Optimal plot size for the evaluation of phenotypic traits in soybean seedlings

Silva, Tiago de Avila; Matsuo, Eder; Sousa, Ithalo Coelho de; Ferreira, Silvana da Costa; Cecon, Paulo Roberto; Noronha, Luiz Felipe Queiroz

doi:10.1590/1984-70332025v25n4a50

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ARTICLE • Crop Breed. Appl. Biotechnol. 25 (4) • 2025 • https://doi.org/10.1590/1984-70332025v25n4a50 linkcopy

Optimal plot size for the evaluation of phenotypic traits in soybean seedlings

Authorship SCIMAGO INSTITUTIONS RANKINGS

Phenotypic traits with discriminatory capacity have been studied in recent years. However, scientific studies often employ experimental plots of varying sizes. Therefore, the objective of this study was to estimate the optimal plot size for the evaluation of phenotypic traits in soybean seedlings. Two blank trials were conducted, each consisting of 120 basic experimental units. Each basic experimental unit consisted of a single plant grown in a pot, which was evaluated during the juvenile stage. Coefficients of variation and optimal plot sizes were estimated using a segmented linear model with plateau response, a segmented quadratic model with plateau response, and the modified maximum curvature method. Model fit quality was assessed based on the adjusted coefficient of determination, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Plot size recommendations were obtained, but there are variations in the optimal plot size for the different traits evaluated in two soybean cultivars.

Keywords:
Glycine max; uniformity trial; basic experimental units; experimental precision

Number of plants (basic units)	Grouping shape
120	1 rectangular plot of 10 columns and 12 rows
60	2 rectangular plots of 10 columns and 6 rows
40	3 rectangular plots of 10 columns and 4 rows
30	4 rectangular plots of 10 columns and 3 rows
24	5 rectangular plots of 2 columns and 12 rows
20	6 rectangular plots of 10 columns and 2 rows
15	8 rectangular plots with 5 columns and 3 rows
12	10 rectangular plots with 2 columns and 6 rows
10	12 rectangular plots with 10 columns and 1 row
8	15 rectangular plots with 2 columns and 4 rows
6	20 rectangular plots of 2 columns and 3 rows
5	24 rectangular plots of 5 columns and 1 row
4	30 square plots of 2 columns and 2 rows
3	40 rectangular plots of 1 column and 3 rows
2	60 rectangular plots of 1 column and 2 rows

Trial A (MG/BR 46 (Conquista))
	Models²	$β_{0}$	$β_{1}$	$β_{2}$	$R_{A d j}^{2}$	AIC	BIC	X₀	${C V P}_{%}$
EL_V2	SLMPR	14.8500**	-0.7208**		0.70	63.04	65.60	12.9	14.8
	SQMPR	26.9089**	-1.6353*	0.0565	0.75	60.48	63.04	14.5	15.1
	MMCM	26.8018**	-0.1827**		0.76	59.11	61.03	3.1	21.7
EL_V3	SLMPR	14.4167**	-0.7746**		0.74	62.55	65.11	13.0	14.4
	SQMPR	29.6923**	-2.5703*	0.1125	0.81	58.68	61.23	11.4	15.0
	MMCM	27.5634**	-0.2003**		0.80	58.35	60.27	3.4	21.5
EL_V4	SLMPR	17.3000**	-2.0946**		0.88	70.39	72.94	10.8	17.3
	SQMPR	43.1384**	-3.4380**	0.1133*	0.90	67.65	70.21	15.2	17.0
	MMCM	46.5862**	-0.3174**		0.91	65.33	67.25	6.8	25.3
Trial B (BRS752S)
EL_V2	SLMPR	16.5250**	-2.6186**		0.86	70.37	72.93	8.5	16.5
	SQMPR	45.2101**	-5.4652**	0.2612*	0.89	65.96	68.52	10.5	16.6
	MMCM	41.7635**	-0.3114**		0.88	67.37	69.28	6.2	23.6
EL_V3	SLMPR	16.3625**	-2.6786**		0.75	78.58	81.14	8.2	16.3
	SQMPR	48.6769**	-7.2333*	0.4089	0.81	74.81	77.36	8.8	16.7
	MMCM	41.0752**	-0.3129**		0.79	75.33	77.25	6.1	23.3
EL_V4	SLMPR	15.7143**	-1.5269**		0.85	63.96	66.52	10.2	15.7
	SQMPR	35.3079**	-3.1194**	0.1247*	0.88	60.56	63.12	12.5	15.8
	MMCM	34.4888**	-0.2549**		0.89	58.28	60.20	4.8	23.0

Trial A (MG/BR 46 (Conquista))
	Models²	$β_{0}$	$β_{1}$	$β_{2}$	$R_{A d j}^{2}$	AIC	BIC	X₀	${C V P}_{%}$
IL_V2	SLMPR	26.7125**	-4.0086**		0.85	83.58	86.13	8.6	26.7
	SQMPR	68.8271**	-7.4961**	0.3333*	0.87	80.80	83.36	11.2	26.6
	MMCM	65.6820**	-0.3010**		0.88	79.71	81.62	8.6	34.3
IL_V3	SLMPR	20.5100**	-5.3000**		0.79	78.25	80.81	5.8	20.5
	SQMPR	61.4084**	-11.0809**	0.7484*	0.84	74.75	77.31	7.4	20.4
	MMCM	45.6352**	-0.2858**		0.79	77.57	79.49	6.4	26.8
IL_V4	SLMPR	21.0667**	-4.6000**		0.85	72.58	75.14	6.3	21.0
	SQMPR	60.4275**	-10.3789**	0.6858*	0.88	69.23	71.79	7.6	21.1
	MMCM	44.6580**	-0.2611**		0.76	78.08	79.99	6.0	27.9
Trial B (BRS752S)
IL_V2	SLMPR	20.4600**	-1.4514**		0.76	84.82	87.38	15.5	20.4
	SQMPR	52.1030**	-4.2205**	0.1470	0.83	79.95	82.50	14.4	21.8
	MMCM	55.5665**	-0.3022**		0.90	72.12	74.04	7.6	30.1
IL_V3	SLMPR	15.9111**	-2.2900**		0.86	59.84	62.40	7.4	15.9
	SQMPR	37.5610**	-4.6342**	0.2485*	0.89	56.96	59.52	9.3	15.9
	MMCM	31.4482**	-0.2289**		0.77	65.70	67.61	4.2	22.6
IL_V4	SLMPR	16.1222**	-2.8800**		0.87	62.77	65.33	7.0	16.1
	SQMPR	42.2493**	-5.8675**	0.3302*	0.90	59.26	61.81	8.9	16.1
	MMCM	34.2629**	-0.2551**		0.75	71.14	73.06	4.8	22.9

Trial A (MG/BR 46 (Conquista))
	Models²	$β_{0}$	$β_{1}$	$β_{2}$	$R_{A d j}^{2}$	AIC	BIC	X₀	${C V P}_{%}$
PL_V2	SLMPR	24.0625**	-3.3800**		0.91	72.54	75.10	9.0	24.0
	SQMPR	61.1125**	-6.2380**	0.2624**	0.94	68.21	70.77	11.9	24.0
	MMCM	59.0849**	-0.2961**		0.89	74.98	76.90	7.9	32.0
PL_V3	SLMPR	13.6111**	-3.5600**		0.88	63.07	65.62	6.5	13.6
	SQMPR	43.5028**	-7.1984**	0.4337*	0.91	58.82	61.37	8.3	13.6
	MMCM	35.4151**	-0.3357**		0.88	63.18	65.10	5.6	19.8
PL_V4	SLMPR	14.5400**	-5.3600**		0.83	69.86	72.42	5.2	14.5
	SQMPR	60.0377**	-15.7387**	1.3665*	0.88	64.40	66.95	5.8	14.7
	MMCM	35.0956**	-0.3208**		0.74	74.78	76.70	5.5	20.3
Trial B (BRS752S)
PL_V2	SLMPR	33.6444**	-9.4800**		0.87	91.47	94.02	6.3	33.6
	SQMPR	112.6488**	-19.9292**	1.2585*	0.89	88.91	91.47	7.9	33.7
	MMCM	88.1406**	-0.3394**		0.81	95.39	97.31	11.2	38.8
PL_V3	SLMPR	22.0400**	-7.3100**		0.88	76.61	79.17	5.6	22.0
	SQMPR	74.3119**	-14.0762**	0.9432**	0.91	71.74	74.30	7.5	21.8
	MMCM	55.1907**	-0.3304**		0.81	82.22	84.14	7.8	27.9
PL_V4	SLMPR	20.4778**	-3.7900**		0.77	76.48	79.03	6.6	20.4
	SQMPR	57.0482**	-9.8041*	0.661	0.82	72.58	75.13	7.4	20.7
	MMCM	42.3368**	-0.2518**		0.78	74.95	76.87	5.6	27.3

Trial A (MG/BR 46 (Conquista))
	Models²	$β_{0}$	$β_{1}$	$β_{2}$	$R_{A d j}^{2}$	AIC	BIC	X₀	${C V P}_{%}$
SH_V2	SLMPR	18.1300**	-8.0100**		0.92	67.45	70.00	5.2	18.1
	SQMPR	70.8502**	-15.4706**	1.1299**	0.94	63.94	66.50	6.8	17.9
	MMCM	48.6294**	-0.3613**		0.77	82.53	84.45	7.3	23.6
SH_V3	SLMPR	15.3286**	-1.2880**		0.86	57.85	60.41	10.4	15.3
	SQMPR	32.0209**	-2.5923**	0.1010*	0.90	53.31	55.87	12.8	15.3
	MMCM	31.0731**	-0.2274**		0.87	56.29	58.21	4.1	22.4
SH_V4	SLMPR	15.5900**	-6.0200**		0.87	66.86	69.42	5.1	15.5
	SQMPR	58.4805**	-13.5351**	1.0674*	0.89	64.49	67.05	6.3	15.5
	MMCM	37.1653**	-0.3154**		0.72	77.21	79.13	5.7	21.4
Trial B (BRS752S)
SH_V2	SLMPR	14.1111**	-5.3900**		0.91	70.24	72.80	6.5	14.1
	SQMPR	60.6636**	-11.7116**	0.7385**	0.94	63.57	66.13	7.9	14.2
	MMCM	50.0245**	-0.4537**		0.87	74.23	76.14	7.9	19.6
SH_V3	SLMPR	12.5182**	-3.0500**		0.83	48.90	51.45	4.8	12.5
	SQMPR	30.9885**	-5.7860**	0.4508*	0.85	46.96	49.52	6.4	12.4
	MMCM	20.5786**	-0.1771**		0.67	56.94	58.86	2.5	17.5
SH_V4	SLMPR	15.1778**	-3.0200**		0.86	61.34	63.89	6.5	15.1
	SQMPR	40.7504**	-6.1906**	0.3752*	0.89	58.60	61.16	8.2	15.2
	MMCM	31.5546**	-0.2504**		0.73	69.79	71.71	4.4	21.7

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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] ¹
** and * significant at 1% and 5% probability, respectively, by the t-test; ² Models: SLMPR = Segmented Linear Model with Plateau Response, SQMPR = Segmented Quadratic Model with Plateau Response and MMCM = Modified Maximum Curvature Method.