V. INHERITANCE OF RESISTANCE TO GROUNDNUT ROSETTE DISEASE IN
5.2 Materials and methods
The study was conducted in 2010/2011 growing season at Nampula Research Station. The station is located about 7 km east of Nampula (15º 09’ S, 39º 30’ S) town in northern Mozambique and is elevated 432 m above sea level. The soil type is sandy loam and the vegetation is predominantly grassland. The average rainfall is slightly over 1000 mm. The rainy season starts around November/December up to April/May with its peak in January. The maximum temperature in the region is about 39o C and the minimum temperature is 19o C.
5.2.2 Germplasm development and field establishment
Seven groundnut cultivars that were originally obtained from ICRISAT-Malawi and were adapted to Mozambican conditions were used in this study. They included two cultivars that were susceptible to groundnut rosette disease (JL-24 and CG 7) and five resistant cultivars (ICG 12991, ICGV-SM 01513, ICGV-SM 01731, ICGV-SM 90704 and ICGV-SM 01711) (Table 5.1).
The seven cultivars were planted in a crossing block on 15th December, 2008. A second planting was made on 30th of December, 2008 to ensure that enough flowers were available for hybridization. At flowering, cultivars were crossed in a half diallel mating design, using standard artificial hybridization procedures for groundnut (Norden, 1980; Knauft and Ozias-Akins, 1995).
The 21 F1 populations resulting from the crosses were planted at Nampula Research Station in 2009/2010 growing season and allowed to self-pollinate to generate F2 populations. The F2 populations along with the parents were evaluated for resistance to groundnut rosette disease in the 2010/2011 growing season.
The test materials (7 parents and 21 F2 populations) were planted at Nampula Research Station on 20th January, 2011 in a randomized complete block design with two replications. The replicates were separated by 2 m alleys. An individual genotype was planted in 2 row plots, 4 m long with 0.5 m between rows and 0.2 m within rows.
The test materials were infected using the spreader-row technique whereby each test genotype was flanked with two spreader rows. The experiment was planted in late January in order to subject the test material to high groundnut rosette disease
pressure that generally occurs late in the season. The spreader rows were planted with a groundnut rosette susceptible cultivar (JL-24) 15 days earlier than the test materials.
Table 5.1: Name, market type and rosette disease reaction of the groundnut cultivars used in this study
Genotype Botanical classification
Reaction to
rosette Remarks
ICG 12991 Spanish bunch Resistant Released in Mozambique JL-24 Spanish bunch Susceptible Released in Mozambique ICGV-SM 01513 Spanish bunch Resistant Released in Mozambique ICGV-SM 01731 Virginia bunch Resistant On-farm trials in Mozambique CG 7 Virginia bunch Susceptible Released in Mozambique ICGV-SM 90704 Virginia bunch Resistant Released in Mozambique ICGV-SM 01711 Virginia bunch Resistant On-farm trials in Mozambique
5.2.3 Data collection and analysis
Individual plants from each genotype were monitored for presence or absence of virus symptoms at 60 days after planting. Disease incidence (DI) for each genotype was calculated as the percentage of plants in a plot with rosette symptoms (Waliyar et al., 2007). Data on DI were subjected to log10 transformation before analysis.
Data was analyzed for combining ability using the Griffing’s diallel analysis Model 1 (fixed effects) Method 2 (parents included, reciprocals excluded) (Griffing, 1956;
Christie and Shattuck, 1992; Dabholkar, 1992). This approach partitions the variance due to diallel progenies into two components (Table 5.2): 1) due to general combining ability (GCA) and 2) due to specific combining ability (SCA). From the mean sum of squares estimates of GCA effects (gi) for each parent and SCA effects (sij) for each cross combination effects were calculated. The statistical model applied was: yijk =µ+gi+gj+sij+εijk,
where,
yijk = Disease incidence of the cross between lines i and j in k replications;
µ = overall mean; gi +gj+ sij = the genotypic contribution forcross i x j;
gi = the GCA of parent i;
gj = the GCA of parent j;
sij = SCA of the cross between parents i and j;
εijl = random error (assumed as normally and independently distributed i.e.
µ=0 and σ²=1).
The combining ability estimates were calculated based on the methods described by Singh and Chaudhary (1985), and Huff and Wu (1992) as follows:
Independent GCA effects were calculated for male and female parents using the same formula. GCA was regarded as significantly different from zero using a t-test,
at 27 degrees of freedom.
Predicted value of a cross = GCA of female parent + GCA of male parent + Grand mean of all crosses.
.
SCA was regarded as significantly different from zero using a t-test, at 27 degree of freedom.
Table 5.2: Analysis of variance and expected mean squares for Model I, Method 2 from Griffing's (1956)
Source df SS MS Expectation of mean squares
GCA
SCA
Error m
where,
Mg = mean square due to GCA, Ms = mean squqre due to SCA, Me = mean error
p = number of parents m = error degrees of freedom
Data were analysed using Diallel-SAS05, a SAS statistical program for Griffing’s diallel analyisis (Zhang and Kang, 1997). The F ratios were used to test for significance of the GCA and SCA main effects and t-values were used to test for significance of GCA and SCA estimate effects. The GCA/SCA ratio to estimate the relative importance of the genetic effects (additive, dominant or epistatic) was calculated as reported by Baker (1978) as follows: . The Chi-square ( 2) was used to test the F2 populations for fit to a 1:3 (resistant:susceptible) or 1:15 (resistant:susceptible) segregation ratio expected from a one-gene and two-gene inheritance using the formula (Gomez and Gomez, 1984),
respectively: .