White oat progenies based on the agronomic ideotype due to response to environmental stimulus

Abstract

The development of a selection strategy and to obtain a precocious white oat progenies, homogeneous for flowering and with additions to panicle grain mass, reconciled to environmental stimuli controlled by air temperature. The experiment was conducted in the municipality of Augusto Pestana, in the state of Rio Grande do Sul, Brazil. The experimental design was in augmented blocks with intermediate control, the regular treatments corresponded to 238 white oat progenies and the common treatment was represented by the commercial control (URS Taura) arranged in three replications. The selection for grains weight per panicle, percentage of flowered plants, days to physiological maturity and accumulation of degrees day are due to a high genetic contribution, which indicated promising selection for these characters already in initial breeding generations. The accumulation of degrees days for physiological maturity defines that 41.94% of the progenies are selected as precocious. The genotype ideotype distance index (MGIDI) selected 20% of the white oat progenies with greater flowering uniformity, less accumulation of degrees days and high potential for grain mass per panicle.

Alternate abstract:

O objetivo deste trabalho foi desenvolver uma estratégia de seleção e obter progênies de aveia branca precoces, homogêneas para o florescimento e com acréscimos a massa de grãos da panícula, conciliados aos estímulos ambientais controlados pela temperatura do ar. O experimento foi conduzido no município de Augusto Pestana, no estado do Rio Grande do Sul, Brasil. O delineamento experimental de blocos aumentados com testemunha intercalar, os tratamentos regulares correspondem a 238 progênies de aveia branca e o tratamento comum foi representado pela testemunha comercial (URS Taura) disposta em três repetições. A seleção para peso de grãos por panícula, porcentagem de plantas floridas, dias para maturidade fisiológica e acúmulo de graus-dia devem-se a uma alta contribuição genética, o que indica uma seleção promissora para esses caracteres já nas gerações iniciais de melhoramento. O acúmulo de graus dias para a maturidade fisiológica define que 41,94% das progênies são selecionadas como precoces. O índice de distância genótipo ideótipo (MGIDI) selecionou 20% das progênies da aveia branca com maior uniformidade do florescimento, menor acúmulo de graus dias e alto potencial de massa de grãos por panícula.

INTRODUCTION

White oat (Avena sativa L.) is a cereal of temperate climate with grains of high nutritional quality. They have high levels of protein, lipids, starch and dietary fiber (HAWERROTH et al., 2014). In Brazil, according to the National Supply Company, in the 2020/2021 harvest, 503.4 thousand hectares were sown with a production of 1.14 million tons and productivity of 2.27 tons of grains per hectare (CONAB, 2023). The high demand for its grain is due to it being a versatile cereal, as it acts in the context of human and animal nutrition and soil cover (HAWERROTH et al., 2014). This projects high responsibility to genetic improvement for the development of genotypes that meet the agronomic ideotype in the binomial of the industry along with the maximization of cultural practices, as well as for the use of environmental resources.

Working with a given ideotype, that is, a model created through the sum of a set of agronomic characters of interest, thus provides breeders with a final purpose for selection, which maximizes effectiveness and assertiveness in the selection of high-performance genotypes. (VAN OIJEN & HÖGLIND, 2016). The expression of some characteristics of interest are crucial to define the agronomic ideotype such as erect growth, reduced stature, stem tolerant to lodging, early cycle, disease tolerance, leaves at the correct angles, compact panicles, high grain mass per panicle, larger grains than two millimeters in diameter and easy to peel (SILVA et al., 2020). To achieve these objectives, it is necessary to maximize selection gains, through effective selection strategies for populations, progenies and lineages that result in greater assertiveness of breeding programs (CARVALHO et al., 2022).

Among the selection strategies used in genetic improvement programs, almost all prioritize obtaining transgressive genetic constitutions in terms of grain productivity, industrial quality, stability, cycle reduction and maximum use of environmental conditions and resources (SILVA et al., 2020). High efficiency is possible when gathering attributes of interest that meet the multivariate selection, with different meaning and pressure. Among the parameters targeted by genetic improvement programs are heritability, additive genetic variations and expected genetic gains in progenies (TROYJACK et al., 2017; SILVA et al., 2020).

The effectiveness of the selection requires that the traits are heritable and that there is genetic variability in the breeding population, then enhancing the obtainment of progenies that respond favorably to the available environmental stimuli. In this context, dividing the phenotypic variation into genetic and environmental effects weighted by the genealogy involved, thus extracting variance components and genetic parameters that will provide subsidies for selection direction and pressure (BALDISSERA et al., 2014).

Obtaining an ideal genotype for a given market niche requires high skill from the breeder, and it is a tough task to gather favorable genes for productivity, quality and precocity in the same genotype. Bertan et al. (2004), studying traits for precocity in white oats, observed that the genetic contribution to the duration of the vegetative period was 40% and 68% to grain weight. Studies define that the duration of the cycle 59% of the effects are genetic (CAIERÃO et al., 2001). Estimates of genetic parameters aim to understand the genetic structure of the population and make inferences about the magnitude of its genetic variability (BALDISERA et al., 2014).

There are many models used in the selection of superior genotypes, but emphasis to the model based on the genetic distance of the genotype to the intended ideotype (MGIDI). This tool considers multiple concomitant characteristics in the selection (OLIVOTO & NARDINO, 2021). Thus, there is a need for the development and selection of superior genotypes that are broadly adapted or specific and phenotypically stable. In this context, this research developed a selection strategy and obtain precocious white oat progenies, homogeneous for flowering and with additions to panicle grain weight, reconciled to environmental stimuli controlled by air temperature.

MATERIALS AND METHODS

The experiment took place in the municipality of Augusto Pestana (28º14’S, 52º22’W), in the state of Rio Grande do Sul, Brazil, at Escola Fazenda of the Universidade Regional do Noroeste do Rio Grande do Sul (UNIJUÍ). The sowing of the progenies took place on March 15, 2022, allocated in the experimental design of augmented blocks with interim control. The regular treatments correspond to 238 white oat progenies and the common treatment was represented by the commercial control (URS Taura) arranged in three replications. The experimental unit consisted of three seeding rows spaced by 0.18 meters and five meters long, totaling 2.70 m², seeding density of 300 viable seeds m². Phytosanitary management took place preventively to minimize biotic effects (pest insects and invasive plants) in the experiment.

The progenies used come from the Genetic Improvement Program of UNIJUÍ line grains and covering, stratified by two origins: recombinant (F) and mutants (M). Totaling 238 progenies (intrinsic to the segregating generations where: 39 F3 (75% homozygous and 25% heterozygous), 3 F4 (87.5% homozygous and 12.5% heterozygous), 63 M4 (93.75 of homozygosity and 6.25% of heterozygosity) and 133 M5 (96.87% of homozygosity and 3.133 of heterozygosity) - tables 1 and 2. Based on the variables measured in the measurement of ten plants in the center line of each experimental unit estimated to represent the population, these being: percentage of flowered plants (PFP, %) of the progeny: a diagrammatic scale (0 - 100%) was used based on the percentage of plants that emitted the inflorescence and that showed their phenotype in full anthesis. Accumulation of degrees days from flowering to physiological maturity (DDM, ºC days), the thermal sum accumulated during the period elapsed from flowering to the physiological maturity of the panicles, using the expression proposed by Arnold (1959): GD = Tmed - Tb, where: Tmed = average daily air temperature, in ºC and Tb = lower base temperature 4 ºC (PEDRO JUNIOR et al., 2004; TRAUTMANN et al., 2017; DORNELLES et al., 2023). Meteorological components extracted through the Power Nasa platform (NASA, 2023). Days from flowering to physiological maturity (DMAT, days): the number of days from flowering to physiological maturity of the panicles was calculated. Grain weight per panicle (GWP, grams), panicles were harvested and threshed, with grain moisture adjusted to 13%. The data obtained were submitted to descriptive analysis where the number of informative progenies, maximum, minimum and average values for the measured variables were obtained. Using the mixed linear technique based on the model:

y=Xβ+Zv+e(1)

Where: y = corresponds to the observation vector at the experimental unit level, ?? = represents the parametric vector of fixed effects, with incidence matrix X, v = is characterized as the parametric vector of random effects, with an incidence matrix Z, e = means the vector responsible for capitalizing the residual variance (RESENDE et al., 2014).

The method based on Restricted Maximum Likelihood (REML) has the objective of estimating the variance components and genetic parameters, the significance was supported by the Deviance analysis at 5% probability by the Chi-square test (χ²), and together we computed the restricted logarithm of likelihood (LogLink), the Akaike criterion (AIC) and the Maximum Likelihood Ratio (LRT). The model significant by the probability of the χ² allowed estimating the parameters: genetic variance (σ²G); residual variance (σ²R); phenotypic variance (σ²P), and the genetic parameters: broad sense heritability (H²); accuracy (acc); genotypic coefficient of variation (CVg); residual coefficient of variation (CVr); ratio between the genotypic and residual coefficient of variation (CVratio).

Based on the genetic parameters, it was possible to obtain the best linear unbiased predictors (BLUP) for each variable analyzed in the progenies. Where obtaining the coefficients of variation, the maximum, minimum and average value. Based on the BLUPs, the multitrait index of the distance between the genotype and the intended agronomic ideotype (MGIDI) was used (OLIVOTO & NARDINO, 2021), the objective of the multiple selection was to increase (positive sense) the percentage of flowering plants (PFP, %) and the grain weight per panicle (GWP, grams), as well as reducing (negative direction) for the accumulation of degrees days to physiological maturity (DDM, ºC days) and days to physiological maturity (DMAT, days).

The MGIDI method allowed estimating the parameters: observed mean (Xo), mean of selected lines (Xs), selection differential (Sd, %), Commonality (Com) and selection direction (increase or decrease). The multivariate index is based on the model:

MGIDIi=∑j=ifϓij-ϓj20,5(2)

Where: MGIDI _i means the distance index of the genotype to the intended ideotype in the progeny, ϓij represents the score of the i-th progeny on the j-th factor, ϓj is the j-th score of the intended ideotype. Thus, the progeny, which have the lowest MGIDI index, are closer to the expected ideotype. The line i of the progeny, explained by the j-th factor (w _ij) is used to demonstrate potentialities and deficiencies of the progenies (OLIVOTO & NARDINO, 2021). Explained by model:

wij=D²ij∑j=1fD²ij(3)

This way: D_{ij =} means the distance between the i-th progeny and the ideotype for the j-th factor. Low contributions of a given factor show that the characters are close to the ideotype (OLIVOTO & NARDINO, 2021). The analyzes were performed using the R software (R CORE TEAM, 2023) using the packages agricolae (MENDIBURU, 2021), metan (OLIVOTO & LUCIO, 2020) and ggplot2 (WICKHAM, 2016).

RESULTS AND DISCUSSION

White oats have a water demand of 400 to 800 mm throughout their cycle (SILVA, et al., 2020). Throughout the cultivation cycle from March to November, an accumulated precipitation of 1790 mm was observed, which is not a limiting factor for the development of the crop. Regarding the temperatures at minimum temperature, it presented lower values in the month of June with 7.6 ºC, so this variable did not appear to be a limiting factor, since white oats have a lower base temperature of 4 ºC. For maximum temperature, all months were below the upper limit of the crop.

The variance components were estimated using restricted maximum likelihood (Table 1), and the Deviance analysis showed significance (P < 0.01) for the percentage of flowered plants, grain weight per panicle, accumulation of degrees days to maturity physiological and days to physiological maturity. This indicates that the variance components and genetic parameters are valid, reliable and reproducible in other white oat breeding scenarios for the generations evaluated (F3, F4, M4 and M5). These results revealed the possibility of selection among progenies for all traits.

For the percentage of flowering plants (PFP), a genetic variance of 92.6% was observed in the progenies, demonstrating high values, and a large part of this variation is due to the genetic origin and the low contribution of the environment in the expression of this character, the residual variance (σ²R) was 7.37%. Accuracy is high (0.987) which results in experimental precision. It is identified that the ratio CVg/CVr greater than 1 allows selection and genetic gains for this trait.

The accumulation of degrees days to physiological maturity (DDM) defines that 69.6% of the phenotypic effects come from genetic variance (σ²G) and 30.4% of the residual variance (σ²R). This attribute reveals an accuracy of 0.934, being high and representative for the statistical model used (RESENDE & DUARTE, 2007). The ratio of the genotypic and residual coefficient of variation (CVratio) indicates magnitude of 1.51, which determines high genetic variability, and high probability of selection of transgressive progenies for this item. Days to physiological maturity (DMAT) expresses 72.3% of genetic effects on the phenotypic manifestation of the trait and 27.7% due to residual effects (σ²R). It shows high accuracy (0.942), and the possibility of selection demonstrated by the ratio of the genetic and residual coefficients of variation. The grain weight per panicle (GWP) demonstrated that the genotypic variance (σ²G) was responsible for 94.7% of the character, due to the great genetic variability and the number of lines, with an accuracy of 0.991. The ratio between the coefficient of genotypic and residual variance was 4.22, which is extremely high. For VENCOVSKY & BARRIGA (1992), the occurrence of values greater than 1 means selection is favorable and promising.

The percentage of flowered plants heritability in a broad sense was high (H²: 0.92). Lower values (H²: 0.34). Similar results to the present study were reported by CHAUDHARY & SING (2020), when studying 38 genotypes of white oat in India, they found high heritability for flowering (H ²: 0.73), similar results were reported by CHAUHAN & SINGH (2019) and CHAWLA et al. (2022), with heritability for this characteristic of 0.85 and 0.90 respectively. CARLSON et al. (2022), evaluating 9 white oat families from generations F7 to F12 and M7, they observed that the heritability of variables related to panicle and caryopsis were moderately high in both populations. These values indicated promising selection for this trait, in the F3, F4, M4 and M5 generations.

The accumulation of degrees days to physiological maturity revealed heritability with a broad sense of H²: 0.696 and days from flowering to physiological maturity showed heritability of H²: 0.723. Characteristics related to the cycle are governed by a high number of genes with small effect and high environmental action (HARTWIG et al., 2007). These same authors reported dominance gene action for these attributes. It is likely that in oats there is a compensatory effect between the vegetative and reproductive periods that define the cycle. Given this, it is possible to infer that the selection to obtain plants with a smaller number of days from emergence to flowering and a shorter period from flowering to maturation can provide the selection of more productive genotypes and responsive to environmental stimuli influenced mainly by air temperature. It is important that there is a balance between the vegetative and reproductive periods, so that the vegetative structures develop fully, with accumulation and translocation of photo assimilates and nutrients efficiently and quickly from the temporary storage structures to the grains (HARTWIG et al., 2007).

Panicle weight is correlated with yield components such as grain weight and number of grains per panicle. However, the effectiveness of the selection can be maximized by evaluating auxiliary traits such as the thousand grain weight and the number of grains per panicle, which help the manifestation of the productive potential through indirect genetic actions. Studies by MARCHIORO et al. (2003), conducted using the population method with eight F4 segregating populations, verified heritability with a broad sense of H²: 0.425 for panicle weight. In the conditions where this study was carried out, it was found that this parameter was high with H²: 0.947 this being potentiated through the high number of progenies conducted in the genealogical method with high intrinsic genetic variability the basis of the improvement program and control of residual variations during the conduction of the trials.

The prediction of the percentage of flowered plants (PFP) indicates that 13.80% of the progenies were superior to the commercial control (Figure 1). Of these, 66.66% of the genetic variability comes from mutation (14 lineages are M₅ with 96.87% homozygosity) and 33.99% from recombination (F3 generation with 25% heterozygosity). The progenies from a mutation process showed the highest percentages of flowering plants, through the lines 315M4, 266M5, 259M5, 184M5 and 228M5, the progenies from recombination show low percentages, which are from the hybridizations of URS Taura x URS Corona, Fapa Slava x Carlasul, Carlasul x URS Corona, and Brisasul x Barbarasul. In a study carried out by PRADEBON et al. (2024), when evaluating the agronomic ideotype of white oats combined with vegetation indices, they reported that in addition to the thermal sum, its expression is related to the vegetation index DF, NR, IGR, SCI, L, GRAY, RGRI, GLI and NG, BI, NRBDI, BGI, BIM and NGBDI.

The accumulation of degrees days from flowering to physiological maturity (DDM) showed precocity in 24.8% of the progenies, especially 389M₅, 179M₅, 250M₅ with just less than 595 ºC day^-1, to complete the partition of assimilates for the grains. For the commercial control, a need for thermal accumulation of 907.5 ºC day^-1 was observed for maturity, with 312 ºC day^-1 being higher for the progenies originating from the mutation. Among the recombinant progenies, 17.14% of these are precocious resulting from the hybridizations of the URS Fapa Slava x Carlasul, and Brisasul x Barbarasul.

The grain weight per panicle showed that 90% of the progenies originating from hybridizations were obtained by crossing Brisasul x Barbarasul, Brisasul x IPR Afrodite, URS Corona x URS Fapa Slava and Carlasul x URS Altiva, based on the mutant progenies it is evident 190M5, 243M5, 145M5, 186M4 and 389M5. Studies by HAVERROTH et al. (2021), when evaluating five genotypes of white oat, they observed that there is great variation in the filling and final weight of oat grains depending on the characteristics of the genotype and position of the grains in the panicle. Even in this study, the authors concluded that meteorological variables can interfere on oat grain filling dynamics.

The multi-trait selection analysis carried out for the white oat progenies in comparison to the commercial cultivar revealed the main characteristics in view of choosing a progeny that has an agronomic ideotype, gathering them to achieve the best result. Observing the variables and analyzing possible increases and decreases (Table 2), where PC1 and PC2, presented a commonality of 0.705, indicating that the factor analysis estimates are adequate (CARVALHO et al., 2016) It is possible to form two groups factorials, factor 1 was composed by the percentage of flowered plants with the objective of positive selection to increase the percentage of flowering of the plants and reduction of the accumulation of degrees days for the physiological maturity aiming at selection of earlier genotypes. The second factor was composed of days to physiological maturity to reduce and weight of grains per panicle to increase the character. The current ideotype of white oats to be developed requires an increase in grain yield and industry yield, reduction of the vegetative period, adjustment of the reproductive period and grain filling.

The MGIDI index allows increasing selection gains in traits of interest, this model makes it possible to identify potentialities of genotypes (OLIVOTO & NARDINO, 2020). The selection of 20% of white oat progenies was observed, divided into two factors (Figure 2). Factor I with the purpose of increasing flowering uniformity and reducing accumulation of degree days for grain filling showed the following progenies (20M5, 332M5, 274M5, 28M5, 191M4, 389M5, 311M5, 49M4, 381M4, 174M4, 138M4, 63M5, 329M4, 36M5, 53M5, 187M5, 193M5, 204M5, 168M5, 206F4, 125M5, 144M5, 106M4, 260M5, 166M5, 429F3, 433F3 ,442F3,301F3, 388F3, 242F3, 430F3, 435F3, 434F3, 432F3, 431F3, 366F4 and 245F3). Factor II aims to reduce the days for grain filling and increase the grain weight per panicle through the progenies (179M5, 213M5, 184M5, 154M5, 268F3, 171F3, 315M4, 385M5, 355F3, 236M5).

By reconciling the use of genetic parameters via mixed models (REML/BLUP) and multi-trait selection (MGIDI), it showed efficiency in the selection of promising progenies for traits of agronomic interest. There is a gain for genetic improvement mainly in the initial generations, where there is a great genetic variability within and between progenies that maximize the selection of characters of interest.

CONCLUSION

The selection for grains weight per panicle, percentage of flowered plants, days to physiological maturity and accumulation of degrees day are due to a high genetic contribution, which indicates promising selection for these characters already in initial breeding generations. They can be used in other white oat breeding scenarios for the generations evaluated. The accumulation of degrees days for physiological maturity defines that 41.94% of the progenies are selected as precocious through 519.74 ºC days. The genotype ideotype distance index selected 20% of white oat progenies with greater uniformity of flowering, less accumulation of degrees days and high potential for grain weight per panicle.

ACKNOWLEDGEMENTS

The reserach was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brasil - Finance code 001.

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