Novel Effects of Leonardite-Based Applications on Sugar Beet.
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ABSTRACT:
SUBMITTER: Della Lucia MC
PROVIDER: S-EPMC8013720 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Novel effects of leonardite-based applications on sugar beet (16s rDNA amplicon sequence) The present study provides important evidence for understanding the effects induced by leonardite-based biostimulant in sugar beet. The microbial populations of plants grown under hydroponics and field conditions were compared.
| PRJEB42500 | ENA
Project description:Leonardite-based biostimulants are a large class of compounds, including humic acid substances. Foliar application of biostimulants at field level improves plant growth, yield and quality through metabolic changes and stimulation of plant proton pumps. The present study aimed at identifying optimum dosage of BLACKJAK, a humic acid-based substance, which is able to modify genes involved in sugar beet growth. Thirty-three genes belonging to various biochemical pathway categories were tested in leaves of treated sugar beet (Beta vulgaris L.) samples to assess gene expression profiling in response to BLACKJAK. Seedlings of a diploid and multigerm variety were grown in plastic pots and sprayed with two dilutions of BLACKJAK (dilution 1:500-1.0 mg C L-1 and dilution 1:1000-0.5 mg C L-1). Leaf samples were collected after 24, 48, and 72 h treatment with BLACKJAK for each dilution. RNA was extracted and the quantification of gene expression was performed while using an OpenArray platform. Results of analysis of variance demonstrated that, 15 genes out of a total of 33 genes tested with OpenArray qPCR were significantly affected by treatment and exposure time. Analysis for annotation of gene products and pathways revealed that genes belonging to the mitochondrial respiratory pathways, nitrogen and hormone metabolisms, and nutrient uptake were up-regulated in the BLACKJAK treated samples. Among the up-regulated genes, Bv_PHT2;1 and Bv_GLN1 expression exerted a 2-fold change in 1:1000 and 1:500 BLACKJAK concentrations. Overall, the gene expression data in the BLACKJAK treated leaves demonstrated the induction of plant growth-related genes that were contributed almost to amino acid and nitrogen metabolism, plant defense system, and plant growth.
| S-EPMC6970231 | biostudies-literature
Project description:The continuous cropping of sugar beet can result in soil degradation and a decrease in the sugar beet yield and quality. However, the role of continuous sugar beet (Beta vulgaris L. var. saccharifera) cropping in shaping the structure and function of the rhizosphere microbial community remains poorly investigated. In this study, we comparatively investigated the impact of different numbers of years of continuous sugar beet cropping on structural and functional changes in the microbial community of the rhizosphere using high-throughput sequencing and bioinformatics analysis. We collected rhizosphere soils from fields continuously cropped for one-year (T1), five-year (T5), and thirty-year (T30) periods, as well as one bulk soil (T0), in the Xinjiang Uygur Autonomous Region. The results demonstrated that continuous sugar beet cropping resulted in a significant decline in the community diversity of soil bacterial and fungal populations from T1 to T5. With continuous change in the structure of the microbial community, the Shannon diversity and observed species were increased in T30. With an abundance of pathogenic microbes, including Acidobacteria, Alternaria, and Fusarium, that were highly enriched in T30, soil-borne diseases could be accelerated, deduced by functional predictions based on 16S rRNA genes. Continuous sugar beet cropping also led to significant declines in beneficial bacteria, including Actinobacteria, Pseudomonas spp., and Bacillus spp. In addition, we profiled and analyzed predictive metabolic characteristics (metabolism and detoxification). The abundance of phenolic acid decarboxylase involved in the phenolic acid degradation pathway was significantly lower in groups T5 and T30 than that in T0 and T1, which could result in the phenolic compounds becoming excessive in long-term continuous cropping soil. Our results provide a deeper understanding of the rhizosphere soil microbial community's response to continuous sugar beet cropping, which is important in evaluating the sustainability of this agricultural practice.
| S-EPMC7017100 | biostudies-literature
Project description:Clone libraries of bacterial 16S rRNA genes (a total of 1,980 clones) were constructed from the leaf blades, petioles, taproots, and lateral roots of sugar beet (Beta vulgaris L.) grown under different fertilization conditions. A principal coordinate analysis revealed that the structures of bacterial communities in above- and underground tissues were largely separated by PC1 (44.5%). The bacterial communities of above-ground tissues (leaf blades and petioles) were more tightly clustered regardless of differences in the tissue types and fertilization conditions than those of below-ground tissues (taproots and lateral roots). The bacterial communities of below-ground tissues were largely separated by PC2 (26.0%). To survey plant growth-promoting bacteria (PGPBs), isolate collections (a total of 665 isolates) were constructed from the lateral roots. As candidate PGPBs, 44 isolates were selected via clustering analyses with the combined 16S rRNA gene sequence data of clone libraries and isolate collections. The results of inoculation tests using sugar beet seedlings showed that eight isolates exhibited growth-promoting effects on the seedlings. Among them, seven isolates belonging to seven genera (Asticcacaulis, Mesorhizobium, Nocardioides, Sphingobium, Sphingomonas, Sphingopyxis, and Polaromonas) were newly identified as PGPBs for sugar beet at the genus level, and two isolates belonging to two genera (Asticcacaulis and Polaromonas) were revealed to exert growth-promoting effects on the plant at the genus level for the first time. These results suggest that a community analysis-based selection strategy will facilitate the isolation of novel PGPBs and extend the potential for the development of novel biofertilizers.
| S-EPMC8209457 | biostudies-literature
Project description:Bacterial root communities in sugar beets grown in potting soil and vermicompost
| PRJEB37140 | ENA
Project description:The recalcitrance of lignocellulose makes enzymatic hydrolysis of plant biomass for the production of second generation biofuels a major challenge. This work investigates an efficient and economic approach for the enzymatic hydrolysis of sugar beet pulp (SBP), which is a difficult to degrade, hemicellulose-rich by-product of the table sugar industry. Three fungal strains were grown on different substrates and the production of various extracellular hydrolytic and oxidative enzymes involved in pectin, hemicellulose, and cellulose breakdown were monitored. In a second step, the ability of the culture supernatants to hydrolyze thermally pretreated SBP was tested in batch experiments. The supernatant of Sclerotium rolfsii, a soil-borne facultative plant pathogen, was found to have the highest hydrolytic activity on SBP and was selected for further hydrolyzation experiments. A low enzyme load of 0.2 mg g(-1) protein from the culture supernatant was sufficient to hydrolyze a large fraction of the pectin and hemicelluloses present in SBP. The addition of Trichoderma reesei cellulase (1-17.5 mg g(-1) SBP) resulted in almost complete hydrolyzation of cellulose. It was found that the combination of pectinolytic, hemicellulolytic, and cellulolytic activities works synergistically on the complex SBP composite, and a combination of these hydrolytic enzymes is required to achieve a high degree of enzymatic SBP hydrolysis with a low enzyme load.
| S-EPMC4162999 | biostudies-literature
Project description:BackgroundGenomic selection exploits dense genome-wide marker data to predict breeding values. In this study we used a large sugar beet population of 924 lines representing different germplasm types present in breeding populations: unselected segregating families and diverse lines from more advanced stages of selection. All lines have been intensively phenotyped in multi-location field trials for six agronomically important traits and genotyped with 677 SNP markers.ResultsWe used ridge regression best linear unbiased prediction in combination with fivefold cross-validation and obtained high prediction accuracies for all except one trait. In addition, we investigated whether a calibration developed based on a training population composed of diverse lines is suited to predict the phenotypic performance within families. Our results show that the prediction accuracy is lower than that obtained within the diverse set of lines, but comparable to that obtained by cross-validation within the respective families.ConclusionsThe results presented in this study suggest that a training population derived from intensively phenotyped and genotyped diverse lines from a breeding program does hold potential to build up robust calibration models for genomic selection. Taken together, our results indicate that genomic selection is a valuable tool and can thus complement the genomics toolbox in sugar beet breeding.
| S-EPMC3848454 | biostudies-literature
Project description:In sugar beet (Beta vulgaris altissima), bolting tolerance is an essential agronomic trait reflecting the bolting response of genotypes after vernalization. Genes involved in induction of sugar beet bolting have now been identified, and evidence suggests that epigenetic factors are involved in their control. Indeed, the time course and amplitude of DNA methylation variations in the shoot apical meristem have been shown to be critical in inducing sugar beet bolting, and a few functional targets of DNA methylation during vernalization have been identified. However, molecular mechanisms controlling bolting tolerance levels among genotypes are still poorly understood. Here, gene expression and DNA methylation profiles were compared in shoot apical meristems of three bolting-resistant and three bolting-sensitive genotypes after vernalization. Using Cot fractionation followed by 454 sequencing of the isolated low-copy DNA, 6231 contigs were obtained that were used along with public sugar beet DNA sequences to design custom Agilent microarrays for expression (56k) and methylation (244k) analyses. A total of 169 differentially expressed genes and 111 differentially methylated regions were identified between resistant and sensitive vernalized genotypes. Fourteen sequences were both differentially expressed and differentially methylated, with a negative correlation between their methylation and expression levels. Genes involved in cold perception, phytohormone signalling, and flowering induction were over-represented and collectively represent an integrative gene network from environmental perception to bolting induction. Altogether, the data suggest that the genotype-dependent control of DNA methylation and expression of an integrative gene network participate in bolting tolerance in sugar beet, opening up perspectives for crop improvement.
| S-EPMC4682430 | biostudies-literature
Project description:Background:Convolvulus sepium (hedge bindweed) detection in sugar beet fields remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and different growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over different fields and environments. Results:Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 field images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 field images, showing that the combination of synthetic and original field images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected field images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-off between accuracy and speed. Specifically, the average precisions (APs@IoU0.5) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800?×?1200) on a NVIDIA Titan X GPU environment. Conclusion:The developed model has the potential to be deployed on an embedded mobile platform like the Jetson TX for online weed detection and management due to its high-speed inference. It is recommendable to use synthetic images and empirical field images together in training stage to improve the performance of models.
| S-EPMC7059384 | biostudies-literature