Simulation Models Relevant To Australian Farming Systems

Miracle Farm Blueprint

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During the twentieth century, world agriculture passed through three revolutionary eras. The first was the mechanical (1930-1950), the second, seed-fertilizer (1960-1970), and the third, information technology (in the closing decades of the century). Within the span of the last two decades of the century, several thousand computer-based plant and animal dynamical models were developed worldwide which have expanded scientific insight into the complex interactions between environmental and biological systems. Australian science and scientists made a substantial contribution in this expansion. Currently, scores of modeling groups and hundreds of individuals are actively engaged in this pursuit. Several crop and pasture models developed in Australia are in use on an international level.

To describe and discuss even a fraction of the models developed in Australia is beyond the scope of this book. The information given on the topic is not conclusive, nor is the list of sources of this information exhaustive. A summary of a limited number of models that are thoroughly tested and are currently in use is given in Table 8.1. Tree and crop models are described first, followed by pasture and animal models.

DECISION SUPPORT SYSTEMS (DSS)

Decision support systems (DSS) are integrated software packages comprising tools for processing both numerical and qualitative information. A DSS points the way for better decision making in the cropping and pastoral industries. It offers the ability to deliver the best information available, quickly, reliably, and efficiently.

The choices of planting time, varietal selection, grazing strategies, and fertilizer, irrigation, and spray applications are complex decisions to be made at the farm level. These are important and decisive because they cannot be postponed, are irreversible, represent a substantial allocation of resources, and have a wide range of outcomes, with consequences that impact the farm business for years to come. They are also hard decisions because they are characterized by uncertainty, mainly due to the highly variable climate. They are complex both in terms of the number of interacting factors and the trade-offs between risk and reward. A successful decision support system focuses on such decisions. A key element in the success of a DSS is the development of trust in its reliability and the willingness and ability of the targeted users to utilize the system.

go TABLE 8.1. Crop, pasture, and animal production simulation models

Model Output Reference

BIOMASS Growth, dry matter, yield McMurtrie and Landsberg,

Tree model 1991

CenW Photosynthetic carbon gain, water use, nitrogen Kirschbaum, 1999

Tree model cycling

APSIM Carbon, water, and nitrogen balances of agricultural McCown et al., 1996

Agricultural Production Systems systems, crop rotations, interspecies competition, etc.

SIMulator

SIMTAG All development phases, plant dry matter, grain yield Stapper, 1984

Simulation model for Triticum aestivum

Wheat model Soil moisture content, crop growth rate, canopy leaf- Wang and Gifford, 1995

area index, crop biomass, grain number, grain yield

QBAR Soil water balance, phenology, leaf area, biomass pro- Hook, 1997

Barley model duction, grain yield

AUSCANE Sugarcane model

Yield of millable cane stalks, sugar content of cane Jones et al., 1989

CERCOT Cotton model

Growth and production

Hook, 1997

OZCOT Cotton model

Soil water content, evapotranspiration, yield, yield components, fruiting dynamics, leaf-area index, nitrogen uptake

Hearn, 1994

GRASP Pasture model

Soil water status, pasture growth, death and detachment, animal intake, diet selection, utilization, live weight gain

McKeon et al., 1990

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