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Silvia Burés, Franklin A. Pokorny, David P. Landau, and Alan M. Ferrenberg

1 Dept. of Horticulture. 2 Center for Simulational Physics. 3 Univ. Computing and Networking Services. Supported by state and Hatch funds allocated to the Georgia Experiment Station. We acknowledge Larry T. West, Crop and Soil Sciences Dept., Univ

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Héctor Germán Rodríguez, Jennie Popp, Michael Thomsen, Heather Friedrich, and Curt R. Rom

) and i is the interest rate. where PV TC is the sum of present values of total costs incurred each year and i is the interest rate. Simulation and assumptions Simetar © (Simetar Inc., College Station, TX) is used to simulate the random PV of GR

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J.I. Lizaso, K.J. Boote, C.M. Cherr, J.M.S. Scholberg, J.J. Casanova, J. Judge, J.W. Jones, and G. Hoogenboom

perishable nature intensify interest in predicting accurately timing and fresh weight production of the crop. A sweet corn simulation model could assist crop production planning by exploring alternative planting dates matching crop requirements with local

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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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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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Kent D. Kobayashi

The simulation programs Stella® (High Performance Systems) and Extend™ (Imagine That!) were used on Apple® Macintosh® computers in a graduate course on crop modeling to develop crop simulation models. Students developed models as part of their homework and laboratory assignments and their semester project Stella offered the advantage of building models using a relational diagram displaying state, rate, driving, and auxiliary variables. Arrows connecting the variables showed the relationships among the variables as information or material flows. Stella automatically kept track of differential equations and integration. No complicated programming was required of the students. Extend used the idea of blocks representing the different parts of a system. Lines connected the inputs and outputs to and from the different blocks. Extend was more flexible than Stella by giving the students the opportunity to do their own programming in a language similar to C. Also, with its dialog boxes, Extend more easily allowed the students to run multiple simulations answering “What if” questions. Both programs quickly enabled students to develop crop simulation models without the hindrance of extensive learning of a programming language or delving deeply into the mathematics of modeling.

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Jingbo Zhang and Duane P. Bartholomew

A simulation model of pineapple growth and development (CERES-Pineapple) was developed, using the structure of CERES-Maize and a heat unit model for pineapple inflorescence development. The model is process-oriented and incremented daily. It simulates the effects of planting date, plant population, plant size at planting and at forcing, and weather on pineapple crop growth and development. CERES-Pineapple was calibrated to field data collected from a plant population trial at Kunia, Hawaii, and validated using data from 11 plantings of pineapple grown in Hawaii. The model accurately simulated pineapple growth and development for most Hawaii conditions but underpredicted fruit yields for pineapple grown at high elevations. CERES-Pineapple also provides a frame-work for the conduct of pineapple research and has potential to serve as a decision aid for pineapple farmers.

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Steven E. Woerner and Douglas A. Hopper

A computer simulation model was developed to be used in evaluating irrigation scheduling techniques and assisting irrigation scheduling decisions under greenhouse conditions in Colorado. The model simulates variable greenhouse conditions and shows how each of four irrigation scheduling techniques responds to these conditions. Reports from the model detail numbers of irrigation events, sensitivities to parameters, and forecasts water usage. The model was also used to determine appropriate accumulation triggers for Colorado conditions.

Four techniques evaluated here include: time clock control; accumulated radiation; accumulated vapor pressure deficit; combination method (radiation and vapor pressure deficit). The model has shown the combination method to be the most sensitive to changes in environmental conditions, while the time clock method proved to be least sensitive (and most wasteful of water).

The model may evaluate additional irrigation scheduling techniques by including additional parameters in the model, and may readily be adapted to different climatic regions.

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Leon H. Allen Jr., Mary P. Brakke, and James W. Jones

A water flow model was developed which uses irradiance, leaf-to-air vapor concentration difference, and soil water potential to establish stomatal conductance. Water flow to the roots was computed using a linear approximation of radial flow through the soil toward the axis of the roots across concentric shells. Root length density and soil rooting volume within four separate layers or compartments were included in the model. The simulation was executed in small time step iterations. A small increment of transpiration was translated to a water content deficit at the root and then sequentially through the concentric shells to simulate water uptake and change of soil water potential. The change in soil water potential was used to increment changes in stomatal conductance and transpiration. The output of the model simulated the pattern of diurnal stomatal behavior observed in other types of experiments, as well as the total soil water extraction patterns of young potted citrus trees.

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Giovanni Piccinni, Thomas Gerik, Evelyn Steglich, Daniel Leskovar, Jonghan Ko, Thomas Marek, and Terry Howell

Improving irrigation water management for crop production is becoming increasingly important in South Texas as the water supplies shrink and competition with urban centers in the region grows. Crop simulators and crop evapotranspiration (ET) are appealing methods for estimating crop water use and irrigation requirements because of the low investment in time and dollars required by on-site (in-field) measurement of soil and/or crop water status. We compared the effectiveness of the Crop.m.an/EPIC crop simulator and Crop-ET approaches estimating the crop water use for irrigation scheduling of spinach. In-ground weighing lysimeters were used to measure real-time spinach water use during the growing season. We related the water use of the spinach crop to a well-watered reference grass crop to determine crop coefficients (Kc) to assist in predicting accurate crop needs using available meteorological data. In addition, we ran several simulations of CropMan to evaluate the best management for growing spinach under limited water availability. Results show the possibility of saving about 61 to 74 million m3 of water per year in the 36,500 ha of irrigated farms of the Edwards aquifer region if proper irrigation management techniques are implemented in conjunction with the newly developed decision support systems. We discuss the implications of the use of these technologies for improving the effectiveness of irrigation and for reducing irrigation water requirements in South Texas.