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Kent D. Kobayashi

A simulation model consists of equations that represent the important relationships between components in a system, e.g., a plant or plant part. One of the purposes of simulation models is to simulate plant growth or plant growth processes to help further our understanding of plant growth and development. Simulation models are mechanistic or process based models that account for the physiological processes occurring in the system.

Model development involves several steps. We define the problem and defuse the system, its entities, their attributes, and important relationships. A conceptual model is often expressed visually in a relational diagram showing the components and their relationships. This diagram is formally expressed as a simulation model through the use of equations repenting the relationships in the system. We often make assumptions regarding the components and their relationships to simply the model or because of a lack of knowledge. Simulation models are generally written using a simulation language such as CSMP or STELLA® or with a programming language such as FORTRAN or BASIC. The model is verified through checking the appropriateness of the relationships and the integrity of the computer program. The model is then validated through seeing bow well it simulates the behavior of the system. Simulation models provide additional insights by enabling us to ask “What if” questions by changing of the conditions of the model and seeing the resulting changes in plant growth.

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Jianyu Chen, Keith A. Funnell, and Ed R. Morgan

frequently so for light ( Faust, 2003 ). Development of reliable models for scheduling flowering requires an understanding of the environmental requirements for flower induction and development through to anthesis ( Funnell, 2008 ; Oh et al., 2009

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Xiaofang Guo, Chengyan Yue, and Charles R. Hall

. Few studies have been conducted that investigate the factors that affect the flow of products in nursery crop trade. The gravity model has been widely used for agricultural trade-related studies. Zahniser et al. (2002) explored the impact of

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Michael S. Reid and Fisun G. Çelikel

use in ornamentals, many of the strategies for commercial use were developed using ornamental models. Ornamentals provide a diversity of species and isolated petal and individual flower systems that can be used to test chemicals such as 1-MCP

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Dennis D. Schulte, George E. Meyer, Jay B. Fitzgerald, and Kevin G. Power

This paper is a forward-oriented review of computer-based simulation models. It focusses on applications to horticultural crops in nurseries and greenhouses. Highlights of systems simulation models for horticultural enterprises are presented. The paper closes with an outline of needs for future simulation models for horticultural crops and trends in computer science and engineering which will facilitate useful applications for the industry.

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Soo-Hyung Kim, Jig Han Jeong, and Lloyd L. Nackley

made valuable contributions in modeling canopy responses using radiation use efficiency and water use efficiency in garlic ( Rizzalli et al., 2002 ; Villalobos et al., 2004 ). Building on these efforts, a mechanistic crop simulation model that

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Julie M. Tarara, Paul E. Blom, Bahman Shafii, William J. Price, and Mercy A. Olmstead

of plant fresh mass recorded through destructive sampling. However, the inherent sensitivity of the TTM to changes in the total mass being supported by the trellis wire ( Tarara et al., 2004 ) highlights the need for nondestructive approaches to model

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Eric H. Simonne, Doyle A. Smittle, and Harry A. Mills

An irrigation scheduling model for turnip (Brassica rapa L.) was validated using a line-source irrigation system in a 2-year field trial. The model used a water balance, a variable root length, and a crop factor function of plant age (i). Evapotranspiration was computed daily as class A pan evaporation times a crop factor [CF(i) = 0.365 + 0.0154i-0.00011i2]. Irrigation according to the model maintained soil water tension at <25 kPa at a 30-cm depth. When rainfall amounts were less than water use, leaf yields responded quadratically to irrigation rates, from 0% to 160% of the model rate, and the highest leaf yield with the lowest water applications corresponded to the model rate. Therefore, this model could replace the “feel or see” methods commonly used for scheduling irrigation of leafy vegetables grown in the southeastern United States.

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Jared Barnes, Paul Nelson, Brian E. Whipker, David A. Dickey, Dean Hesterberg, and Wei Shi

; however, their occurrence is variable in comparison with the others and not majorly significant for those wishing to model substrate pH change for a crop. The majority of growers raise propagules in soilless, usually sphagnum–peatmoss-based substrate

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Doyle A. Smittle, W. Lamar Dickens, and James R. Stansell

An irrigation scheduling model for snap bean (Phaseolus vulgaris L.) was developed and validated. The irrigation scheduling model is represented by the equation: 12.7(i - 4) × 0.5ASW = Di-1 + [E(0.31 + 0.01i) - P - I]i, where crop age is i; effective root depth is 12.7(i - 4) with a maximum of 400 mm; usable water (cm3·cm-3 of soil) is 0.5 ASW, deficit on the previous day is Di-1; evapotranspiration is pan evaporation (E) times 0.31 + 0.01i; rainfall (mm) is P, and irrigation (mm) is I. The model was validated using a line source irrigation system with irrigation depths ranging from 3% to 145% of tbe model rate in 1985 and from 4% to 180% of the model rate in 1986. Nitrogen fertilization rates ranged from 50% to 150% of the recommended rate both years. Marketable pod yields increased as irrigation rate increased in 1985. Irrigation at 4%, 44%, 65%, 80%, 150%, and 180% of the model rate produced yields that were 4%, 39%, 71%, 85%, 92%, and 55% as great as yields with the model rate in 1986. Marketable pod yields increased as N rate increased when irrigation was applied at 80%, 100%, or 150% of the model rate in 1986, but pod yields varied less with N rate when irrigation was applied at 4%, 44%, 65%, or 180% of the model.