### Abstract

Inexpensive estimates of broad-sense heritability (BSH) may be valuable in plant breeding. This research evaluated two methods for estimating BSH with data from stands of equidistantly spaced genotypes. Both methods depend on the assumption that genetic and environmental contributions to plot variance (plot = group of contiguous plants) change at different rates as plot size changes if genetic variation is distributed randomly. For the method proposed by V.J. Shrikhande, variances among plot means are computed and least-squares regression used to estimate environmental and genetic variances and the change in a plot variance with changes in plot size. The other method involves the same approach, but uses a two-parameter model suggested by G.H. Freeman but not previously used to estimate BSH. Both methods produce biased BSH estimates since genotypic and genotypic x environmental components of variation are inseparable. Our objectives were to: (i) develop software to calculate variances for the methods, and (ii) compare BSH estimates generated using these methods with each other and with those from analysis of variance (ANOVA) of data from families grown in the same environment. Data were from a perennial herb, Sphaeralcea emoryi Torr. grown in Tucson, AZ. Shrikhande's method produced parameter estimates with large variances and BSH estimates that averaged 3.6 times larger than those from Freeman's method. Only BSH estimates from Freeman's method agreed well with those from ANOVA for most traits. Freeman's method may be useful for rapidly and inexpensively generating BSH estimates in a variety of situations, especially when traditional genetic analysis are difficult.

Original language | English (US) |
---|---|

Pages (from-to) | 1125-1129 |

Number of pages | 5 |

Journal | Crop Science |

Volume | 38 |

Issue number | 5 |

State | Published - Sep 1998 |

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### ASJC Scopus subject areas

- Agronomy and Crop Science

### Cite this

*Crop Science*,

*38*(5), 1125-1129.

**Evaluation of simple methods for estimating broad-sense heritability in stands of randomly planted genotypes.** / Smith, Steven E; Kuehl, R. O.; Ray, I. M.; Hui, R.; Soleri, D.

Research output: Contribution to journal › Article

*Crop Science*, vol. 38, no. 5, pp. 1125-1129.

}

TY - JOUR

T1 - Evaluation of simple methods for estimating broad-sense heritability in stands of randomly planted genotypes

AU - Smith, Steven E

AU - Kuehl, R. O.

AU - Ray, I. M.

AU - Hui, R.

AU - Soleri, D.

PY - 1998/9

Y1 - 1998/9

N2 - Inexpensive estimates of broad-sense heritability (BSH) may be valuable in plant breeding. This research evaluated two methods for estimating BSH with data from stands of equidistantly spaced genotypes. Both methods depend on the assumption that genetic and environmental contributions to plot variance (plot = group of contiguous plants) change at different rates as plot size changes if genetic variation is distributed randomly. For the method proposed by V.J. Shrikhande, variances among plot means are computed and least-squares regression used to estimate environmental and genetic variances and the change in a plot variance with changes in plot size. The other method involves the same approach, but uses a two-parameter model suggested by G.H. Freeman but not previously used to estimate BSH. Both methods produce biased BSH estimates since genotypic and genotypic x environmental components of variation are inseparable. Our objectives were to: (i) develop software to calculate variances for the methods, and (ii) compare BSH estimates generated using these methods with each other and with those from analysis of variance (ANOVA) of data from families grown in the same environment. Data were from a perennial herb, Sphaeralcea emoryi Torr. grown in Tucson, AZ. Shrikhande's method produced parameter estimates with large variances and BSH estimates that averaged 3.6 times larger than those from Freeman's method. Only BSH estimates from Freeman's method agreed well with those from ANOVA for most traits. Freeman's method may be useful for rapidly and inexpensively generating BSH estimates in a variety of situations, especially when traditional genetic analysis are difficult.

AB - Inexpensive estimates of broad-sense heritability (BSH) may be valuable in plant breeding. This research evaluated two methods for estimating BSH with data from stands of equidistantly spaced genotypes. Both methods depend on the assumption that genetic and environmental contributions to plot variance (plot = group of contiguous plants) change at different rates as plot size changes if genetic variation is distributed randomly. For the method proposed by V.J. Shrikhande, variances among plot means are computed and least-squares regression used to estimate environmental and genetic variances and the change in a plot variance with changes in plot size. The other method involves the same approach, but uses a two-parameter model suggested by G.H. Freeman but not previously used to estimate BSH. Both methods produce biased BSH estimates since genotypic and genotypic x environmental components of variation are inseparable. Our objectives were to: (i) develop software to calculate variances for the methods, and (ii) compare BSH estimates generated using these methods with each other and with those from analysis of variance (ANOVA) of data from families grown in the same environment. Data were from a perennial herb, Sphaeralcea emoryi Torr. grown in Tucson, AZ. Shrikhande's method produced parameter estimates with large variances and BSH estimates that averaged 3.6 times larger than those from Freeman's method. Only BSH estimates from Freeman's method agreed well with those from ANOVA for most traits. Freeman's method may be useful for rapidly and inexpensively generating BSH estimates in a variety of situations, especially when traditional genetic analysis are difficult.

UR - http://www.scopus.com/inward/record.url?scp=0031697403&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031697403&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0031697403

VL - 38

SP - 1125

EP - 1129

JO - Crop Science

JF - Crop Science

SN - 0011-183X

IS - 5

ER -