Modeling spatial trends and selecting tropical wheat genotypes in multi-environment trials

Silva, Caique Machado e; Signorini, Victor Silva; Chaves, Saulo Fabrício da Silva; Souza, Diana Jhulia Palheta de; Lima, Gabriel Wolter; Casagrande, Cleiton Renato; Mezzomo, Henrique Caletti; Ribeiro, João Paulo Oliveira; Nardino, Maicon

doi:10.1590/1984-70332024v24n1a10

Brasil

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ARTICLE • Crop Breed. Appl. Biotechnol. 24 (2) • 2024 • https://doi.org/10.1590/1984-70332024v24n1a10 linkcopy

Modeling spatial trends and selecting tropical wheat genotypes in multi-environment trials

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

In many cases, traditional analysis of breeding trials based on analysis of variance (ANOVA) do not allow a suitable genetic evaluation. Alternatively, mixed model-based approaches create the possibility of dealing with unbalanced data and modeling spatial trends. The aims of this study were to compare the goodness-of-fit of the model and the genotype ranking through different residual modeling approaches and to select the best performing tropical wheat genotypes based on the best-fitting model. A panel of tropical wheat genotypes was evaluated in three field trials conducted between 2020 and 2021 for grain yield. Linear mixed model analyses were used on the data to estimate the genetic parameters and to predict the genotypic values in analyses of single- and multi-environment trials. Accounting for spatial trends in the analyses of single- and multi-environment trials provides better outcomes than the compound symmetry model does.

Keywords: BLUP; mixed-model; spatial analysis; Triticum aestivum L

Single-environment analyses
Trial	Model	$σ_{g}^{2}$	$σ_{r}^{2}$	$ρ_{r}$	$ρ_{c}$	h²	Ac	AIC
FT1	NSPM	193778.00**	342251	-	-	0.62	0.78	1436.41
	SPM1	193396.80**	341808	-0.13	-	0.63	0.78	1437.75
	SMP2	209833.80**	342199	-	0.23	0.66	0.8	1435.79
	SPM3	215433.00**	341589	-0.15	0.25	0.68	0.82	1436.94
FT2	NSPM	130158.30**	198333	-	-	0.65	0.81	1371.2
	SPM1	127232.50**	198514	-0.24	-	0.67	0.81	1370.51
	SPM2	120592.70**	210282	-	0.4	0.67	0.81	1363.66
	SPM3	120293.60**	205084	-0.16	0.38	0.68	0.82	1364.34
FT3	NSPM	341879.20**	757517	-	-	0.57	0.74	1422.96
	SPM1	340942.50**	759050	0.03	-	0.57	0.75	1424.86
	SPM2	320041.80**	801840	-	0.3	0.58	0.74	1421.09
	SPM3	320077.20**	801980	0.01	0.3	0.57	0.74	1423.08
Multi-environment analyses
	Model	$σ_{g}^{2}$	$σ_{g e}^{2}$	h²	Ac	GS%	AIC
	SSM1	188338.85**	48012.57	0.66	0.81	15.25%	4251.75
	SSM2	164764.95**	1848.40	0.67	0.82	12.11%	4210.93
	SSM3	147657.80**	20642.48	0.65	0.79	11.21%	4200.86

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Crop Breeding and Applied Biotechnology Universidade Federal de Viçosa, Departamento de Fitotecnia, 36570-000 Viçosa - Minas Gerais/Brasil, Tel.: (55 31)3899-2611, Fax: (55 31)3899-2611 - Viçosa - MG - Brazil
E-mail: cbab@ufv.br

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[TFN1] FT1, field trial 1; FT2, field trial 2; FT3, field trial 3; NSPM, non-spatial model; SPM1, spatial model 1; SMP2, spatial model 2; SPM3, spatial model 3;
σg2
, genotypic variance component;
σr2
, residual variance component;
ρr
, auto-correlation coefficient for rows;
ρc
, auto-correlation coefficient for columns; h², heritability; Ac, accuracy; AIC, Akaike Information Criterion; SSM1, single-stage model 1; SSM2, single-stage model 2; SSM3, single-stage model 3;
σge2
, genotype-by-environment variance component; GS%, percentage predicted genetic gain. Bold values indicate the best-fitting model; ** significant genotype effects at 1% probability by the Chi-square test.