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Evaluation of Seedling Emergence in Cicer Milkvetch by Linear Regression 1
Author(s) -
Townsend C. E.,
Remmenga E. E.,
Dewald C. L.,
Ditterline R. L.,
Melton B. A.,
Smoliak S.
Publication year - 1979
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci1979.0011183x001900050037x
Subject(s) - seedling , adaptability , biology , sowing , linear regression , agronomy , regression analysis , precipitation , stability (learning theory) , botany , horticulture , ecology , statistics , mathematics , geography , machine learning , computer science , meteorology
We used linear regression to evaluate the stability and adaptability of polycross progenies of cicer milkvetch ( Astragalus cicer L.) for seedling emergence in diverse environments. The environments included four sites in Colorado and one each in Montana, Oklahoma, New Mexico, and Alberta, Canada, with 2 or more years of evaluation at some sites. Data from the last two sites were omitted from regression analysis because of extreme variation. Although there were large differences among environments for seedling emergence, several progenies ranked high in all environments. Such progenies were average or above average for stability and could be considered well adapted to most environments. The progeny with the highest average emergence had below average stability because it was poorly adapted to the most unfavorable environment. Soil moisture conditions at planting time and subsequent precipitation greatly influenced seedling emergence. Linear regression was a simple and effective method of demonstrating the relative stability and adaptability of polycross progenies for seedling emergence in these environments.