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This article in SSSAJ

  1. Vol. 46 No. 1, p. 158-161
     

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doi:10.2136/sssaj1982.03615995004600010039x

Regression Models for Predicting Corn Yields from Climatic Data and Management Practices1

  1. J. W. Bauder and
  2. G. W. Randall2

Abstract

Abstract

The purpose of this study was to determine the relationship between climatic factors, management-controlled factors, and corn (Zea mays L.) grain yield in the northern Corn Belt. Harvest grain moisture and early plant growth were also investigated. Models were developed to quantify the effect of variations in seasonal climatic conditions and annual management practices on corn grain yield. The study was conducted on Webster clay loam, a fine-loamy, mixed mesic Typic Haplaquoll in south-central Minnesota.

Soil-stored moisture and early and mid-season precipitation were significantly related to corn grain yield. Other inputs included date of planting, final populations, and previous crop residue on the soil surface at planting time. The models accounted for 86 to 89% of the variability in corn grain yield and 93% of the variability in grain moisture percentage over a 5-year period, where five different tillage practices were compared. The models provide a simplistic means of identifying and demonstrating the significance of management practices that maximize yields. They also provided a means of approximating grain yield potential.

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