9
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Allometric relationship and leaf area modeling estimation on chia by non-destructive method Translated title: Relação alométrica e modelagem da estimativa da área foliar em chia por método não destrutivo

      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 This study aimed to obtain equations to estimate leaf area from linear leaf dimensions and establish the allometric relationship between leaf area and the number of leaves on the main stem of chia (Salvia hispanica L.) at different sowing times. The experiment was conducted in the agricultural year 2016/2017 on five sowing times in Santa Maria, RS, Brazil, in a randomized block design with four repetitions. In each plot, ten random plants were marked weekly during the vegetative phase to determine the number of leaves (NL) in the main stem, and three of these for the determination of leaf area (LA). A total of 70 leaves of different sizes were used to calibrate the model. Another 106 leaves were used to test the predictive capacity of the equations by various statistical indices. The length (L) and the largest leaf width (W) were measured. Leaf collection was carried out during the cycle, in all sowing times to represent all leaf sizes. The linear, quadratic, exponential, and potential models were adjusted. The non-destructive method, through the linear dimensions of the leaf, is appropriate for estimating the leaf area in chia. The general equation LA = 0.642 (L x W) can be used to estimate the leaf area of the chia plants without loss of precision. The potential model is appropriate to characterize the allometric relationship between leaf area evolution and the number of leaves accumulated in the main stem of chia at different sowing times.

          Translated abstract

          RESUMO Objetivou-se neste estudo obter equações para estimar a área foliar a partir de dimensões lineares da folha e estabelecer a relação alométrica entre a área foliar e o número de folhas da haste principal de chia (Salvia hispanica L.) em diferentes épocas de semeadura. O experimento foi conduzido no ano agrícola 2016/2017 em cinco épocas de semeadura em Santa Maria, RS, Brasil, em delineamento de blocos ao acaso com quatro repetições. Em cada parcela, dez plantas ao acaso foram marcadas para determinação semanal do número de folhas (NF) na haste principal durante a fase vegetativa, e três para determinação da área foliar (AF). Um total de 70 folhas de diferentes tamanhos foram usadas para calibrar o modelo e outras 106 folhas foram usadas para testar a capacidade preditiva das equações por vários índices estatísticos, nas quais foram medidos o comprimento (C) e a maior largura da folha (L). A coleta de folhas foi realizada durante o ciclo de desenvolvimento de todas as épocas de semeadura para ter representação de todos os tamanhos de folhas. Os modelos linear, quadrático, exponencial e potencial foram ajustados. O método não destrutivo, através das dimensões lineares da folha, é adequado para estimar a área foliar em chia. A equação geral AF = 0.642 (L x W) pode ser usada para estimar a área foliar das plantas de chia sem perda de precisão. O modelo potencial é adequado para caracterizar a relação alométrica entre a evolução da área foliar e o número de folhas acumuladas na haste principal de chia em diferentes épocas de semeadura.

          Related collections

          Most cited references27

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

          metan: An R package for multi‐environment trial analysis

            Bookmark
            • Record: found
            • Abstract: not found
            • Book: not found

            On the validation of models

              Bookmark
              • Record: found
              • Abstract: not found
              • Book: not found

              R: a language and environment for statistical computing

              (2020)
                Bookmark

                Author and article information

                Journal
                rbeaa
                Revista Brasileira de Engenharia Agrícola e Ambiental
                Rev. bras. eng. agríc. ambient.
                Departamento de Engenharia Agrícola - UFCG (Campina Grande, PB, Brazil )
                1415-4366
                1807-1929
                May 2021
                : 25
                : 5
                : 305-311
                Affiliations
                [2] Santa Maria Rio Grande do Sul orgnameUniversidade Federal de Santa Maria Brazil
                Article
                S1415-43662021000500305 S1415-4366(21)02500500305
                10.1590/1807-1929/agriambi.v25n5p305-311
                c825c08b-15cf-41a9-b9d6-c98473ea2e35

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

                History
                : 03 February 2021
                : 08 September 2019
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 27, Pages: 7
                Product

                SciELO Brazil

                Self URI: Full text available only in PDF format (EN)
                Categories
                Articles

                dimensões lineares,área foliar,Salvia hispanica L.,modelos matemáticos,crescimento de planta,plant growth,linear dimensions,mathematical models,leaf area

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content433

                Cited by8

                Most referenced authors543