Prediction and calculation of morphological characteristics and distribution of assimilates in the ROSGRO model

  • Authors:
  • E. Dayan;E. Presnov;M. Fuchs

  • Affiliations:
  • Agricultural Research Organization, Besor Exp. Station, Mobile Post: Hanegev 4, 85400, Israel;Agricultural Research Organization, Besor Exp. Station, Mobile Post: Hanegev 4, 85400, Israel;Agricultural Research Organization, Besor Exp. Station, Mobile Post: Hanegev 4, 85400, Israel

  • Venue:
  • Mathematics and Computers in Simulation - Special issue: Selected papers of the IMACS/IFAC fourth international symposium on mathematical modelling and simulation in agricultural and bio-industries
  • Year:
  • 2004

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Abstract

ROSGRO is a mechanistic photosynthesis-based model for better understanding of rose growth under a controlled environment. The rose canopy is composed of two types of shoots: flower shoots (FSs) and side shoots (SSs). Each shoot type is a complex of three components: stem internodes, compound leaflets and whorled petals, characterized by number, weight and morphological dimension. Light interception by the leaf area, photosynthesis and respiration are calculated in order to determine assimilates production and conversion into structural dry matter (DM). Subsequently, the model partitions the DM among plant organs and estimates spatial distribution of plant material from dry weight. DM partitioning between shoots derives from the potential growth rates established according to the potential growth of shoot templates. The potential growth can be estimated by morphological measurements on basal shoots (BSs). The growth and development of each shoot is arbitrarily divided into 20 age classes (ACs). In each AC, the apex of an FS or SS has similar morphogenetic information to the BS apex, but is deficient in its supply of assimilates. The model handles the daily bookkeeping of the number, weight, and length, area or volume of each component by considering birth and growth, death, entry and exit of components in each AC. The model predicts harvest dates and rates of picking by number and weight. It predicts flower quality characteristics and their seasonal evolution. The calculated numbers, weights, and average weights and lengths of picked flowers agree well with measured values.