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2022 Volume 2
Article Contents
Abstract
Introduction
Auxin regulates cucumber morphological development
Root development
Hypocotyl and shoot development
Leaf development
Tendril development
Flower development
Fruit development
Auxin regulates cucumber stress responses
Abiotic stresses
Biotic stresses
Conclusions
Acknowledgments
Conflict of interest
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References
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ARTICLE   Open Access    

Heritability estimates for seed yield and its components in Cynodon dactylon var. dactylon (L.) Pers.

  • 1.

    Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078, USA

  • 2.

    Oklahoma Department of Agriculture, Food and Forestry, 2800 N. Lincoln Blvd., Oklahoma City, OK 73105, USA

  • 3.

    Department of Horticulture and Landscape Architecture, Oklahoma State University, Stillwater, OK 74078, USA

More Information
  • Bermudagrass [Cynodon dactylon var. dactylon (L.) Pers.] is a major warm-season turf and forage grass worldwide. Seed yield is an important trait targeted for improvement in bermudagrass breeding programs because of the increased interest in seed-propagated cultivars. Understanding the nature of genetic variation for seed yield and its components in bermudagrass would aid the development of seed-propagated cultivars. The objective of this study was to estimate the genetic variation and narrow-sense heritability for seed yield and its two major components, inflorescences prolificacy and seed set percentage in bermudagrass. Twenty-five half-sib families and their respective clonal parents were evaluated at two Oklahoma locations, Perkins and Stillwater (Oklahoma, USA), over two years. Half-sib families were different for seed yield, inflorescences prolificacy and seed set percentage, indicating the expression of additive genes in controlling these traits. Family × location effects were observed for seed set percentage and seed yield. All three traits showed family × year interaction effects. There was a significant family × location × year interaction in inflorescences prolificacy and seed set percentage. Narrow-sense heritability estimates for seed yield was 0.18 based on variance component analysis among half-sib families and ranged from 0.26 to 0.68 based on parent-offspring regressions, indicating genetic complexity of seed yield. Heritability estimates were moderate (0.30−0.55) for inflorescences prolificacy and moderate to relatively high (0.41−0.78) for seed set percentage. The results indicate that sufficient magnitudes of additive genetic variation for seed set percentage and inflorescence prolificacy permit positive response to selection and conventional progeny-based genotypic evaluation is necessary for seed yield improvement.

  • Introduction

    Cucumber (Cucumis sativus L.), with a total cultivation area of 1.98 million hectares in 2020, is popular worldwide for its crispy texture and special flavor[1,2]. Cucumber fruit is rich in minerals and nutrients, including calcium, protein, iron, and vitamins and thus provides numerous health benefits to the human body[3]. Cucumber fruit also supplies polyols, flavonoids, and polysaccharides with antioxidant properties, which help scavenge free radicals, delay aging, and boost immunity and mental health[4,5]. Cucumber fruit contains Cucurbitacin B and C that inhibit the growth of various tumors and cerebral reperfusion injury and protect the liver from inflammation[6,7]. In addition, the wild cucumber fruit has been used as a herbal medicine in several health-related products[6].

    Auxin was first isolated from maize in 1941[8]. Initially, it was proposed as a mobile molecule regulating the phototropic growth of coleoptiles. Since then, auxin received great attention in plant biology research as it is involved in every developmental process from embryogenesis to postharvest ripening[9]. In cucumber, auxin is involved in various agronomic trait development and stress responses[10,11]. Auxin participation is largely dependent on its biosynthesis, transportation and signaling. Auxin biosynthesis pathways mainly include IAOx (indole-3-acetaldoxime), tryptamine (TAM), IAM (indole-3-acetamide), and Trp-IPyA (tryptophan-indole-3-pyruvic acid). Among them, the most discussed auxin biosynthesis pathway is the Trp-IPyA pathway. TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS (TAA1) genes begins the conversion of Trp into IPyA. Following that, the irreversible and rate-limiting reaction catalyzed by YUCCA genes (flavin-containing monooxygenases) ensures the decarboxylation of IPyA into IAA[12−16]. PIN and AUX1/LAX are key auxin transporter genes, instrumental in regulating plant phenotype and auxin homeostasis[17−19]. Auxin signaling genes such as AUX/IAA, TIR1/AFB and ARF are responsible for tightly regulating the different processes, including embryo development, seed abortion and fruit setting[20−22].

    The present review summarizes recent progress on the auxin regulation of growth and stress response in the cucumber plant. The future directions to develop high-yielding and climate-resilient cucumber lines are also emphasized.

    Auxin regulates cucumber morphological development

    Auxin regulates plant development mainly by its biosynthesis, transport, and signal transduction. It is generally regarded as the growth hormone owing to its involvement in almost every developmental process[23]. In cucumber, auxin regulates the development of different organs (root, hypocotyl, shoot, leaf, tendril, flower, and fruit) (Fig. 1). Below, we summarize how auxin regulates the growth of cucumber.

    Figure 1.  Auxin mediated cucumber growth and development. Auxin genes involved in the modulation of cucumber developmental stages has been presented in each box. (a) In roots, the auxin signaling genes (CsIAA2, CsARF8, CsARF17), auxin biosynthesis genes (CsYUCCA3, CsYUCCA5) and auxin transporting gene (CsPIN5) are shaping its structure and directional growth. (b) Majority of the auxin signaling genes (CsIAA4, CsIAA14, CsIAA29, CsIAA32, CsSAUR23 and CsSAUR32) are the main drivers of the hypocotyl length. (c) For shoot development, the auxin transporting gene CsPIN3 regulate the IAA accumulation in the wildtype cultivar hardwickii. Reduced IAA accumulation intensified the shoot branching in hardwickii. (d) The upregulation of CsYUCCA4 in v-2 mutant caused the rolled phenotype by inducing the IAA accumulation. (e) Suppression of auxin signaling genes SAUR and IAA/AUX resulted in the tendril-less phenotype in mutant line CG9192. (f) The enhanced expression of auxin biosynthesis genes (CsYUCCA2 and CsYUCCA10) amplified the IAA accumulation, increased the number of petals in male and female flower bud. (g) The higher accumulation of IAA in fruit caused its length to increase. The curved fruit phenotype was caused by the asymmetric distribution of IAA in the concave and convex side of the fruit.
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    Root development

    Auxin plays a key role in maintaining the root growth of the cucumber plant. From directional growth to lateral and adventitious root developments, auxin influence is instrumental[24]. Adventitious roots (ARs) and Lateral roots (LRs) arise from the stem and root tissues, respectively, and are pivotal in plant growth and development[25]. A series of research articles have highlighted the auxin role in regulating cucumber ARs and LRs development. Applying gibberellic acid (GA3) to cucumber has significantly induced LR through enhancing auxin biosynthesis genes CsYUCCA3 (Csa3G133910) and CsYUCCA5 (Csa3G619930) and suppressing the auxin inactivation genes CsGH3.3 (Csa3G198490) and CsGH3.5 (Csa3G431430)[26]. Application of methane (CH4) induced AR in cucumber[27]. Amplified expression of auxin signaling genes CsAux22D-like and CsAux22B-like was observed in the CH4 treated cucumber[27]. Exogenously applied glutathione (GSH) promoted AR formation in cucumber by upregulating the expression of CsARF8, CsARF17, CsAux22D-like, and CsAux22B-like[28].

    The movement of roots toward water is called hydrotropism[24]. Auxin transporter genes CsPIN5 were reported to regulate cucumber root hydrotropism. The Clinorotation method was employed to analyze the hydrotropic response of cucumber roots. Four hours after hydrosimulation in the microgravity (µG) environment, faster and better root growth was observed on the high humidity side (concave) than on the low humidity side (convex). Elevated expression of CsPIN5 was recorded on the concave compared to that of the convex. The study revealed that under µG conditions, roots become hydrotropically sensitive. This hydrotropic sensitivity of cucumber root is mainly driven by the higher accumulation of the CsPIN5 gene[29]. Another study reported the prominent role of auxin-inducible genes in regulating hydrotropism. The gene CsIAA2 is expressed more dominantly on the concave side than the convex[30].

    Hypocotyl and shoot development

    Hypocotyl length is a key factor in determining the quality of seedlings. Seedlings with moderate hypocotyl length are easy to handle during transplantation and generally grow well. Local auxin biosynthesis in the leaves and cotyledon promote hypocotyl elongation in Arabidopsis[31]. In cucumber, an elongated hypocotyl 1 (elh1) mutant, which showed an elongated hypocotyl because of the longitudinal cells, was identified[32]. CsHY2 (CsGy1G030000) encoding a phytochromobilin (PΦB) synthase was found to be responsible for the elongated hypocotyl phenotype in elh1. Elevated expression of CsHY2 was recorded in elh1 in comparison with WT. RNA-seq analysis of the WT and elh1 revealed the upregulated expression of AUXIN RESPONSIVE GENES (CsIAA4, CsIAA14, CsIAA29, CsIAA32, CsSAUR23, CsSAUR32) in elh1[32].

    Shoot branching is an important agronomic trait determining cucumber fruit yield[33]. These branches must be removed manually from the cucumber plant to avoid unnecessary nutrient consumption[33]. Auxin's role in cucumber shoot branching has been investigated in detail[34]. The cucumber line Gy14, in comparison to wild ancestor hardwickii, has relatively small numbers of shoot branches. CsBRC1 (BRANCHED1) exhibited high expression in the auxiliary buds of Gy14 than the hardwickii. CsBRC1 binds to CsPIN3 and suppresses its expression level, thus conferring a low number of shoot branching. The RNAi lines of CsBRC1 generated a high number of branches by increasing the expression level of CsPIN3 and decreasing the IAA accumulation in auxiliary buds[34]. Dramatic inhibition of auxiliary buds was observed in the TIBA (2,3,5-Triiodobenzoic acid) treated hardwickii. The IAA accumulation in the TIBA treated plants was also increased[34]. This revealed the crucial role of the auxin transporter gene in regulating shoot branching.

    Leaf development

    Auxin regulating cucumber leaf development has been reported numerous times[35,36]. Leaf mutation provides the perfect platform in understanding the genetics and genomics of leaf biology. One cucumber round leaf (rl) mutant was identified from an ethyl methanesulfonate-induced mutagenesis population. Genetic screening and MutMap analysis mapped a single recessive gene encoding PINOID kinase protein (Csa1M537400), active in auxin transport, as the candidate gene responsible for rl mutation. The gene displayed relatively lower expression in the rl mutant than in the WT plant. Obvious induction in the endogenous IAA content and the higher expression level of CsYUCCA4 were also recorded in the rl mutant. The auxin transport inhibitor NPA (1-N-naphthylphthalamic acid) -treated plants displayed similar leaf phenotypes to the rl mutant, which further validates the involvement of IAA in leaf development[37]. Auxin was also found to be responsible for regulating the leaf color of cucumber. A single recessive gene v-2 (Csa3G890020) encoding an auxin F-box protein was fine mapped, controlling the virescent leaf that developed yellow cotyledon and upper five true leaves. Auxin signaling and chlorophyll biosynthesis genes were decreased significantly in the v-2 mutant[38]. Overall, these finding elucidates auxin involvement in leaf development and color change.

    Tendril development

    The tendril is a specialized organ arising from the lateral meristem, providing extra support to the main stem and branches. Cucumber has typically branchless tendrils. In response to a touch stimulus, tendrils twine and climb quickly by sensing a thigmotropic signal[33]. Since cucumber cultivation is often carried out in a protected environment, tendrils become a problem by causing disorderly growth and increasing crop management labor[33]. Several tendril-less cucumber mutants have been previously reported[39,40]. Auxin was found to play a key role in regulating the growth and numbers of tendrils in cucumbers. A unique tendril-less mutant was identified from more than 3,000 cucumber lines by Wang et al.[41]. Instead of developing tendrils, the tendril-less mutant line CG9192 forms branches and, in the process, loses its climbing ability. The gene TEN (Csa5G644520) encoding a TCP transcription factor was responsible for the underlying tendril-less phenotype in the CG9192 line[41]. In situ and PCR analysis revealed that TEN was expressed specifically in tendrils. The variation in the protein motif CNNFYFP of TEN impairs the transcriptional activation domain (TAD), hindering the normal tendril development in cucumbers. Transcriptome-wide analysis showed genes downstream of TEN required for sensing and climbing ability[41]. Small auxin up-regulated RNA (SAUR) (Csa7G009080, Csa7G009070, Csa2G258640), and IAA/AUX transcription factors (Csa1G397130, Csa3G143570) were all downregulated in TEN. Also, these genes are preferentially expressed in tendrils. Therefore, it can be assumed that TEN recruits and suppresses the expression of SAUR and IAA/AUX genes in the tendril-less mutant[41]. The CsPID gene controlling the rl mutant phenotype was shown to facilitate the initiation of tendrils in the first 20 nodes[42]. Further, the transcriptomic analysis revealed that auxin transporting CsABCB13, CsABCB19 (Csa3G873270, Csa5G636450), and signaling CsIAA16 (Csa1G397130) genes were significantly downregulated in the rl mutant[41]. Auxin's role in modulating tendril development in cucumbers is influential. The rarity of cucumber tendril-less lines provides grounds for the application of auxin in controlling the growth of tendrils.

    Flower development

    Cucumber flowers are generally small and yellow, with 3-6 petals per blossom. Auxin plays a fundamental role in cucumber flower development. For instance, a cystatin-like protein, CsDFB1 (DEFORMED FLORAL BUD1) was identified as a key gene indirectly regulating local auxin biosynthesis and distribution[43]. The CsDFB1 gene displayed dominant expression in the floral meristem, primordia, and vasculature. RNAi-mediated silencing of CsDFB1 increased the IAA accumulation in the shoot by triggering and suppressing the expression of the CsYUCCA2 and CsPIN1 genes, respectively. Conversely, significantly lower IAA accumulation was observed in the CsDFB1 overexpressed lines. The CsDFB1-RNAi lines had an increase of 20%−80% in floral organ (petals, stamens, and carpels) numbers. CsDFB1 interacted with CsPHB (HD-ZIP III transcription factor PHABULOSA), resulting in the truncated expression of CsYUCCA2 and CsPIN1 in WT plants. Overexpression of CsPHB resulted in a similar phenotype to CsDFB1-RNAi lines[43]. It highlighted the importance of auxin distribution in cucumber flower organogenesis.

    Female flowers, on the other hand, determine the fate of cucumber fruit yield. Phytohormone such as ethylene has long been linked with female flower induction[44]. Recently, the participation of auxin in cucumber female flower production has been reported. For instance, blue light-induced female flower by up-regulating auxin biosynthesis and signaling genes in cucumber[45]. A more detailed explanation of the auxin role in female flower induction was presented in two androecious cucumber lines (406an, 406a)[46]. Different concentrations of IAA were used to investigate whether IAA induces femaleness in the 406an and 406a lines. An increased number of female flowers was observed under 50 and 500 mg/L of IAA. However, 500 mg/L IAA suppressed the plant height because IAA over-accumulation caused toxicity. Therefore, 50 mg/L of IAA-treated flowers were used for further experimental work. Post IAA treatment, induction in the endogenous ethylene was also observed. The expression of ethylene biosynthesis genes (CsACS1, CsACS2 and CsACS11) were enhanced whereas sex controlling genes (CsWIP1) were significantly inhibited. The ENHANCER OF SHOOT REGENERATION 2 (ESR2) was also triggered by exogenous auxin. The CsESR2 binds with the promoter region of CsACS2 and activates its expression. Therefore, the application of auxin enhanced femaleness in the cucumber androecious lines via CsESR2-CsACS2 signaling cascade[46].

    Male sterility is an important agronomic trait in cucumber hybrid seed production[47]. A normal supply of carbohydrates is important for the development of male gametophytes. In this regard, a sucrose transporter gene, CsSUT1, was functionally characterized in cucumber to understand its role in male sterility[48]. The CsSUT1 silenced plants bare male flowers with abnormal anthers and microsporocytes producing unviable pollens. Compared to WT, the CsSUT1 antisense lines possessed a significantly low starch, sucrose, and hexose level in the male flower buds. Auxin biosynthesis (CsYUCCA10) and signaling (CsIAA4, CsIAA11, CsIAA12) genes were downregulated in the CsSUT1-RNAi lines showing the association of auxin with male sterility[48].

    Fruit development

    Auxins regulate key cucumber traits, including fruit setting, length, shape and trichomes/fruit spines[22]. There are two types of fruit setting, i.e., pollination dependent and parthenocarpic[49]. Auxin has been widely utilized to achieve a successful fruit setting without fertilization[22]. A study by Qian et al.[1] presented the benefits of exogenously applied auxin inducing parthenocarpic fruit in cucumber. Naphthaleneacetic acid (NAA) at 100 mg/L was sprayed on the unpollinated cucumber ovaries. The NAA-treated unpollinated ovaries had successful fruit settings, whereas untreated ovaries were deceased[1]. Similarly, Su et al.[10] used two cucumber inbred lines, namely ZK (weak parthenocarpic) and DDX (strong parthenocarpic). Unfertilized ovaries of ZK failed to grow. On the contrary, unfertilized ovaries of DDX generated normal-sized parthenocarpic fruits. ZK, compared to DDX, was quantified for low endogenous IAA content. Applying 100 mg/L NAA to the unpollinated ovaries of the ZK line produced parthenocarpic fruit[10]. The spraying of sugars (sucrose, glucose, and fructose) on the ZK line caused parthenocarpic fruit induction[50]. The auxin signaling gene CsIAA14 was induced significantly in the sugar-treated ovaries. On the other hand, the auxin degradation gene CsGH3.1 was suppressed post-sugar treatment[50].

    Fruit length is a prominent agronomic trait in cucumber breeding. Two alleles (CsFUL1A, CsFUL1C) of the FRUIT (FUL)-like gene were mapped across 150 cucumber lines. The CsFUL1A was enriched specifically in the long-fruited cucumber lines of East Asia (China and Japan). Random distribution of CsFUL1C was observed in the wild and semiwild cucumber lines[51]. Silencing of CsFUL1A resulted in the elongated fruit phenotype. By contrast, overexpressing the CsFUL1A generated shorter fruits. Higher IAA accumulation was observed in CsFUL1A antisense transgenic fruits. Also, an elevated expression level of CsPIN1 and CsPIN7 was observed in the CsFUL1A -RNAi fruits[51]. A natural cucumber mutant with short fruit (sf1) was isolated from the North China-type inbred line CNS2[52]. A total of 15 genes were identified in the 174.3 kb region on chromosome 6. Various auxin signaling genes were downregulated in the sf1 mutant[52]. Genetic analysis showed that the CsKTN1 gene encoding the katanin p60 subunit was responsible for the short fruit3 (sf3) phenotype[53]. Interestingly, no change was noted in the expression of CsKTN1 between WT and sf3. However, suppressed expression of CsYUCCA6 and lowered IAA accumulation were recorded in the cucumber (sf3) mutant[53]. Apart from the cucumber fruit length, its fruit shape is also a key factor affecting the cosmetic appearance and marketability of cucumber fruits. Asymmetric auxin distribution is central to fruit shape. For example, samples were taken from the curved and straight cucumber fruit to measure endogenous auxin levels[54]. Compared to the concave side, high auxin concentration was recorded on the convex side of the curved fruit. No significant difference was recorded in the auxin level on both sides of the straight fruits[54]. This asymmetric distribution in the curved cucumber fruit might be brought about by the impaired local auxin biosynthesis and transport. The application of 0.15 μM NAA inhibited the curvature at 2 d post-anthesis (DPA). Transcriptomic analysis revealed that the CsYUC10b gene level was significantly higher in the convex side in comparison to the concave side. Overexpression of CsYUC10b induced the equal biosynthesis of auxin on both sides and finally produced straight fruits[54]. Overexpression of CsDFB1 suppressed the IAA accumulation by impeding the CsYUCCA2 expression resulting in a curved fruit phenotype, whereas CsDFB1-RNAi lines showed contrasting results for fruit phenotype[43]. Interestingly, CsYUC10b and CsDFB1 had no relationship with the previously identified loci involved in fruit shape[55,56]. Therefore, further genetic work is required to develop markers for fine mapping the QTL and identify major loci regulating cucumber fruit shape.

    Fruit trichomes or spines are important traits affecting fruit smoothness, storage, and transportation. Thus far, various fruit spine mutants have been identified, namely tiny branched hair (tbh), microtrichome (mict), and glabrous (csgl1)[57]. Auxin's role in cucumber fruit spine formation has been previously studied. Such as, a numerous spine (ns) locus harboring a Csa2G264590 gene was mapped in the F2 population (numerous fruit spines lines X few fruit spines lines). The Csa2G264590 gene was annotated as the auxin transporter-like 3 and was significantly downregulated in the fruit skin of ns lines. Several signaling genes (Csa7M440550, Csa5M610430, Csa1M397130) were upregulated in the ns lines[58]. More recently, nine loci for cucumber fruit spine density were identified by using genome wide association mapping analysis[59]. Among them, fsdG2.1 showed a closer association with the previously reported ns loci (Csa2G264590). Overexpression of NS generated fruits with lower spine density by increasing the IAA accumulation in fruit peel. On the contrary, CRISPR/Cas9 generated ns-cr lines developed fruit with a high number of spines, and the level of endogenous IAA also plummeted in peel tissue. NPA treatment significantly induced the fruit spine density, confirming the auxin regulation in spine formation[59]. However, Yang et al.[60] discovered that auxin level was unaltered between the two cucumber fruit spine-specific parental lines (S-SB and L-SB). It indicated that other unknown pathways might be regulating fruit spine density, independent of auxin.

    Auxin regulates cucumber stress responses

    Some environmental stresses such as drought, waterlogging, salinity, heat, and other biotic stressors inflict serious damage on cucumber growth by disturbing the physiological activities[61−65]. As an essential growth hormone, auxin ensures the proper functioning of plant physiology under stressful conditions. In the subsequent sections, we discuss auxin's role in regulating cucumber stress biology. Summarized information regarding the auxin-mediated stress response in cucumber is presented in Table 1.

    Table 1.  Auxin participation in regulating cucumber response to multiple stresses.
    StressAuxin activityFunctionsReference
    HeatCsYUC8, CsYUC9 ↑Enhanced IAA accumulation under high temperature stress.[68]
    IronYUC1, PIN1 ↑Higher expression of PIN1 gene increased cucumber tolerance.[70]
    WaterloggingIAA accumulation ↑Boosted AR formation, ethylene accumulation, and expression of CsRBOHB and CsRBOHF3.[71]
    Cold
    		CsYUCCA2 ↑Decrease harmful ROS and stress-induced electrolyte leakage.[75]
    CsARF6 ↑Activating the expression of cold stress-responsive gene CsDREB3.[76]
    Salinity
    		AUX/IAA ↓Reversed the harmful effects of salinity stress.[79]
    Csa6G104650 ↑Regulate the silicon-mediated salinity resistance.[81]
    DroughtIAA accumulation ↓CO2 suppressed the IAA accumulation and boosted GA.[83]
    CadmiumABP19a-like ↑Improve photosynthesis and antioxidant enzyme activities.[85]
    Powdery mildewCsIAA4, CsIAA6 ↓Suppressed auxin signaling hinders the pathogenicity of powdery mildew pathogen.[88]
    Downy mildewIAA accumulation ↑Positive regulation of the salicylic acid pathway.[91]
    NematodeAuxin transport ↓Augmented flavonoid biosynthesis inhibited the formation of the giant cell on the root.[93]
    Downregulated = ↓, Upregulated = ↑.
     | Show Table
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    Abiotic stresses

    Global climate change events causing the surface and air temperature to rise. Temperature above the optimum level required for normal growth induces heat stress in plants. This causing detrimental and irreversible damage not only to plant growth, but also threatening the world food security by plummeting the overall crop productivity[66]. The phytohormone auxin plays an important role in heat stress-induced thermomorphogenesis, including stem (hypocotyl) elongation and leaf hyponasty[67]. In cucumber, the IAA accumulated in abundance under high temperature stress (38 °C)[68]. The auxin biosynthesis genes CsYUC8 and CsYUC9 also displayed an induced expression pattern under 38 °C[68]. Therefore, it can be suggested that auxin regulates the response of cucumber seedlings to high temperature stress; however, no functional study is available.

    Iron (Fe) is an essential micronutrient involved in photosynthesis, respiration, nucleic acid synthesis, protein functions and chlorophyll structure[69]. However, Fe deficit or excess is harmful to plants for normal growth and development and causes a significant yield penalty in terms of quantity and quality. The cucumber plant was grown in Fe deficient soil to understand its response to Fe stress[70]. Increased Gamma-aminobutyric acid (GABA) was observed in the cucumber plants grown in Fe deficient soil. Because of this, exogenous GABA was used over cucumber plants subjected to Fe deficiency. Plant treated with 20 mM GABA suppressed the chlorosis by increasing the expression of iron transporter genes FRO2, IRT1 and HA1. Additionally, GABA application induced the endogenous IAA level by boosting the expression of YUC1 and PIN1 genes. However, the use of NPA, an auxin transport inhibitor, reversed the beneficial effects of GABA[70]. It can be suggested that iron and auxin transporting genes work in concert, thus regulating the response of cucumber to Fe stress.

    Excess water blocks oxygen to the root and can cause moderate to severe damage[11]. The development of AR in cucumber is one kind of major phenotype response to waterlogging stress. AR formation largely depends on the local auxin biosynthesis and transportation[71]. For instance, the level of endogenous auxin in the hypocotyl increased at 72 h post waterlogging stress. Applying 10 mg/L of NAA enhanced the AR formation[71]. Further analysis revealed that auxin treatment upregulated the expression of ethylene biosynthesis genes (CsACS1, CsACS2, CsACO5) and reactive oxygen species (ROS) signaling genes such as CsRBOHB and CsRBOHF3 under waterlogging stress. By contrast, NPA treatment substantially hindered AR formation[71]. Exogenous NAA improved the formation of AR; however, elongation was unnoticed. AR elongation has been achieved by sugar treatment, as revealed by the study[72]. The removal of shoots inhibited the AR formation and elongation. Sugar treatment (300 µL) positively modulated the AR formation and elongation by strongly up-regulating the expression of CsPIN1, CsPIN1b, CsPIN8, CsARF5, CsARF6, and CsSAUR29[72]. Interestingly, no significant difference in ethylene biosynthesis genes was noted. However, the previous research did not separate the AR emergence and elongation. Further study focusing on these two different processes might explain the exact role of auxin in each stage.

    Cold stress mainly confines plant growth by causing chilling injuries to the tissue. Cucumber is highly sensitive to cold stress, and the plant will stop growth under 15 °C[73]. QTL named qLTT6.2 was identified in the F2 population of 'CG104 (LT-tolerant inbred line) and 'CG37' (LT-sensitive inbred line). Two candidate genes (Csa6G445210, an auxin response factor, and Csa6G445230, an ethylene-responsive transmembrane protein) were fine mapped in the 42-kb interval region of qLTT6.2. Compared to CG37, the dominant expression of Csa6G445210 was recorded in the CG104 line[74]. Though, the study failed to provide any detail about crosstalk of auxin and ethylene in the CG104. A study by Zhang et al.[75] explained the role of endogenous IAA in cucumber plants subjected to cold stress. The 1.0 mM sodium hydrosulfide (H2S) application enhanced cucumber tolerance to cold stress. Post H2S treatment, a sharp increase in the endogenous IAA content was observed. The transcription of auxin biosynthesis gene CsYUCCA2 was also triggered in the H2S treated cucumber plants. Conversely, NPA application significantly compromised the cucumber defense against the cold by decreasing the endogenous IAA and H2S contents[75]. Similarly, H2S treatment to cucumber boosted the expression of CsARF6, an auxin-responsive gene. Overexpression of CsARF6 confers cucumber plant tolerance to cold stress by augmenting the endogenous IAA and H2S levels. Molecular analysis revealed that CsARF6 bind to the promoter region of CsDREB3 (DEHYDRATION-RESPONSIVE ELEMENT-BINDING 3) and transactivate its expression[76].

    Salinity/salt stress is the second biggest abiotic factor affecting agricultural productivity worldwide by damaging numerous physiological, biochemical, and molecular processes[77,78]. Cucumber is extremely sensitive to salinity stress. Auxin involvement in enhancing cucumber tolerance to salinity has been recently documented. At the seedling stage, the cucumber cultivar 'Jinyou 1' was subjected to 100 mM NaCl[79]. Transcriptomic analysis revealed numerous differentially expressed genes. Among them auxin signaling genes SAUR (LOC105436055), Aux/IAA (LOC101219209, LOC101217817), and GH3 (LOC101208132) displayed a downregulated trend[79]. Silicon application has been proved to be pivotal in alleviating the detrimental effects of salinity stress[80]. Cucumber seedlings were subjected to 75 mM NaCl stress. Silicon at the rate of 0.3 mM was added to the nutrient solution. Meanwhile, silicon addition to nutrient solution significantly minimized the harmful effects by augmenting the stomatal conductance, net photosynthesis rate, and dry weight of fully expanded cucumber leaves. The auxin-induced protein 5NG4-like (Csa6G104650), a key molecule transporting gene, was upregulated in silicon-NaCl treated seedlings[81]. It can be assumed that auxin signaling genes are key in cucumbers' silicon-mediated salinity tolerance. However, functional studies are missing to elucidate the underlying mechanism.

    Similarly, drought stress adversely affects agricultural productivity worldwide and is expected to rise in the coming years[82]. There are only a few auxin-related studies on drought stress in cucumbers. For instance, cucumbers have been subject to drought stress along with CO2 to understand its effects on root biology. Drought stress alone inhibits the root growth and root biomass. CO2 enrichment reversed the adverse effects of drought stress on the cucumber plant by regulating the endogenous phytohormones. IAA accumulation decreased in the drought, and CO2 treated cucumber roots. GA, on the other hand, induced significantly. Auxin may work downstream of GA in regulating cucumber response to drought stress[83]. However, functional studies are lacking that highlight the constitutive role of auxin in cucumbers under drought stress.

    Increasing industrialization has increased heavy metal content in air and soil. Heavy metals cause injury to plant cells and cause the malfunction a variety of physiological processes[84]. Studies on auxin regulating the response to heavy metals in plants have been reported; however, not enough literature is available on cucumber. A recent study highlighted the role of auxin in cucumber response to cadmium stress[85]. The application of 3 µM selenium to cadmium stressed cucumber was performed. Selenium application significantly inhibited the detrimental effects of cadmium. Auxin binding protein (ABP19a-like) showed abundance in the selenium-treated seedlings compared to the control[85]. However, further functional studies are required to rewire the auxin involvement to mitigate the stress of the cadmium or other heavy metals.

    Biotic stresses

    Modern research has emphasized the role of auxin homeostasis in plant-pathogen interconnections. Below, we have briefly discussed the role of auxin in regulating cucumber response to various biotic stresses.

    Powdery mildew is Cucurbitaceae's most devastating fungal disease, causing serious damage to the yield. The Podosphaera fusca (Fr.) and Sphaerotheca fuliginea are the main fungi causing powdery mildew in cucumber[86]. So far, no functional study has characterized the direct involvement of auxin in mitigating powdery mildew stress in cucumbers. However, RNA-seq studies have highlighted the participation of auxin in regulating cucumber response to powdery mildew. For instance, transcriptomic analysis was applied to two cucumber lines XY09-118 (resistant) and Q10 (susceptible). Two auxin-responsive protein genes, six auxin-induced proteins, and one auxin efflux carrier gene were downregulated in XY09-118 compared to Q10[87]. Similar downregulation of auxin signaling genes was recorded in the powdery mildew resistant line 'BK2'[88]. From the studies, auxin signaling genes could be involved in the negative regulation of plant immunity. The recent work of Navarette et al.[89] also supported this notion. Functional studies will expand our understanding of auxin signaling in cucumber immune response to powdery mildew stress.

    Downy mildew caused by a biotrophic fungus Pseudoperonospora cubensis, is another serious fungal disease in cucumbers. Jasmonic acid and salicylic acid are key in regulating the response of plants to biotrophic fungus[90]. However, studies relating to auxin participation in the defense mechanism against P. cubensis are rare. The irregular vasculature patterning (CsIVP) is a transcription factor from basic Helix-Loop-Helix (bHLH) family[91]. Post P. cubensis infection, the mRNA level of irregular vasculature patterning (CsIVP) reduced significantly. The CsIVP interacts with NIM1-INTERACTING1 (CsNIMIN1), a key suppressor of the salicylic acid pathway. Knockdown of CsIVP increased resistance against P. cubensis by denying CsNIMIN1 from suppressing the production of salicylic acid. Interestingly, the IAA accumulation in the leaves of CsIVP-RNAi lines was boosted many fold. By contrast, a low level of IAA accumulation was observed in the WT leaves which also showed increased susceptibility to P. cubensis[91]. There may be synergistic crosstalk between auxin and salicylic acid to regulate the response of the cucumber plant to downy mildew.

    The root-knot nematode such as Meloidogyne incognita is a serious parasite, infecting the roots of almost all land plants. In cucumber, Meloidogyne incognita causes massive losses to overall yield by infecting the root and disturbing the hormonal pathways[92]. Despite the large germplasm, very few Meloidogyne incognita cucumber resistant lines have been developed. The Meloidogyne incognita generally make gigantic galls on the roots via parasitism. The cucumber line IL10–1 displayed resistance against Meloidogyne incognita by inhibiting the development of giant cells on the root[93]. Compared to CC3 (susceptible line), the enriched flavonoid biosynthesis pathway 3 d after Meloidogyne incognita inoculation was observed in IL10-1. Curtailed auxin transport in the roots of IL10-1 was also recorded. The induced flavonoid biosynthesis could be involved in the restricted auxin transport, which resulted in the inhibition of giant cells on cucumber root, thus conferring resistance to Meloidogyne incognita[93]. However, the study failed to explain how auxin transport regulates gall formation on cucumber roots.

    Conclusions

    Auxin plays an important role in cucumber growth and stress response, and the application of auxin in cucumber production is very popular in farming operations. For example, auxin control of branch numbers and parthenocarpy in cucumber are key factors significantly reducing labor and increasing the overall production. In the last decade, extensive genetic studies in cucumbers have identified key genes regulating agronomic traits by directly or indirectly modulating auxin. Nonetheless, the auxin-mediated stress response of cucumber added to its versatility. Despite the evidence, future research work is required to answer the following queries:

    • Identifying the functional genes and QTLs controlling fruit setting and shape and explain its regulatory mechanisms;

    • Auxin induces femaleness in cucumbers by activating ethylene biosynthesis. However, the question remains open whether auxin treatment affects other hormones such as GA when enhancing femaleness. It is important to understand these different signaling cascades;

    • A preliminary study indicated auxin's involvement in regulating cucumber response to heat stress. It would be interesting to see how auxin minimizes the detrimental effects of heat stress on cucumber, particularly at the fruit setting stage;

    • Auxin is still less understood in response to environmental stresses which requires further exploration.

    Acknowledgments

    This work was supported by National Natural Science Foundation of China, Grant/Award Number: 31972422, 32272739.

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Tan C, Wu Y, Taliaferro CM, Bell GE, Martin DL, et al. 2022. Heritability estimates for seed yield and its components in Cynodon dactylon var. dactylon (L.) Pers.. Grass Research 2:7 doi: 10.48130/GR-2022-0007
    Tan C, Wu Y, Taliaferro CM, Bell GE, Martin DL, et al. 2022. Heritability estimates for seed yield and its components in Cynodon dactylon var. dactylon (L.) Pers.. Grass Research 2:7 doi: 10.48130/GR-2022-0007
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Heritability estimates for seed yield and its components in Cynodon dactylon var. dactylon (L.) Pers.

  • 1.

    Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078, USA

  • 2.

    Oklahoma Department of Agriculture, Food and Forestry, 2800 N. Lincoln Blvd., Oklahoma City, OK 73105, USA

  • 3.

    Department of Horticulture and Landscape Architecture, Oklahoma State University, Stillwater, OK 74078, USA

  • Received: 01 August 2022
  • Accepted: 26 October 2022
  • Published online: 22 November 2022
Grass Research  2,  Article number: 7  (2022)  |  Cite this article

Abstract: Bermudagrass [Cynodon dactylon var. dactylon (L.) Pers.] is a major warm-season turf and forage grass worldwide. Seed yield is an important trait targeted for improvement in bermudagrass breeding programs because of the increased interest in seed-propagated cultivars. Understanding the nature of genetic variation for seed yield and its components in bermudagrass would aid the development of seed-propagated cultivars. The objective of this study was to estimate the genetic variation and narrow-sense heritability for seed yield and its two major components, inflorescences prolificacy and seed set percentage in bermudagrass. Twenty-five half-sib families and their respective clonal parents were evaluated at two Oklahoma locations, Perkins and Stillwater (Oklahoma, USA), over two years. Half-sib families were different for seed yield, inflorescences prolificacy and seed set percentage, indicating the expression of additive genes in controlling these traits. Family × location effects were observed for seed set percentage and seed yield. All three traits showed family × year interaction effects. There was a significant family × location × year interaction in inflorescences prolificacy and seed set percentage. Narrow-sense heritability estimates for seed yield was 0.18 based on variance component analysis among half-sib families and ranged from 0.26 to 0.68 based on parent-offspring regressions, indicating genetic complexity of seed yield. Heritability estimates were moderate (0.30−0.55) for inflorescences prolificacy and moderate to relatively high (0.41−0.78) for seed set percentage. The results indicate that sufficient magnitudes of additive genetic variation for seed set percentage and inflorescence prolificacy permit positive response to selection and conventional progeny-based genotypic evaluation is necessary for seed yield improvement.

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    INTRODUCTION

    • Bermudagrass [Cynodon dactylon var. dactylon (L.) Pers.] is economically the most important and genetically most diverse species in the genus Cynodon Rich. It has been widely used for turf, forage, soil conversation and remediation of contaminated soil in southern United States, other temperate and tropical regions of the world[1, 2]. Bermudagrass has long been characterized as an outcrossing species[3, 4].

      Sexual reproduction via cross-pollination and self-incompatibility is responsible for the immense genetic and phenotypic variability among bermudagrass[3, 5, 6]. The broad genetic diversity within the species has been primarily derived from combinational functions of mutations, sexual reproduction, and natural selection during its dispersion and subsequent adaption around the world. The sexual reproduction capability of individual bermudagrass plants varies from none to very high[7]. In general, seed production of bermudagrass is low, but most plants have the ability to produce some seed that effects genetic recombination and segregation. According to Harlan & de Wet[7], the variability within C. dactylon for many characteristics, including fertility, was enormous and generated by population fragmentation based on chromosomal changes such as translocations and deletions. As one of the most prominent causes of the variation in seed yield in bermudagrass, the chromosomal irregularities are sufficient to cause high, but usually not complete sterility.

      Extensive variability among selected genotypes of bermudagrass for components of seed yield has been documented[3, 8−11]. Burton[3] studied variation in many characteristics of 147 bermudagrass polycross progenies, including seedhead abundance, seed set and seed yield. He concluded that a considerable amount of variation existed among polycross progenies for the three traits. Seed yield was correlated (r = 0.59) with seed set percentage. Ahring et al.[8] collected data from seed yield and its components in bermudagrass using seven single-crosses with seven male clones and one cultivar as maternal parent. The components included number of racemes per head, number of florets per raceme, number of florets per head and number of florets containing a caryopsis per head (seed set percentage). Significant differences were found between progenies and parents for all components except florets per head. Paternal parentage could affect the seed set, because the differences in progenies were a result of different male clones. Seed yield of any offspring did not exceed that of the highest yielding parents.

      Wu et al.[11] reported an enormous amount of genetic variability and relationships for seed yield and its components existing in Chinese tetraploid C. dactylon accessions. Using path coefficient analyses, they concluded that inflorescence prolificacy (r = 0.51) and seed set percentage (r = 0.38) were significantly positively correlated with seed yield and had the highest direct effects on seed yield. They indicated that selection for increased inflorescence prolificacy and seed set should be the best indirect method for the improvement of seed yield.

      Since the 1980s, the number of seed-propagated turf bermudagrass cultivars released for commercial production has dramatically increased, especially in the breeding programs belonging to commercial companies in the US[2]. According to Ahring et al.[8], seed-propagated bermudagrass cultivars are needed because commercial planting equipment and establishment technology for vegetative propagation are not effective for confined areas, such as home lawns or on steep embankments such as dam faces or roadsides. In addition, vegetative propagation requires more time and physical labor than seeding. Establishment of bermudagrass turf is less expensive by seeding than by sodding[12]. With the increased interest in breeding seed-propagated bermudagrass cultivars, seed yield has become a major trait targeted by bermudagrass breeding programs.

      Heritability is a measure of the magnitude to which genetic factors contribute to the quantitative phenotypic variation in a population. In breeding programs, information regarding heritability is most useful as a measure of potential response to selection, i.e. predicted genetic gains per selection cycle. Broad sense heritability (H) measures the magnitude to which phenotypic variance (Vp) is determined by variation in genetic factors (Vg) of additive, dominant and interactive effects. Narrow sense heritability (h2) only quantifies the proportion of phenotypic variation that is due to additive genetic effects. Robust information on narrow sense heritability estimates for seed yield and its components is warranted to increase the selection efficiency for such traits during the development of improved seed-propagated bermudagrass cultivars. Accordingly, the objectives of this study were: (i) to determine if significant genetic variability existed for seed yield and its related components among selected half-sib families of C. dactylon accessions, (ii) to calculate narrow-sense heritability estimates for seed yield and its two major components using variance components analysis and parent-offspring regression, and (iii) to use this information to suggest appropriate breeding strategies for the development of seed-propagated bermudagrass cultivars.

    RESULTS AND DISCUSSION

      Weather conditions

    • Severe drought prevailed during the summer of 2012 while the 2013 summer received consistent rainfall during May, June and July. The monthly precipitation at the Stillwater site was 2.84, 5.49 and 0.18 cm in May, June and July of 2012 respectively, while 15.80, 10.03 and 14.15 cm rainfall occurred in the respective months of 2013. At the Perkins site the monthly rainfall was 2.84, 7.39 and 0.66 cm in May, June and July of 2012, compared to 17.81, 10.52 and 15.42 cm precipitation in the respective months of 2013. It has been documented that too much rainfall during these months can be detrimental to seed production[13]. The average air temperatures during May (23.06, 23 °C), June (26.22, 26.22 °C) and July (30.83, 30.89 °C) 2012 at Perkins and Stillwater respectively were similar. In 2013, the average air temperatures during May (18.89, 19.06 °C), June (25.44, 25.56 °C) and July (26.28, 26.44 °C) at Perkins and Stillwater respectively were lower on average than those in 2012.

      Phenotypic variation

    • Means, standard errors and ranges of seed yield and its components in parents and progeny half-sib families are given in Table 1. For inflorescence prolificacy rating, the ranges of offspring observations fell within the ranges of the parents from either single year or two year data combined. Considering seed set percentage, only the range of offspring at the Stillwater site in 2013 fell within the range of the parents. The ranges of offspring for seed yield fell within the ranges of the parents except the data at the Perkins site in 2013. The means of offspring for seed set percentage and seed yield were mostly greater than the means of parents at the same site in the same years. The ranges and means for these two variables indicate that transgressive segregation may exist in the offspring population.

      Table 1.  Means, standard errors and ranges of seed yield and its components in parents and progeny half-sib families.

      VariablesYearSite‡ParentOffspring
      MeanSE§RangeMeanSERange
      Inflorescence prolificacy rating†
      		2012Pks6.310.241.00−9.005.170.142.00−8.00
      Stw6.290.211.00−9.005.400.163.00−8.00
      2013Pks6.240.251.00−9.006.170.192.00−9.00
      Stw6.080.241.00−9.005.590.182.00−8.00
      Combined6.230.121.00−9.005.590.092.00−9.00
      Seed set (%)2012Pks21.581.910.73−63.6937.662.231.83−86.29
      Stw27.372.340.00−85.5544.282.404.16−89.54
      2013Pks18.060.020.14−62.7830.012.071.38−68.89
      Stw29.412.340.20−82.1424.632.000.10−73.29
      Combined24.111.070.00−85.5534.151.170.10−89.54
      Seed yield (kg ha−1)2012Pks152.2316.291.26−742.44198.269.5031.75−373.86
      Stw114.3911.230−419.67165.759.104.31−403.18
      2013Pks10.541.540−70.7523.473.650−136.30
      Stw8.050.950−39.377.800.840.33−33.61
      Combined71.306.160-742.4498.825.940-403.18
      †Rating scale was 1−9, where 1 indicated the least inflorescences while 9 was the greatest inflorescence prolificacy.
      ‡ Sites were Stw = Stillwater, Pks = Perkins.
      § Standard error of the group mean.

      Bermudagrass (C. dactylon) is a cross-pollinated species. Its seed yield largely depends on water conditions during pollination[13]. Dry and wet cycles enhance seed set, seedhead prolificacy, and seed yield. In this experiment, the bermudagrass seed yields in 2012 in two locations were similar to that previously reported[11]. However, seed yields in 2013 were very low due to abundant rainfall in the flowering season of bermudagrass from May to July. It is common to have low seed yields in bermudagrass if high soil moisture results in too much vegetative growth.

      Estimated components of variation

    • Based on the data of offspring in 2012 (Table 2), half-sib families differed greatly in all the variables measured, indicating a significant role of additive genes in seed yield and its related components of bermudagrass. The fixed effect of location was only significant for seed yield. Seed set percentage showed a significant family × location interaction. The effects of replication nested within location were observed for both seed set percentage and seed yield. In 2013, half-sib families were also different for all the variables measured (Table 2). The fixed location effects were shown for inflorescence prolificacy and seed yield. Seed set percentage and seed yield were also influenced by family × location interaction. In both single-year analyses, a significant residual variance existed in all three variables, which may suggest that the phenotypic variation involving some genetic variance associated with among individuals of the same half-sib families within plots. In order to exploit the within family genetic variance, individual data within a family is required, but were not collected in this experiment.

      Table 2.  Tests of fixed effects due to location by year, location and year, and expected mean squares due to random effects of various sources for inflorescences prolificacy, seed set percentage and seed yield among half-sib families combined across locations (Stillwater and Perkins) or/and years (2012 and 2013).

      SourcesDfInflorescence prolificacy†Seed set
      (%)      		Seed yield
      (kg ha−1)
      2012 test of fixed effects (F values)
      Location (L)11.533.7310.16*
      Expected mean squares
      Family (F)244.16*0.11*23,960.13*
      F × L241.260.04*3,902.04
      Rep/L41.810.0412,709.81*
      F × Rep/L961.16*0.02*2,513.33*
      2013 test of fixed effects (F values)
      Location (L)14.59*3.3613.25*
      Expected mean squares
      Family (F)245.67*0.09*910.22*
      F × L242.810.03*694.42*
      Rep/L42.570.011,787.60*
      F × Rep/L961.74*0.02*336.71*
      Across 2-location and 2-year test of fixed effects (F values)
      Location (L)10.810.067.99*
      Year (Y)19.20*45.33*199.86*
      L × Y16.45*9.55*3.32
      Expected mean squares
      Family (F)246.97*0.170*14,483.50*
      F × L242.150.048*3,060.98*
      Rep/L42.330.0073,986.73*
      F × Y253.81*0.085*93,009.25*
      F × L × Y252.34*0.038*1,686.89
      F × Y × Rep/L1961.46*0.020*1,610.44*
      * Significant at the 0.05 probability level.
      † Rating scale was 1−9, where 1 indicated the least inflorescences while 9 was the greatest inflorescence prolificacy.

      In the two-year combined analysis (Table 2), the year effect was of greater magnitude than the effects due to location or location × year interaction for inflorescence prolificacy, seed set percentage and seed yield. The year effect was likely related to the uncommon rainfall that occurred in 2013. The results also demonstrated a significant amount of variation among half-sib families for inflorescence prolificacy and seed set percentage, suggesting a significant contribution of additive genes in these two components associated with the seed production. For seed yield, however, the expected means square due to half-sib families did not exceed the expected mean square due to family × year interaction. This could indicate greater genotype × environment interaction in controlling seed yield variation than its two components. The family × year interaction effect was likely due to different responses of the families to the obvious climate difference between the two years. On average, the seed yield in 2013 was a 10th to 15th of the yield in 2012 (Table 1). This significant year effect is likely caused by the unusually high precipitation during the seed production season (May, June and July) in 2013 compared to 2012 with intermittent rainfall under drought conditions. Growth-stress cycles with alternate wet and dry conditions are needed to stimulate bermudagrass seed production and flowering during May, June and July, which afterwards could produce a good seed crop in August[13]. In 2013, the weather provided consistent rather than intermittent precipitation during the seed production season.

      The significant genetic variances in the half-sib families for inflorescences prolificacy and seed set percentage indicated that additive genetic components account for these two traits comparing with small interaction variances. Thus, breeding strategies commonly used for bermudagrass improvement, such as recurrent phenotypic selection, should be effective in improving these two seed yield related components.

      Estimates of narrow sense heritability

    • From the single year data, narrow-sense heritability estimates based on half-sib families' variance components for inflorescence prolificacy, seed set percentage and seed yield in 2012 were 0.70, 0.61% and 0.84 kg ha−1 respectively, while in 2013 were 0.50, 0.63% and 0.23 kg ha−1 respectively (Table 3). A large decline occurred in the seed yield estimates between 2012 and 2013. In 2013, when unusually consistent precipitation was received and no growth-stress cycles occurred during the seed production season, the seed yields of families at two locations were significantly affected compared to the relatively drought conditions in 2012. Greater family × location effect was shown in both 2013 than 2012, which could be accounted for by differences in precipitation pattern. Some families at Perkins may have been influenced more severely than those at Stillwater due to the difference between soil types at the two locations. In the combined year analysis, the narrow-sense heritability estimates were 0.55, 0.78 and 0.18, respectively, for inflorescence prolificacy, seed set percentage and seed yield. Seed set percentage showed relatively high heritability estimates with single year and year combined analysis. Low narrow-sense heritability estimates for seed yield in this study were comparatively lower than the result (h2 = 0.42) reported by Cluff & Baltensperger[14]. This could be attributed to the large family × year interaction and residuals associated with random effects from replications nested within location, including sampling error. Low heritability estimates may be the result of large phenotypic variability caused by differing microenvironments and are indicated by a large residual variance term.

      Table 3.  Narrow-sense heritability estimates for seed yield and its components in bermudagrass based on component of genetic variation among half-sib families.

      Variables2012†2013†Combined‡
      Inflorescence prolificacy0.700.500.55
      Seed set (%)0.610.630.78
      Seed yield (kg ha−1)0.840.230.18
      † Estimates were based on the single year data across locations.
      ‡ Estimates were based on the two-year data across locations.

      The heritability estimates based on parent-offspring regression for all variables are given in Table 4. The regressions were only performed with half-sib families and parents from different locations in the same year to exclude the family × location interaction effect and the significant amount of year effect in reverse year analysis. The heritability estimates were statistically different from zero (p < 0.05) for inflorescence prolificacy, seed set percentage and seed yield in 2012 when regressing half-sib families performance at one location on parents at another location, except the estimate for seed set percentage obtained by regression with half-sib families at the Perkins site. In 2013, only estimates for seed set percentage were statistically different from zero. The estimates based on parent-offspring regressions for inflorescence prolificacy ranged from 0.30−0.53, and the estimates for seed set percentage ranged from 0.41−0.76, which were fairly comparable with the estimates for both traits obtained from variation among half-sib families. This may suggest the low effect of non-additive gene actions in influencing inflorescence prolificacy and seed set. The heritability estimates for seed yield ranged from 0.26−0.68, which were only statistically significant in 2012. The heritability estimates for seed yield based on parent-progeny regressions in 2012 were from 0.26 to 0.68 while 2013 heritability values were not different from zero. The inconsistent heritability estimates for seed yield by years and in two methods indicate the genetic complexity of the trait.

      Table 4.  Narrow-sense heritability estimates and their standard errors (in parentheses) for inflorescences prolificacy, seed set percentage and seed yield in 2012 and 2013 based on parent-offspring regression.

      TraitsStillwater†Perkins‡Stillwater†Perkins‡
      2012201220132013
      Inflorescence prolificacy0.30*(0.15)0.53*(0.14)0.21(0.16)0.33(0.18)
      Seed set (%)0.76*(0.28)0.33(0.21)0.66*(0.27)0.41*(0.20)
      Seed yield
      (kg ha−1)      		0.26*(0.13)0.68*(0.18)−0.10(0.13)1.38(0.88)
      †Data for half-sib families were at the Stillwater site, and for parents at the Perkins site.
      ‡ Data for half-sib families were at the Perkins site, and for parents at the Stillwater site.
      * Significant at the 0.05 probability level.

      Predicted genetic gain

    • Narrow-sense heritability estimates based on half-sib families' variance components were used to predict genetic gain for all the traits. Predicted genetic gains for inflorescence prolificacy were 0.86 and 0.72 for the years of 2012 and 2013, respectively. With two-year data combined, the predicted genetic gain was 0.62 for inflorescence prolificacy. For seed set percentage, predicted genetic gains were 11.14, 11.36 and 13.00% for 2012, 2013 and two-year combined data respectively. Predicted genetic gains for seed yield were 77.87, 4.30 and 9.29 kg/ha for 2012, 2013 and both year combined. The lowest predicted genetic gain for seed yield in 2013 was resulted from the much smaller phenotypic variance 151.70 than 3,993.36 for 2012 and 1,214.94 for 2012 and 2013 combined, respectively (data not presented). The consistent rainfall pattern in 2013 compared with the drought conditions in 2012 resulted in the obvious differences in predicted gains for seed yield obtained individual year separately and combined. The result indicated the risks of selecting for the improvement of seed yield that requires intermittent rainfall pattern to predict seed production using one year's data. Seed yield selection using this population in 2012 would have resulted in greater progress than the predicted gain of the trait in 2013. For accurate selection of seed yield that requires particular environment conditions, the use of multiple years of data would probably result in more reliable progress.

    CONCLUSIONS

    • The population of 25 half-sib families used in the current study was derived from open pollination in a field planting in a randomized complete block design with three replications. The families were selected because of relatively high fertility, winter survivability and spring green-up of their maternal parents. Relatively high to moderate narrow-sense heritability estimates for inflorescence prolificacy and seed set percentage in this population indicated not only that a substantial genetic variation existed in the two traits, but also the variability was significantly controlled by additive gene action in nature and therefore of value to breeders. The results suggest that a significant improvement for seed yield could be possible when applying phenotypic selection to the two components. Low and unstable heritability estimates for seed yield indicated the sole phenotypic recurrent selection when obtaining seed yield directly may not be very effective to improve the trait in bermudagrass. Conventional genotypic selection procedures are necessary to achieve improvement for seed yield increase.

    MATERIALS AND METHODS

      Plant materials

    • Plant materials used in the study included 25 C. dactylon clonal accessions introduced from China and half-sib progeny from the accessions. The 25 clonal accessions were selected from a larger population of Chinese accessions based on their winter hardiness and relatively high fertility. Half-sib seed from the 25 respective clonal accessions was harvested from plots in a replicated nursery in 2002[11].

      About 0.1g (~2,000 seeds/g) seed of each half-sib family was planted in Metro Mix 250 growing medium (Sun Gro Horticulture, Bellevue, WA, USA) in labeled 12.4 cm × 19.7 cm black pots placed inside a 26.7 cm × 53.3 cm white tray in a greenhouse at the Agronomy Research Station, Oklahoma State University (OSU, USA). Forty-eight progeny seedlings from each pot were transplanted to two 24-cell trays (26.7 cm × 53.3 cm) representing one half-sib family. After a 2−3 months growing period, 40 plants were randomly selected, and each of the selected plants was split into six sprigs (shoots with attached roots) to grow six identical plants, which were used in establishing experiments on the OSU Agronomy Research Station, Stillwater, OK, USA and the Cimarron Valley Research Station, Perkins, OK, USA. Propagating material for each of the 25 maternal parent plants was dug from the '2007 OSU turf bermudagrass germplasm nursery'. Six identical potted plants were vegetatively prepared for each of the 25 parents in a greenhouse at the OSU Agronomy Research Station, Stillwater (OK, USA).

      Experimental design

    • The experimental design for the Stillwater and Perkins experiments was a randomized complete block with three replications. Within each replication block, a total of 50 entries (one plot for each entry), including 25 randomly selected half-sib families and their respective 25 maternal clones were randomly arranged and planted into each 1.52 m × 2.44 m (5 feet × 8 feet) plot with 1.52 m (5 feet) bare borders between neighboring plots. Each of 40 progeny plants per family was planted on 30.5 cm (one foot) centers in each progeny plot. Each potted maternal plant was cut into four equal plugs (shoots and attached roots and soil) in the field. The four plugs of each maternal parent were transplanted to its respective parent plot.

      Management procedures

    • The soil type at the Stillwater research site is an Easpur loam while the soil type at the Cimarron Valley Research Station is a Teller fine sandy loam (Natural Resources Conservation Service at http://websoilsurvey.nrcs.usda.gov). Based on the soil test reports for the two locations, fertilizers [N-P(P2O5)-K(K2O)] were applied at 56-112-112 kg ha−1 respectively to both fields to achieve optimum rates for bermudagrass seed production before transplanting. The Stillwater and Perkins experiments were respectively established on July 7−8 and July 22, 2011. Dual® herbicide (metalochlor) was applied to both fields at 3.36 kg ha−1 a.i. Weeds within the alleys were suppressed by applying 2.24 kg ha−1 a.i. Roundup® (glyphosate, N-phosphonomethyl glycine) plus surfactant (0.5% v:v−1) and 2.24 kg ha−1 (NH4)2SO4 herbicide in the middle of September.

      In 2012 there was no mowing of plant residues before initiation of spring regrowth due to early spring green up following the establishment. In early March 2013, plant residues in the plots were mowed off at a 5.08 cm height. Before the initiation of bermudagrass spring green up, Roundup® (glyphosate, N-phosphonomethyl glycine) at 4.68 L a.i. ha−1, 2,4-D [(2,4-dichlorophenoxy) acetic acid] at 1.17 L a.i. ha−1 and Barricade® (prodiamine) at 2.58 kg a.i. ha−1 with 1.17 L ha−1 of surfactant were applied in early March according to the labels of each herbicide. In early May for both years of 2012 and 2013, nitrogen fertilizer was applied at a rate of 67 kg ha−1 for both plots in Stillwater and Perkins. During the growing season, glyphosate tank mixed with a surfactant and ammonium sulfate was applied to control weeds in alleys if needed on a weekly basis.

      Data collection

    • Data were collected in August and September in both 2012 and 2013. Measured and visually rated response variables were: (i) inflorescence prolificacy, (ii) seed set percentage [total seed number per inflorescence and collective raceme length (mm) per inflorescence], and (iii) seed yield (kg ha−1).

      Inflorescence prolificacy were visually assessed for each plot with a rating scale from 1 to 9, with 1 indicating no inflorescences and 9 the most abundant inflorescences. This variable was taken at the beginning of August 2012 and early September 2013 due to the difference at inflorescence maturity. To obtain seed set percentage on the plot mean basis, 80 inflorescences were randomly hand-picked from 40 30.5 cm (one foot) centers within the offspring plot using a 30.5 cm × 30.5 cm (one square foot) grid, where originally the 40 individual progeny were planted. For the parent plots, 20 individual inflorescences collected to obtain this variable. For achieving seed set for each plot, 10 inflorescences per replication of each entry were randomly chosen to determine seed number and raceme length (mm) per inflorescence, which were used to calculate seed set percentage. Seed set percentage was calculated as: (number of caryopses inflorescence−1 / number of spikelets inflorescence−1) * 100. The number of spikelets inflorescence−1 was estimated with a linear formula: Y = 8.4 + 0.79X (r2 = 0.68, p < 0.01)[11]. The number of caryopses inflorescence−1 was counted by soaking the seedhead samples in a 20% (v/v) bleach solution then the seedhead was examined under a dissecting microscope at 10× magnification[11]. For seed yield data collection in September 2012, the variable was estimated by harvesting all biomass of each progeny plot using a sickle-bar mower. All harvested biomass was bagged, dried thoroughly and then threshed by hammermilling at 800 rpm using a 0.371 cm round hole screen[15]. Due to incomplete coverage for some parent plots in 2012, the biomass from 30.5 cm × 30.5 cm fully covered area was randomly selected and hand-clipped within each plot. The parent plot samples were threshed by rubbing in pans lined with ridged rubber matting. In 2013, all parent and progeny plots were harvested by a sickle-bar mower to obtain the seed yield for each plot. All bagged biomass samples were threshed following the same procedure as in 2012. All threshed samples from both parent and progeny plots were cleaned into pure seed with a Model B South Dakota seed blower using an air-valve setting of 15°[15].

      Statistical analyses

    • Data were analyzed using the MIXED procedure of SAS version 9.3[16] to obtain estimates of variance components and GLM procedure to obtain mean squares and significance for each of various sources of variation. Estimates of narrow-sense heritability were computed for all three variables, inflorescence prolificacy, seed set percentage and seed yield. Narrow-sense heritability was estimated for the three variables based on the genetic components of variation among half-sib families. In this experiment, the genetic variance of half-sib families predominantly measured the additive genetic variation in the population. Estimates of narrow-sense heritability were obtained for two individual years 2012 and 2013 and for both years combined. For single year data analysis, the estimates of variance components of half-sib families were based on combined data at two locations. The half-sib families data were collected on the plot mean basis, the narrow-sense heritability on a phenotypic variance among half-sib family mean basis averaging over replications, years, and locations can be estimated as: hPFM2 = ϭF2/(ϭF2 + ϭFL2/l + ϭFY2/y + ϭFLY2/ly + ϭγ2/rl + ϭε2/ryl)[17]. For single year analysis, the estimates of narrow-sense heritability on a phenotypic mean basis is hPFM2 = ϭF2/( ϭF2 + ϭFL2/l + ϭγ2/rl)[17].

      Parent-offspring regression was the other method used for the estimates of narrow sense heritability. Regression of progeny means on parental means evaluated under different environments can remove the potential bias due to non-genetic covariance between parent and offspring. The estimate in this case would be free of genotype × environmental interaction effect. In our study, the parent offspring regression was performed with parent and offspring data from different locations for the single year data to reduce upward bias caused by genotype × environment interactions[18]. The estimates of hn2 were calculated by the following formula: hn2 = 2 × β1, where β1 = the slope of the parent offspring regression[19].

      Predicted genetic gain was calculated for seed yield and its two components using the formula: ΔG = ck hPFM2 ϭPFM, where c represents parental control factor, k represents the standardized selection differential, hPFM2 and ϭPFM represent heritability and phenotypic standard deviations on a phenotypic mean basis[17]. Parental control factor c = 2 in this experiment, because superior parents are selected based on the mean performance of their half-sib progenies and intermated in isolation to produce the improved population. For a selection intensity of 30%, k = 0.736[19].

      Acknowledgments

      • The authors would like to acknowledge Mr. Gary Williams, Ms. Sharon Williams and lab colleagues for their help with the inflorescence sampling and biomass harvest. The work has been supported, in part, by the United State Department of Agriculture Specialty Crop Research Initiative award 2010-51181-21064, the Oklahoma Agricultural Experiment Station, and the United States Golf Association.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2022 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Tan C, Wu Y, Taliaferro CM, Bell GE, Martin DL, et al. 2022. Heritability estimates for seed yield and its components in Cynodon dactylon var. dactylon (L.) Pers.. Grass Research 2:7 doi: 10.48130/GR-2022-0007
    Tan C, Wu Y, Taliaferro CM, Bell GE, Martin DL, et al. 2022. Heritability estimates for seed yield and its components in Cynodon dactylon var. dactylon (L.) Pers.. Grass Research 2:7 doi: 10.48130/GR-2022-0007
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  • Introduction
  • Results and discussion

  • Weather conditions

  • Phenotypic variation

  • Estimated components of variation

  • Estimates of narrow sense heritability

  • Predicted genetic gain

  • Conclusions
  • Materials and methods

  • Plant materials

  • Experimental design

  • Management procedures

  • Data collection

  • Statistical analyses

  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
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