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Suitability of Corn Growth Models for Incorporation of Weed and Insect Stresses
Author(s) -
Retta A.,
Vanderlip R. L.,
Higgins R. A.,
Moshier L. J.,
Feyerherm A. M.
Publication year - 1991
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj1991.00021962008300040021x
Subject(s) - phenology , agronomy , pest analysis , yield (engineering) , biology , leaf area index , biomass (ecology) , infestation , weed , horticulture , materials science , metallurgy
Shattercane [ Sorghum bicolor (L.) Moench] and second generation European corn borer (ECB) [ Ostrinia nubililis (Hubner)] are pests that singly or in combination reduce corn ( Zea maize L.) production in the northcentral regions of the USA. Shattercane reduces corn growth and yield because it competes effectively with corn for light and water. Second generation ECB larvae, in tunneling through the vascular system, apparently affect yield by disrupting water and photosynthate movements. Pest models may be linked to physiological models for assessing the effects of pest stresses on corn growth and yield. CERES‐Maize and CORNF corn growth models were chosen to test accuracy and consistency in predicting corn growth and yield parameters. The objectives were to evaluate corn growth models to which pest models could be attached and to test the sensitivity of the selected model to variations in light and water. Simulated leaf area index; vegetative, grain, and total biomass; and yield components were compared to measured data. CERES‐Maize modified for leaf growth and phenology computations (VO/SAT) gave more accurate predictions of date of silking (bias = 1 d) than CORNF (bias = 6 d) or original version CERES‐Maize (bias = −5 d). Accurate estimation of phenology is important because the severity of yield reduction from ECB infestation is dependent on the stage of growth. Sensitivity of VO/SAT to reductions in light and water inputs was tested by simulating combinations of light and water levels ranging from 50 to 100% of actual. A 50% reduction in light resulted in average reductions of 26% in yield, 16% in kernel weight, 16% in kernel number, and 20% in leaf area index. Similarly, a 50% reduction in precipitation resulted in average reductions of 47% in yield, 51% in kernel weight, 1% in kernel number, and 1% in leaf area index. The combination model showed adequate sensitivity to light and water, and thus could be modified to mimic weed competition.