The effectiveness of a product evaluation system largely depends on the genetic correlation between multi-environment trials (MET) and the target population of environments (TPE) (Comstock 1977). Previous classifications of maize environments relied mainly on climatic and soil data (e.g., Pollak and Corbett l993; Runge 1968). While useful to describe environmental variables affecting crop productivity I these efforts did not quantify the impact of these variables on the genetic correlations among testing sites. Consequently I plant breeders have more extensively used classifications of environments based on similarity of product discrimination in product evaluation trials (e.g., Cooper et al. 1993). However, these efforts frequently fail to provide a long-term assessment of the TPE, mainly due to the cost and impracticality of collecting empirical performance data for long-term studies. Using a crop simulation modeL Chapman et al. (2000) integrated soils and long-term weather data to classify highly variable sorghum environments in Australia. For a subset of six testing locations, they found that three drought stress environment types had a consistent relationship with simulated yield. The purpose of this study was to investigate the applicability of this approach to the characterization of the milder US maize environments.

English

0309|AGRIS 0301|AL-Maize Program

Juan Carlos Mendieta

CIMMYT Publications Collection