Wheat is the main raw material widely used in making flour and bread (Kumar
Development of tropical wheat can be done through the development of plant varieties. In general, there are several accessions of wheat grown in Indonesia (Nur
Decrease in the growing environmental elevation of wheat plants will result in an increase in environmental temperature which causes the plants to experience heat stress (Akter and Islam 2017; Farid 2018). In general, this condition will reduce the growth rate of wheat production components and have an impact on wheat yield (Bányai
The best secondary trait identification can be done with several approaches, such as the use of genetic parameters (Erkul
The study was conducted in three growing environments. The first environment was conducted in Kelara Village, South Tolo District, Jeneponto Regency, South Sulawesi province, Indonesia (latitude 5°24'58.0"S, longitude 119º54'58.2"E, and 135 m above sea level (asl)) from March to June 2018. The second experiment was conducted in Allopolea Village, Lau District, Maros Regency, South Sulawesi province, Indonesia (latitude -4º-58'-55.1"S, longitude 119º34'27.4"E, and 100 m asl) in the same time frame of the first experiment. The last and third experiment was carried out in Bonto Manai Village, Bissappu District, Bantaeng Regency, South Sulawesi province, Indonesia (latitude 5º32'03.0"S, longitude 119º54'17.5"E, and 120 m asl) from July to October 2018. The plant conditions in each environment was shown in Fig. 1.
The experiment used a nested randomized complete block design (RCBD) with two factors namely genotype and environment. The replications were three times and nested in the environment. 20 genotypes were used consisting of 16 mutant lines and 4 control varieties, namely Dewata, Selayar, Nias, and Munal. Based on the design, the total experimental units were 180. Each plot is 1 m × 4 m divided into 4 rows. Each genotype was planted with spacing of 25 cm × 2 cm (12 g /row according to CIMMYT standards).
Fertilization was conducted twice during plant growth. The first fertilization was carried out 10 days after planting (DAP) with 150 kg/ha Urea, 200 kg/ha SP36, and 100 kg/ha NPK. The second fertilization was at 30 DAP using 150 kg/ha Urea. Weeding was done twice, at 30 and 45 DAP. Pest control was conducted by spraying insecticide on wheat. Harvesting was conducted when the plants reached its physiological maturity, marked 80% of panicles, stems and leaves of plants have turned yellow, and the seeds have hardened. Harvesting was done by cutting the base of the stem above the soil surface using a sickle. Panicles that have been harvested are then dried and seeded. Observations were carried out for plant height before harvest, number of productive tillers, days to flowering, days to harvest, number of spikelet, panicle length, number of seeds per panicle, seed weight per panicle, weight 100 seeds, percentage of unfilled grains, and yield.
Data analysis conducted on the observed data in this study consists of analysis on the genetic parameters and multivariate analysis. Data analysis carried out were analysis of variance using STAR 2.1, and identification on genetic variability, repeatability, genetic correlation and phenotypic correlation for each character using META-R software (Anshori
Four analyses were conducted in the multivariate analysis on the genotypes average values at the three locations consisted of cluster analysis, principal component analysis, factor analysis and path analysis (Mattjik and Sumertajaya 2011). Cluster and path analysis were carried out using Rstudio software with Cluster Maechler
Analysis of variance in Table 1 showed that all responses to growth character were influenced by environmental variability, genotypes and their interactions (
Table 1 . Analysis of variance and repeatability of the characters of wheat mutant adaptive to tropical lowland.
Character | Location (E) | Genotype (G) | G × E | CV | Vg | R |
---|---|---|---|---|---|---|
PH | 2211.88^{**} | 64.97^{**} | 55.31^{**} | 8.63 | 0.02 | 14.86% |
NPT | 30.55^{*} | 5.97^{**} | 0.55ns | 20.05 | 0.37 | 89.38% |
DF | 181.52^{**} | 19.44^{*} | 31.12^{**} | 5.48 | 0.00 | 0.00% |
DH | 1886.16^{**} | 15.97^{**} | 17.84^{**} | 2.76 | 0.00 | 0.00% |
NS | 202.43^{**} | 7.0^{**} | 3.13^{**} | 7.95 | 0.09 | 55.41% |
PL | 46.12^{**} | 1.15^{**} | 0.55^{**} | 5.46 | 0.08 | 51.96% |
NGP | 778.65^{**} | 57.61^{**} | 45.56^{**} | 16.32 | 0.04 | 20.92% |
WPG | 1.821^{**} | 0.085^{**} | 0.032^{**} | 19.1 | 0.13 | 61.98% |
W100 | 0.28^{**} | 0.32^{**} | 0.23^{**} | 8.51 | 0.07 | 27.36% |
PUG | 411.84^{**} | 133.96^{**} | 175.34^{**} | 12.05 | 0.00 | 0.00% |
Yield | 7.49^{**} | 1.59^{**} | 0.19ns | 22.07 | 0.38 | 87.92% |
CV: coefficient of variance, Vg: genetic varaince, R: repeatability, ns: not significant. *: significant at
The results of phenotype and genotype correlation analysis showed that plant height (0.503, 0.999), number of productive tillers (0.771, 0.835), number of spikelets (0.831, 0.999), panicle length (0.559, 0.705), number of grains per panicle (0.674, 0.999), grain weight per panicle (0.663, 0.921), and weight of 100 grains (0.645, 0.999) have significant correlations with yield. On the other hand, days to flowering, days to harvest and percentage of unfilled grains had a low phenotype correlation and are undefined in the genetic correlations. Based on Table 2, the number of productive tillers (0.065) was the character showing the lowest difference between phenotype and genetic correlations compared to other characters, followed by characters of panicle length (0.146) and number of spikelets (0.169).
Table 2 . Analysis genetic correlation (above the diagonal line) and phenotypic correlation (below diagonal line) on the yield of wheat mutant adaptive to tropical lowland.
Traits | PH | NPT | DF | DH | NS | PL | NGP | WGP | W100 | PUG | Yield |
---|---|---|---|---|---|---|---|---|---|---|---|
PH | 0.599 | NA | NA | 0.999 | 0.999 | 0.999 | 0.999 | 0.999 | NA | 0.999^{**} | |
NPT | 0.167 | NA | NA | 0.682 | 0.364 | 0.797 | 0.441 | 0.423 | NA | 0.835^{**} | |
DF | ‒0.508 | 0.019 | NA | NA | NA | NA | NA | NA | NA | NA | |
DH | 0.057 | ‒0.380 | ‒0.287 | NA | NA | NA | NA | NA | NA | NA | |
NS | 0.606 | 0.443 | ‒0.315 | ‒0.379 | 0.822 | 1.000 | 1.000 | 1.000 | NA | 0.999^{**} | |
PL | 0.632 | 0.216 | ‒0.241 | ‒0.173 | 0.745 | 0.550 | 0.978 | 1.000 | NA | 0.705^{**} | |
NGP | 0.386 | 0.247 | ‒0.331 | ‒0.172 | 0.755 | 0.377 | 1.000 | 1.000 | NA | 0.999^{**} | |
WGP | 0.671 | 0.262 | ‒0.228 | 0.054 | 0.667 | 0.765 | 0.633 | 1.000 | NA | 0.921^{**} | |
W100 | 0.729 | 0.189 | ‒0.198 | ‒0.180 | 0.642 | 0.669 | 0.517 | 0.749 | NA | 0.999^{**} | |
PUG | ‒0.108 | ‒0.011 | 0.176 | 0.011 | ‒0.366 | ‒0.020 | ‒0.874 | ‒0.424 | ‒0.325 | NA | |
Yield | 0.503^{**} | 0.771^{**} | ‒0.137 | ‒0.413 | 0.831^{**} | 0.559^{**} | 0.674^{**} | 0.663^{**} | 0.645^{**} | ‒0.386 | |
Difference | 0.497 | 0.065 | 0.169 | 0.146 | 0.326 | 0.258 | 0.355 |
ns: not significant, *: significant at
The results of the cluster analysis showed that there were two large groups with a degree of dissimilarity and agglomerative coefficients reaching values of 0.6 and 0.89, respectively (Fig. 2). The yield as the main character was grouped in group 2. In general, group 2 was divided into two sub-groups with a degree of dissimilarity of 0.2. Based on the division of the subgroups in group 2, the yield was in subgroup 1 together with the number of productive tillers, the weight of 100 grains, seed weight per panicle, and panicle length. On the other hand, the number of spikelets and number of grains per panicle were in subgroup 2. Meanwhile, plant height, days to flowering, percentage of unfilled grains, and days to harvest were in group 1.
The results of the principal components analysis were visualized in the 3D plot form (Fig. 3). Based on Fig. 3, the yield was in quadrant 2 together with the number of productive tillers, panicle length, number of spikelets and weight of 100 grains. On the other hand, days to harvest was in quadrant which is opposite to quadrant 2 or there was in quadrant 8. Meanwhile, the closest quadrant that has a vector close to the yield was quadrant 3 (plant height and weight of grains per panicle) and 7 (number of grains per panicle).
Factor analysis in this study showed representative results. This was supported by a total variance of data that reached 85.58% and the overall character trait that was above 0.8, except for plant height (0.774) and days to harvest (0.698) (Table 3). Based on the factor loading values, panicle length and weight of 100 grains were the determinants of the first-factor variance represented 34.2% of the total initial data variance. Then the second factor, which represented 19.9% of the total data variance, had a variance direction that was largely determined by the number of grains per panicle and the percentage of unfilled grains in the opposite directions. The greatest variance of the yield, as the main character, was in the third factor with a factor loading value of 0.321. Besides the yield, another main character in factor 3 was the number of productive tillers (0.592).
Table 3 . Factor analysis of wheat mutant adaptive to tropical lowland character.
Variable | Factor1 | Factor2 | Factor3 | Factor4 | Communality |
---|---|---|---|---|---|
PH | 0.218 | 0.14 | ‒0.017 | ‒0.233 | 0.774 |
NPT | ‒0.185 | 0.121 | 0.592 | ‒0.219 | 0.86 |
DF | 0.127 | ‒0.027 | ‒0.126 | 0.775 | 0.878 |
DH | 0.015 | ‒0.011 | ‒0.293 | ‒0.364 | 0.698 |
NS | 0.105 | ‒0.034 | 0.152 | ‒0.035 | 0.836 |
PL | 0.366 | 0.202 | ‒0.086 | 0.118 | 0.85 |
NGP | ‒0.067 | ‒0.43 | 0.011 | ‒0.03 | 0.976 |
WPG | 0.274 | ‒0.068 | ‒0.163 | 0.101 | 0.841 |
W100 | 0.326 | 0.001 | ‒0.167 | 0.203 | 0.805 |
PUG | 0.135 | 0.59 | 0.125 | ‒0.069 | 0.978 |
Yield | 0.002 | ‒0.043 | 0.321 | ‒0.035 | 0.946 |
Variance (Var) | 3.764 | 2.1886 | 2.177 | 1.3137 | 9.4433 |
% Var | 0.342 | 0.199 | 0.198 | 0.119 | 0.858 |
DF: Days to flowering, DH: Days to harvest, NGP: Number of grains per panicle, NPT: The number of productive tillers, NS: Number of spikelet, PH: Plant height, PL: Panicle length, PUG: Percentage of unfilled grains, W100: Weight of 100 grains, WGP: Weight of grains per panicle.
Path analysis showed fairly representative results with a determination value of 79.01% (Table 4). Based on the path analysis, the number of productive tillers was the character that had the greatest direct influence value with a value of 0.563. As for the other characters that have a significant direct effect were the weights of 100 grains (0.239) and the number of spikelets (0.237). However, the direct influence of the two characters was only half of the direct effect of the number of productive tillers.
Table 4 . Path analysis on yield of wheat mutant adaptive to tropical lowland.
Character | Direct effect | JAP | JS | JBM | BBM | B100 | Residual |
---|---|---|---|---|---|---|---|
NPT | 0.563 | 0.104 | 0.051 | 0.017 | 0.045 | 0.044 | |
NS | 0.237 | 0.248 | 0.142 | 0.041 | 0.153 | 0.044 | |
NGP | 0.189 | 0.152 | 0.178 | 0.039 | 0.122 | 0.044 | |
WGP | 0.061 | 0.152 | 0.157 | 0.121 | 0.179 | 0.044 | |
W100 | 0.239 | 0.107 | 0.152 | 0.096 | 0.046 | 0.044 |
R^{2} = 79.01%. NPT: the number of productive tillers, NS: Number of spikelet, NGP: Number of grains per panicle, WGP: Weight of grains per panicle, W100: Weight of 100 grains.
Evaluation of conformity of all multivariate analysis results and genetic parameters was shown in Table 5. The number of productive tillers was a character with the highest degree of conformity value of 91.67%. Meanwhile, other characters that have a suitability value above 50% were the number of spikelets (75%), weight of 100 grains (75%), weight of grains per panicle (66.67%) and panicle length (66.67%). Based on these results, the number of productive tillers, number of spikelets, weight of 100 grains, weight of grains per panicle, and panicle length could be used as a selection character in the development of tropical wheat in Indonesia. However, the number of productive tillers was the best selection character highly recommended in developing wheat under the lowland.
Table 5 . Conformity value of secondary trait on the yield of wheat mutant adaptive to tropical lowland.
Character | R | CA | Dr | PCA | FcA | PA | Total | Conformity |
---|---|---|---|---|---|---|---|---|
Plant height | 5 | 3 | 5 | 3 | 5 | 5 | 26 | 16.67 |
Number of productive tillers | 1 | 1 | 1 | 3 | 1 | 1 | 8 | 91.67 |
Days to flowering | 5 | 5 | 5 | 5 | 5 | 5 | 30 | 0.00 |
Days to harvest | 5 | 5 | 5 | 5 | 5 | 5 | 30 | 0.00 |
Number of spikelet | 1 | 1 | 3 | 1 | 5 | 1 | 12 | 75.00 |
Panicle length | 1 | 1 | 3 | 1 | 3 | 5 | 14 | 66.67 |
Number of grains per panicle | 3 | 3 | 3 | 3 | 5 | 3 | 20 | 41.67 |
Weight of grains per panicle | 1 | 3 | 1 | 1 | 5 | 3 | 14 | 66.67 |
Weight of 100 grains | 3 | 3 | 1 | 1 | 3 | 1 | 12 | 75.00 |
Percentage of unfilled grains | 5 | 5 | 5 | 5 | 3 | 5 | 28 | 8.33 |
Max score | 5 | 5 | 5 | 5 | 5 | 5 | 30 | |
Min Score | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
R: Repeatability, CA: Correlation analysis, Dr: Dendrogram, PCA: Principal component analysis, FcA: Factor analysis, PA: path analysis.
Based on the analysis results of variance and repeatability, the number of productive tillers and yield were genetically stable characters in the lowlands (Table 1). This was also reported by Erkul
Phenotypic and genetic correlation is one way to determine the stability of a selection character (Garg
Cluster analysis is a semi-qualitative and subjective multivariate analysis (Mattjik and Sumertajaya 2011). The results of this analysis are groupings with a certain degree of closeness based on the grouping method chosen by the researcher (Malek
The principal component analysis is a multivariate analysis used by many researchers to reduce and simplify character dimensions with still maintaining most of the initial data variance (Awan
Factor analysis is a multivariate analysis that has similarities with the principal component analysis (PCA) (Mattjik and Sumertajaya 2011). The difference in this analysis lies in the process of reducing the variance of the non-specific covariates and optimizing variances on specific characters that have high covariance in a factor dimension (Mattjik and Sumertajaya 2011; Dormann
Path analysis is a multivariate analysis used to divide a multivariate correlation into direct and indirect effects (Carvalho
Productive tillers become one of the main keys in supporting wheat yield (Monpara 2011). Every tiller may not be a productive one (Moral and Moral 1995). This is due to the competition of the energy distribution in initiating flowering apparatus including the spikelet formation (Xie
In conclusions, the number of productive tillers, number of spikelets, panicle length, weight of 100 grains and weight of grains per panicle can be used as secondary traits in the selection of lowland wheats. The number of productive tillers is the main secondary trait for adaptive wheat in the lowlands. These characters can be recommended for selection criteria in testing wheat lines in the lowlands to make selection effective.