Search Results (2,149)

Northern corn leaf blight (NCLB), caused by the fungal pathogen Setosphaeria turcica, is a devastating foliar disease that significantly threatens maize production in China. Previous studies have demonstrated that SET domain gene 102 (SDG 102), a gene encoding an H3K36 methyltransferase, plays a crucial role in regulating maize growth, development, and stress responses. This study used the wild-type (WT), SDG102 overexpression line (OE), and silencing line (SL) of the corn inbred line B73 as materials. After artificial inoculation with S. turcica, the phenotypic characteristics, disease index, yield, and other related traits of different strains were compared, and RNA-Seq was used to analyze the changes in the gene expression profile. The results showed that overexpression of SDG102 significantly inhibited pathogen spore germination and hyphal growth and enhanced the activity of antioxidant enzymes and the ability to scavenge reactive oxygen species in plants prior to S. turcica infection, the opposite trend was observed in SDG102 silencing lines. Compared with the wild-type, 1546 and 1837 differentially expressed genes (DEGs) responsive to S. turcica were identified in OE and SL, respectively. These differentially expressed genes primarily function in pathways such as plant–pathogen interactions, plant hormone signaling, and secondary metabolite biosynthesis. In the OE lines, genes related to plant–pathogen interactions, reactive oxygen species (ROS) production, and key phenylpropanoid biosynthesis genes exhibited higher expression levels. Furthermore, SDG102 regulates the synthesis of auxin (JA) and abscisic acid (SA) as well as the transcription of their signaling pathway genes, thereby influencing maize resistance to large leaf spot disease. Under corn leaf blight conditions, SDG102 overexpression increased yield by 9.29% compared to WT, while SL reduced yield by 10.10%. In conclusion, SDG102 enhances maize resistance to NCLB by positively regulating the expression of disease resistance genes, antioxidant enzyme activity, and hormone-mediated defense pathways. Full article

Melatonin and hydrogen sulfide (H₂S) have both been demonstrated to enhance plant drought tolerance. However, the relationship between melatonin and H₂S during the drought resistance response remains unclear. In this study, under drought stress, the synthesis pathways for both melatonin and H₂S in maize seedlings were activated. The application of exogenous melatonin enhanced the expression of key genes, namely LCD and DCD, which are involved in H₂S synthesis, thereby promoting the accumulation of H₂S. Conversely, the application of NaHS did not significantly influence the expression of genes related to melatonin synthesis or the levels of endogenous melatonin. Melatonin enhanced drought tolerance in maize through the H₂S signaling pathway, as evidenced by a 124.1% increase in the photosynthetic rate and improved activity of antioxidant enzymes. Specifically, there were increases of 66.5%, 75.6%, and 51.0% in the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), respectively. Furthermore, there was an elevation in the levels of osmotic regulatory substances and non-enzymatic antioxidants. The application of the H₂S scavenger (HT) significantly inhibited the drought tolerance effects mediated by melatonin, whereas the melatonin synthesis inhibitor (p-CPA) did not exert a significant impact on the drought resistance induced by H₂S. Overall, our findings suggest that H₂S plays a role in the melatonin-mediated enhancement of drought tolerance in maize, primarily through coordinated modulation of osmotic balance and antioxidant defense systems. Full article

The induction of resistance in plants involves prior activation of physiological and biochemical systems in the face of external stimuli, promoting greater tolerance to biotic stresses. Faced with the growing challenge of emerging diseases in agricultural plants and the search for more sustainable phytosanitary practices, natural substances are promising alternatives. Xylopia frutescens, known as “pindaiba-da-folha-pequena”, native to the Brazilian Cerrado and traditionally used as an antimicrobial treatment, is still little-explored in the literature and presents potentially effective compounds for the control of plant diseases. This study characterized the chemical composition and thermal stability of the X. frutescens essential oil (XEO), while evaluating its physiological and phytotoxic effects and the potential for disease control in maize and cowpea plants. The main constituents found in X. frutescens essential oil were nopinone (13.75%), spatulenol (12.94%), myrtenal (12.47%), and β-pinene (11.02%). Thermogravimetric analysis indicated that X. frutescens is highly volatile, with a large mass loss at 94.74 °C. In bioassays, the oil preserved chlorophyll levels at adequate amounts and activated several antioxidant mechanisms, but also showed a dose-dependent phytotoxic effect. In vitro assays further confirmed its antifungal activity against key phytopathogens, supporting its potential use in disease control. A general increase in the activities of the enzymes superoxide dismutase (SOD), ascorbate peroxidase (APx) and—partially—chitinase (CHIT) was observed. Catalase (CAT) decreased in both maize and cowpea plants treated with this essential oil but was higher in untreated infected plants. Such enzymatic changes suggest that the oil acts as a natural elicitor of resistance, strengthening biochemical and physiological defenses. Finally, disease severities, as measured by AUDPCs, demonstrated significant reductions in the progress of maize “Curvularia leaf spot” (Curvularia lunata) and cowpea “Web blight” (Rhizoctonia solani). The results highlight the potential of X. frutescens essential oil as an active compound stimulating defense mechanisms for applications in sustainable agricultural systems. Full article

Although purple non-sulfur bacteria (PNSB) have been studied as good biofertilizers, their direct effects on maize seed vigor remain unclear. Additionally, the seedling stage is a vital factor for the later growth of maize. This study was conducted to evaluate the effectiveness of potassium-solubilizing PNSB (K-PNSB) in enhancing the vigor of hybrid maize seeds. A completely randomized design was employed, incorporating single strains, Luteovulum sphaeroides M-Sl-09, Rhodopseudomonas thermotolerans M-So-11, and Rhodopseudomonas palustris M-So-14, as well as a mixture of all three strains. Each was tested at bacterial suspension dilution ratios with sterile distilled water of 1:2000; 1:2250; 1:2500; 1:2750; and 1:3000 (v/v), with three replications per treatment. Each replicate consisted of a Petri dish containing 10 hybrid maize seeds of each hybrid of LVN 10, C.P. 511, and NK7328 Gt/BT, and was incubated for five days. The results showed that K-PNSB significantly enhanced root and shoot development compared to the control (p < 0.05). The 1:2500 dilution of the individual strains and the mixture notably improved germination rate, root length, shoot length, and seedling vigor index compared to the control. At the 1:2500 dilution, the improved vigor index increased by 73.5% for L. sphaeroides, 48.7% for R. thermotolerans, 47.4% for R. palustris, and 78.5% for the mixed inoculum in the LVN 10 hybrid. Similar trends were observed for C.P. 511 and NK7328 hybrids, confirming strain- and hybrid-specific responses. The findings highlight that K-PNSB can serve as effective bio-priming agents to enhance maize seed vigor through mechanisms related to potassium solubilization and phytohormone production. Field-scale validation is recommended to assess their long-term agronomic potential. Full article

This study evaluates the thermal performance of a prototype vacuum-tube solar cooker adapted to the climatic conditions of the Amazon region, Peru. Four grain types (Zea mays L., Triticum aestivum, Zea mays var. morochon, and Hordeum vulgare) were tested to assess temperature evolution, exposure time, and incident solar radiation. Hordeum vulgare was selected as a food model for calibration due to its well-characterized thermophysical properties and reproducible heating behavior. The results showed individual thermal efficiencies ranging from 19.3% to 35.3%, with an average of 27.3% across the three tubes. The most efficient treatment, obtained with Zea mays L., reached 180 °C under an irradiance of approximately 980 W/m². A direct relationship was observed between solar radiation intensity, exposure time, and thermal efficiency. These findings confirm that the proposed hybrid design combining a cylindrical parabolic collector with vacuum U-tubes achieves higher and more stable performance than conventional box-type cookers. The system allows complete grain cooking without fossil fuels, demonstrating its potential as a sustainable and low-cost solution for rural communities in the Andean Amazonian region, promoting clean energy adoption and reducing environmental impact. Full article

This study sought to screen 45 maize (Zea mays L.) inbred lines for tolerance to combined drought and heat stress (CDHS) and identify the leaf proteome patterns of two inbred lines with contrasting stress response at early vegetative stage. Biomass accumulation was significantly reduced under CDHS compared to optimum conditions. Furthermore, CDHS-tolerant inbred lines exhibited significantly lower (p < 0.05) leaf temperatures (28.6 °C) and higher sub-stomatal CO₂ concentration (9012 mol mol⁻¹) and photosynthetic yield (0.69) under stress. The tolerant (CIM18) and susceptible (QS21) inbred lines were exposed to stress by maintaining a field capacity of 25% for 7 days and increasing the daily ambient temperature by 5 °C from 25 °C to 40 °C. Conventional two-dimensional electrophoresis analysis was used to compare leaf protein expression profiles, and significant differences (p < 0.05) were observed. Out of a total of 505 proteins, 114 showed significant quantitative variation. Of these, 62 proteins had a twofold upregulation in CIM18, while 52 were downregulated. Twenty upregulated proteins were selected for amino acid micro-sequencing, and 11 proteins were uniquely expressed in CIM18. The other nine proteins had ≥ twofold upregulation in CIM18 compared to QS21. The functions of the identified proteins included defence, metabolism, photosynthesis and structure. Full article

Maize (Zea mays L.) is a cornerstone of food security and livestock production in Paraguay. However, its productivity and grain safety are increasingly threatened by Fusarium species because of their pathogenic capacity and ability to produce mycotoxins. In this study, symptomatic maize leaves collected from commercial fields in Pirapó, Itapúa, during the 2022 growing season were processed to isolate and characterize fungal pathogens. Three isolates displaying typical Fusarium morphology were obtained and examined through macroscopic and microscopic traits. Molecular identification was conducted using translation elongation factor 1-α 1-α (TEF) sequences, followed by phylogenetic inference using maximum likelihood and Bayesian methods. The Paraguayan isolates (PYF-MZE22-01, -02, -03) clustered with the ex-type strain Fusarium awaxy CBS139380 in a strongly supported clade, confirming species identity. This finding constitutes the first record of F. awaxy associated with maize in Paraguay, thereby expanding its known geographical distribution. Considering that members of the Fusarium fujikuroi species complex are recognized producers of regulated mycotoxins, the detection of F. awaxy raises concerns regarding its pathogenic potential and possible implications for food safety. These results underscore the importance of integrating molecular diagnostics, toxigenic profiling, and surveillance programs to monitor emerging Fusarium taxa in South American agroecosystems. Full article

A pot experiment was carried out to evaluate the effects of composts, vermicomposts, and mineral fertilization on maize (Zea mays L.) growth, grain quality, soil chemical properties, and nematode communities. Eight treatments were tested, including organic amendments combined with mineral nitrogen, exclusive mineral fertilization, and an unfertilized control. Soil chemical properties, including pH, salinity, nitrogen compounds, and macro- and microelements, varied notably across treatments. Nematode community analysis revealed distinct patterns among treatments: Shannon diversity was moderate and relatively stable across most treatments, but a statistically significant reduction was recorded in treatment 7. In contrast, the Plant Parasitic Index (PPI) varied significantly, reflecting differences in community maturity and parasitic pressure. Bacterivores and fungivores indicated active nutrient cycling, while omnivores and predators reflected soil food web stability. Fertilization treatments significantly affected maize grain development. The highest thousand-kernel weight (TKW) was recorded in treatment 6 (+8.9% vs. control) and treatment 4 (+7.4% vs. control). The kernel number per cob was greatest in treatments 4 and 5 (+38% and +32%), with corresponding increases in grain mass per cob (+48% and +40%). The mean cob core weight ranged from 20.1 g in the control treatment to 30.2 g in treatment 1. The greatest increases compared to the control were observed in treatments 1 and 5, amounting to 50.2% and 44.8%, respectively. Overall, fertilization influenced grain quality, soil chemistry, and nematode communities, highlighting the importance of integrating organic and mineral amendments for sustainable crop production. Full article

Potassium (K) is present in soils mainly in minerals, including feldspar. However, most of it is unavailable to plants. In the in-dyked alluvial soils of the Mekong Delta, available K is typically low despite the abundance of K-bearing feldspar, leading to nutrient imbalances and yield constraints. This study aimed to (i) select potential feldspar-potassium-solubilizing purple nonsulfur bacteria (K-PNSB), (ii) determine their ability to enhance hybrid maize seed vigor (Zea mays L.), and (iii) evaluate their effects on the growth of maize seedlings. Fifty-eight K-PNSB strains were isolated from maize-cultivated in-dyked alluvial soils, with soluble K concentrations ranging from 0.108 to 15.0 mg L⁻¹. Among these, strain M-Sl-03 released the highest K concentration under microaerobic light conditions, whereas strains M-Sl-01 and M-Sl-06 produced best under aerobic dark conditions. In addition, two more strains, M-Sl-02 and M-Wa-06, were also selected for their K solubilization ability. The selected strains were identified as Cereibacter sphaeroides strains M-Sl-01 and M-Sl-02, Rhodopseudomonas palustris strain M-Sl-03, and Rhodoplanes pokkaliisoli strains M-Sl-03 and M-Wa-06, according to their 16S rDNA region. None of them exhibited toxicity to germinating maize seeds. Both individual strains and the five-strain mixture significantly improved seed vigor. At a 1:1000 dilution, individual and mixed inoculants increased the vigor index of maize seeds by 47.5–68.8%. In addition, the selected PNSB strains contributed to improving the growth of maize seedlings, particularly plant height and root dry biomass. These promising strains have potential for application as biofertilizers to support hybrid maize cultivation. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

Corn (Zea mays L.) yield productivity is driven by a multitude of factors, specifically genetics, environment, and management practices, along with their corresponding interactions. Despite continuous monitoring through proximal or remote sensors and advanced predictive models, understanding these complex interactions remains challenging. While predictive models are improving with regard to accurate predictions, they often fail to explain causal relationships, rendering them less interpretable than desired. Process-based or biophysical models such as the Agricultural Production Systems sIMulator (APSIM) incorporate these causalities, but the multitude of interactions are difficult to tease apart and are largely sensitive to external drivers, which often include stochastic variations. To address this limitation, we developed a novel methodology that reveals these hidden causal structures. We simulated corn production under varied conditions, including different planting dates, nitrogen fertilizer amounts, irrigation rules, soil and environmental conditions, and climate change scenarios. We then used the simulation results to rank features having the largest impact on corn yield through Random Forest modeling. The Random Forest model identified nitrogen uptake and annual transpiration as the most influential variables on corn yield, similar to the existing research. However, this analysis alone provided limited insight into how or why these features ranked highest and how the features interact with each other. Building on these results, we deployed a Causal Bayesian model, using a hybrid approach of score-based (hill climb) and constraint-based (injecting domain knowledge) models. The causal analysis provides a deeper understanding by revealing that genetics, environment, and management factors had causal impacts on nitrogen uptake and annual transpiration, which ultimately affected yield. Our methodology allows researchers and practitioners to unpack the “black box” of crop production systems, enabling more targeted and effective model development and management recommendations for optimizing corn production. Full article

Northern corn leaf blight is a major fungal disease hindering maize production worldwide. Among the various strategies of disease management, the deployment of host plant resistance is the most economic means to mitigate the yield losses, as it is cost-effective and durable. In this study, we performed the genome-wide association study (GWAS) analysis in a set of 336 maize inbred lines. The experimental material was evaluated for northern corn leaf blight disease response across two seasons during the rainy seasons of 2023 and 2024. The ANOVA results and estimates of genetic variability parameters indicated the existence of a substantial amount of genetic variability. High heritability and high genetic advance as percent mean suggested the presence of additive genetic effects in controlling the disease response. GWAS analysis was performed employing GLM, MLM, CMLM, MLMM, FarmCPU and BLINK. The results from GWAS identified 74 marker associations from GLM and FarmCPU models. The QTN S1_7356398, located on chromosome 1, identified from the GLM model, explained 12.12 percent of phenotypic variation. Another QTN S2_51098833 located on chromosome 2, identified from the FarmCPU model, explained 6.14 percent variation. Remaining associations explained lesser PVE, suggesting the quantitative inheritance of NCLB resistance. Candidate gene identification was performed by keeping B73 as a reference genome. The identified QTNs from the current study were found to be located in annotated genes with functional domains implicated in defence mechanisms in maize and other crops. Many candidate genes, including chitinase, putative serine/threonine protein kinase, and aldehyde oxygenase, were identified and found to play a crucial role in plant defence mechanisms against several biotic and abiotic stresses. Full article

The low fertility of plinthosols is a major constraint on agricultural production, largely due to the presence of plinthite, which restricts the availability of water and nutrients. This study aimed to simulate the growth and yield of grain maize on a loosened plinthosol amended with termite mound (from Macrotermes falciger) material in the Lubumbashi region. A 660-hectare perimeter was established, subdivided into ten maize blocks (B1–B10) and a control block (B0), which received the same management practices as the other blocks except for subsoiling and termite mound amendment. The APSIM model was used for simulations. The leaf area index (LAI) was estimated from Sentinel-2 imagery via Google Earth Engine, using the Simple Ratio (SR) spectral index, and integrated into APSIM alongside agro-environmental variables. Model performance was assessed using cross-validation (2/3 calibration, 1/3 validation) based on the coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE). Results revealed a temporal LAI dynamic consistent with maize phenology. Simulated LAI matched observations closely (R² = 0.85 − 0.93; NSE = 0.50 − 0.77; RMSE = 0.29 − 0.40 m² m⁻²). Maize grain yield was also well predicted (R² = 0.91; NSE > 0.80; RMSE < 0.50 t ha⁻¹). Simulated yields reproduced the observed contrast between treated and control blocks: 10.4 t ha⁻¹ (B4, 2023–2024) versus 4.1 t ha⁻¹ (B0). These findings highlight the usefulness of combining remote sensing and biophysical modeling to optimize soil management and improve crop productivity under limiting conditions. Full article

This study investigates how different vegetation types influence the molecular structure and abundance of soil organic carbon (SOC), as well as their influence on microbial metabolic pathways and community composition. Soil samples were collected from four different sites: a woodland dominated by Drypetes perreticulata (DP), a woodland dominated by Horsfieldia hainanensis (HM), a Zea mays L. field (ZL), and a citrus reticulata orchard (CB). The molecular structure of soil organic carbon (SOC) was characterised using Fourier Transform Infrared (FTIR) spectroscopy, identifying aromatic carbon (ArC), polysaccharide carbon (PSC), alkyl carbon (AlkC), amine carbon (AmC), ether carbon (EtC), and olefin carbon (OleC). Our results indicated significant variations across vegetation types: DG exhibited a significantly higher ArC content, while maize fields showed lower PSC levels. To analyse the relationships between different samples, we employed principal component analysis (PCA), which revealed distinct organic carbon structures across vegetation types, with the forests (DG and HM) significantly differing from agricultural sites (ZL and CB). Additionally, the 16S V3_V4 region of soil bacteria was sequenced using high-throughput sequencing. We employed PICRUSt2 to predict microbial metabolic pathways, revealing consistent core metabolic functions across samples but significant variations in secondary metabolism, with HM samples exhibiting the most distinctive metabolic profiles. Redundancy analysis (RDA) further demonstrated that microbial metabolic pathway variation explained 55.66% of organic carbon structure variance. Key microbial taxa exhibited significant associations with specific carbon source types and functional pathways. These findings highlight the pivotal mechanisms by which different vegetation types regulate soil organic carbon structure and composition by driving changes in microbial metabolic traits and community assembly. This study provides a mechanistic basis for understanding the coupling between vegetation, microorganisms, and carbon cycling, offering significant guidance for optimising vegetation restoration strategies, enhancing soil carbon sequestration capacity, and advancing carbon management practices based on microbial regulation. Full article

Purple waxy corn cobs (PWCCs) represent an underutilized agricultural waste rich in anthocyanins with promising cosmeceutical potential. This study investigated niosome-based encapsulation to enhance the stability and bioactivity of PWCC anthocyanin extracts. PWCC extract was macerated in 50% ethanol. The extract exhibited a high total anthocyanin content (3.02 ± 0.81 mg C3GE/L), while cyanidin-3-glucoside identified as the major anthocyanin (1.17 ± 0.02 mg/g dry weight). Furthermore, the extracts showed strong antioxidant activities as evidence by DPPH, ABTS, and FRAP assays. The optimized niosome preparations synthesized by the probe sonication method exhibited better entrapment efficiency (80–85%), nanoscale particle size (185–296 nm), and stable zeta potential (−29 to −32 mV). TEM verification of the spherical morphology and FT-IR spectra confirmed the successful loading of anthocyanins. The thermal stability test exhibited negligible changes in the particle size and zeta potential. Furthermore, in vitro release profile followed the Higuchi model, indicating enhanced release kinetics. Biological assays demonstrated moderate UVB protection effects and potent anti-melanogenesis activity in B16F10 cells. Notably, formulation N5 exhibited the highest tyrosinase inhibition and melanin synthesis suppression. These findings indicate that niosome-based encapsulation represents a promising strategy for enhancing the stability, bioavailability, and biological efficacy of anthocyanin extracts, especially in the cosmetic and pharmaceutical industries. Full article