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This study aimed to detect genotypic differences in the resistance to sprouting of wheat grains, evaluate the effectiveness of different methods for inducing sprouting and identify, using repeatability estimates, the minimum number of spikes necessary for the adequate evaluation of the percentage of grain sprouting in the spike in order to assist in the selection of superior genotypes in breeding programs. Spikes from four wheat cultivars (Frontana, IPR Catuara, Quartzo and BRS 220) were evaluated using three methods for inducing grain sprouting in the spike (water immersion, rainfall simulation and germination chamber). To determine the most efficient method, repeatability coefficients were estimated through analysis of variance, principal components analysis and structural analysis based on correlation and covariance matrices. The induction of sprouting by immersion in water was the most effective method for indicating genotypic differences and may be used in breeding programs for this purpose. The repeatability method based on the components of covariance was more efficient. A minimum of 11 spikes is required to make a high-reliability estimate of the percentage of sprouted grains in the spike.

The environmental conditions in the areas where wheat is growing in Brazil favor the occurrence of rainfall in the harvest period, which in most cultivars may cause a high loss in germination potential and industrial quality due mainly to grain sprouting in the spike [

The sprouting of wheat grains in the spike is induced by the absorption of water by the grains immediately after full maturation. This problem affects both rural and industrial producers because it reduces the potential yield of crops, negatively affecting hectoliter weight [

The sprouting of wheat grain triggers a sequence of physiological processes, including the release of hormones and hydrolytic enzymes [

Similarly, the search for efficient methodologies that provide greater accuracy in the stratification of genotypes should be conducted because of their usefulness in the selection of cultivars [

The central issue is to define not only the most appropriate parameters or methods but also those that best simulate the effect of preharvest sprouting in a homogenous manner and that provide a high level of repeatability; i.e., the results can be confirmed through the repetition of the experiment by the same researcher or by others when using different samples or genotypes from the ones originally tested.

Through the repeatability coefficient, one can determine the number of measurements required to predict the true value of each individual with a certain degree of probability (R^{2}), which represents the percentage of certainty of the prediction of the true value of the selected individuals based on “n” measurements; this value is easily estimated and requires no controlled crossbreeding or study of progenies [

Thus, this study aimed 1) to evaluate the effectiveness of different methods for inducing sprouting in spikes with genotypic differences in the resistance to sprouting and 2) to obtain repeatability estimates using different statistical methods to identify the minimum number of spikes necessary for the proper evaluation of the percentage of grain sprouting in the spike.

The spikes originated from an experiment arranged in a randomized block design with three replications at the Agronomic Institute of Paraná (Instituto Agronômico do Paraná—IAPAR), in Londrina, Paraná State (latitude 23˚22'S, longitude 51˚10'W) during the 2012 harvest season. The harvesting of the spikes was conducted in the preharvest period. Four wheat cultivars with different levels of sprouting in the spike were evaluated: Frontana (resistant), IPR Catuara (moderately resistant), Quartzo (moderately susceptible) and BRS 220 (susceptible). These cultivars were subjected to three methods of inducing grain sprouting in the spike, as described below.

a) Immersion in water: Three samples of 10 spikes per genotype were randomly collected and subjected to water immersion for 16 hours, followed by a six-hour period of exposure to air to remove the excess water contained between the spikelets in accordance with the method described by [

b) Rainfall simulator: Rainfall simulation was performed using a sprinkler (Model P5 with 3/8 nozzle) set at a height of 1.5 m from the spikes. Three replicates of 10 spikes per genotype were sampled, vertically allocated and fixed on a Styrofoam board. The simulation lasted six hours with a total of 630 mm of rain.

c) Germination chamber: Spikes were wrapped in a paper towel moistened with a volume of water corresponding to 2.5 times the weight of the paper substrate, then placed in a germination chamber. For each genotype, three samples of 10 spikes were taken.

Spikes from the three sprouting methods were wrapped in paper towels and placed in a germination chamber at 85% ± 3% relative humidity and 27˚C ± 2˚C temperature and were subsequently removed for evaluation after six days.

Each spike was individually assessed and identified before the induction of grain sprouting. Spike mass (SM), spike length (SL) and number of grains per spike (NGS) were evaluated without the destruction of the spikes. After the six-day period in the germination chamber, the spikes were dried and threshed manually, and the sprouted and unsprouted grains were counted for each individual spike. The percentage of sprouting (S %) was calculated from the ratio between the number of sprouted and unsprouted grains.

The results obtained for spike mass, spike length, numbers of grains per spike and percentage of sprouting were evaluated by the tests for normality and homogeneity of variances, indicating no need for data transformation. Analysis of variance was performed in a 3 × 4 factorial design, i.e., three methods of sprouting induction and four genotypes, with three replications. Comparisons between the means were performed using the Tukey’s test at a 5% probability level.

Because repeatability estimates vary depending on the nature of the variables, the properties of the genotypes and the conditions under which the plants are grown [

For the ANOVA method, the number of repeated measurements was considered equal for all genotypes, using Model 2 as described by [

The minimum number of measurements required to predict the true values of the evaluated traits, based on pre-established coefficients of determination (R^{2}) (0.80, 0.85, 0.90 and 0.95), was calculated using the following expression: η = R^{2} (1 − r)/(1 − R^{2})r, based on the mean value of η cycles (η = 30) representing the 30 spikes used to estimate the repeatability. In the estimation of the r-values obtained by the different methodologies, R^{2} was calculated according to the following expression: R^{2} = ηr/1 + r (η − 1).

All statistical analyses were performed using the GENES software [

The analysis of variance (

The Frontana wheat genotype showed superior performance, compared to the other genotypes, for SM, SL and NGS (

Effective methods that contribute to the selection of cultivars with resistance to grain sprouting in spikes are of fundamental importance to breeding programs [

. Mean squares of analysis of variance (ANOVA) for Spike mass (SM), spike length (SL) and number of grains per spike (NGS) and the percentage of sprouting (S %) for four wheat genotypes under three different forms of induction sprouting

Source of Variation | D.F. | TRAITS | |||
---|---|---|---|---|---|

SM | SL | NGS | S (%) | ||

Inducing sprouting (I) | 2 | 0.047 | 0.150 | 4.210 | 982.55^{**} |

Genotypes (G) | 3 | 4.74^{**} | 11.40^{**} | 539.12^{**} | 7146.54^{**} |

I × G | 6 | 0.018 | 0.290 | 4.230 | 340.77^{**} |

Error | 24 | 0.031 | 0.132 | 7.074 | 27.643 |

Means | 1.45 | 7.15 | 31.14 | 17.46 | |

C.V. (%) | 12.15 | 5.09 | 8.54 | 30.11 |

D.F.: Degrees of freedom. ^{**} and ^{*}: 1 and 5% probability by F test, respectively.

. Comparison of means between four wheat genotypes to traits of Spike mass (SM), spike length (SL) and number of grains per spike (NGS)

TRAITS | GENOTYPES | |||
---|---|---|---|---|

BRS 220 | QUARTZO | IPR CATUARA | FRONTANA | |

SM (g) | 0.86 C^{(1)} | 1.11 BC | 1.32 B | 2.50 A |

SL (cm) | 6.23 C | 6.51 BC | 7.13 B | 8.74 A |

NGS | 27.54 B | 28.12 B | 26.21 B | 42.69 A |

^{(1)}: Means followed by the same letter (horizontal) indicate no significance at 5% of probability by Tukey test.

. Effect of three different forms of grains germination induction in the percentage of sprouting per spike trait (S %) in four wheat genotypes

TRAITS | GENOTYPES^{(1)} | |||
---|---|---|---|---|

BRS 220 | QUARTZO | IPR CATUARA | FRONTANA | |

S % | ||||

Immersion in water method | 85.02 aA^{(1)} | 17.35 aB | 6.71 aBC | 1.25 aC |

Rainfall simulator | 56.11 bA | 1.14 bB | 1.20 aB | 0.20 aB |

Germination chamber | 37.59 cA | 1.61 bB | 0.59 aB | 0.81 aB |

^{(1)}: Means followed by the same uppercase letter (horizontal) and by the same lowercase letter (horizontal) indicate no significance at 5% of probability by Tukey test.

Quartzo and BRS 220 were resistant, moderately resistant, moderately susceptible and susceptible for grain sprouting in the spike, respectively.

This differentiation of the genotypes was not observed for the rainfall simulator and germination chamber methods, which grouped the Frontana, IPR Catuara and Quartzo genotypes in the same category of spike sprouting response. [

After the immersion in water method was identified as the most suitable for the stratification of wheat parental lines, the repeatability analysis was conducted (^{2}) ranged between 99.64 and 99.90. The high magnitudes of the repeatability coefficients and the coefficients of determination indicated that the mathematical model used was a satisfactory fit to the dataset [^{2}, respectively. Other authors have also observed higher estimates using the PCCOV method in other species [

High estimates of repeatability for a given trait indicate that it is feasible to predict the true value of the individual for that trait using a relatively small number of measurements, and the opposite is true when the repeatability is low [^{2} = 90%, is considered quite satisfactory.

The method based on the ANOVA Model 2 gave less accurate estimates compared to the other methods. [

Cruz et al. [^{2} = 95% (

However, the high accuracy obtained for all analysis methods (above 99%) indicates that any of the methods evaluated in this study would be suitable for a highly accurate evaluation of repeatability. Thus, even in the comparatively lower accuracy ANOVA method, the minimum number of spikes required to determine the true value of the percentage of sprouting was 11, for an accuracy of 99%.

. Estimates of the repeatability coefficient (r), determination coefficients (R^{2}) and number of measurements (η) for percentage of sprouting per spike trait (S %) of four wheat genotypes whose germination was induced by the water immersion method

METHODS^{(1)} | ||||
---|---|---|---|---|

ANOVA-2 | PCCOV | PCCOR | EVCOR | |

r | 0.90 | 0.97 | 0.93 | 0.93 |

R^{2} | 99.64 | 99.90 | 99.77 | 99.75 |

η | ||||

R^{2} | ANOVA-2 | PCCOV | PCCOR | EVCOR |

0.80 | 1 | 1 | 1 | 1 |

0.85 | 1 | 1 | 1 | 1 |

0.90 | 1 | 1 | 1 | 1 |

0.95 | 3 | 1 | 2 | 2 |

0.99 | 11 | 3 | 7 | 8 |

^{(1)}: ANOVA: analysis of variance; PCCOV and PCCOR: principal components, obtained from both covariance and correlation matrices, respectively; EVCOR: structural analysis based on the theoretical eigenvalues of the correlation matrix.

It is worth mentioning that a reduced number of evaluation cycles does not allow the researcher to obtain detailed information on the genotype x environment interaction or on the individual response of the genotype to environmental variations [

The water immersion method for inducing sprouting in spikes was the most effective for identifying contrasting genotypes and can be used in breeding programs for this purpose.

The repeatability method based on the covariance components was the most efficient.

A minimum of 11 spikes is required for to estimate the percentage of grain sprouting in the spike with 99% confidence.

The authors gratefully acknowledge CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for their financial support.