8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identification of Plant Growth Promoting Rhizobacteria That Improve the Performance of Greenhouse-Grown Petunias under Low Fertility Conditions

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The production of greenhouse ornamentals relies on high fertilizer inputs to meet scheduling deadlines and quality standards, but overfertilization has negative environmental impacts. The goals of this study were to identify plant-growth-promoting rhizobacteria (PGPR) that can improve greenhouse ornamental crop performance with reduced fertilizer inputs, and to identify the best measurements of plant performance for assessing the beneficial impact of PGPR on ornamentals. A high-throughput greenhouse trial was used to identify 14 PGPR isolates that improved the flower/bud number and shoot dry weight of Petunia × hybrida ‘Picobella Blue’ grown under low fertility conditions in peat-based media. These 14 PGPR were then applied to petunias grown under low fertility conditions (25 mg L −1 N). PGPR-treated plants were compared to negative (untreated at 25 mg L −1 N) and positive (untreated at 50, 75, 100, and 150 mg L −1 N) controls. Multiple parameters were measured in the categories of flowering, vegetative growth, and vegetative quality to determine the best measurements to assess improvements in ornamental plant performance. Caballeronia zhejiangensis C7B12-treated plants performed better in almost all parameters and were comparable to untreated plants fertilized with 50 mg L −1 N. Genomic analysis identified genes that were potentially involved in plant growth promotion. Our study identified potential PGPR that can be used as biostimulants to produce high-quality greenhouse ornamentals with lower fertilizer inputs.

          Related collections

          Most cited references114

          • Record: found
          • Abstract: not found
          • Article: not found

          Fitting Linear Mixed-Effects Models Usinglme4

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Simultaneous inference in general parametric models.

            Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline

              Abstract Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, β-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.
                Bookmark

                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Plants (Basel)
                Plants (Basel)
                plants
                Plants
                MDPI
                2223-7747
                09 July 2021
                July 2021
                : 10
                : 7
                : 1410
                Affiliations
                Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH 44691, USA; south.63@ 123456osu.edu (K.A.S.); nordstedt.1@ 123456osu.edu (N.P.N.)
                Author notes
                [* ]Correspondence: jones.1968@ 123456osu.edu ; Tel.: +1-330-263-3885
                Article
                plants-10-01410
                10.3390/plants10071410
                8309264
                586e3c1e-bb79-4652-b6e8-2a642dafaf4e
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 17 June 2021
                : 07 July 2021
                Categories
                Article

                abiotic stress,biostimulants,caballeronia zhejiangensis,floriculture,flower quality,greenhouse production,horticulture,nutrient stress,ornamentals,soilless media

                Comments

                Comment on this article