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Simulation of Leaf Area Development Based on Dry Matter Partitioning and Specific Leaf Area for Cut Chrysanthemum

机译:基于干物质分配和切叶特定叶面积的叶面积发育模拟

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摘要

This work aims to predict time courses of leaf area index (LAI) based on dry matter partitioning into the leaves and on specific leaf area of newly formed leaf biomass (SLAn) for year‐round cut chrysanthemum crops. In five glasshouse experiments, each consisting of several plant densities and planted throughout the year, periodic destructive measurements were conducted to develop empirical models for partitioning and for SLAn. Dry matter partitioning into leaves, calculated as incremental leaf dry mass divided by incremental shoot dry mass between two destructive harvests, could be described accurately (R2 = 0·93) by a Gompertz function of relative time, Rt. Rt is 0 at planting date, 1 at the start of short‐days, and 2 at final harvest. SLAn, calculated as the slope of a linear regression between periodic measurements of leaf dry mass (LDM) and LAI, showed a significant linear increase with the inverse of the daily incident photosynthetically active radiation (incident PAR, MJ m–2 d–1), averaged over the whole growing period, the average glasshouse temperature and plant density (R2 = 0·74). The models were validated by two independent experiments and with data from three commercial growers, each with four planting dates. Measured shoot dry mass increase, initial LAI and LDM, plant density, daily temperature and incident PAR were input into the model. Dynamics of LDM and LAI were predicted accurately by the model, although in the last part of the cultivation LAI was often overestimated. The slope of the linear regression of simulated against measured LDM varied between 0·95 and 1·09. For LAI this slope varied between 1·01 and 1·12. The models presented in this study are important for the development of a photosynthesis‐driven crop growth model for cut chrysanthemum crops.
机译:这项工作旨在根据常年切割的菊花作物中划分为叶片的干物质和新形成的叶片生物量(SLAn)的特定叶面积来预测叶面积指数(LAI)的时程。在五个温室实验中,每个实验都由几种植物密度组成,并且全年种植,进行了定期破坏性测量,以建立分区和SLAn的经验模型。通过将相对干时间的Gompertz函数准确地描述(R 2 = 0·93),将干物质分配到叶片中,将其计算为叶片的干物质增量除以两个破坏性收获之间的枝干增量。 Rt。 Rt在播种日期为0,在短日开始时为1,在最终收获时为2。以叶片干重(LDM)和LAI的定期测量值之间的线性回归斜率计算的SLAn显示,随着日入射光合有效辐射的倒数,线性显着增加(入射PAR,MJ m d –1 ),在整个生长期平均,温室平均温度和植物密度(R 2 = 0·74)。该模型通过两个独立的实验以及来自三个商业种植者的数据进行了验证,每个种植者都有四个种植日期。将测量的枝干质量增加,初始LAI和LDM,植物密度,日温度和入射PAR输入模型。该模型可准确预测LDM和LAI的动力学,尽管在耕种的最后阶段经常会高估LAI。相对于测得的LDM,模拟的线性回归的斜率在0·95和1·09之间变化。对于LAI,此斜率在1·01和1·12之间变化。这项研究中提出的模型对于开发光合作用驱动的菊苣作物生长模型非常重要。

著录项

  • 期刊名称 Annals of Botany
  • 作者

    J. H. LEE; E. HEUVELINK;

  • 作者单位
  • 年(卷),期 2003(91),3
  • 年度 2003
  • 页码 319–327
  • 总页数 9
  • 原文格式 PDF
  • 正文语种
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