Sunflower (Helianthus annuus L.) is one of the most important oil crops in South Africa and genetic improvement for grain yield and oil content was initiated in the country in the early 1970s. It also provides a historical overview of sunflower breeding in South Africa and how genetic gain in trial plots translates into improved performance in farmers' fields.
Sunflower production in South Africa
- Sunflower production trends
- Sunflower production environments in South Africa
History of sunflower breeding in South Africa
- Germplasm acquisition and screening
- National cultivar trials
The National Sunflower Cultivar Trials evaluate superior sunflower hybrids from different seed companies in the same trials at a number of locations to compare performance. For breeders, the performance of cultivars in national cultivar trials objectively quantifies the progress of breeding programs while allowing the various seed companies to showcase their hybrids.
Breeding objectives versus production constraints
- Oil yield and related traits
- Yield stability and reliability
In addition to seed yield and oil concentration, a major breeding consideration for diverse and heterogeneous environments such as those in the tropics is performance stability/reliability (Guillen-Portal et al., 2003; de la Vega, 2012). Abiotic stresses affecting the stability and reliability of sunflower production in South Africa include high soil temperatures and variable moisture conditions mainly in the main sunflower producing areas of the Free State and North West provinces (Nel, 1998).
Genetic gain and its quantification
Genetic improvement for seed yield and oil content in sunflower (Helianthus annuus L.) was started in South Africa in the early 1970s. Oil yield (kg ha-1) is the mathematical product of seed yield (kg ha-1) and oil content.
Selection methods and heterosis in sunflower
- Selection methods
- Heterosis in sunflower
Heritability and combining ability for yield related traits
- Heritability
- Combining ability and gene action
The studies of Miller et al. 1980) confirm earlier conclusions of Sindagi et al. 1979) who found that additive genetic effects were more predominant than non-additive for the traits studied. Recent research by Khan et al. 2008) found a greater manifestation of non-additive gene effects in all traits studied in different environments using a 5 x 5 tester line analysis.
Selection strategies for variable moisture conditions
- Drought tolerance in sunflower
- Secondary traits associated with drought tolerance in sunflower
- Selection environments
- Genetic correlations
- Indirect selection and correlated response to selection
Grain yield under water-limited conditions is the mathematical product of the amount of water evaporated (the amount of water available to the plant), the water use efficiency (WUE) and the harvest index (HI) (Passioura classified putative secondary traits associated with drought tolerance in maize in three categories: (i) traits related to increased water uptake; (ii) traits related to water use efficiency; and (iii) traits related to harvest index. Type A genetic correlation is particularly important because the genetic Variance and heritability of maize most important trait in crop improvement: yield is greatly reduced under stress (Chapman and Edmeades, 1999).
Synopsis of the Literature Review
The effect of the complex of sunflower growth regions on the genetic progress achieved by breeding programs. Genetic characterization of sunflower breeding resources from Argentina: assessment of diversity in major pollinated and composite populations.
Introduction
Increases in sunflower production area and yield per ha were characterized by fluctuations caused by a combination of factors including seed price, the introduction of hybrid cultivars in the late 1970s, drought in drier regions, and Sclerotinia stem and head rot. (Sclerotinia sclerotiorum) in colds. area (Birch et al., 1978). Formal sunflower breeding in South Africa spans more than four decades and hybrid cultivars became commercially available in the late 1970s (Birch et al., 1978).
Methods and materials
- Side-by-side evaluation
- Genotypes
- Trial sites and agronomic practices
- Data collection
- Statistical analysis
- Contribution of new cultivars to total seed yield increase
The 2007 season trial in Potchefstroom was planted in the first week of December 2006 and was irrigated to field capacity at planting. The Bothaville trials were planted in the first week of January each year (2007 and 2008) and no irrigation was applied.
Results and Discussion
- Side-by-side trial evaluation
- Growing season conditions
- Genotype and environmental effects
- Absolute genetic gain and response to the environment
- Relative genetic gain and estimation of genetic yield gain
- Phenotypic correlations between seed yield and other traits
- Contribution of new cultivars to genetic gain
Environmental mean, absolute genetic gain (b) and r2 for seed yield and related traits in each environment and between different environments. The estimate of genetic gain for seed yield is within the range of most crops reported in the literature. The relative genetic gain per year for oil yield is high due to selection on both seed yield and oil content.
Conclusions
The post-green revolution in soft corn yield potential in the North-Central United States. Genetic gain in yield and agronomic characteristics of cowpea cultivars developed in the Sudan Savannas of Nigeria over the past three decades. Reanalysis of the historical series of UK variety trials to quantify the contribution of genetic and environmental factors to yield trends and variability over time.
Introduction
The strategy usually involves recycling elite inbred lines as a way to avoid linkage resistance and thereby preserve past gains (Condón et al., 2008). Genetic diversity studies of cultivated sunflower germplasm using molecular markers have found significantly higher diversity and genetic differentiation among sunflower groups (Cheres et al., 2000; Yue et al., 2009). There are two commonly used estimates of heterosis: (i) interparental heterosis (MPH), the relative performance of a hybrid compared to the average of its parents; and (ii) better parent heterosis (BPH), which is the performance of the hybrid compared to the average of its better parent (Betrán et al., 2003).
Materials and methods
- Genetic material
- Test environments and experimental design
- Agronomic traits recorded
- Statistical analysis
- Genetic variability and usefulness of base breeding populations
- Determining the commercial breeding potential of new inbred lines
Various versions of the KB series elite founder inbred parental lines exist and have been widely used in South African sunflower breeding programs (Barend 2008, personal communication). Each of the 109 S3CMS lines, together with the CMS versions of the basic parental lines: H52CMS, HA89CMS, KB61CMS, KB16CMS, KB189CMS and H55CMS, were test crossed to two male fertility restorer lines RP865 (T1) and RP29 produced by a total of 218 experimental TCHs and 12 founder parental testcross hybrids (FPTC). Where: mean TCH within a group is the mean value across the three environments for each test cross hybrid within a group; and the average best FPTC is the average across the three environments of the best testcross hybrid of the four founder testcross hybrids in each corresponding group.
Results and discussion
- Genetic variability and usefulness of base breeding populations
- Breeding potential of new inbred lines
Genotype by environment interaction for seed yield and oil yield was only significant (p<0.01) in Pop1TC and across the TCH groups. Genotypic and phenotypic coefficients of variation were high for seed yield and oil yield, and low for oil content. The mean estimates of FPH for seed yield, oil content and oil yield were positive for Pop2TC, Pop3TC and Pop4TC but negative for Pop1TC, indicating that the founder parents for Pop1TC were also generally high yielders when tested to the same testers is (Table 3.3).
Conclusions
Genetic distance as a predictor of heterosis and hybrid performance within and between heterotic groups in sunflower. Seed yield and associated trait improvement in sunflower cultivars during four decades of breeding in South Africa. Variability for agronomic traits in random mating populations of sunflower: correlations, estimated benefits of selection, and correlated responses to selection.
Introduction
Similar trends were reported in maize and barley (Hordeum vulgare L.) in the USA by Bernado et al. 2008), indicating widespread use and success of advanced pedigree breeding. GCA is generally considered a measure of additive reactivity, while SCA is equated with the non-additive (dominance and epistatic) effects (Comstock et al., 1949). In a 3 x 10 tester line assay in sunflower, Miller et al. 1980) found that additive genetic variance accounted for a large proportion of the genetic variation for all traits examined except head diameter.
Materials and methods
- Genetic material and field evaluation
- Test environments, experimental design and agronomic traits recorded
- Statistical analysis
Environments and tester effects were considered fixed while effects of female lines and replicates within environment were considered random. Narrow-sense heritability (h2) based on GCA effects of lines for each breeding population and for all breeding populations was calculated according to Grieder et al. The significance of the GCAj and SCAij effects of the j-th female line and its interaction with the i-th tester were determined by paired t-tests, where.
Results
- Mixed model analysis of variance: mean squares
- Mixed model analyses: variance components
- Combining ability effects
σ2SCA was significant in three of the four breeding populations for seed and oil yield, except for Pop2. Out of 109 S3CMS lines in four breeding populations, 33 had positive and significant effects of GCA on oil yield (Table 4.3). For oil content, the proportion of SCA effects was slightly higher than GCA effects for three of the top five TCHS based on oil yield in each of Pop1, Pop3 and Pop4 (Figure 4.6).
Discussion
- Analysis of variance and variance component analysis
- Combining ability effects
Generally low narrow-sense heritability was obtained for oil content in three of the four breeding populations, with the exception of Pop4. The significant positive and negative effects of GCA and SCA on seed yield, oil content and oil yield indicated that across and within breeding populations, some S3CMS lines had good general and/or specific combining ability, while others were weak in this aspect (Figures 4.2, 4.3 and 4.4). Similarly, the five TCHs with the highest SCA effects were from all four breeding populations, indicating that beneficial SCA effects on oil yield were generally equally distributed across all breeding populations.
Conclusions
The relative magnitude of SCA effects is an important consideration in determining when to test and evaluate, i.e. Line x tester analysis of morphological traits and their correlations with seed yield and oil content in sunflower (Helianthus annuus L.). General and specific combinatorial ability of partial diallels of rays: implications for the usefulness of SCA in breeding and deployment populations.
Introduction
Drought ranks second only to soil infertility as the major dominant factor of abiotic stress and the greatest source of uncertainty limiting crop productivity in the tropics (Edmeades et al., 1997). Breeding of drought-tolerant and non-drought-tolerant cultivars stabilizes yield in intermittent stress environments (Edmeades et al., 1997) although methodologies that facilitate good selection progress remain a challenge. According to Bänziger et al. 2004), good selection progress in the development of drought tolerant cultivars can be achieved through: (i) accurate assessment and identification of genetic variation in traits that confer drought tolerance under conditions relevant to the target production environments; and (ii) the use of highly discriminatory, phenotyping environments relevant to the target environments.
Methods and materials
- Genetic material
- Test environments and management of moisture stress
- Experimental design
- Traits recorded
- Statistical analysis of data
- Means, combining ability effects, and heritability estimates
- Type A and B genotypic and additive genetic correlations
- Indirect response to selection based type A genetic correlation
- Indirect response to selection based on type B genetic correlations
Where: rG(x,y) is the Type A genotypic correlations between two traits x and y; Cov(x,y) is the genetic covariance between traits x and y that mean within or across environments;. Where: r'G(x,x*) is the type B genotypic correlation coefficients between the same trait x and x* measured in different environments; r'P(x,x*) is the phenotypic correlation coefficient between TCH averages for the same trait at the different environmental pairs; and H2x and H2x* are the broad sense heritabilities of the same trait at the different environments. Where: r'A(x,x*) is the type B additive genetic correlation coefficient between the same trait measured in different stress treatments; r''P(GCAx,GCAx*) is the Pearson correlation coefficient between the GCA effects of the same trait in different environments; h2x and h2x*.
Results and discussion
- Soil moisture monitoring
- Combined analysis of trials within and across environments
- Means and ranges of genotypes for oil yield
- General and specific combining ability effects of female lines for oil yield
- Heritability within and across the three different stress environments
- Genetic correlations and selection efficiency
- Type A genotypic and additive genetic correlations and selection efficiency
- Type B genotypic and additive genetic correlations
For all the traits measured, both H2 and h2 in the NSE and across the three stress environments were moderate to high. The phenotypic correlations, rP, of the secondary traits HD, SD and SG with oil yield in the RSE ranged from 0.34 for HD to 0.44 for SG. In both cases, indirect selection for oil yield using secondary traits was least in the RSE conditions except for SG (Table 5.4).
Conclusion
The efficiency of indirect selection on MSE or NSE compared to direct selection on RSE based on ISE'A was consistently higher for all traits compared to selection based on ISE'G (Table 6.5). For type B genetic correlations, indirect selection using r'A in MSE was as effective as direct selection under RSE, indicating that additive genetic correlation using GCA effects is effective for estimating correlated response to selection, especially if breeding populations are from factorial mating designs. Genetic analysis of yield and related traits in sunflower (Helianthus annuus L.) in dry and irrigated environments.
- Introduction
- Seed yield and associated trait improvements in South African sunflower cultivars
- Genetic variability and heterosis during advanced cycle pedigree breeding in
- Genetic variability
- Heterosis in advanced cycle pedigree breeding populations
- Combining ability during advanced cycle pedigree breeding in sunflower
- Selection strategies for improving drought tolerance
- Conclusion
Therefore, similar initiatives are needed not only in sunflower breeding programs in South Africa, but also in other commercially important crops in which the strategy of advanced pedigree breeding cycle is commonly used. Heterosis based on middle parent (MPH) or best parent (BPH) has been widely used in cross-pollinated crops (Duvick, 1999), but the cost of determining these parameters in cross-pollinated crops has been subject to a great review. Smith, 1997). Therefore, advanced cycle pedigree breeding will continue to be used in most sunflower breeding programs.
