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Observed Gains from Three Recurrent Selection Regimes for Increased Groat‐Oil Content of Oat
Author(s) -
Schipper H.,
Frey K. J.
Publication year - 1991
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/cropsci1991.0011183x003100060022x
Subject(s) - selection (genetic algorithm) , avena , biology , agronomy , greenhouse , field experiment , crop , grain yield , zoology , horticulture , computer science , artificial intelligence
Increasing the groat‐oil content (GO) of aot ( Avena sativa L.) could increase the profitability of oat when grown as an oilseed crop or for high‐energy feed grain. In this study, three recurrent selection regimes for increasing GO of oat were evaluated and compared with predicted responses. For Regime 1, selection for GO was practiced among S 0 plants grown in the field; for Regime 2, selection occurred among S 0 plants grown in the greenhouse; and for Regime 3, selection among field‐grown S 0 plants was followed by selection among among and within S 0:1 progenies grown in the greenhouse. Gains from selection for GO via the three regimes were evaluated in field experiments by using random lines from each selection for GO via the three regimes were evaluated in field experiments by using random lines from each selection cycle of each regime. Groat‐oil contents increased significantly in Regimes 1 and 3, but not in Regime 2. Annual gains in GO were 6.04, 5.85, and 11.98 g kg ‐1 in Regimes 1, 2, and 3, respectively, whereas the predicted gains were 9.69, 4.92, and 11.60 g kg ‐1 , respectively. Selection among S 0 in the field and among and withing S 0:1 progenies in the greenhouse both contributed to the increased GO in Regime 3. In each regime, the genetic variance of GO remained significant. Line M022‐4‐5 with a mean GO of 162.85 g kg ‐1 had the largest GO in the evaluation experiment. Selection for groat‐oil content on the basis of data from greenhouse‐grown oat plants can be successfully incorporated into a recurrent selection program.