| Literature DB >> 31086493 |
Shin Kato1, Takashi Sayama2, Fumio Taguchi-Shiobara3, Akio Kikuchi1, Masao Ishimoto3, Elroy Cober4.
Abstract
Although an indeterminate growth habit is attractive to develop high-yield soybean varieties with higher number of pods (Glycine max (L). Merr.), lodging in indeterminate varieties remains a problem in Japan. As the semi-determinate varieties have shorter main stem length than the indeterminate varieties, this trait can be useful to improve varieties with high yield and low lodging risk. We introduced the genes Dt1 and Dt2, which regulate stem growth habit, into three determinate varieties by backcrossing and evaluated the resulting effects on yield and lodging tendency under four different growing environments. The yield and lodging degree of the semi-determinate and indeterminate lines were higher and more severe than those of the determinate lines. Despite the lower overall lodging score, the semi-determinate lines had marginally lower overall yield than that of the indeterminate lines. However, the effect of introduction of semi-determinate traits on yield and lodging degree was different in the three backgrounds, with the yield of semi-determinate lines being the highest and the difference in lodging degree between the semi-determinate and determinate lines being under 1.0 in one background. Therefore, semi-determinate growth habit has potential to develop high yielding varieties with low lodging risk.Entities:
Keywords: DNA marker; lodging; near isogenic line; semi determinate; soybean; stem growth habit
Year: 2019 PMID: 31086493 PMCID: PMC6507727 DOI: 10.1270/jsbbs.18112
Source DB: PubMed Journal: Breed Sci ISSN: 1344-7610 Impact factor: 2.086
Fig. 1Comparison of main stem length, number of main stem nodes, seed yield, lodging, and 100-seed weight among the stem growth habits and genetic backgrounds. Error bars represent standard errors. The same letters are not significantly different at 0.05 probability level by the Tukey–Kramer test.
Analysis of split-plot variance (ANOVA) of agricultural characteristics of near isogenic lines with different growth habits
| Sources of variation | Degree of Freedom | Mean of square | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||||||||||
| Flowering time | Seed-filling period | Maturation time | Lodging | Main stem length | Number of main stem nodes | Main stem internode length | Number of branches | ||||||||||
| Year (Y) | 1 | 1.1 | NS | 1330.4 | *** | 1254.2 | *** | 6.4 | NS | 388 | NS | 8.4 | NS | 0.001 | NS | 6.4 | NS |
| Location (L) | 1 | 21.1 | * | 1535.5 | *** | 1916.8 | *** | 0.3 | NS | 261 | NS | 3.4 | NS | 0.005 | NS | 21.7 | * |
| Y × L | 1 | 117.6 | ** | 770.3 | *** | 286.0 | ** | 0.8 | NS | 360 | NS | 41.4 | ** | 0.183 | NS | 9.8 | NS |
| Genetic backround (G) | 2 | 7813.3 | *** | 1309.8 | *** | 15493.0 | *** | 35.1 | *** | 17846 | *** | 418.9 | *** | 8.227 | *** | 5.3 | NS |
| Pooled error a | 8 | 2.3 | 7.3 | 7.9 | 0.9 | 40 | 0.9 | 0.090 | 1.2 | ||||||||
| Stem growth habit (S) | 2 | 55.8 | *** | 2.4 | NS | 78.8 | *** | 67.8 | *** | 42372 | *** | 1049.6 | *** | 17.198 | *** | 26.4 | *** |
| S × G | 4 | 2.1 | NS | 7.2 | NS | 8.2 | * | 3.6 | *** | 343 | *** | 15.9 | *** | 0.074 | NS | 2.3 | * |
| Pooled error b | 24 | 1.1 | 3.2 | 2.7 | 0.3 | 41 | 1.5 | 0.037 | 0.7 | ||||||||
*, **, and *** are significant at 5%, 1%, and 0.1% probability levels, respectively.
NS: not significant.
Mean values of agricultural characteristics for each level of near isogenic lines with different growth habits
| Factor | Flowering time (days) | Seed-filling period (days) | Maturation time (days) | Lodging | Main stem length (cm) | Number of main stem nodes | Main stem internode length (cm) | Number of branches |
|---|---|---|---|---|---|---|---|---|
| Determinate | 61.8 | 68.7 | 130.5 | 1.1 | 55.5 | 14.7 | 3.8 | 4.0 |
| Semi-determinate | 62.7 | 69.0 | 131.7 | 2.4 | 86.0 | 19.9 | 4.3 | 3.7 |
| Indeterminate | 63.3 | 69.0 | 132.3 | 2.7 | 95.7 | 20.8 | 4.6 | 4.7 |
| Wasesuzunari | 52.5 | 64.9 | 117.4 | 2.3 | 63.9 | 16.2 | 3.9 | 3.9 |
| Tohoku 162 | 65.6 | 69.7 | 135.3 | 2.5 | 83.1 | 18.7 | 4.4 | 4.3 |
| Tohoku 164 | 69.8 | 72.1 | 141.9 | 1.4 | 90.2 | 20.4 | 4.4 | 4.3 |
| Ishinazaka | 62.9 | 71.2 | 134.1 | 2.1 | 80.0 | 18.5 | 4.2 | 3.9 |
| Uenodai | 62.3 | 66.6 | 128.9 | 2.0 | 78.1 | 18.3 | 4.2 | 4.4 |
| 2015 | 62.5 | 71.1 | 133.6 | 2.2 | 80.2 | 18.6 | 4.2 | 4.0 |
| 2016 | 62.7 | 66.8 | 129.4 | 1.9 | 77.9 | 18.3 | 4.2 | 4.3 |
Fig. 2Relationship between lodging score and main stem length. Regression of lodging score to main stem length was analyzed in each genetic background. Each dot represents the average value obtained in all cultivation environments and year grown. White, grey, and black filled shapes represent the determinate, semi-determinate, and indeterminate lines, respectively. Error bars represent standard errors. Regression analysis y = bx + a; BG/Wase (circle), b = 0.062 ± 0.003; BG/T162 (square), b = 0.040 ± 0.007; BG/T164 (triangle), b = 0.021 ± 0.003.