Experimental site
This research was conducted at the research farm of the College of Agriculture, Isfahan University of Technology, Isfahan, Iran (32° 30′ N, 51° 20′ E, 1630 m asl). The soil at the site was clay loam (pH 7.5) with an average bulk density of 1.48 g/cm3 in the top 60 cm layer of the soil profile. The average annual precipitation and temperature were 122 mm and 17 °C, respectively.
Plant materials, field management, and measurements
The primary plant materials included of 21 genotypes of tall fescue (Table 1) which were chosen from a broad base germplasm collection according to various agro-morphological, physiological and root traits and used as parents for crossing [11, 26]. In order to generate a reference breeding population, these genotypes were crossed following a polycross design. As a results, 960 progenies (from 21 half-sib families) were obtained and evaluated for agro-morphological traits [26]. From these 960 progenies, 120 genotypes genotyped using diagnostic EST-SSR primers in the previous study [26]. These 120 genotypes which were divided in to 42 full-sib families along with the 21 parental genotypes were used as the plant material in the present study. Identification of the tall fescue genotypes used in this study has been done in the botanical laboratory of Isfahan University of Technology (IUT). A voucher specimen of this material has been deposited in a publicly available herbarium of IUT (Deposition number: 36594).
This germplasm was evaluated in the field under normal irrigation condition according to the randomized complete block design with two replications during 2017–2020. The clone of each genotype was space planted in the field with inter-row and intra-row spacing 50 and 45 cm, respectively. Plants were irrigated using a surface drip tape irrigation system. No limitation of irrigation was conducted during the whole experiment. Irrigation was applied when 45% of the total available water was depleted from the root-zone to maintain the soil water content at the field capacity [49].
The above-ground biomass (forage) was harvested manually three times in each year. The first harvest was in late spring after flowering, the second and third one was in late summer and autumn to assess complete growth, respectively. At each harvest, the grass was cut from 5 cm above the ground and the weight of dry forage yield per plant was recorded after drying at 72 °C for 48 h. The annual dry forage yield of each year (ADFY) was calculated by the sum of the spring (SPDFY), summer (SUDFY) and autumn (AUDFY) forage yield. Number of stems per plant (NS), plant height (H), crown diameter (CD), and flowering time (FL) were measured as recommended by Pirnajmedin et al. [7].
Statistical analysis
Data (residuals) were tested for normality using the Kolmogorov-Smirnov test; subsequently, the analysis of variance (ANOVA) was performed using the PROC Mixed by repeated measures in SAS software (v9.2) [50]. The means were compared using the Fisher’s LSD test (P < 0.05). Stability analysis was calculated for forage yield using the stability parameter proposed by Eberhart and Russell [51]. The stability parameter was the regression coefficient of the average forage yield of each family in each year on the average of all families in each year.
The pedigree of all the families were known and the recode of genotypes was done using CFC software. The pedigree information in BLUP analysis was used for constructing the relationship matrix and then estimating the genetic parameters and predicting the breeding values, which is done by the DMU software [52]. The first analysis was performed by individual harvest and then a multi-harvest model was fitted. Year and cut were treated as fixed and genotype was treated as random effects. All analyses were conducted using the mixed linear model given by Henderson as follow [53]:
$$\left[\begin{array}{cc}{X}^{\prime }{R}^{-1}X& {X}^{\prime }{R}^{-1}Z\\ {}{Z}^{\prime }{R}^{-1}X& {Z}^{\prime }{R}^{-1}Z+{G}^{-1}\end{array}\right]\left[\begin{array}{c}\hat{\beta}\\ {}\hat{\mathrm{u}}\end{array}\right]=\left[\begin{array}{c}{X}^{\prime }{R}^{-1}y\\ {}{Z}^{\prime }{R}^{-1}y\end{array}\right]$$
where, Y is the vector of observation, β and u are vectors of fixed and random effects, respectively, X and Z are the associated design matrices, and e is a random residual vector. The random effects are assumed to be distributed as u ~ MVN (0, G) and e ~ MVN (0, R), where MVN (u, V) denotes the multivariate normal distribution with mean vector u and variance-covariance matrix V. The G is the genetic variance/covariance matrix and R is the residual variance/covariance matrix.
Individual harvest analysis was performed using the model:
$$Y=\mu + Xy+Z\mathrm{g}+e$$
(2)
where, the u is the overall mean; X and Z represent the incidence matrices for fixed and random effects, respectively; y is the fixed effect for year; g is the random vector of genotype, g ~ MVN (0, Aσ2g), which σ2g is variance of genotype and A is a relationship (kinship) matrix; e is the random vector of error, e ~ MVN (0, Iσ2e), which σ2e is the variance of residual and I is an identity matrix of its proper size.
Multiple harvest analysis was performed using the model:
$$Y=\mu + Xc+ Xy+Z\mathrm{g}c+ Wp+e$$
(3)
where, the u is the overall mean; where, the u is the overall mean; c is the fixed vector of cut; y is the fixed vector of year; gc is the random vector of genotype within each cut, gc ~ MVN (0, GA), which G is the genetic variance/covariance matrix and A is a relationship (kinship) matrix; p is the random vector of the permanent environment, p ~ MVN (0, I σ2e); e is the random vector of error within each cut, e ~ MVN (0, RI), which σ2e is the variance of residual, R is the residual variance/covariance matrix, and I is an identity matrix of its proper size. X, Z, and W represent the incidence matrices for these effects.
Variance component and narrow sense heritability of traits were estimated using the restricted maximum likelihood (REML/BLUP) analysis by DMUAI procedure and breeding values (BVi) were computed by DMU4 procedure in DMU software, respectively [52]. The narrow sense heritability was estimated by dividing of additive genetic variance to phenotyping variance by the following formula:
$${h}_{n=}^2\frac{\sigma_a^2}{\sigma_g^2+{\sigma}_p^2+{\sigma}_e^2}$$
(4)
where σ2a, σ2g, σ2p, σ2e are the additive genetic variance, genotypic variance, permanent environment variance, and residual variance, respectively.
Using the bivariate analysis, the genetic correlations for each pair of traits were estimated from the genetic variance-covariance matrices from the model described above.
Relative selection efficiency (RSE) for improvement of dry forage yield (DFY) were estimated as described by Falconer and Mackay [45] and Searle [54] by the following formula:
$$\mathrm{RSE}=\frac{{\mathrm{CR}}_{\mathrm{y}}}{{\mathrm{R}}_{\mathrm{y}}}=\frac{\mathrm{i}\times \mathrm{rg}\times \mathrm{hx}\times \mathrm{hy}\times \upsigma \mathrm{p}\left(\mathrm{y}\right)}{\mathrm{i}\times \times {h}_x^2\times \upsigma \mathrm{p}\left(\mathrm{y}\right)}$$
(5)
where CRy is correlated response to selection, Ry is response to selection, i is the selection intensity of 10% (1.75), h2x is heritability of trait x, σp(x) is the square root of genotypic variance of trait x, rg is the genotypic correlation coefficient between two traits, hx and hy are the root square of narrow-sense heritability of traits of x and y, respectively. The correlated trait (y) is dry forage yield and RSE was only calculated based on dry forage yield.
Principal component analysis (PCA) was performed based on correlation matrix to reduce the multiple dimensions of data space using SAS (Proc princomp), and biplots were drawn using Stat Graphics statistical software [55].
We confirm that all methods complied with relevant institutional, national, and international guidelines and legislation.
