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This article in AJ

  1. Vol. 85 No. 3, p. 725-735
     
    Received: Nov 18, 1991
    Published: May, 1993


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doi:10.2134/agronj1993.00021962008500030038x

A Sunflower Simulation Model: I. Model Development

  1. Scott C. Chapman,
  2. Graeme L. Hammer and
  3. Holger Meinke 
  1. Maize Program, CIMMYT, Apartado Postal 6-641, 06600, Mexico D.F., Mexico

Abstract

Abstract

In dryland farming systems, opportunities to improve sunflower (Helianthus annuus L.) yields are mostly associated with management decisions made at planting. Dynamic crop simulation models can assist in making such decisions. This study reports the structure of QSUN, a simple and mechanistic crop model for sunflower, and how it accounts for the dynamic interaction of the crop with the soil and aerial environment. The model incorporates several recent approaches to simulation of crop growth in dryland conditions. QSUN estimates growth, development, and yield of a sunflower crop. Daily inputs of temperature and photoperiod drive a phenology submodel to predict stages of emergence, bud visibility, 50% anthesis, and maturity. Using these stages, the growth submodel, driven by daily inputs of radiation, rainfall, and temperature, estimates leaf area production and senescence and soil water extraction. Biomass production is calculated from the amount of radiation intercepted by leaves or from the amount of water accessible in the root zone, depending on whether radiation or water is limiting crop growth. Seed yield is calculated from the allocation of biomass to the grain following anthesis. Sensitivity testing of the model under several irrigation regimes indicated that QSUN was most sensitive to the rate at which partitioning of biomass to grain increased, the ratio of biomass produced to water transpired, and the rate of soil water extraction in a water limited situation. The model was tested against independent data, with actual phenological data and was able to satisfactorily predict leaf area index (r2 = 0.65), total biomass (r2 = 0.96), and grain yield (r2 = 0.93), thus providing tool for use in simulations studies and to assist in management decision making.

Contribution from the Agric. Production Systems Res. Unit, Queensland Dep. of Primary Industries and Commonwealth Scientific and Industrial Research Organisation, PO Box 102, Toowoomba, Qld, 4350, Australia.

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