Multi-trait selection of wheat lines under drought-stress condition

Silva, Caique Machado e; Mezzomo, Henrique Caletti; Ribeiro, João Paulo Oliveira; Freitas, Davi Soares de; Nardino, Maicon

doi:10.1590/1678-4499.20220254

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Article • Bragantia 82 • 2023 • https://doi.org/10.1590/1678-4499.20220254 copy

Multi-trait selection of wheat lines under drought-stress condition

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The immediate need for the increase in wheat production to meet the future world demand, associated with the occurrence of drastic climatic events, such as drought, makes it necessary to develop drought-resilient genotypes. The aims of this work were to evaluate the use of drought-tolerance indices for the selection of wheat genotypes, to compare the genetic gains in grain yield using different selection strategies by means of a multi-trait index, and to select superior drought-tolerant wheat genotypes. The total of 31 tropical wheat lines was evaluated in two experiments. Five agronomic traits were accessed. The grain yield data from the stress and nonstress experiments were used to obtain five drought-tolerance indices. The data were subjected to mixed model analysis, and four selection scenarios were designed. There was a significant effect of genotype for all traits. The inclusion of drought-tolerance indices in the selection index provided superior genetic gains in drought condition. Seven lines were selected due the high frequency of favorable alleles for drought-tolerance and other important agronomic traits. Drought-tolerance indices are appropriate for characterizing the response of wheat genotypes to drought-stress. The inclusion of drought-tolerance indices along with agronomic traits in multi-trait selection strategies provides for superior gains in grain yield compared to the non-inclusion of the indices.

Key words
drought-tolerance; genetic gain; mixed models; selection index; Triticum aestivum L

Drought-tolerance indices	Code	Equation	Reference
Stress tolerance index	STI	$\frac{Y_{S} * Y_{C}}{{(Y_{C})}^{2}}$	Fernández (1992)Fernández, G. C. J. (1992). Effective selection criteria for assessing plant stress tolerance. In C. G. Kuo (Ed.), Adaptation of food crops to temperature and water stress (p. 257-270). Taiwan: AVRDC. https://doi.org/10.22001/wvc.72511 https://doi.org/10.22001/wvc.72511...
Yield index	YI	$\frac{Y_{S}}{{\bar{Y}}_{S}}$	Gavuzzi et al. (1997)Gavuzzi, P., Rizza, F., Palumbo, M., Campanile, R. G., Ricciardi, G. L. and Borghi, B. (1997). Evaluation of field and laboratory predictors of drought and heat tolerance in winter cereals. Canadian Journal of Plant Science, 77, 523-531. https://doi.org/10.4141/P96-130 https://doi.org/10.4141/P96-130...
Geometric mean productivity	GMP	$\sqrt{Y_{S} * Y_{C}}$	Schneider et al. (1997)Schneider, K. A., Serna, R. R., Pérez, F. I., Enríquez, B. C., Gallegos, J. A. A., Vallejo, P. R., Wassimi, N. and Kelley, J. D. (1997). Improving common bean performance under drought stress. Crop Science, 37, 43-50. https://doi.org/10.2135/cropsci1997.0011183X003700010007x https://doi.org/10.2135/cropsci1997.0011...
Mean productivity	MP	$\frac{Y_{S} * Y_{C}}{2}$	Rosielle and Hambling (1981)Rosielle, A. A. and Hambling, J. (1981). Theoretical aspects of selection for yield in stress and non-stress environments. Crop Science, 21, 943-946. https://doi.org/10.2135/cropsci1981.0011183X002100060033x https://doi.org/10.2135/cropsci1981.0011...
Harmonic mean	HM	$\frac{2 (Y_{C} - Y_{S})}{Y_{C} + Y_{S}}$	Jafari et al. (2009)Jafari, A., Paknejada, F. and Jami Al-Ahmadi, M. (2009). Evaluation of selection indices for drought tolerance of corn (Zea mays L.) hybrids. International Journal of Plant Protection, 3, 33-38. https://doi.org/10.22069/IJPP.2012.661 https://doi.org/10.22069/IJPP.2012.661...

Parameters	Traits¹ 1 Deviance analysis for agronomic traits in nonstress experiment;
Parameters	GY	DH	PH	LR	TS
${\hat{σ}}_{g}^{2}$	193,721.00^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	4.20^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	13.50^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	0.54^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	0.26^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.
${\hat{σ}}_{r}^{2}$	342,300.00	2.39	18.10	0.15	0.19
${\hat{σ}}_{p}^{2}$	536,021.00	6.59	31.70	0.69	0.45
h²_m	0.63	0.88	0.69	0.91	0.80
Accuracy	0.79	0.92	0.83	0.96	0.89
CV_g (%)	10.50	3.53	4.13	31.50	25.90
CV_r (%)	14.00	2.66	4.78	16.80	22.40
p-value	5.62E-04	8.28E-11	2.95E-05	1.32E-17	1.13E-08
Parameters	Traits² 2 deviance analysis for agronomic traits in stress experiment;
Parameters	GY	DH	PH	LR	TS
${\hat{σ}}_{g}^{2}$	130,146.00^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	3.75^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	12.10^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	0.18^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	0.10^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.
${\hat{σ}}_{r}^{2}$	198,311.00	1.20	16.00	0.25	0.24
${\hat{σ}}_{p}^{2}$	328,457.00	4.95	28.10	0.42	0.34
h²_m	0.66	0.90	0.69	0.68	0.56
Accuracy	0.81	0.95	0.83	0.82	0.75
CV_g (%)	9.85	3.41	4.14	18.00	15.00
CV_r (%)	12.20	1.93	4.76	21.40	22.80
p-value	3.71E-04	3.07E-16	2.69E-05	5.34E-05	3.53E-03
Parameters	Traits³ 3 deviance analysis for drought stress indices;
Parameters	GMP	MP	HM	STI	YI
${\hat{σ}}_{g}^{2}$	169,244.00^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	168,354.00^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	17,139.00^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	0.03^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.	0.01^* ** significant at 5% or 1% error probability by the Chi-square test: χ1% = 6.63.
${\hat{σ}}_{r}^{2}$	134,502.00	137,723.00	132,421.00	0.03	0.02
${\hat{σ}}_{p}^{2}$	303,745.00	306,077.00	302,560.00	0.06	0.03
h²_m	0.79	0.79	0.79	0.78	0.64
Accuracy	0.89	0.89	0.89	0.88	0.80
CV_g (%)	10.60	10.50	10.70	20.60	9.64
CV_r (%)	9.41	9.47	9.40	19.20	12.40
p-value	3.56E-07	5.05E-07	2.79E-07	1.05E-0.6	1.18E-03

Trait	1^st Scenario
Trait	FA1	FA2	h
GY	-0.03	0.88	0.78
DH	-0.79	0.38	0.76
PH	-0.14	-0.63	0.42
LR	-0.90	-0.18	0.85
TS	-0.90	-0.22	0.85
Trait	2^nd Scenario
Trait	FA1	FA2	h
GY	-0.94	0.11	0.93
DH	-0.45	-0.70	0.69
PH	0.26	-0.25	0.13
LR	0.02	-0.92	0.85
TS	0.08	-0.92	0.85
GMP	-0.99	-0.08	0.98
MP	-0.98	-0.09	0.97
HM	-0.99	-0.07	0.98
STI	-0.98	-0.07	0.97
YI	-0.96	0.11	0.93
Trait	3^rd Scenario
Trait	FA1	FA2	h
GY	-0.31	-0.69	0.57
DH	-0.69	-0.45	0.68
PH	-0.08	0.78	0.61
LR	-0.95	0.00	0.90
TS	-0.95	-0.08	0.91
Trait	4^th Scenario
Trait	FA1	FA2	h
GY	-0.40	0.48	0.38
DH	-0.28	0.77	0.67
PH	0.14	-0.19	0.06
LR	0.12	0.92	0.85
TS	0.08	0.94	0.89
GMP	-0.98	0.12	0.98
MP	-0.98	0.12	0.98
HM	-0.99	0.12	0.99
STI	-0.98	0.11	0.97
YI	-0.90	0.07	0.81

Trait	1^st Scenario
Trait	OB	SB	SG (%)
DH	56.80	55.20	-2.49
LR	2.33	1.99	-9.90
TS	2.12	1.95	-4.64
GY	3664.00	3732.00	1.24
PH	84.10	83.80	-0.19
Trait	2^nd Scenario
Trait	OB	SB	SG (%)
GY	3,664.00	3,967.00	5.48
PH	84.10	84.70	0.50
GMP	3,896.00	4,307.00	8.35
MP	3,919.00	4,329.00	8.23
HM	3,873.00	4,285.00	8.44
STI	0.89	1.08	16.30
YI	1.00	1.08	5.04
DH	56.80	54.90	-3.03
LR	2.33	2.17	-4.71
TS	2.12	2.05	-1.80
Trait	3^rd Scenario
Trait	OB	SB	SG (%)
DH	58.10	57.30	-1.09
LR	2.33	2.59	10.2
TS	1.96	2.13	6.64
GY	4,174.00	4,271.00	1.47
PH	89.01	90.2	0.88
Trait	4^th Scenario
Trait	OB	SB	SG (%)
GY	4,174.00	4,394.00	3.32
PH	89.01	90.90	1.48
GMP	3,896.00	3,948.00	0.17
MP	3,919.00	3,976.00	0.20
HM	3,873.00	3,920.00	0.14
STI	0.89	9,131.00	0.37
YI	1.00	1.03	0.43
DH	56.80	54.90	-2.18
LR	2.33	2.09	-11.93
TS	1.96	1.68	-11.21

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[1] *Corresponding author: nardino@ufv.br