7
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      MULTIVARIATE ANALYSIS APPLIED TO SPRAY DEPOSITION IN GROUND APPLICATION OF PHYTOSANITARY PRODUCTS IN COFFEE PLANTS

      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

          ABSTRACT An adequate combination of factors involved in the technology used for phytosanitary product application contributes to an efficient spray deposition on the target. The objective of this study was to use multivariate analysis to characterize the magnitude of effects and the order of influence of three factors that interfere with the quality of phytosanitary product application in coffee plants. An entirely randomized design was adopted, with four repetitions, using a 2 × 2 × 3 factorial scheme, with two classes of droplets quality (fine and coarse), two application rates (250 and 400 L ha-1), and the use of adjuvants (with no adjuvant or with Fighter®and Aureo® adjuvants). The quality of the application was determined by jointly analyzing the spray deposition on three thirds of leaves, in their internal and external layers, the runoff to soil, coverage, droplet density, relative amplitude, and the volumetric median diameter. The results underwent analysis of variance (ANOVA) to measure the effect sizes (η2). After testing the assumptions of multivariate analysis, clustering and principal component analyses were performed. The class of droplets was found to be the most influential factor in the quality of the phytosanitary product application (spray deposition and runoff to soil). When focusing on spray deposition on leaves, the second-most influential factor was the application rate and the relation between the application rate and the adjuvants. For the other variables, the second-most influential factor was the application rate.

          Related collections

          Most cited references36

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

          Effect size estimates: current use, calculations, and interpretation.

          The Publication Manual of the American Psychological Association (American Psychological Association, 2001, American Psychological Association, 2010) calls for the reporting of effect sizes and their confidence intervals. Estimates of effect size are useful for determining the practical or theoretical importance of an effect, the relative contributions of factors, and the power of an analysis. We surveyed articles published in 2009 and 2010 in the Journal of Experimental Psychology: General, noting the statistical analyses reported and the associated reporting of effect size estimates. Effect sizes were reported for fewer than half of the analyses; no article reported a confidence interval for an effect size. The most often reported analysis was analysis of variance, and almost half of these reports were not accompanied by effect sizes. Partial η2 was the most commonly reported effect size estimate for analysis of variance. For t tests, 2/3 of the articles did not report an associated effect size estimate; Cohen's d was the most often reported. We provide a straightforward guide to understanding, selecting, calculating, and interpreting effect sizes for many types of data and to methods for calculating effect size confidence intervals and power analysis.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            The varimax criterion for analytic rotation in factor analysis

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

              Understanding the effect size and its measures

              The evidence based medicine paradigm demands scientific reliability, but modern research seems to overlook it sometimes. The power analysis represents a way to show the meaningfulness of findings, regardless to the emphasized aspect of statistical significance. Within this statistical framework, the estimation of the effect size represents a means to show the relevance of the evidences produced through research. In this regard, this paper presents and discusses the main procedures to estimate the size of an effect with respect to the specific statistical test used for hypothesis testing. Thus, this work can be seen as an introduction and a guide for the reader interested in the use of effect size estimation for its scientific endeavour.
                Bookmark

                Author and article information

                Journal
                eagri
                Engenharia Agrícola
                Eng. Agríc.
                Associação Brasileira de Engenharia Agrícola (Jaboticabal, SP, Brazil )
                0100-6916
                1809-4430
                August 2021
                : 41
                : 4
                : 458-467
                Affiliations
                [1] Uberlândia Minas Gerais orgnameUniversidade Federal de Uberlândia Brazil
                Article
                S0100-69162021000400458 S0100-6916(21)04100400458
                10.1590/1809-4430-eng.agric.v41n4p458-467/2021
                98e98d2a-b14e-498b-b46b-8e60740ab1cd

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 12 July 2021
                : 14 March 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 36, Pages: 10
                Product

                SciELO Brazil

                Categories
                Scientific Papers

                Application rate,droplet size,adjuvants,Eta squared,PCA,cluster analysis

                Comments

                Comment on this article