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This article in CS

  1. Vol. 45 No. 5, p. 1708-1716
    Received: June 17, 2004

    * Corresponding author(s): carlos.loeffler@pioneer.com
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Classification of Maize Environments Using Crop Simulation and Geographic Information Systems

  1. Carlos M. Löffler *,
  2. Jun Wei,
  3. Tim Fast,
  4. Joe Gogerty,
  5. Steve Langton,
  6. Marlin Bergman,
  7. Bob Merrill and
  8. Mark Cooper
  1. Pioneer Hi-Bred International, Inc., 7250 NW 62nd Ave., P.O. Box 552, Johnston, IA 50131


The effectiveness of a cultivar evaluation system largely depends on the genetic correlation between genotype performance in multienvironment trials (MET) and in the target population of environments (TPE). Previous classifications of maize (Zea mays L.) environments on the basis of climate and soil did not quantify their impact on the genetic correlations among environments. Consequently, plant breeders have favored classifications based on the similarity of cultivar discrimination in trials. However, these efforts frequently fail to provide adequate assessments of the TPE, since they require long-term performance data, which are not normally collected due to high cost. To describe the TPE, we performed crop simulations for each U.S. Corn Belt Township for the period 1952 through 2002, using standard CERES-Maize model inputs. To classify METs, input data were collected at or near the trial sites. Grain yield and biotic stress data for model confirmation were collected from 18 hybrids grown in replicated trials in 266 environments in 2000–2002. On the basis of prevailing conditions during key growth stages, and observed patterns of genotype × environment interactions (GEI), six major environment classes (EC) were identified. The relative frequency of each EC varied greatly from year to year and significant hybrid × EC interaction variance was observed. Our environmental classification system provided a useful description of some of the features of both the TPE and MET. Knowledge of the spatial (locations) and temporal (years) distributions of ECs that influence the incidence of GEI can be used to improve cultivar performance predictability in the U.S. Corn Belt TPE.

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