Evaluation of the Plant Growth Model GREENLAB-Maize

Plant architecture and topology are important determinants of crop performance and agro-ecological adaptation, and should thus be taken into account in crop modelling approaches. This has not been done in most crop models designed to answer agronomic questions. GREENLAB-Maize model combines the dynamic simulation of the complete plant architecture with simple algorithms of biomass formation, for which the growing organs are competing. Plant is considered at organ level (roots, leaves, internodes, cobs, etc.) and thus as a set of sinks competing for assimilates. Morphogenetic processes are governed by generic organ expansion laws associated with organ-specific parameters supposed to be independent from environmental conditions. Environment will then define the carbon supply available to the plant at any given time step. An advantage of the model is that it can be used to get the values of parameters for a given species (target file) to retrieve morphogenetic and organogenetic history of the plant. Based on the above reasoning, multi-fitting technique was introduced in GREENLAB-Maize model to compute the values of endogenous parameters, which can trace back the dynamical process between source and sink as plant growth. The aim of this study was to make a first evaluation of the ability of GREENLAB-Maize to get the values of endogenous parameters taking both field heterogeneity and inter-annual trials according to the variability in environmental condition. This study focused on the case of one maize genotype cropped in three years in non-limiting conditions but with natural seasonal variation. Four field experiments were conducted at the China Agricultural University (39° 50'N, 116° 25'E). ND108 cultivar seeds were sown in rows in north- south direction with a row spacing 0.6 m and plant spacing 0.6 m within the row (28000 plants ha1). Water and nutrients were supplied to maintain non-limiting conditions. Meteorological data were acquired from a field station located on the site. Tillers were pruned systematically when they appeared to maintain "mono-culm" architecture. The avenge number of leaves in a plant of this genotype at maturity was 21. Four plants were taken to measure the fresh weights and dimension of individual organs (i. e. internodes, leaf sheaths, leaf blades, cobs and tassels). Leaf blade area was characterized using a LI-COR Model 3100 Area Meter (Lincoln, NB, USA) Statistical analysis of the results showed that: (1) multi-fitting clearly improves the stability of parameters more among experiments and growths than single fitting; (2) the biomass of the plant and its parts were significantly different between years and seasons (except leaf sheath biomass), but not among plants sampled at the same time; (3) the differences of endogenous parameters were little between different years, growth stages and individual maize plant. The parameters optimized with multi-fitting of year 2000 experiment were then considered as the reference set of parameters and used to simulate plant growth for other experiments. There was a good agreement between simulated and measured data for organ biomass and geometry. Then three-dimensional visualization of maize plant among different growth stages were brought about based on these processes to study the growth of individual maize plant

Dual-scale automaton model for virtual plant development

in ChineseA variety of plant growth models for plant morphogenesis have been reported, but most of them are based on computer graphics to represent natural scenery. In this paper, a dual-scale automaton model for virtual plant development is presented, in which the concepts of microstate and macrostate are proposed from the viewpoints of botany. The parameters of this model have explicitly physical meaning for relating to the plant growth mechanisms, and they can be structured for input with diagram. All of these features make it easy to understand and implement in programming. And a probability process, which accords with the growth of plant's apical bud and axillary bud, is also used in this model. The model is demonstrated a simpler but effective method by comparison with 'L-system' and 'reference axis technique'. While the model has been tested for generating almost all plant architectural models defined by botanists, only one example is given in the paper to confirm the advantages of the model

Parameter optimization and field validation of the functional-structural model GREENLAB for maize

Background and Aims: There are three reasons for the increasing demand for crop models that build the plant on the basis of architectural principles and organogenetic processes: (1) realistic concepts for developing new crops need to be guided by such models; (2) there is an increasing interest in crop phenotypic plasticity, based on variable architecture and morphology; and (3) engineering of mechanized cropping systems requires information on crop architecture. The functional–structural model GREENLAB was recently presented that simulates resource-dependent plasticity of plant architecture. This study introduces a new methodology for crop parameter optimization against measured data called multi-fitting, validates the calibrated model for maize with independent field data, and describes a technique for 3D visualization of outputs. Methods: Maize was grown near Beijing during the 2000, 2001 and 2003 (two sowing dates) summer seasons in a block design with four to five replications. Detailed morphological and topological observations were made on the plant architecture throughout the development of the four crops. Data obtained in 2000 was used to establish target files for parameter optimization using the generalized least square method, and parameter accuracy was evaluated by coefficient of variance. In situ plant digitization was used to establish 3D symbol files for organs that were then used to translate model outputs directly into 3D representations for each time step of model execution. Key: Results and Conclusions Multi-fitting against several target files obtained at different growth stages gave better parameter accuracy than single fitting at maturity only, and permitted extracting generic organ expansion kinetics from the static observations. The 2000 model gave excellent predictions of plant architecture and vegetative growth for the other three seasons having different temperature regimes, but predictions of inter-seasonal variability of biomass partitioning during grain filling were less accurate. This was probably due to insufficient consideration of processes governing cob sink size and terminal leaf senescence. Further perspectives for model improvement are discussed