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Annual grassland ecosystem model
Authors
Dennis F. PendletonJohn W. Menke
William A. Williams
Robert G. Woodmansee
Authors Affiliations
Dennis F. Pendleton was Program Specialist, University Extension, Davis; John W. Menke was Associate Professor, Department of Agronomy and Range Science, Davis; William A. Williams was Professor, Department of Agronomy and Range Science, Davis; Robert G. Woodmansee was Associate Professor, Department of Range Science, Colorado State University, Fort Collins.Publication Information
Hilgardia 51(1):1-44. DOI:10.3733/hilg.v51n01p044. April 1983.
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Abstract
A dynamic computer model of the annual grassland ecosystem of California (ELMAGE) has been developed through the adaptation of ELM, a compartment/flow computer model constructed in the US/IBP Grassland Biome. The modeling effort was part of a larger project involving an interdisciplinary team of researchers with the objective of compiling and synthesizing diverse information on the annual grassland ecosystem. Primary data from the San Joaquin Experimental Range and other information from published and unpublished sources were used in developing the model.
The general objective was to simulate the seasonal dynamics of biomass in the annual grassland on a daily basis. The components of the grassland ecosystem—weather factors, primary producers, decomposers, nutrients, and mammalian consumers—are described in linked submodels. Process mechanisms in each of the submodels control flow rates of carbon (biomass), nutrients, and water through the simulated system. Driving variables are temperature, precipitation, relative humidity, wind speed, and cloud cover.
Specific model objectives included assessment of the effect of changes in weather factors, fertilization practices, seeding practices, and stocking level of livestock on forage production and botanical composition of a simulated site. Implicit goals of the work included the use of the model to facilitate organization of information on the annual grassland, to test hypotheses concerning poorly understood biological phenomena, and to suggest future research needs.
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