Scientia Agricultura Sinica ›› 2026, Vol. 59 ›› Issue (11): 2325-2339.doi: 10.3864/j.issn.0578-1752.2026.11.003

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Identification and Comprehensive Evaluation of the Barren-Tolerant Germplasm Resources of Foxtail Millet in Shanxi

XIE YongXiang1,2(), PAN YiMin1,2, HUANG Rui2, QIN HuiBin2, HOU Sen2, HE Qiang3, LING Liang4,5(), MU ZhiXin2(), WANG HaiGang2()   

  1. 1 College of Agronomy, Shanxi Agricultural University, Taigu 030801, Shanxi
    2 Center for Agricultural Genetic Resources Research, Shanxi Agricultural University, Taiyuan 030031
    3 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/State Key Laboratory of Crop Gene Resources and Breeding, Beijing 100081
    4 Shanxi Institute for Functional Food, Shanxi Agricultural University, Taiyuan 030031
    5 Agricultural Science Research Institute of the Sixth Division of Xinjiang Production and Construction Corps, Wujiaqu 830301, Xinjiang
  • Received:2025-10-31 Accepted:2025-12-15 Online:2026-06-01 Published:2026-06-03
  • Contact: LING Liang, MU ZhiXin, WANG HaiGang

Abstract:

【Objective】This study aimed to select superior barren-tolerant germplasm, establish an evaluation model, and identify diagnostic indicators in foxtail millet germplasm resources from Shanxi, so as to provide the theoretical basis and methodological support for the efficient breeding of barren-tolerant foxtail millet varieties. 【Method】617 local foxtail millet varieties were used as experimental materials. Trials were carried out under low-fertility stress in Taiyuan (23TY, 24TY) and under normal fertility conditions in Dongyang (23DY, 24DY) from 2023 to 2024. Eleven indicators were measured, including stem node number (SNN), stem diameter (SD), panicle diameter (PD), plant height (PH), peduncle length (PeL), panicle length (PaL), leaf length (LL), leaf width (LW), spike weight per plant (SWP), grain weight per plant (GWP), and thousand-grain weight (TGW). A comprehensive evaluation and identification were carried out using multiple analytical methods, such as normal distribution test, barren tolerance coefficient difference analysis, correlation analysis, cluster analysis, and regression analysis. 【Result】The results showed significant differences in agronomic traits, with coefficients of variation ranging from 9.23% to 48.05%. Specifically, the coefficient of variation for LW in 23TY was the smallest (9.23%), while the coefficient for GWP in 24TY was the largest (48.05%). K-S test results indicated that, except for SNN, PaL, LW, and SWP, other traits showed varying degrees of normal distribution in different environments (P>0.05). Correlation analysis showed that SWP and GWP exhibited a significant positive correlation with the highest correlation coefficient, followed by SNN with PH, PaL with LL. In addition, PeL and SNN showed a significant negative correlation in 23TY and 23DY; LL and PH, LL and SNN, PeL and PD had a significant negative correlation in 23TY; and PeL was significantly negatively correlated with PH in 23DY. Principal component analysis results revealed that 11 phenotypic traits were converted into 2 comprehensive indices, with a cumulative explanation rate of 59.68%. Using the membership function method calculated the barren tolerance (D value), and cluster analysis was performed. The varieties were classified into three categories: Class I included 13 barren-sensitive varieties with D values ranging from 0.09 to 0.28; Class Ⅱ included 595 moderately barren-tolerant varieties with D values ranging from 0.40 to 0.81; Class Ⅲ included 9 barren-tolerant varieties with D values ranging from 0.84 to 0.93. Maorangu was the most barren-tolerant variety from Yangquan city. Through multiple regression analysis, a predictive evaluation model for barren tolerance was established: Y=-0.031+0.129X9+0.167X10+0.141X6+0.233X8+ 0.205X4 (R2=0.951, P<0.001). 【Conclusion】Using multivariate statistical analysis methods, it is reliable to evaluate and predict the barren tolerance of foxtail millet germplasm. Five phenotypic traits are selected as evaluation indicators for barren tolerance: plant height, panicle length, leaf width, spike weight per plant, grain weight per plant. The Maorangu variety from Yangquan city was the strongest barren tolerance.

Key words: foxtail millet, germplasm resources, barren tolerance, comprehensive evaluation, subordinate function method

Table 1

Soil nutrient content of the experimental field"

地点
Location
全氮
Total nitrogen (g·kg-1)
有效磷
Available phosphorus (mg·kg-1)
速效钾
Available potassium (mg·kg-1)
有机质
Organic matter (g·kg-1)
酸碱度
pH
太原TY 0.58 5.67 116.90 9.45 8.51
东阳DY 0.80 5.30 118.05 13.95 8.65

Fig. 1

Monthly average temperature and total precipitation from May to October under four different environments 23TY: 2023Taiyuan; 24TY: 2024Taiyuan; 23DY: 2023Dongyang; 24DY: 2024Dongyang. The same as below"

Table 2

Statistical analysis of agronomic traits of 617 foxtail millet germplasm"

性状Trait 环境Environment 平均值Mean 变异系数Coefficient variation (%) 偏度Skewness 峰度Kurtosis PP value
节数
SNN
23TY 11.75±1.43 12.24 -0.01 1.03 0.000
24TY 11.20±1.60 14.36 -0.26 0.05 0.000
23DY 10.79±1.81 16.75 -0.05 0.00 0.000
24DY 11.43±1.68 14.71 -0.07 -0.30 0.000
茎粗
SD
23TY 4.03±0.76 18.77 0.62 1.21 0.001
24TY 5.44±1.12 20.63 0.64 1.28 0.000
23DY 7.29±0.99 13.63 0.46 2.63 0.200*
24DY 5.88±1.02 17.30 0.18 0.03 0.200*
穗粗
PD
23TY 15.51±2.72 17.52 1.19 5.46 0.000
24TY 19.08±3.97 20.83 0.88 1.14 0.000
23DY 23.94±4.60 19.29 0.28 0.02 0.200*
24DY 24.29±4.78 19.73 0.62 1.59 0.000
株高
PH
23TY 80.91±13.37 16.55 -0.15 0.13 0.200*
24TY 89.82±16.67 18.57 -0.09 -0.37 0.011
23DY 106.17±16.41 15.42 -0.26 0.13 0.060*
24DY 104.14±16.84 16.18 -0.32 -0.02 0.000
颈长
PeL
23TY 38.41±5.01 13.04 0.07 1.20 0.001
24TY 36.97±5.83 15.84 0.34 0.43 0.009
23DY 33.93±6.81 20.05 0.24 0.03 0.077*
24DY 33.98±5.69 16.77 0.35 0.45 0.000
穗长
PaL
23TY 18.75±3.85 20.53 0.46 0.43 0.001
24TY 18.41±4.87 26.47 0.51 -0.14 0.001
23DY 25.58±4.80 18.86 0.21 0.21 0.004
24DY 26.61±5.49 20.71 0.48 0.23 0.001
叶长
LL
23TY 34.28±5.14 15.06 0.28 0.14 0.171*
24TY 33.45±6.78 20.33 0.21 -0.48 0.077*
23DY 39.94±4.98 12.54 0.11 0.59 0.009
24DY 41.07±6.26 15.32 0.12 0.53 0.184*
叶宽
LW
23TY 2.17±0.20 9.23 1.51 7.42 0.000
24TY 2.34±0.44 18.93 0.36 0.26 0.003
23DY 2.90±0.41 14.24 0.07 1.09 0.000
24DY 2.72±0.30 10.99 1.08 3.29 0.000
穗重
SWP
23TY 11.85±4.61 38.81 0.50 1.47 0.000
24TY 12.20±5.46 45.08 0.66 0.20 0.000
23DY 21.19±5.67 26.80 0.52 0.52 0.003
24DY 17.87±4.61 26.01 0.37 0.06 0.022
穗粒重
GWP
23TY 8.67±3.87 44.52 0.52 1.65 0.006
24TY 6.57±3.15 48.05 0.60 -0.15 0.000
23DY 16.91±4.78 28.41 0.51 0.49 0.029
24DY 13.58±4.00 29.74 0.39 0.20 0.200*
千粒重
TGW
23TY 2.88±0.34 11.84 -0.72 4.23 0.000
24TY 3.04±0.39 12.83 -0.73 3.07 0.004
23DY 3.43±0.39 11.50 -0.08 0.04 0.200*
24DY 3.40±0.39 11.69 -0.13 0.40 0.200*

Fig. 2

Boxplots of 11 phenotypic traits in 4 environments **, and *** indicate significance at the 0.01 and 0.001 levels. The same as below"

Fig. 3

Correlation of phenotypic values of various traits in foxtail millet A: 23Taiyuan; B: 23Dongyang; C: 24Taiyuan; D: 24Dongyang"

Table 3

Loadings and contribution rates of principal components"

指标
Index
主成分1
Principal component 1
主成分2
Principal component 2
节数SNN 0.689 -0.271
茎粗SD 0.668 -0.242
穗粗PD 0.703 -0.057
株高PH 0.748 -0.210
颈长PeL 0.470 0.071
穗长PaL 0.764 -0.148
叶长LL 0.751 -0.334
叶宽LW 0.772 -0.238
穗重SWP 0.664 0.687
穗粒重GWP 0.646 0.715
千粒重TGW 0.592 0.239
特征值Eigenvalue 5.150 1.41
贡献率Contributive rate (%) 46.82 12.86
累积贡献率
Cumulative contributive rate (%)
46.82 59.68

Fig. 4

Clustering map of the barren tolerance coefficient of 617 varieties of millet The figure shows the variety numbers, as in the Supplementary Table 1"

Table 4

Correlations of the comprehensive evaluation value (D) tolerance with index in foxtail millet"

指标Index 相关系数Correlation coefficient
节数SNN 0.579**
茎粗SD 0.567**
穗粗PD 0.654**
株高PH 0.653**
颈长PeL 0.471**
穗长PaL 0.687**
叶长LL 0.619**
叶宽LW 0.668**
穗重SWP 0.837**
穗粒重GWP 0.828**
千粒重TGW 0.637**

Table 5

Prediction model of barrenness resistance characteristics of foxtail millet varieties"

多元回归方程
Multiple regression equation
相关系数
r
判定系数
R2
F
F value
P
P value
Y=0.354+0.227X9+0.182X10 0.847 0.718 779.906 ≤0.001
Y=0.174+0.159X9+0.172X10+0.319X6 0.925 0.856 1217.475 ≤0.001
Y=0.048+0.14X9+0.174X10+0.189X6+0.286X8 0.958 0.917 1699.921 ≤0.001
Y=0.027+0.138X9+0.161X10+0.154X6+0.244X8+0.121X3 0.966 0.932 1681.664 ≤0.001
Y=-0.031+0.129X9+0.167X10+0.141X6+0.233X8+0.205X4 0.975 0.951 2386.573 ≤0.001
Y=-0.039+0.128X9+0.158X10+0.12X6+0.207X8+0.186X4+0.089X3 0.979 0.959 2381.147 ≤0.001
Y=-0.133+0.124X9+0.134X10+0.117X6+0.162X8+0.161X4+0.098X3+0.188X11 0.992 0.983 5131.342 ≤0.001
Y=-0.138+0.122X9+0.139X10+0.082X6+0.128X8+0.143X4+0.102X3+0.186X11+0.082X7 0.994 0.988 6237.503 ≤0.001

Table 6

Average agronomic traits of 9 barren-resistant foxtail millet varieties over two years"

材料
Materials
地点
Location
节数
SNN
茎粗
SD
穗粗
PD
株高
PH
颈长
PeL
穗长
PaL
叶长
LL
叶宽
LW
穗重
SWP
穗粒重
GWP
千粒重
TGW
竹叶青
Zhuyeqing
TY 11.33 5.48 17.88 69.48 38.98 24.28 36.37 2.34 14.51 9.85 3.10
DY 9.50 5.91 21.82 91.83 38.83 25.17 38.67 2.62 13.03 9.50 2.86
称锤白谷
Chengchuibaigu
TY 10.17 4.55 15.95 78.83 42.18 18.14 40.77 2.08 11.90 7.81 3.02
DY 8.50 3.67 15.28 93.33 31.33 17.50 36.67 2.10 11.94 8.96 3.45
羊毛糙
Yangmaocao
TY 13.63 4.06 16.81 111.30 38.73 22.66 39.60 2.23 19.00 11.43 2.74
DY 12.83 6.91 32.19 118.00 25.33 19.50 35.67 2.85 15.03 11.95 2.85
青达谷
Qingdagu
TY 12.17 4.37 16.14 84.78 34.58 21.25 34.52 2.38 17.18 11.15 3.05
DY 11.50 6.17 16.06 106.67 27.33 19.00 39.33 2.72 13.68 10.42 3.40
刘沟谷
Liugougu
TY 13.23 6.90 12.40 76.93 22.33 20.66 34.73 3.98 20.50 12.31 2.88
DY 11.83 9.54 28.91 83.67 18.00 20.83 35.17 4.36 15.54 10.73 2.96
一层黄
Yicenghuang
TY 14.37 6.02 28.15 113.23 32.60 25.58 45.67 2.55 21.05 13.82 2.57
DY 13.50 6.57 29.34 127.67 28.00 29.33 46.17 2.53 17.32 13.12 2.89
红石榴
Hongshiliu
TY 12.03 5.85 19.31 97.47 37.30 23.05 39.43 2.74 20.55 15.91 3.39
DY 10.83 7.11 22.99 108.83 33.17 31.50 43.17 2.83 14.90 11.16 3.22
黄熟谷
Huangshugu
TY 10.10 4.37 17.62 81.83 48.22 20.59 45.35 2.49 13.62 6.96 3.01
DY 8.50 5.73 18.62 90.83 45.50 28.33 45.17 2.92 7.70 5.40 3.22
毛软谷
Maoruangu
TY 12.67 5.20 23.49 93.00 35.88 25.30 36.62 2.23 14.30 10.33 2.71
DY 10.50 5.99 22.22 103.00 33.33 20.67 32.17 2.20 11.69 8.04 2.68
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