Abstract
Background: Finger millet is an underutilised crop with high nutritional value and resilience in marginal environments, yet limited genetic research on yield and yield-related traits has constrained the development of improved varieties.
Aim: This research analysed yield traits in adapted finger millet germplasm to establish a foundation for systematic breeding and the development of high-yielding varieties through the exploitation of heterosis.
Setting: Trials were conducted at the Lupane State University farm during the 2023 and 2024 summer seasons.
Methods: Ten F1 finger millet hybrids were developed in season one using the Line*Tester method and evaluated in season two for combining ability and heterosis.
Results: Line 5045 exhibited highly significant general combining ability (GCA) effects for number of productive tillers, threshing percentage and grain yield. The cross 5327*FMV1 displayed highly significant specific combining ability (SCA) effects for grain yield. Most F1 hybrids expressed significant heterosis, largely driven by overdominance gene action. Baker’s ratios close to unity for finger length (0.83), grain yield (0.84) and thousand-grain weight (0.67) indicated a predominance of additive gene effects. Early genetic gain is expected from selecting superior parents (5045 and 5327) and their hybrids (5045*FMV2 and 5327*FMV1), which combine high GCA, strong SCA and favourable heterotic effects.
Conclusion: The study confirms that heterosis and combining ability can be effectively harnessed to improve yield in finger millet.
Contribution: This research advances knowledge on finger millet genetics, providing practical insights for breeding improved varieties of this crop to strengthen food security.
Keywords: combining ability; heterosis; gene action; lines; testers.
Introduction
Finger millet (Eleusine coracana) is a nutrient-dense dual crop used for food and fodder that has utilitarian value in addressing prevalent nutritional and health issues in tropical regions where it is widely cultivated (Ojulong et al. 2020; Opole 2019). The crop is climate resilient and is therefore cultivated in various agro-ecological regions (Parvathi et al. 2019; Ramashia et al. 2018). Consequently, finger millet acts as a vanguard and safety net against hunger and malnutrition, particularly in regions where exotic staples fail to thrive (Ojulong et al. 2020; Sharma et al. 2018). Albeit its valuable properties, a paradigmatic shift is necessary to elevate finger millet from its overlooked and under-researched ‘orphan crop’ status to a priority elite status, worthy of dedicated investment and scientific attention. Major constraints in finger millet production include the limited availability of improved varieties, market void, and antiquated and unfit equipment, constraining large-scale full mechanisation (Krishna et al. 2020; Opole 2019; Sharma et al. 2018).
Low recombination rates and high inbreeding coefficients (Gimode et al. 2016) have narrowed the genetic base of cultivated finger millet (Hittalmani et al. 2017), necessitating the identification and incorporation of novel genes to enhance genetic variability, enabling assessment of combining ability for strategic selection of parents. Combining ability studies are a valuable tool for understanding the genetic mechanisms underlying quantitative trait inheritance, evaluating the breeding value of inbred lines, facilitating selection and enabling accumulation and fixation of desirable alleles for exploitation of heterosis (Nisha & Veeraragavathatham 2014; Zewdu 2020). Given the quantitative nature of yield, an in-depth understanding of factors that influence yield is crucial for achieving appreciable increases in grain yield (Fasahat et al. 2016; Sharma et al. 2018). The simplicity of the Line*Tester mating design makes it most suitable for testing inbred lines from various species, regardless of their floral phenology (Muthoni & Shimelis 2020). Furthermore, the design discriminates the combining ability of lines (GCAf), testers (GCAm) and their interactions (SCAfm) by partitioning mean square variances (Kempthorne 1957).
General combining and specific combining ability are the two partitions of combining ability. General combining ability (GCA) is an estimate of a parent’s breeding value, which is determined by the average performance of its progeny in a series of crosses (Sprague & Tatum 1942; Temesgen 2021). General combining ability is primarily associated with additive gene action and additive*additive gene interactions. A low GCA value suggests that a parent’s average performance is not statistically different from that of its hybrid offspring (Fasahat et al. 2016), and high GCA effects imply a higher heritability and marginal environmental effects. Specific combining ability (SCA) describes a parent’s propensity to form remarkable progeny in a specific cross (Rukundo et al. 2017). Specific combining ability is an interaction effect associated with non-additive gene action, which includes dominance, dominance*dominance effects, epistatic interactions and genotype-by-environment interactions (Sprague & Tatum 1942).
Although not widely used in breeding self-pollinated crops, heterosis breeding is gaining traction as a solution for increasing yield in crops such as finger millet and pearl millet (Divya et al. 2022; Savitha et al. 2013; Srivastava et al. 2020). Heterosis is defined as a phenomenon in which F1 hybrids exhibit accentuated performance that exceeds that of their parents. The magnitude and direction of heterosis are a product of environmental influences that interact with genes underlying the heritability of yield-contributing traits that can be exploited to augment finger millet yield (Yu et al. 2020). Finger millet improvement should also incorporate performance along with SCA effects in leveraging gains emanating from hybrids exhibiting meaningfully quantifiable heterosis (Divya et al. 2022; Patroti & Gowda 2013; Temesgen 2021).
Heterosis can be classified into three types: mid-parent, heterobeltiosis and standard heterosis. Mid-parent heterosis (MPH) is the term for a hybrid’s superior performance compared to the average of its parents’ performance, which serves as a breeding value benchmark (Hosen et al. 2022). This phenomenon can be attributed to epistatic interactions and dominant gene action. Better parent heterosis (BPH) (heterobeltiosis) describes the phenomenon where a hybrid’s performance for a specific trait exceeds that of its better parent (Shull 1952; Temesgen 2021). Additionally, when the performance of a commercial check variety falls below that of a hybrid variety, this phenomenon is called standard heterosis. The expression of heterosis is directly linked to an organism’s genetic diversity and the underlying gene action controlling trait inheritance (Temesgen 2021).
The potence ratio estimates the extent and direction of dominance of parental alleles on the F1 hybrid’s phenotype, in comparison to the mid-parent value (Aditika et al. 2020). Upregulation of overdominance genes has a multiplier effect on crop performance. Heterosis emanating from increased heterozygosity in hybrids can be harnessed to augment finger millet yield in breeding programmes. This study aimed to determine the nature and the magnitude of gene action controlling the inheritance of yield and yield-related traits in adapted finger millet germplasm. A further objective was to evaluate the GCA and SCA of lines, testers and their interactions, and to quantify the heterotic effects observed in their crosses.
Experimental materials, design and procedures
The germplasm used in this study was acquired from the National Gene Bank of Zimbabwe (Table 1) and was characterised in an independent study. A Line*Tester mating design was used to cross five genetically diverse lines with two adapted, high-yielding varieties (FMV1 and FMV2). The 10 resulting hybrids were subsequently evaluated for combining ability and heterotic effects.
| TABLE 1: Finger millet germplasm and their biological status used for genetic analysis of yield and yield-related traits. |
At Zadok growth stage 45 (Figure 1), finger millet hybridisation was performed to generate F1 hybrids. In the subsequent season, these confirmed F1 hybrids and their parents were planted in a Randomised Complete Block Design with three replicates for the analysis of combining ability and heterotic effects. Morphological markers for anther and stem pigmentation at flowering and seed colour were used to confirm the hybridity of the F1 finger millet plants. The plots comprised five rows, each 4 m in length, with an in-row spacing of 0.10 m and an inter-row spacing of 0.90 m. The central row consisted of F1 hybrid plants, whereas the two outer rows were the parental plants.
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FIGURE 1: Show steps followed in finger millet hybridization. A) identification of inflorescence that has just flowered, B) the tips of inflorescence are cut off, and the inflorescence is treated with hot water at 48 °C–52 °C for 2 minutes to 5 minutes. C) drying and tying of male inflorescence inside the female inflorescence and bagging. |
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Data collection
To mitigate the border effect, data collection was restricted to five randomly chosen plants located in the central row of each plot. Data were collected at three distinct stages of plant development. At the dough stage, measurements included plant height, ear-head size, peduncle length, finger number, finger length and spikelet density. The maturity stage was used to record the number of productive tillers and the number of grains per spikelet. Post-harvest, final data for ear-head weight, threshing percentage, thousand-grain weight and grain yield per plant were collected. Data for all traits, except for mean ear weight and threshing percentage, were collected according to the Finger Millet Descriptors List (International Board for Plant Genetic Resources [IBPGR] 1985). Mean ear weight (Equation 1) and threshing percentage (Equation 2) were calculated as described by Reddy and Gowda (2023). The characters assessed were plant height, peduncle length, ear-head size, finger number, finger length, spikelet density, number of productive tillers, number of grains per spikelet, mean ear weight, 1000-grain weight, grain yield per plant and threshing percentage.
Statistical and genetic analyses
Combining ability analyses, including estimates for GCA and SCA, analysis of variance (ANOVA), the contributions of lines, testers, and their interactions to total genetic variance were computed in TNAUSTAT-Statistical package (Manivannan 2014). The computations of heterosis, potence ratios and gene numbers were performed using Microsoft Excel 2016.
The Line*Tester mating design is described by the following statistical model (Kempthorne 1957) (Equation 3):
where the phenotypic value for each experimental unit is denoted by t, u is the overall population mean, ai is the location effect, bkl is the block effect within each location or replication within each location, and vij is the effect of the F1 hybrid.
The F1 hybrid effect, vij, can be further decomposed into (Equation 4):
where gi is the GCA effect of the ith parent line, gj is the GCA effect of the jth tester, sij is the SCA effect of the ijth hybrid in the lth location, eijkl is the environmental error effect.
Variance analysis of various traits was performed to calculate the variances for GCA and SCA in relation to the error variances, as indicated below (Equation 5 and Equation 6).
Half-sib covariance of a line is given by:
Half-sib covariance of a tester is represented as:
Full-sib covariance can be calculated using the formula (Equation 7):
where (l) denotes the total lines, (t) represents the total testers, and (r) represents the total replications.
If the absence of epistasis and a breeding coefficient (F) of one is assumed, then the variance components for GCA and SCA can be computed as follows (Equation 8 and Equation 9):
and
In these formulae, σ2 A represents the additive variance and σ2 D denotes the dominance variance. The term (F) is the inbreeding coefficient, which is set to unity.
The following formula was used to calculate the Genetic Ratio or Baker Ratio (Baker 1978) (Equation 10):
where, σ2g = GCA variances and σ2s = SCA variances.
Heterosis was estimated with its magnitude measured relative to better-parent (BPH), mid-parent (MPH) and commercial hybrids (Equation 11 and Equation 12):
where F1 = mean hybrid value, BP = mean better-parent value, and
where F1 = mean hybrid value and MP = mean mid-parent value, and (Equation 13)
where F1 = mean hybrid value, CC = mean commercial check variety.
The potence ratio, as defined by Mather (1949) and Smith (1952), was calculated to quantify the degree of dominance (Equation 14):
where P = potence of genes, F1 = mean of first generation, P1 and P2 are the mean values of the lower and higher parents, and MP is the mid-parent value. The degree of dominance was interpreted as follows: a potence ratio of +1 indicates complete dominance; values between −1 and +1 indicate partial dominance; values greater than ±1 indicate overdominance; and a value of 0 indicates absence of dominance.
Ethical considerations
This article followed all ethical standards for research without direct contact with human or animal subjects.
Results
Analysis of variance revealed highly significant (p < 0.01) differences in genotypes, crosses, lines, and parents versus crosses (Table 2). Highly significant mean squares were recorded for genotypes, parents and lines for all evaluated traits. For parents versus crosses comparison, all traits showed highly significant mean squares, except for spikelet density and ear-head weight. Significant variation among parents was observed for all traits. Specifically, the mean squares for parents were significant (p < 0.05) for threshing percentage (130.61*) and highly significant (p < 0.01) for grain yield per plant (752.51**), number of grains per spikelet (0.52**), ear-head weight (4.74**) and plant height (167.78**).
| TABLE 2: Analysis of variance of finger millet yield and yield-related traits. |
The variances for GCA (σ2GCA) and additive effects (σ2A) were larger than the variances for SCA (σ2SCA) and dominance (σ2D), respectively, for finger length, threshing percentage and grain yield per plant. For these same traits, Baker’s ratio, which indicates the prevalence of additive gene action when its value approaches unity, was 0.83 for finger length, 0.84 for grain yield per plant, 0.67 for thousand-grain weight and 0.63 for ear-head weight.
The contributions of lines, testers and their interactions to total genetic variation indicated that lines contributed the largest proportion of the total variation for several traits: grain yield per plant (97%), ear-head weight (92.64%), finger length (93.02%), plant height (88.12%) and grain yield per plant (Table 3). However, tester contributions were high for spikelet density (37.91%) and ear-head size (17.00%). The Line*Tester interactions were also substantial in the number of productive tillers (48.17%), ear-head size (37.91%), spikelet density (35.77%) and finger number (30.18%).
| TABLE 3: Lines, testers, and their interaction with total variation. |
The GCA of lines and testers was evaluated to identify superior parents for yield and yield-related traits in finger millet. The results identified Line 5045 as the best general combiner, exhibiting highly significant (p < 0.01) and positive GCA effects the number of productive tillers (0.73*), ear-head weight (3.41**), number of grains per spikelet (0.21**), threshing percentage (5.30*), grain yield per plant (32.91**) and thousand-grain weight (0.31**) (Table 4). Conversely, Line 2307 was the poorest general combiner, showing highly negative significant GCA effects for number of productive tillers (−1.34**), grain yield per plant (−20.20**), thousand-grain weight (−0.37**), threshing percentage (−4.42**), and number of grains per spikelet (−0.33**).
| TABLE 4: Estimates of general combining effects of lines and testers. |
All lines exhibited significant GCA effects in plant height, with negative effects associated with shorter plants and positive effects associated with taller plants. Highly significant (p < 0.01) negative GCA effects in plant height were observed in Line 5045 (−14.25**) and Line 5244 (−8.91**). For peduncle length, significant (p < 0.05) GCA effects were recorded in Line 5157 (1.20*) and Line 5244 (1.35*), while highly significant (p < 0.01) effects were recorded in Line 5327 (1.72**). Furthermore, significant (p < 0.05) and positive GCA effects for finger number (0.88*) were observed in Line 5157 and for finger length (1.63**) in Line 2307.
In terms of the SCA effects on yield and yield-related traits in finger millet, the hybrid Line 5327*FMV1 stood out as a strong specific combiner, showing favourable SCA effects for both grain yield per plant (5.01*) and its key component spikelet density (0.77*) (Table 5). The hybrid Line 5157*FMV2 also exhibited significant positive SCA effects for grain yield per plant (4.02*). In contrast, hybrid Line 2307*FMV1 was generally a poor combiner across most traits. However, both Lines 2307*FMV1 (−6.22*) and 2307*FMV2 (6.22*) hybrids were exceptional specific combiners for plant height, making them valuable for breeding programmes targeting improved plant productivity.
| TABLE 5: Estimates of specific combing ability effects on finger millet yield and yield-related traits. |
The hybrids Line 5327*FMV1, Line 5244*FMV1 and Line 5045*FMV1 showed significant positive SCA effects for the number of grains per spikelet, number of productive tillers and ear-head weight, respectively. Conversely, tester FMV2 was a poor combiner for the number of productive tillers, contributing to negative SCA effects in all lines.
Regarding heterosis and its magnitude for yield and yield-related traits in finger millet, the Line 2307*FMV1 combination did not record significant heterotic effects in most traits, except for finger length (Table 6a and Table 6b) Highly statistically significant (p < 0.001) BPH was observed in the Line 2307*FMV2 combination for plant height (14.62**), finger length (28.84**) and spikelet density (25.18**). In the Line 5045*FMV1 combination, highly significant heterosis was observed for ear-head weight, grain yield per plant, thousand-grain weight and the number of grains per spikelet. Highly significant MPH was observed for the number of productive tillers (47.22**), grain yield per plant (39.67**) and thousand-grain weight (22.87**). The Line 5045*FMV2 combination showed significant BPH for the number of productive tillers (41.63**), number of grains per spikelet (15.58**), grain yield per plant (9.49**) and thousand-grain weight (14.72**).
| TABLE 6a: Heterosis and its magnitude for yield and yield-related traits in finger millet. |
| TABLE 6b: Heterosis and its magnitude for yield and yield-related traits in finger millet. |
Highly significant heterotic effects were expressed in plant height, grain yield per plant, thousand-grain weight and number of grains per spikelet in Line 5327*FMV1. In contrast, the Line 5327*FMV2 exhibited significant heterosis for peduncle length, number of productive tillers and thousand-grain weight. Highly significant positive heterotic effects for finger length were recorded only in Line 5157*FMV1 and Line 5157*FMV2. Similarly, positive heterotic effects for plant height were recorded in Line 5045*FMV2, Line 5045*FMV1 and Line 5244*FMV2 combinations, and highly significant positive heterotic effects were recorded in Line 2307*FMV1, Line 5327*FMV1 and Line 5157*FMV1. Line 2307*FMV2, Line 5054*FMV2 and Line 5327*FMV1 exhibited significant (p < 0.05) heterosis in threshing percentage, and highly significant (p < 0.01) heterotic effects were recorded for grain yield per plant in Line 5045*FMV1, Line 5045*FMV2 and Line 5327*FMV1.
In terms of potence ratios for yield and yield-related traits in finger millet, the absence of dominance (P = 0) was noted in Line 5045*FMV2 in spikelet density (Table 7), and overdominance (P > ±1) was observed in most line combinations for all traits. Overdominance gene effects were expressed in all combinations for grain yield per plant and threshing percentage. Overdominance gene effects were also observed in the number of productive tillers, number of grains per spikelet, finger number, finger length, ear-head size, spikelet density, peduncle length and plant height. Conversely, partial dominance effects were observed in a limited number of crosses. Partial dominance (−1 < P < 1) in grain yield per plant was observed in the following combinations: Line 5327*FMV2, Line 5244*FMV1, Line 5244*FMV2 and Line 5157*FMV1.
| TABLE 7: Potence ratios in finger millet yield and yield-related traits for crosses. |
Discussion
The ANOVA revealed that highly significant (p < 0.01) variation for finger millet yield and yield-related traits was detected in genotypes, crosses, parents versus crosses, lines, testers, and Line*Tester interaction. This significant variation suggests that these genotypes are valuable genetic resources containing the diverse alleles necessary for selective breeding and the development of improved finger millet varieties (Abrha, Zeleke & Gissa 2013; Daudi et al. 2021; Owere et al. 2016). Of the two testers used, FMV1 demonstrated superior performance over FMV2 in several key traits, including number of productive tillers, finger number and grain yield per plant. Conversely, FMV2 proved to be the better tester for plant height, finger length, threshing percentage and thousand-grain weight. These findings are similar to those of Gupta and Mushonga (1994), who studied variability, heritability and correlation of morphological traits of a diverse panel of 324 finger millet genotypes.
The Line*Tester interaction revealed that there were significant (p < 0.05) differences in the means squares for peduncle length, number of productive tillers, ear-head size, spikelet density, number of grains per spikelet and grain yield per plant. This highlights the importance of additive and non-additive gene action for predicting the offspring performance and exploiting heterosis in hybrid breeding (Abdel-Aty et al. 2023). Significant variance between parents and crosses indicated heterosis in all traits, except spikelet density and ear-head weight.
The combining ability analysis revealed that additive gene action was predominant for several traits, as indicated by the GCA effects being greater than the SCA effects. This was particularly true for grain yield per plant, threshing percentage and finger length. This suggests that pure-line selection is the most effective method for improvement of these traits. However, SCA effects were found to be more significant in the inheritance of the other traits studied, making hybrid breeding the most effective approach for their improvement. The greater importance of SCA effects over GCA effects in the inheritance of yield and yield-related traits in finger millet corroborates the findings of Priyadharshini et al. (2017), Savitha et al. (2023) and Divya et al. (2022).
Baker’s Ratio for plant height, ear-head weight, finger length and grain yield per plant was close to unity, suggesting that these traits are primarily governed by additive gene action. This indicates that selection based on phenotypic performance of the progeny is highly likely to be effective (Agaba et al. 2017). The inheritance of ear-head size, finger number, number of productive tillers and spikelet density is governed by non-additive gene action as indicated by Baker’s Ratio close to zero and high dominance variance (Daudi et al. 2021). Consequently, selection for these traits is most effective in later generations (Daudi et al. 2021; Santhiya et al. 2024).
Moreover, total variation in the F1 hybrids was largely attributable to lines, with significant positive mean squares confirming the predominance of additive gene effects for finger length, finger number, ear-head size, plant height and grain yield per plant. Our findings align with those of Divya et al. (2022), who reported on the combining ability of 20 finger millet hybrids in a Line*Tester study. Unlike the observations of Divya et al. (2022), where Line*Tester interaction was the primary contributor to total observable variation, our analysis showed that lines contributed most to the total variation in peduncle length. Also, Line*Tester interactions significantly contributed to the observed variance in spikelet density and the number of productive tillers, indicating a good SCA emanating from dominance gene effects (Hosen et al. 2022), which is likely to lead to early genetic gain (Patroti & Gowda 2013). The partition of variance because of tester effects was pronounced in the number of productive tillers, spikelet density, number of grains per spikelet and thousand-grain weight, where significant mean squares were observed, highlighting the importance of the tester in influencing genetic variability.
In terms of GCA for yield and yield-related traits, all the lines exhibited highly significant GCA effects in plant height; however, their magnitude and direction varied. A highly significant GCA variance was detected in the number of productive tillers, ear-head weight, spikelet density and grain yield per plant in Line 5045. This finding suggests that additive and additive*additive gene action primarily control these traits, indicating that effective selection can be performed in early generations to fix genetic gains (Daudi et al. 2021; Kumar et al. 2019). The non-additive gene action in Line 2307 governs the inheritance of the number of grains per spikelet, number of productive tillers, thousand-grain weight, threshing percentage and grain yield per plant; however, it proved to be the poorest general combiner because of the negative effects of these genes. Line 5045 and Line 5244 are essential genetic materials for breeding lodging-resistant machine-harvestable varieties with short stature. Line 2307, Line 5327 and Line 5157 can be selected for the development of tall plants that can maximise yield through higher biomass accumulation in both grain and fodder.
For SCA, the 5327*FMV1 hybrid showed significant positive effects for spikelet density and grain yield per plant, despite low GCA effects for the same traits. A high SCA from a cross with low GCA effects suggests that dominance*dominance gene action is the primary contributor, indicating that these genetic gains are non-fixable (Hosen et al. 2022). The high SCA effects exhibited by the hybrids 5327*FMV1 and 5157*FMV2 for grain yield per plant make them promising genetic material for hybrid variety development in finger millet. These findings align with previous research by Patroti et al. (2013), Savitha et al. (2013) and Divya et al. (2022), who also reported the existence of superior specific combiners for yield and yield-related traits in finger millet. This is significant because the improvement of traits whose inheritance is controlled by non-additive gene effects, which are expressed through SCA, is best achieved through hybrid breeding (Kumar et al. 2017; Priyadharshini et al. 2011).
The magnitude of heterosis for yield and yield-related traits was greatest in crosses where the traits also exhibited high GCA effects. However, significant heterosis was also observed in crosses with High*Low GCA effects, supporting the notion that parents with low GCA effects should not be discarded in breeding programmes (Allahgholipour & Ali 2006; Dorosti & Monajjem 2014; Hosen et al. 2022). For example, Line 2307*FMV1 and Line 2307*FMV2 expressed heterosis for plant height and finger length, whereas Line 5045*FMV1 and Line 5045*FMV2 showed heterosis for ear head weight, number of grains per spikelet, number of productive tillers, thousand-grain weight and grain yield per plant. Similarly, the combinations Line 5327*FMV1 and Line 5327*FMV2, Line 5157*FMV1, and Line 5244*FMV2 were derived from crosses with low and negatively significant GCA effects, yet they exhibited significant heterosis, indicating a dominance*dominance gene action in the number of grains per spikelet, thousand-grain weight and ear head weight. Therefore, while parental GCA provides a useful baseline for predicting hybrid performance, its sole use can be misleading. A more accurate prediction of heterosis and selection of elite hybrids requires an integrated approach that evaluates SCA to capture non-additive gene effects and utilises advanced methods such as genomic prediction.
Furthermore, high significant positive heterosis estimates for plant height in Line 5327*FMV1, Line 2307*FMV1, and Line 5157*FMV1 combinations can be selected to develop varieties that efficiently partition photo-assimilates into grains, thereby increasing yield (Singh et al. 2023). Conversely, combinations from Line 5045*FMV1, Line 5045*FMV2 and Line 5244*FMV2 are suitable for developing shorter, machine-harvestable varieties with an improved canopy structure that is resistant to lodging, thereby reducing yield losses (Divya et al. 2022; Savitha et al. 2013). High and significant heterotic effects for peduncle length were observed in Line 5327*FMV2 and Line 5244*FMV1. This finding is agronomically important as longer peduncles are positively correlated with grain yield and can reduce ear lodging (Babu et al. 2016; Divya et al. 2022; Kandel, Bhadhur Dhami & Shrestha 2019; Manyasa et al. 2016; Singh et al. 2023), likely because of their role in the transportation of water and photo-assimilates during grain filling (Wang et al. 2022).
The negative BPH for grain yield per plant suggests incompatibility in gene interactions; therefore, such combinations may not be suitable for further breeding. Highly significant BPH was expressed in Line 5327*FMV1, Line 5157*FMV2, Line 5045*FMV1 and Line 5045*FMV2 combinations. However, significant SCA effects were only expressed in Line 5327*FMV1 and Line 5157*FMV2 combinations. These observations indicated that positive gene interactions between the diverse parental genotypes were governed by dominance*dominance gene effects (Santhiya et al. 2024), further consolidating that sufficient variation existed among genotypes, parents, and parents versus crosses (Table 2).
Regarding potence ratios in crosses, most of the studied traits in finger millet yield were governed by overdominance gene action, especially thousand-grain weight, peduncle length, threshing percentage, number of grains per spikelet, number of productive tillers, finger number and finger length. The intra-allelic interactions in heterozygote hybrids lead to the expression of heterobeltiosis in hybrids because of their superior performance compared to their homozygote parents (Ghosh et al. 2018; Sweet et al. 2024). Because of overdominance, the crosses recorded considerable heterotic effects in grain yield per plant, thousand-grain weight, threshing percentage, number of grains per spikelet, number of productive tillers, peduncle length, finger length and finger number. The study highlighted that overdominance genes positively influenced yield in all line combinations by increasing threshability, which has a direct impact on the quantity, quality and processing costs of harvested grain. Genotypes with heterotic threshability overexpression are ideal for augmented grain yield and should be incorporated into finger millet improvement programmes. Overdominance gene effects exert heterosis by increasing heterozygosity, which must be exploited in superior cultivar development programmes (Ghosh et al. 2018).
Conclusion
This study demonstrates that both additive and non-additive gene actions govern the inheritance of yield and yield-related traits in finger millet, with additive effects predominating in grain yield per plant, threshing percentage and finger length, while non-additive effects were more important for traits such as spikelet density, finger number and productive tillers. Line 5045 emerged as the best general combiner, while Line 2307 showed potential for developing short, lodging-resistant varieties despite its overall poor combining ability. Several hybrids, particularly 5327*FMV1 and 5157*FMV2, exhibited high SCA and significant heterotic effects for yield and yield-related traits, making them promising candidates for hybrid development. The predominance of overdominance gene action in many traits further underscores the value of heterosis breeding as an effective strategy for genetic improvement. Collectively, these findings provide valuable insights into the genetic architecture of finger millet and highlight practical opportunities for breeding programmes to accelerate yield improvement, develop machine-harvestable varieties and strengthen the role of this underutilised crop in food security and smallholder resilience.
Acknowledgements
The authors would like to thank Lupane State University for support through the provision of research facilities, which include laboratory equipment and experimental plots.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
Authors’ contributions
O.S.M. was responsible for the research trial establishment, data collection, analysis and original write-up of the article. M.M. was responsible for the research idea, supervision, original write-up of the article, review and editing.
Funding information
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data availability
The authors confirm that the data supporting the findings of this study are available within the article.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
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