,612)

Hydroponic systems can drain nutrient-rich waste into the environment. Increasing irrigation efficiency would decrease effluent and improve cost efficiency for growers. However, current methods accessible to small- and mid-sized growers to determine moisture content in growth media are often imprecise. Simplified transpiration models could inform irrigation needs. This study aimed to improve transpiration estimates using vapor pressure deficit (VPD) and solar radiation. We compared our model to an existing transpiration model. Three years of transpiration and environmental data from tomato production were used to calibrate (year 2) and validate (years 1 and 3) the model. Randomly chosen subsets from all years of data were also used. The new model (TVPD) predicted the observed values more closely than the previous model (PG) in year 1 (TVPD: RMSE = 0.1570 mm, r² = 0.95; PG: RMSE = 0.5594 to 0.6875 mm, r² = 0.27 to 0.78) but not in year 3 (TVPD: RMSE = 0.5430 mm, r² = 0.44; PG: RMSE = 0.1873 to 0.2065 mm, r² = 0.95). TVPD calibrated using random subsets of the combined data improved consistency and predictive capacity (RMSE = 0.2387 to 0.2419 mm, r² = 0.87 to 0.91). TVPD is a simpler alternative to complex models and to those focusing on solar radiation alone. TVPD is less reliable under low solar radiation (year 3); however, reliability could be improved by calibration across a broader environmental range. TVPD also allows for exploration of the relative influences of low VPD and high solar radiation on evapotranspiration found in greenhouse settings. Full article

Rapid and green detection of soil nutrients is essential for soil fertility and plant growth. However, traditional methods cannot meet the needs of rapid detection, and the reagents easily cause environmental pollution. Hence, we proposed a multivariable output weighting-network (MW-Net) combined with laser-induced breakdown spectroscopy (LIBS) to achieve rapid and green detection for three soil nutrients. For a better spectral signal-to-background ratio (SBR), the two important parameters of delay time and gate width were optimized. Then, the spectral noise was removed by the near-zero standard deviation method. Three common quantitative models were investigated for single-element prediction, which are usually applied in LIBS analysis. Also, multi-element prediction was investigated using MW-Net. The results showed that MW-Net outperformed other models generally with very good quantification for soil total N and K (the determination coefficients in the prediction set (R_p²) of 0.75 and 0.83 and the relative percent difference in the prediction sets (RPD) of 2.05 and 2.43) and excellent indirect determination for soil exchangeable Ca (R_p² of 0.93 and RPD of 3.91). Finally, the interpretability was realized through feature extraction from MW-Net, indicating its design rationality. The preliminary results indicated that MW-Net combined with LIBS technology could quantify the three soil nutrients simultaneously, improving the detection efficiency, and it could possibly be deployed on a LIBS portable instrument in the future for precision agriculture. Full article

3D bioprinting has emerged as a key tool in tissue engineering by facilitating the creation of customized scaffolds with properties tailored to specific needs. Among the design parameters, porosity stands out as a determining factor, as it directly influences critical mechanical and biological properties such as nutrient diffusion, cell adhesion and structural integrity. This review comprehensively analyses the state of the art in scaffold design, emphasizing how porosity-related parameters such as pore size, geometry, distribution and interconnectivity affect cellular behavior and mechanical performance. It also addresses advances in manufacturing methods, such as additive manufacturing and computer-aided design (CAD), which allow the development of scaffolds with hierarchical structures and controlled porosity. In addition, the use of computational modelling, in particular finite element analysis (FEA), as an essential predictive tool to optimize the design of scaffolds under physiological conditions is highlighted. This narrative review analyzed 112 core articles retrieved primarily from Scopus (2014–2025) to provide a comprehensive and up-to-date synthesis. Despite recent progress, significant challenges persist, including the lack of standardized methodologies for characterizing and comparing porosity parameters across different studies. This review identifies these gaps and suggests future research directions, such as the development of unified characterization and classification systems and the enhancement of nanoscale resolution in bioprinting technologies. By integrating structural design with biological functionality, this review underscores the transformative potential of porosity research applied to 3D bioprinting, positioning it as a key strategy to meet current clinical needs in tissue engineering. Full article

This study illustrates the complex interaction between environmental parameters and carbonate distribution in marine sediments along the Tarfaya–Boujdour coastline (26–28° N) of Northwest Africa. Analysis of 21 surface sediment samples and their associated bottom water properties (salinity, temperature, dissolved oxygen, nutrients) reveals CaCO₃ content ranging from 16.8 wt.% to 60.5 wt.%, with concentrations above 45 wt.% occurring in multiple stations, especially in nearshore deposits. Mineralogy indicates a general decrease in quartz, with an arithmetic mean and standard deviation of 52.5 wt.% ± 19.8 towards the open sea, and an increase in carbonate minerals (calcite ≤ 24%, aragonite ≤ 10%) with depth. Sediments are predominantly composed of fine sand (78–99%), poorly classified, with gravel content reaching 6.7% in energetic coastal stations. An inverse relationship between organic carbon (0.63–3.23 wt.%) and carbonates is observed in upwelling zones, correlated with nitrate concentrations exceeding 19 μmol/L. Hydrological gradients show temperatures from 12.41 °C (offshore) to 21.62 °C (inshore), salinity from 35.64 to 36.81 psu and dissolved oxygen from 2.06 to 4.21 mL/L. The weak correlation between carbonates and depth (r = 0.10) reflects the balance between three processes: biogenic production stimulated by upwelling, dilution by Saharan terrigenous inputs, and hydrodynamic sorting redistributing bioclasts. These results underline the need for models integrating hydrology, mineralogy and hydrodynamics to predict carbonate dynamics in desert margins under upwelling. Full article

Pyruvate kinase (PK) is a key enzyme in glycolysis that regulates sugar metabolism and energy production, thereby influencing fruit quality. The ‘Feizixiao’ litchi, widely cultivated in Hainan Province, faces sugar reduction during fruit ripening. This study evaluated the effects of the foliar application of calcium and magnesium (Ca+Mg) during the fruit expansion stage to alleviate this problem. Ca+Mg foliar application significantly enhanced soluble sugar content, promoted peel coloration, and reduced respiration and PK activity. Genome-wide analysis identified 19 PK genes (LcPKs) exhibiting diverse exon-intron structures and conserved motifs. Phylogenetic analysis revealed both conserved and species-specific features, while subcellular localization predicted that most LcPK proteins are likely to be localized in the cytoplasm. Synteny analysis showed closer evolutionary relationships with species in the same genus than with Arabidopsis. Cis-regulatory element analysis implicated LcPKs in light response, hormone signaling, growth, and stress adaptation. Hormonal assays at 63 and 70 DAA after treatment revealed increased abscisic acid (ABA) and ethylene levels under Ca+Mg application. These hormonal changes correlated with the downregulation of LcPK3, LcPK4, LcPK5, LcPK8, and LcPK15, as confirmed by qRT-PCR, indicating negative regulation by ABA and ethylene. This regulatory mechanism likely contributes to overcoming sugar receding in litchi pulp. These findings offer insights into the regulation of sugar metabolism and strategies for enhancing fruit quality through the management of genes and nutrients. Full article

The high-elevation ecosystems of the Tibetan Plateau provide crucial ecosystem services including watershed protection and water provision for downstream human and wildlife communities. Thus, understanding the relationship between soil properties and vegetation under different management regimes is important as a warming climate alters these systems. This study assessed vegetation cover, quantified the distribution of soil nutrients, and examined the relationships among soil chemical properties and plant cover in the high-elevation shrublands (3300 to 3700 m) in the Qilian Mountains on the northeastern Tibetan Plateau of China. These vegetation surveys and soil sample collections were conducted on 15 shrubland plots at different soil depths and soil chemical properties were investigated at each elevation. The content of soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) fluctuated along the elevational gradient, while soil pH was close to neutral (pH 7.4). At our sites, SOM and TN contents generally increased with elevation, and AK was positively correlated with Salix plant cover. Using PCA, we determined that PC1 captured 43% of the total variance, and SOM and TN were the top contributing features. As climate in the region warms and precipitation becomes more variable, understanding the current soil–vegetation equilibria and how vegetation may migrate in future years is important to predicting changes in this region, especially at high elevations. From a managerial perspective, our goal was to provide additional information for restoring and managing subalpine and alpine shrubland vegetation in the Qilian Mountains to ensure the future sustainable use of these systems. Full article

Rhizosphere microbiota are key drivers of plant nutrition, immunity, and stress tolerance. Abies ziyuanensis L. K. Fu & S. L. Mo (Pinaceae) is an endangered conifer endemic to China, and its persistence may depend on its interactions with its belowground microbiome. However, how soil-borne bacterial functional groups respond to, and potentially support, A. ziyuanensis remains unclear. Based on amplicon high-throughput sequencing data of the 16S rRNA gene and soil physicochemical properties, the bacterial community structure in the rhizosphere soil of A. ziyuanensis in Yinzhu Laoshan National Nature Reserve in Guangxi Zhuang Autonomous Region, China, was analyzed, and the potential ecological functions and phenotypic characteristics of the bacterial community were predicted to determine the functional taxa characteristics (nitrogen cycle, phototrophy, and chemoheterotrophy) and dominant soil environmental factors. Proteobacteria, Acidobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia, and Chloroflexi were the dominant bacterial taxa in the A. ziyuanensis rhizosphere soil, and all bacteria were significantly positively correlated with soil NO₃⁻-N (R = 0.47, p = 0.0079). Based on FAPROTAX, the A. ziyuanensis rhizosphere soil bacterial community had chemoheterotrophic-related functions, which were more prevalent than nitrogen cycle- and phototrophic-related functions, and the relative abundance of bacteria with nitrogen cycle-related functions was higher than that of those with phototrophic functions. The nitrogen nutrient- and phototrophic-related functional taxa in the rhizosphere soil bacterial community had significant correlations with soil physicochemical properties, whereas the chemoheterotrophic-related functional taxa did not show a significant correlation. Based on BugBase phenotype prediction, Acidobacteria, Proteobacteria, and Chloroflexi made the greatest contribution to the phenotype, with pathogenic and stress tolerance being the most important phenotypes. The pathogenic and stress-tolerant bacteria all belonged to Proteobacteria. The rhizosphere bacteria exhibited rich diversity and dominated several biogeochemical cycling processes. This study identifies beneficial rhizosphere bacteria of A. ziyuanensis, providing a theoretical basis for conserving soil bacterial diversity and guiding the targeted recruitment of functional bacteria by the endangered plant. Full article

Leaf phenology is a crucial functional trait in temperate forest trees that integrates environmental signals and reflects species’ adaptive capacity to stress. This study examined how moderate drought and elevated phosphorus availability, alone and in combination, affect the spring and autumn phenology of juvenile Fagus sylvatica and Quercus petraea saplings from two climatically distinct Croatian provenances. In a common garden experiment, saplings were subjected to four treatments involving drought and phosphorus addition. Phenological stages were scored using standardized ordinal scales across two growing seasons. Results revealed that phosphorus consistently advanced autumn leaf senescence in both species, independent of drought, while drought effects were species- and provenance-specific. Spring phenology was more sensitive to drought: beech from the drier provenance advanced budburst, suggesting an escape strategy, whereas oak delayed leaf-out under the same conditions. Notably, combined drought and phosphorus treatments often neutralized individual effects, indicating physiological compensation. Provenance-level differences highlighted contrasting strategies—phenotypic plasticity versus stress tolerance—under multi-stressor conditions. These findings underscore the dominant role of phosphorus in regulating phenology and the complex, non-additive nature of drought–nutrient interactions, emphasizing the need for integrative approaches in predicting phenological responses under climate change. Full article

Rice (Oryza sativa), a crucial global staple, grapples with environmental stress and resource constraints, necessitating sustainable farming. This review explores the transformative role of transcription factors (TFs) in revolutionizing rice agriculture and their potential impact on global food security. It underscores TFs’ pivotal role in gene expression, particularly in responding to environmental stimuli, presenting a promising avenue for enhancing rice resilience. Delving into key TF families in rice, it highlights their multifaceted roles in abiotic stress responses, defense mechanisms, yield improvement, nutrient uptake, seed development, photosynthesis, and flowering regulation. Specific TFs, including DREB (Dehydration-Responsive Element-Binding), WRKY, NAC, MYB (Myeloblastosis), AP2/ERF (APETALA2/Ethylene Responsive Factor), and bHLH (basic Helix–Loop–Helix), are examined for their contributions to stress resilience, defense mechanisms, and yield enhancement. Concrete examples from cutting-edge research illustrate the tangible benefits of harnessing these molecular regulators. However, manipulating TFs presents challenges, necessitating innovative approaches such as predictive models, collaborative field testing, and transparent communication to navigate intricate regulatory networks and regulatory hurdles. Ultimately, a promising future emerges where manipulating rice TFs leads to the development of resilient, high-yielding, and nutritious varieties. Embracing research advancements and addressing existing challenges is imperative to unlock the full potential of these concealed regulators, ensuring sustainable food security for a growing global population. Full article

Arid and semi-arid regions show distinctive bacterial groups important for the sustainability of ecosystems and soil health. This study aims to investigate how environmental factors across five Qatari soils influence the taxonomic composition of bacterial communities and their predicted functional roles using 16S rRNA amplicon sequencing and soil chemical analysis. Soil samples from five different locations in Qatar (three coastal and two inland) identified 26 bacterial phyla, which were dominated by Actinomycetota (35–43%), Pseudomonadota (12–16%), and Acidobacteriota (4–13%). Species-level analysis discovered taxa such as Rubrobacter tropicus, Longimicrobium terrae, Gaiella occulta, Kallotenue papyrolyticum, and Sphingomonas jaspsi, suggesting the presence of possible novel microbial families. The functional predictions showed development in pathways related to amino acid metabolism, carbohydrate metabolism, and stress tolerance. In addition, heavy-metal-related taxa, which are known to harbor genes for metal resistance mechanisms including efflux pumps, metal chelation, and oxidative stress tolerance. The presence of Streptomyces, Pseudomonas, and Bacillus highlights their roles in stress tolerance, biodegradation, and metabolite production. These findings improve the understanding of microbial roles in dry soils, especially in nutrient cycling and ecosystem resilience. They highlight the importance of local bacteria for sustaining desert soil functions. Further research is needed to validate these relationships, using metabolomic approaches while monitoring microbial-community-changing aspects under fluctuating environmental conditions. Full article

Antheraea pernyi (Lepidoptera: Saturniidae) is an economically important silk-producing insect, whose gut microbiota play a crucial role in growth, development, and nutrient metabolism. This study focused on the entire larval developmental stages of A. pernyi. Using the Illumina MiSeq high-throughput sequencing platform, we performed 16S rRNA gene amplicon sequencing on the gut microbiota of laboratory-reared A. pernyi larvae, analyzing in detail the composition and diversity characteristics of the gut microbial communities across all five instars (1st to 5th instar). Additionally, functional predictions were conducted to explore the potential roles of these microbiota during larvae development. The study revealed that the core gut microbiota of A. pernyi larvae primarily consisted of Actinomycetota (39.78%), Cyanobacteriota (32.46%), Bacillota (18.08%), and Pseudomonadota (9.02%). Among these, Actinomycetota dominated in the 1st to 4th-instar larvae, while Cyanobacteriota became the predominant phylum in the 5th instar. Linear discriminant analysis effect size identified statistically significant biomarkers across different instar larvae of A. pernyi. Alpha diversity analysis showed that gut microbiota diversity initially increased and then decreased with larval development, peaking in the 3rd instar, and reaching its lowest level in the 5th instar. Principal coordinate analysis (PCoA) of beta diversity indicated that the gut microbiota structures of the 1st to 4th instars were similar but significantly differed from that of the 5th instar. Functional prediction analysis based on the KEGG database revealed that Carbohydrate metabolism and Amino acid metabolism-related genes were significantly lower in the 5th instar compared to other instars, while Energy metabolism and Cofactor and vitamin metabolism-related genes were significantly higher. This study offers valuable insights for the development of gut microbial resources in Lepidoptera insects. Full article

Given that suckling lambs with immature rumen development rely on intestinal microbiota for nutrient utilization, investigating the composition and functional characteristics of their intestinal microbiota is therefore of paramount importance. In this study, 16S rRNA gene amplicon sequencing technology was adopted to characterize and analyze the diversity and composition of the jejunum, ileum and cecum bacterial communities of lambs at 0, 7 and 28 days of age, and to predict the functions of the bacterial communities. The α-diversity analysis results revealed that in the jejunum of lambs, the Chao1, PD, Simpson and Shannon indexes differed significantly among the three age groups (p < 0.05). In the ileum, Shannon and Simpson indexes of the 0-days-of-age group were slightly lower than those of the 7 (8.84% and 12.66% reductions, respectively) and 28-days-of-age groups (19.34% and 15.85% reductions, respectively) (0.05 < p < 0.10). In the cecum, Simpson and Shannon indexes differed significantly (p < 0.05) among the three age groups. At the phylum level, Firmicutes (64.68%) and Proteobacteria (21.76%) dominated the bacterial communities across all intestinal segments, with a total of 42 phyla detected. At the genus level, 19 dominant genera were identified in the jejunum. Except for Bifidobacterium, which showed no significant age-related variation (p > 0.05), the relative abundance of the remaining 18 genera changed significantly with age (p < 0.05). In the ileum, compared with the 0-days-of-age group, the Lactobacillus abundance was significantly higher in the 7- and 28-days-of-age groups (p < 0.05), while the Escherichia-Shigella, Mannheimia and Enterobacter abundances were significantly reduced (p < 0.05). In the cecum, the genera, including Blautia, Sellimonas and Ruminococcaceae UCG-014, exhibited significant age-related differences (p < 0.05), whereas other genera showed no significant variation (p > 0.05). Collectively, the bacterial community α-diversity, compositional structure and specific genus abundance in the jejunum, ileum and cecum of lambs demonstrated pronounced age-dependent variation and intestinal segment specificity patterns. This study provides a foundation for a deeper understanding of the succession patterns of the early digestive tract microbiota in lambs, and is conducive to the development of early nutrition strategies based on precise regulation of the microbiome. Full article

Rapid and accurate assessment of nutritional quality, particularly crude protein content and essential nutrient concentrations, remains a major challenge in the food and feed industries. In this study, laser-induced breakdown spectroscopy (LIBS) was combined with advanced chemometric modeling to predict the levels of crude protein and key macro- and micronutrients (Ca, Mg, K, Na, Fe, Mn, P, Zn) in 61 barley forage samples composed of whole aerial plant parts ground prior to analysis. LIBS offers a compelling alternative to traditional analytical methods by enabling real-time analysis with minimal sample preparation. To minimize interference from atmospheric nitrogen, nitrogen spectral lines were excluded from the protein calibration model in favor of spectral lines from elements biochemically associated with proteins. We compared the performance of Partial Least Squares (PLSR) regression and Extreme Learning Machine (ELM) using fivefold cross-validation. ELM outperformed PLS in terms of prediction, achieving a coefficient of determination (R²) close to 1 and a ratio of performance to deviation (RPD) exceeding 2.5 for proteins and several nutrients. These results underscore the potential of LIBS-ELM integration as a robust, non-destructive, and in situ tool for rapid forage quality assessment, particularly in complex and heterogeneous plant matrices. Full article

Nitrogen is an essential nutrient for the growth and development of maize (Zea mays L.), and soil nitrogen deficiency is an important factor limiting maize yield. Although excessive application of nitrogen fertilizer can increase yield, it can also cause environmental problems. Therefore, screening low-nitrogen-tolerant (LNT) germplasm resources and analyzing their genetic mechanisms are of great significance for the development of efficient and environmentally friendly agriculture. In this study, 201 maize inbred lines were used as materials. Two levels of low nitrogen (LN) (0.05 mmol/L, N1) and normal nitrogen (4 mmol/L, N2) were set up. Phenotypic indicators such as seedling length, root length and biomass were measured, and they were classified into LNT type (18 samples), nitrogen-sensitive (NS) type (27 samples) and intermediate type (156 samples). A total of 47 significant SNP loci were detected through a genome-wide association study (GWAS), and 36 candidate genes were predicted. Transcriptome sequencing (RNA-seq) analysis revealed that the differentially expressed genes (753 upregulated and 620 downregulated) in LNT materials under low nitrogen stress (LNS) were significantly fewer than those in NS materials (2436 upregulated and 2228 downregulated). Further analysis using WGCNA identified a total of eight co-expression modules. Among them, the red module was significantly correlated with root length and underground fresh weight under LN conditions (r = 0.75), and three key genes for stress response (Zm00001d005264, Zm00001d053931, Zm00001d044292) were screened out. Combined with GWAS, RNA-seq and qRT-PCR verification, eight candidate genes closely related to LNT at the seedling stage of maize were finally determined, involving biological processes such as stress response, nitrogen metabolism and substance formation. This study initially revealed the molecular mechanism of maize tolerance to LN through multi-omics analysis, providing a theoretical basis and genetic resources for breeding new nitrogen-efficient maize varieties. Full article

It is well known that physico-chemical soil parameters can influence, or even determine, the concentrations of heavy metals in soil. Moreover, in recent decades, there has been growing concern about the role of climatic variables such as temperature fluctuations, drought, or extreme rainfall in affecting heavy metal availability. To examine the combined influence of soil properties and climatic changes on pollution levels, a 10-year study was conducted in an intensively cultivated region of central Greece. This work builds on an earlier study that established predictive relationships for Aqua Regia (Aq-Re)-extracted (pseudo)-total Fe and toxic Cd levels from a set of soil parameters, macronutrients or coexisting metals. The present investigation extends this approach by including DTPA-extracted metal concentrations and additional climatic predictors. The updated methodology applies Linear and Quadratic Regression models as well as Linear and Quadratic Mixed-Effects Models to account for the temporal variation driven by climate. The models were trained and validated on continuous, decade-long measurements. In many cases, this led to substantial revisions of the previously established correlations. Incorporating climate-related variables improved the predictive power of the models, revealing a more complex soil–metal dynamic than previously considered. The newly developed models demonstrated more accurate estimations of both total and available metal concentrations, even under the extreme weather conditions observed in autumn 2020. Given the importance of the Thessaly plain to the Greek agricultural sector, these models serve as a valuable tool for monitoring and risk assessment. Quantifying nutrient and toxic element availability under climate shifts is key to safeguarding Mediterranean soil health and addressing the broader impacts of the climate crisis in agroecosystems. Full article