Generation of mechanistic variability in a process-based object-oriented plant model

Abstract A cotton crop model based on individual plant developmental behavior and variability was developed. Object-oriented simulation (OOS) provided the conceptual basis for the new model structure. The procedural model, COTSIM, provided the theoretical background for cotton plant development. Data collected during 1987 from field-grown cotton were used for model development and verification, and data from 1988 were used for model validation. The model predicted mass accretion and production of organs within the patterns and magnitudes observed in the field. The model also predicted crop development aspects that had not previously been described by procedural models. Age and size of leaves and fruit and associated developmental variability were included in the model through representation of objects and their variable behavior defined by their position on the plant and how this constrains their growth. Observed variability was the result of the aggregate behavior of components. Variability in our OOS model is an output as opposed to being an input in most procedural plant models. The model has recreated both realistic plants and populations in a mechanistic simulation. Object-oriented models are an important step towards common structures and languages for model design and the development of simulations. It was noted that increased mechanistic detail resulted in an increase of procedure calls (messages) and a five-fold increase in model run time.

[1]  N. D. Stone,et al.  Object-oriented simulation: plant growth and discrete organ to organ interactions , 1991 .

[2]  Timothy S. Larkin,et al.  Simulation and object-oriented programming: the development of SERB , 1988, Simul..

[3]  Thomas B. Starr,et al.  Hierarchy: Perspectives for Ecological Complexity , 1982 .

[4]  P. H. Leslie On the use of matrices in certain population mathematics. , 1945, Biometrika.

[5]  A. J. Lotka,et al.  Elements of Physical Biology. , 1925, Nature.

[6]  D. R. Buxton,et al.  Cotton: A Computer Simulation of Cotton Growth , 1974 .

[7]  Richard M. Feldman,et al.  Mathematical foundations of population dynamics , 1987 .

[8]  Bryan L. Deuermeyer,et al.  Approximating a closed-form solution for cotton fruiting dynamics , 1981 .

[9]  D. N. Baker,et al.  Simulation of Growth and Yield in Cotton: I. Gross Photosynthesis, Respiration, and Growth 1 , 1972 .

[10]  P. Sharpe,et al.  Modeling Distributions of Insect Development Time: a Literature Review and Application of the Weibull Function , 1984 .

[11]  John Vansickle,et al.  Attrition in Distributed Delay Models , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  A. B. Hearn,et al.  A simple model for crop management applications for cotton (Gossypium hirsutum L.) , 1985 .

[13]  R. O'Neill A Hierarchical Concept of Ecosystems. , 1986 .

[14]  James W. Sinko,et al.  A New Model For Age‐Size Structure of a Population , 1967 .

[15]  Grady Booch,et al.  Object-Oriented Design with Applications , 1990 .

[16]  G. D. Butler,et al.  DISTRIBUTION MODEL OF HELIOTHIS ZEA (LEPIDOPTERA: NOCTUIDAE) DEVELOPMENT TIMES , 1981, The Canadian Entomologist.

[17]  D. DeAngelis,et al.  New Computer Models Unify Ecological TheoryComputer simulations show that many ecological patterns can be explained by interactions among individual organisms , 1988 .

[18]  A. J. Lotka Elements of mathematical biology , 1956 .

[19]  Richard M. Feldman,et al.  Statistical Procedure for Validating a Simple Population Model , 1984 .

[20]  Thomas J. Manetsch,et al.  Time-Varying Distributed Delays and Their Use in Aggregative Models of Large Systems , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[21]  Andrew Paul Gutierrez,et al.  A general distributed delay time varying life table plant population model: Cotton (Gossypium hirsutum L.) growth and development as an example☆ , 1984 .

[22]  W. Thomas Modeling within-plant distribution of Heliothis spp. (Lepidoptera: Noctuidae) damage in cotton☆ , 1989 .

[23]  Andrew Paul Gutierrez,et al.  A POPULATION MODEL FOR PLANT GROWTH AND DEVELOPMENT: COUPLING COTTON–HERBIVORE INTERACTION , 1977, The Canadian Entomologist.

[24]  H Lemmon,et al.  Comax: An Expert System for Cotton Crop Management , 1986, Science.

[25]  William E. Grant,et al.  AN ARTIFICIAL INTELLIGENCE MODELLING APPROACH TO SIMULATING ANIMAL/HABITAT INTERACTIONS , 1988 .

[26]  James H. Torrie,et al.  Principles and procedures of statistics: a biometrical approach (2nd ed) , 1980 .

[27]  D. Wallach An empirical mathematical model of a cotton crop subjected to damage. , 1980 .

[28]  P. Sharpe,et al.  Distribution model of organism development times. , 1977, Journal of theoretical biology.

[29]  Lloyd T. Wilson,et al.  Within-plant distribution of the immatures of Heliothis zea (Boddie) on cotton. , 1980 .