A dynamic, architectural plant model simulating resource-dependent growth.

BACKGROUND AND AIMS Physiological and architectural plant models have originally been developed for different purposes and therefore have little in common, thus making combined applications difficult. There is, however, an increasing demand for crop models that simulate the genetic and resource-dependent variability of plant geometry and architecture, because man is increasingly able to transform plant production systems through combined genetic and environmental engineering. MODEL GREENLAB is presented, a mathematical plant model that simulates interactions between plant structure and function. Dual-scale automaton is used to simulate plant organogenesis from germination to maturity on the basis of organogenetic growth cycles that have constant thermal time. Plant fresh biomass production is computed from transpiration, assuming transpiration efficiency to be constant and atmospheric demand to be the driving force, under non-limiting water supply. The fresh biomass is then distributed among expanding organs according to their relative demand. Demand for organ growth is estimated from allometric relationships (e.g. leaf surface to weight ratios) and kinetics of potential growth rate for each organ type. These are obtained through parameter optimization against empirical, morphological data sets by running the model in inverted mode. Potential growth rates are then used as estimates of relative sink strength in the model. These and other 'hidden' plant parameters are calibrated using the non-linear, least-square method. KEY RESULTS AND CONCLUSIONS The model reproduced accurately the dynamics of plant growth, architecture and geometry of various annual and woody plants, enabling 3D visualization. It was also able to simulate the variability of leaf size on the plant and compensatory growth following pruning, as a result of internal competition for resources. The potential of the model's underlying concepts to predict the plant's phenotypic plasticity is discussed.

[1]  A. Lindenmayer.  Mathematical models for cellular interactions in development. II. Simple and branching filaments with two-sided inputs. , 1968, Journal of theoretical biology.

[2]  Alvy Ray Smith,et al.  Plants, fractals, and formal languages , 1984, SIGGRAPH.

[3]  J. R. Kiniry,et al.  CERES-Maize: a simulation model of maize growth and development , 1986 .

[4]  Marc Jaeger,et al.  Plant models faithful to botanical structure and development , 1988, SIGGRAPH.

[5]  Przemyslaw Prusinkiewicz,et al.  Development models of herbaceous plants for computer imagery purposes , 1988, SIGGRAPH.

[6]  F.W.T. Penning de Vries,et al.  Concepts for a new plant type for direct seeded flooded tropical rice. , 1991 .

[7]  Liangzhi Gao,et al.  Rice clock model―a computer model to simulate rice development , 1992 .

[8]  Jean Dauzat.  Radiative transfer simulation on computer models of Elaeis guineensis , 1993 .

[9]  M. Dingkuhn,et al.  Climatic determinants of irrigated rice performance in the Sahel : II. Validation of photothermal constants and characterization of genotypes , 1995 .

[10]  Jari Perttunen,et al.  LIGNUM: A Tree Model Based on Simple Structural Units , 1996 .

[11]  Winfried Kurth,et al.  Growth grammars simulating trees - an extension of L-systems incorporating local variables and sensitivity , 1997 .

[12]  Jean Dauzat,et al.  Simulating light regime and intercrop yields in coconut based farming systems , 1997 .

[13]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[14]  Bruno Andrieu,et al.  The nested radiosity model for the distribution of light within plant canopies , 1998 .

[15]  G. Hoogenboom,et al.  Understanding Options for Agricultural Production , 1998, Systems Approaches for Sustainable Agricultural Development.

[16]  James W. Jones,et al.  Decision support system for agrotechnology transfer: DSSAT v3 , 1998 .

[17]  Ep Heuvelink,et al.  Modelling biomass production and yield of horticultural crops: a review , 1998 .

[18]  Philippe de Reffye,et al.  Calibration of a hydraulic architecture-based growth model of cotton plants , 1999 .

[19]  Martin J. Kropff,et al.  A model analysis of yield differences among recombinant inbred lines in barley , 2000 .

[20]  Gilles Lemaire,et al.  Grassland Ecophysiology and Grazing Ecology , 2000 .

[21]  T. C. Hsiao,et al.  Predicting water use efficiency of crops. , 2000 .

[22]  Philippe de Reffye,et al.  Architectural modeling of wheat growth and validation study , 2000 .

[23]  Harri Hakula,et al.  Components of functional-structural tree models , 2000 .

[24]  C. J. Nelson,et al.  Shoot morphological plasticity of grasses: leaf growth vs. tillering. , 2000 .

[25]  Pierre Martin,et al.  Virtual COTONS®, the Firstborn of the Next Generation of Simulation Model , 2000, Virtual Worlds.

[26]  C. Prakash.  The genetically modified crop debate in the context of agricultural evolution. , 2001, Plant physiology.

[27]  Marc Jaeger,et al.  Fast Algorithms of Plant Computation Based on Substructure Instances , 2002, WSCG.

[28]  M. Dingkuhn,et al.  Effects of shading on sink capacity and yield components of cotton in controlled environments , 2002 .

[29]  Eric Justes,et al.  STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize , 2002 .

[30]  Bao-Gang Hu,et al.  Interactive simulation of plant architecture based on a dual-scale automaton model , 2003 .

[31]  Samuel M. Scheiner,et al.  Phenotypic plasticity : functional and conceptual approaches , 2004 .