Selection of High Yield and Stable Maize Hybrids in Mega-Environments of Java Island, Indonesia

Abstract

Determination of grain yields of stable and high-yielding maize hybrids in a wide environment requires high accuracy. Many stability measurement methods have been used in multi-environment experiments. However, the relationships among the different methods are still difficult to understand. The objectives of this study were to 1. Identify the effect of growing season and location (Environments = E), hybrids (Genotypes = G), and their interactions (GEIs) on grain yields; 2. Select high-yielding and stable maize hybrids in a wide range of environments; 3. Determine the relationship between each stability estimation; and 4. Determine the mega-environment of maize hybrid and identify the best locations for testing. Field experiments were conducted at ten locations in Java Island, Indonesia, for two growing seasons using a randomized completed block design with three replications. The experimental results showed that the main effects of the growing season, location, hybrid, and GEIs, significantly affected maize hybrid yields. Stability estimations of TOP, S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, KR, NP⁽⁴⁾, CV_i, and b_i, belong to the concept of dynamic stability that can be used to select maize hybrids in favorable environments, while other estimations were classified as in the static stability. Three maize hybrids were successfully selected, with high and stable yields based on numerical and visual stability estimations, namely SC2, SC7, and SC9. The three hybrids can be used as candidates for sustainable maize development programs. The dry season, the rainy season, and the combination of two growing seasons produced three mega-environments. GJRS and KARS were the most discriminative environments. Both environments can be used as favorable environments for selecting the ideal maize hybrid.

1. Introduction

Maize is an important carbohydrate source. It also contains other important nutrients for the human body, such as protein and minerals [1,2]. This commodity, is among the most important staple foods after rice and wheat. Nowadays, the demand for maize is high, but the existing production is still unable to fulfill these demands. Several factors limit maize production, i.e., varieties; environmental conditions including cropping system, location, season, and infection due to insects and pathogens; and interaction between genotype and environment [3,4]. Due to the importance of maize and the limitations in their production, the development of maize hybrids possessing high-yield and adaptability to environmental changes is required.

Evaluation of maize hybrids in various locations and growing seasons are required to select high-yield and stable varieties. Thus, it can determine the mega-environment, which can effectively select a representative location for an efficient selection. However, selection under these various environments, is very complex due to genotype and environmental interactions (GEIs) [5,6,7]. The emergence of GEIs in multi-environmental experiments is due to unpredictable macro- and micro-environmental influences such as temperature, rainfall, and humidity [8]. According to Karuniawan et al. (2021) [9] and Ruswandi et al. (2022) [10], differences in the growing environment gave different responses for each genotype tested.

There are various estimation methods to determine the GEIs and to identify stable genotypes, including parametric, non-parametric, and graphical estimation. The parametric estimations include linear regression [11], Wricke ecovalence [12], Shukla variance [13], the mean-variance component (θ_i) [14], the GE variance component (θ_(i)) [15], Hanson’s genotype stability measure (Di) [16], and coefficient of variation (CVi) [17]. In addition, the non-parametric estimations are the following: thennarasu measurement (NP⁽ⁱ⁾) [18], Stability nonparametric (S⁽ⁱ⁾) models [19,20], Kang rank [21], and TOP-rank [22]. Thus, the graphical estimation of stability includes AMMI [23,24] and GGE biplot [25]. However, the selection of stable and high-yielding genotypes based on a single stability estimation was described as less accurate [1,26,27]. Compared to single stability estimation, combined stability estimation was considerably more effective and accurate in selecting stable and high-yielding genotypes. This combined stability method for the selection of stable varieties in different environments or specific varieties in specific environments is advantageous because they are: (i) suitable for data with statistical assumptions such as interaction effects and normal distribution of errors since it is estimated by parametric method, (ii) simple since estimation based on the performance of ranks of data and no assumptions are needed for homogeneity of variances and distribution of model residuals and, (iii) fit to determine the pattern of genotypic responses across environments visually and could determine mega-environment for high yield and adaptive genotype by the GGE biplot [28,29]. Some researchers have successfully selected stable and high-yielding genotypes in a wide environment with combined stability estimations, including barley [28,30], chickpeas [31], durum wheat [32], grass bean [26], maize [33], peanut [34], sweet corn [1], sweet potato [9,27], black soybean [35], and wheat [36]. The objectives of this study were to 1. Identify the effect of growing season and location (Environments = E), hybrids (Genotypes = G)), and their interactions (GEIs) on grain yield; 2. Select high-yielding and stable maize hybrids in various environments; 3. Determine the relationship between each stability measurement; and 4. Determine the mega-environment of maize hybrid and identify the best locations for testing.

2. Materials and Methods

2.1. Plant Materials

The study used nine maize hybrids from The Plant Breeding Laboratory, Faculty of Agriculture, Universitas Padjadjaran (UNPAD). These hybrids had broad genetic backgrounds (

Table 1. The maize hybrid materials used in the experiment.

Code	Hybrid	Parental Line			Pedigree
		Female		Male
SC1	Pxy				Hybrid commercial of Pioneer
SC2	NKxx				Hybrid commercial of Monsanto
SC3	Bisi x				Hybrid commercial of Bisi
SC4	PA	1011	×	1016	Female is a downy mildew resistant line; male is a high nutrition line
SC5	PB	1014	×	1018	Female is a downy mildew resistant line and male is a high protein line
SC6	PC	1019	×	1020	Both parents are high nutrition lines
SC7	PE	1007	×	1008	Female is a high yield line; and male is a high nutrition line
SC8	PF	1006	×	1007	Female is a high nutrition line; and male is a high yield line
SC9	PG	1008	×	1009	Female is a high nutrition line; male is a downy mildew resistant line

) [37].

2.2. Field Experiments and Data Collection

Field experiments were conducted at ten locations in Java Island, Indonesia, for two growing seasons (

Table 2. Location, cropping seasons, altitude, type of agroclimate, mean yield and average yield for stability experiments in Java.

Location Code	Location	Cropping Season	Altitude (m asl)	Type of Agroclimate	Mean Yield (t.ha−1)	Average 2-Seasons Yield (t.ha−1)
JTDS	Jatinangor, Sumedang, West Java	Dry	784	AII2. Wet; yearly rainy of >2500 mm; dry month per year 3–7; wet month 5–9; crop index 2	8.31	9.68
JTRS	Jatinangor, Sumedang, West Java	Rainy	784		11.06
ASDS	Arjasari, Bandung, West Java	Dry	991	AII2. Wet; yearly rainy of >2500 mm; dry month per year 3–7; wet month 5–9; crop index 2	5.64	6.40
ASRS	Arjasari, Bandung, West Java	Rainy	991		7.16
LBDS	Lembang, Bandung, West Java	Dry	1250	BIII3. Wet; yearly rainy of 1500–2500 mm; dry month per year < 3; wet month 3–4; crop index 2	5.46	7.76
LBRS	Lembang, Bandung, West Java	Rainy	1250		10.06
KADS	Karanganom, Klaten, Central Java	Dry	166	BII2 Moderate; yearly rainy of 1500–2500 mm; dry month per year 3–7; wet month 5–9; crop index 2	7.64	8.61
KARS	Karanganom, Klaten, Central Java	Rainy	166		9.58
JKDS	Jogonalan, Klaten, Central Java	Dry	168	BII3. moderate; yearly rainy of 1500–2500 mm; dry month per year 3–7; wet month 3–4; crop index 2	8.95	8.78
JKRS	Jogonalan, Klaten, Central Java	Rainy	168		8.60
BBDS	Banyudono, Boyolali, Central Java	Dry	170	AII2. wet; yearly rainy of >2500 mm; dry month per year 3–7; wet month 5–9; crop index 2	9.46	8.49
BBRS	Banyudono, Boyolali, Central Java	Rainy	170		7.53
PPDS	Paiton, Probolinggo, East Java	Dry	13	CI3. Dry; yearly rainy of <1500 mm; dry month per year >7; Wet month 3–4 month; crop index 1	8.04	7.99
PPRS	Paiton, Probolinggo, East Java	Rainy	13		7.94
GJDS	Gumukmas, Jember, East Java	Dry	10	CII3. Dry; yearly rainy of <1500 mm;dry month per year 3–7; wet mont 3–4 bulan; crop index 2	9.81	8.61
GJRS	Gumukmas, Jember, East Java	Rainy	10		7.41
NNDS	Ngronggot, Nganjuk, East Java	Dry	52	BII3. moderate; yearly rainy of 1500–2500 mm; dry month per year 3–7; wet month 3–4; crop index 2	7.47	9.61
NNRS	Ngronggot, Nganjuk, East Java	Rainy	52		11.75
NKDS	Ngadiluwih, Kediri, East Java	Dry	79	BII2 moderate; yearly rainy of 1500–2500 mm; dry month per year 3–7; wet month 5–9; crop index 2	9.53	9.26
NKRS	Ngadiluwih, Kediri, East Java	Rainy	79		8.99

, Figure 1). The experiment used a randomized completed block design which was repeated three times. Each hybrid was planted on a plot measuring 3 × 5 m, with a spacing of 0.75 × 0.2 m. The data was gathered at harvest, 93 days after planting following the standard Descriptor for Maize [38]. Sowing and harvesting were done manually. The number of plants harvested in each plot was 100. The yield of each hybrid in each experimental plot was converted in ton.ha⁻¹.

2.3. Data Analysis

Combined analysis of variance (ANOVA) was used to estimate the GEIs. The statistical equation was as follows:

Y_opqr = μ + G_o + E_p + GE_op + R_q(p) + B_r(q) + ε_opqr

(1)

where Y_opqr is the value of line o in plot r, and the value in location p of each replication q; μ is the grand mean; G_o is the effect of line o; E_p is the effect of the environment p; GE_op is the effect of genotype by environment interactions on line o and environment p; R_q(p) is the effect of replicate q on location p; B_r(q) is the effect of replication q on plot r; and ε_opqr is the error effects from line o in plot r and repeat q of location p, respectively. The combined ANOVA was calculated using the R program.

Yield stability was estimated based on parametric and non-parametric measurements. Details of the stability measurements are presented in

Table 3. Formula of parametric and non-parametric stability measurements.

Parametric Stability Measurements
Formula	Source
$bi-1=\frac{{{\displaystyle \sum }}_i\left(x_{ij}-{\overline{X}}_{i.}-{\overline{X}}_{.j}+{\overline{X}}_{\dots }\right)\left({\overline{X}}_{.j}-{\overline{X}}_{\dots }\right)}{{{\displaystyle \sum }}_j{\left({\overline{X}}_{.j}-{\overline{X}}_{\dots }\right)}^2}$	Eberhart and Russel [11]
$S_{di}^2=\frac{1}{N-2}\left[{\displaystyle \sum _i}\left({\overline{X}}_{ij}-{\overline{X}}_{i.}-{\overline{X}}_{.j}+{\overline{X}}_{\dots }\right)-{\left(bi-2\right)}^2{\displaystyle \sum }_j{\left({\overline{X}}_{.j}+{\overline{X}}_{\dots }\right)}^2\right]$
${\theta }_i=\frac{p}{2\left(p-1\right)\left(q-1\right)}{\displaystyle \sum _{j-1}^q}{\left(x_{ij}-{\overline{X}}_{i.}+{\overline{X}}_{.j}\right)}^2+\frac{SSGE}{2\left(p-2\right)\left(q-1\right)}$	Plaisted and Peterson [14]
${\theta }_{\left(i\right)}=\frac{-p}{\left(p-1\right)\left(p-2\right)\left(q-1\right)}{\displaystyle \sum _{j-1}^q}{\left(x_{ij}-{\overline{X}}_{i.}-{\overline{X}}_{.j}+{\overline{X}}_{..}\right)}^2+\frac{SSGE}{\left(p-2\right)\left(q-1\right)}$	Plaisted [15]
${\mathrm{W}}_i^2={{\displaystyle \sum }}^{ }{(X_{ij}-{\overline{X}}_{i.}-{\overline{X}}_{.j}+{\overline{X}}_{..})}^2$	Wricke and Weber [12]
${\sigma }_i^2=\left|\frac{p}{\left(p-2\right)\left(q-1\right)}\right|{\mathrm{W}}_i^2-\frac{{{\displaystyle \sum }}^{ }{W}_i^2}{\left(p-1\right)\left(p-2\right)\left(q-1\right)}$	Shukla [13]
$C{V}_i=\frac{S{D}_l}{\overline{X}}x 100$	Francis and Kannenberg [17]
$Di={\left[{\displaystyle \sum _j}{Z}_{ij}^2\right]}^2$	Hanson [16]
Non-parametric stability measurements
$S_i^{\left(1\right)}=2{\displaystyle \sum }_j^{n-1}\frac{{{\displaystyle \sum }}_{j\prime =j+1}^n\left|r_{ij}-r_{ij}^{\prime }\right|}{\left[N\left(n-1\right)\right]}$	Huehn; Nassar and Huehn [19,20]
$S_i^{\left(2\right)}=\frac{{{\displaystyle \sum }}_{j=1}^n{\left(r_{ij}-{\overline{r}}_{i.}\right)}^2}{\left(N-1\right)}$
$S_i^{\left(3\right)}=\frac{{{\displaystyle \sum }}_{j=1}^n{\left(r_{ij}-{\overline{r}}_{i.}\right)}^2}{{\overline{r}}_i}$
$S_i^{\left(6\right)}=\frac{{{\displaystyle \sum }}_{j=1}^n\left|r_{ij}-{\overline{r}}_{i.}\right|}{{\overline{r}}_{i.}}$
$N{P}^{\left(1\right)}=\frac{{{\displaystyle \sum }}_{j=1}^n\left|r_{ij}^{*}-M_{di}^{*}\right|}{N}$	Thennarasu [18]
$N{P}^{\left(2\right)}=\frac{\left[{{\displaystyle \sum }}_{j=1}^n\left|r_{ij}^{*}-M_{di}^{*}\right|/M_{di}\right]}{N}$
$N{P}^{\left(3\right)}=\frac{\sqrt{\frac{{{\displaystyle \sum }}^{ }{\left(r_{ij}^{*}-r_{i.}^{*}\right)}^2}{N}}}{{\overline{r}}_{i.}}$
$N{P}^{\left(6\right)}=\frac{2x\left[{{\displaystyle \sum }}_{j=1}^{n-1}{{\displaystyle \sum }}_{j^{\prime }=j+1}^n\left|r_{ij}^{*}-r_{i.}^{*}\right|/{\overline{r}}_{i.}\right]}{N\left(\mathrm{N}-1\right)}$
$\mathrm{KR}=\mathrm{RGY}+\mathrm{R}{\sigma }^{2}i$	Kang [21]
The computation of the TOP-rank is based on scoring of genotypes as ‘Top’, ‘Mid’ or ‘Low’ within each environment. The genotypes that are frequently occurred in the ‘Top’ third are considered to be stable.	Fox [22]

where, X_ij: grain yield total in of the i^th hybrid in j^th environment, ${\overline{X}}_{i.}$: average of the grain yield total from i^th hybrid at all (sixteen) environments, ${\overline{X}}_{.j}$: mean of the grain yield in the j^th environment, ${\overline{X}}_{\dots }$: average of the grain yield total, p and q: numbers of environments and hybrids; SD_l: standard deviation of GEIs. r_ij: stability rank of the i^th hybrid in the j^th environment; ${\overline{r}}_{i.}$: average rank if i^th hybrid in all environments; and N: number of environment. $r_{ij}^{*}$: stability rank of the i^th hybrid in the j^th environment (adjusted data); $M_{di}^{*}$: adjusted data (median rank); M_di: unajusted data (median rank’s of the same parameters). RGY: rank of grain yield; Rσ²i = Rank of Shukla stability measurement.

. The stability of grain yields based on parametric and non-parametric measurements was analyzed using the online software STABILITYSOFT [39].

The graphical stability of grain yields and determination of discriminative environment and mega-environment was analyzed by GGE biplot with the following equation [25]:

$${Ῡ}_{mn}-{\mathsf{\mu }}_m={\mathsf{\beta }}_n+{\sum }_{k=1}^t{\mathsf{\lambda }}_o{\alpha }_{mo}{\gamma }_{no}+{\epsilon }_{mn}$$

(2)

where ${Ῡ}_{mn}$; μ_m; β_n; k; λ_o; α_mo and γ_no; ε_mn are the performance in location ‘n’ from line ‘m’; overall average grain yield; the influence of location ‘n’; the number of primer components; the singular value from primer component ‘o’; the value of line ‘m’ and location ‘n’ for primer component ‘o’; and the error of the line ‘m’ in location ‘n,’ respectively.

The R program was used to visualize the distribution pattern of the hybrids and environments tested in the dry season, rainy season, and the average of both. Spearman rank correlation and Principal Component Analysis (PCA) were used to estimate the relationship between stability measurements and classify them into clear groups. The SPSS 19th software was used to analyze correlation and PCA [40].

The combined stability analysis includes parametric and non-parametric stability measurements. The stability of maize hybrids based on ranks of parametric and non-parametric stability estimation was combined by using Hierarchical Clustering Analysis (HCA). The SPSS 19th software was used to estimate HCA [40,41].

The sustainability index (SI) was estimated by the following formula used by [42]:

$$SI=\left[\frac{\left(Y-\sigma n\right)}{YM}\right]\times 100$$

(3)

where Y is the mean performance of a maize hybrid, σn is the standard deviation, and YM is the best performance of a maize hybrid in any environment. The SI values were classified arbitrarily into five groups, i.e., very low (up to 20%), low (21% to 40%), moderate (41% to 60%), high (61% to 80%), and very high (above 80%) [43]. SI was calculated using Microsoft Excel 2013.

3. Results and Discussion

3.1. Genotype by Environment Interactions of Maize Hybrids Yield

The combined analysis of variance (ANOVA) for maize hybrids evaluated in ten locations during two growing seasons in Java island was presented in

Table 4. Combined ANOVA for yield in ten locations for two cropping seasons in Java Island.

	Df	SS	MS	F Value	Pr (>F)		Total Variation Explained (%)
Genotype (G)	8	142.65	17.83	37.10	0.00	**	7.54
Location (E)	9	462.04	51.34	106.81	0.00	**	24.43
Season (S)	1	130.52	130.52	271.57	0.00	**	6.90
Replication	2	1.69	0.85	1.76	0.17		0.09
G × E	72	195.98	2.72	5.66	0.00	**	10.36
G × S	8	34.42	4.30	8.95	0.00	**	1.82
E × S	9	719.60	79.96	166.36	0.00	**	38.04
Replication/E	18	22.50	1.25	2.60	0.00	**	1.19
Replication/S	2	1.03	0.51	1.07	0.35		0.05
G × E × S (GEIs)	72	175.36	2.44	5.07	0.00	**	9.27
Replication/E × S	18	5.67	0.32	0.66	0.85		0.30
Residuals	320	153.80	0.48
CV (%)		8.14

** p < 0.01; Df = Degree freedom; SS = Sum of square; MS = Mean of square; Pr = Probability.

. There was a very significant variation (p < 0.01) in yields among hybrids (genotypes), environment (season, location, season × location), and their interactions (Genotype × season, Genotype × Location, and Genotype × season × Location) (Table 4). The highest difference was shown by the interaction effect of growing season and location (L × S) of 40.13%, whereas the main effect of genotype (hybrid) accounted for 14.59%, the location was 12.55%, and their interaction (GEIs) was 12.69%. This result confirmed that the hybrid was a significant factor in environmental interactions in this experiment. The significant variation of the main effects (genotypes and environment) and their interactions indicated differences in the hybrid performance under a broad environment for maize production on Java Island. It was presented in Table 2 that the range of average yield in 20 environments was from 5.46 t.ha⁻¹ (LBDS) to 11.75 t.ha⁻¹ (NNRS) (Table 2). The average data for two growing seasons recorded the highest average yield at Jatinangor, Sumedang, West Java (9.68 t.ha⁻¹⁾ and the lowest at Arjasari, Bandung, West Java (6.40 t.ha⁻¹⁾ (Table 2).

In some locations, the average yield was higher in the dry than in the rainy season (Gumukmas, Ngadiluwih, Jogonalan) (Table 2). This is because the land used during the dry season in the three locations is ex-paddy land; therefore, the condition of the land is still wet in the dry season. Alibu et al. [44] reported that wet and humid land conditions in the dry season showed excellent maize yields. Therefore, maize planting on wetlands or ex-paddy during the dry season is preferable.

The effect of GEIs frequently occurred for maize yields in multi-environmental experiments [1]. The difference in yield performance of maize hybrids was probably due to differences in genetic background and various environmental conditions. The hybrids used resulted from directed crosses between two parental lines with a far genetic background [37]. Thus, the environmental factors, i.e., locations, seasons, and cultivation systems, affected yield performances [10,45]. The percentage of environmental influence, which is high on maize yields, indicated that the environment for maize production in Java Island is very broad. Variances in environmental conditions for maize production can lead to differences in yield and yield quality of maize hybrid [10,33,46,47]. The response of maize hybrids to the various environments indicates the importance of GEIs.

The GEIs effect has a great impact on the plant selection process. The emergence of the effect of GEIs in multi-environment experiments makes the selection process complicated and less efficient [7,27,48,49]. Breeders need to allocate more resources, time, and money to evaluate a set of potential superior genotypes. In addition, more locations need to be surveyed to establish the multi-locations field of evaluation. Determination of the mega-environment, establishing a representative location, and applying stability methods for testing and analysis are required to effectively select high-yielding and stable genotypes in a wide range of environments.

3.2. Selection of High-Yielding and Stable Maize Hybrids in a Wide Environment Using Combined Stability Analysis

Stability analysis of maize yields using parametric measurements for every maize hybrid in twenty environments (ten locations and two planting seasons) were presented in

Table 5. Stability estimation for yield of maize based on parametric measurements.

Genotype	Y	Wi2	σ2i	S2di	bi	CVi	θ(i)	θi	Di
SC1	8.35	18.24	1.11	2.17	1.25	26.22	0.87	1.05	7.99
SC2	9.73	32.71	2.09	4.67	1.01	21.34	0.75	1.48	9.02
SC3	8.43	17.28	1.04	2.42	0.92	20.74	0.88	1.02	8.10
SC4	8.56	16.71	1.00	2.36	0.94	20.62	0.88	1.01	8.06
SC5	7.86	8.43	0.44	1.13	1.10	23.92	0.95	0.76	7.51
SC6	7.90	11.03	0.62	1.57	0.96	21.73	0.93	0.84	7.71
SC7	8.68	11.96	0.68	1.69	0.95	19.63	0.92	0.87	7.78
SC8	8.48	8.36	0.44	1.16	0.93	19.18	0.95	0.76	7.53
SC9	8.69	11.40	0.64	1.61	0.94	19.47	0.93	0.85	7.69
Genotype	Y	Wi2	σ2i	S2di	bi	CVi	θ(i)	θi	Di
SC1	7	8	8	6	9	9	8	2	6
SC2	1	9	9	9	1	6	9	1	9
SC3	6	7	7	8	7	5	7	3	8
SC4	4	6	6	7	5	4	6	4	7
SC5	9	2	2	1	8	8	2	8	1
SC6	8	3	3	3	2	7	3	7	4
SC7	3	5	5	5	3	3	5	5	5
SC8	5	1	1	2	6	1	1	9	2
SC9	2	4	4	4	4	2	4	6	3

Y = Grain yield; Genotype code see Table 1.

. According to Eberhart and Russell [11], the stability of each maize hybrid was determined by its regression coefficient (bi) and deviation of variance (S²di). An estimation of b_i = 1 and a low estimate of S²di indicates a very stable hybrid. The SC2, SC6, SC7, and SC9 hybrids had b_i values that were not significantly different from one (1), where the SC6 hybrid produced yields lower than the overall average, while SC2, SC7, and SC9 hybrids were higher than the overall average. Estimation of S²d_i indicated SC5, SC8, SC6, and SC9 as maize hybrids possessing the lowest values. Based on the linear regression estimation, hybrids with values of b_i = 1 and S²d_i = 0 were the most stable, so SC6 and SC9 were the most stable maize hybrids based on this measurement.

Average grain yields for the hybrids tested in 20 environments ranged from 5.46 t.ha⁻¹ to 11.75 t.ha⁻¹, with SC2 and SC9 maize hybrids having the highest average yields and SC5 and SC6 the lowest (Table 2). Based on the stability ranking of W_i², σ²_i, CVi, and θ_(i) estimation, SC8 was determined as the most stable maize hybrid, followed by SC5 and SC6. Of the three selected hybrids, only SC8 had above-average yields. The stability estimation of S²di selected SC5 as the most stable, followed by SC8 and SC6. Stability estimation of Di also revealed SC5 as the most stable maize hybrid, followed by SC8 and SC9. The maize hybrid of SC3 was the highest yield performance hybrid in all test environments. Stability measurement of bi and θ_i selected SC2 as the most stable maize hybrid, followed by SC6 and SC7 for bi and SC1 and SC3 for θ_i, where the SC2 maize hybrid had an above-average overall performance.

Non-parametric stability estimation is presented in

Table 6. Stability estimation for yield of maize based on non-parametric measurements.

Genotype	S(1)	S(2)	S(3)	S(6)	NP(1)	NP(2)	NP(3)	NP(4)	KR	TOP
SC1	2.84	5.92	25.87	9.43	2.00	0.50	0.53	0.65	15	5.00
SC2	2.47	6.13	15.86	5.47	2.80	0.32	0.42	0.34	10	14.00
SC3	3.26	7.80	31.53	10.04	2.50	0.46	0.62	0.69	13	6.00
SC4	2.84	5.96	21.77	7.77	2.30	0.47	0.51	0.55	10	6.00
SC5	2.04	3.17	18.24	9.27	1.85	0.67	0.68	0.62	11	1.00
SC6	2.74	5.83	30.78	11.78	2.20	0.83	0.73	0.76	11	5.00
SC7	2.56	5.00	17.27	6.91	2.30	0.33	0.46	0.47	8	10.00
SC8	2.55	4.93	17.86	6.86	1.75	0.37	0.44	0.49	6	6.00
SC9	2.80	6.01	20.04	7.23	2.00	0.33	0.41	0.49	6	7.00
Rank’s	S(1)	S(2)	S(3)	S(6)	NP(1)	NP(2)	NP(3)	NP(4)	KR	TOP	SR	AR	SD
SC1	7	5	7	7	3	7	6	7	9	7	128	6.74	1.80
SC2	2	8	1	1	9	1	2	1	4	1	84	4.42	3.60
SC3	9	9	9	8	8	5	7	8	8	4	133	7.00	1.65
SC4	8	6	6	5	6	6	5	5	4	4	104	5.47	1.14
SC5	1	1	4	6	2	8	8	6	6	9	92	4.84	3.08
SC6	5	4	8	9	5	9	9	9	6	7	111	5.84	2.39
SC7	4	3	2	3	6	2	4	2	3	2	70	3.68	1.26
SC8	3	2	3	2	1	4	3	3	1	4	54	2.84	2.03
SC9	6	7	5	4	3	2	1	4	1	3	69	3.63	1.60

SR = sum of rank; AR = average of rank; SD = standard deviation; genotype code see Table 1.

. It was shown that each hybrid had different potential in terms of stability. In non-parametric measurements, SC2 hybrids were designated as the most stable hybrid by stability measurements of S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽⁴⁾, and TOP. The SC5 hybrid was indicated as the most stable by S⁽¹⁾ and S⁽²⁾ measurements. The SC8 hybrid was indicated as the most stable by NP⁽¹⁾ and KR measurements. The SC9 hybrid was revealed as the most stable maize hybrid by stability measurements of NP⁽³⁾ and KR. This maize hybrid also has above-average yield performance.

According to Ahmadi et al. (2015) [26], the selection of stable genotypes with one stability measurement was considered less effective and accurate. Similar results were also revealed by several researchers who used various stability measurements to select stable and high-yielding genotypes, including barley [28], maize [33], sweet potatoes [7,9], turmeric [50], and soybeans [35,51]. Every stability estimation generally selected a different hybrid as a stable genotype. However, several stability estimations selected similar hybrids as stable hybrids. These stability estimation included CV_i, θ_i, S⁽¹⁾, S⁽³⁾, S⁽⁶⁾, NP⁽³⁾, and NP⁽⁴⁾ which determined SC3 as a stable hybrid. In addition, stability measurements had the same output in terms of ranking the stability of all maize hybrids, namely Wi², σ²_i, and θ_(i). In this case, measurements with the same stability ranks can select stable genotypes [28].

Applying combined stability by the use of parametric and non-parametric stability estimation can increase the accuracy of the maize hybrid selections. The application could help select high-yield performance and stability of potential genotypes in a wide environment based on a single measurement [7,26]. Some researchers use the average sum rank (AR) to determine the stability of the tested genotypes, wherein the genotype with the smallest AR value was determined as the most stable genotype [9,26,27,28]. In this study, SC9 was identified as a hybrid with the smallest AR, followed by maize hybrids of SC9, SC7, and SC2. These three hybrids also had high average yields.

The stability of maize hybrids based on ranks of parametric and non-parametric stability estimation was combined using the Hierarchical Clustering Analysis (HCA) (Figure 2). Based on this HCA, maize hybrids were divided into three main clusters, namely: 1. stable low yield cluster consisting of SC5 and SC6 maize hybrids; 2. unstable medium yield cluster consisting of SC1, SC3, and SC4 maize hybrids; 3. stable high yield cluster consisted of SC2, SC7, SC8, and SC9 maize hybrids. The first group was not recommended since they have a low yield. The second group can be used as hybrids with medium yield performance in specific environments. Ruswandi et al. (2020) [1] mentioned that maize hybrids with high-yield performance in a specific environment could be superior hybrids in this particular area. A way to increase the income/economics of maize farmers in certain environments is by utilizing the potential of these genotypes. According to Maulana et al. (2020) [7], high agricultural product yields will impact the community’s economy. The third group of maize hybrids was considered the most ideal group because of its yield performance and wide adaptability [26,51]. Thus, the third group was identified as more stable with high yield performance in a wide environment based on parametric and non-parametric stability measurements. Similar studies revealed that combined stability by measuring parametric and non-parametric stability could successfully determine stable and high-yielding genotypes in various commercial crops and a wide environment [27,28,30,31,34,35]. This current study found that the combined stability analysis can be effectively used to select high-yielding and stable maize hybrids in various crop environments and seasons on Java island.

3.3. The Relationship between Parametric and Non-Parametric Stability Measurements

To determine the relationship between the different stability measures and combine them into clear groups, Principal Component Analysis (PCA) was used. The first four PCs with eigenvalues >1 resulted in a cumulative value of 96.37% of the total variation between parametric and non-parametric measurements (

Table 7. Principal Component Analysis (PCA) on the stability measurements in maize grain yields.

PC	PC1	PC2	PC3	PC4
Eigenvalue	8.65	6.84	1.74	1.09
Variance (%)	45.51	36.02	9.13	5.71
Cumulative (%)	45.51	81.53	90.66	96.37

PC = Principal Component.

). The first two components were used to visualize the PCA biplot because they had the highest variability values (PC1 = 45.51% and PC2 = 36.02%) and eigenvalues of 8.65 and 6.84, respectively, as shown in Figure 3. Parametric and non-parametric measurements were classified into four main groups, namely: the first group consisted of NP⁽¹⁾, S⁽²⁾, D_i, W_i², S²d_i, θ_(i), and σ²_i; the second group consisted of S⁽¹⁾; the third group consisted of yields (Y) with TOP, S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, KR, NP⁽⁴⁾, CV_i, and b_i measurements; and the fourth group consisted of θ_i measurement.

Based on the Spearman rank correlation coefficient, the average yield was positively and significantly correlated with S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾, KR, b_i, TOP, and CV_i (p < 0.05) (

Table 8. Correlation of Spearman’s rank on parametric and non-parametric measurements on maize hybrid yields in Java Island, Indonesia.

	Y	S(1)	S(2)	S(3)	S(6)	NP(1)	NP(2)	NP(3)	NP(4)	KR	Wi2	σ2i	s2di	bi	CVi	θ(i)	θi	Di
S(1)	0.00
S(2)	−0.50	0.60
S(3)	0.57	0.77	0.30
S(6)	0.77	0.52	0.05	0.92
NP(1)	−0.47	0.31	0.73	0.02	−0.04
NP(2)	0.93	0.15	−0.40	0.66	0.83	−0.38
NP(3)	0.90	0.13	−0.28	0.63	0.85	−0.07	0.90
NP(4)	0.80	0.50	0.03	0.93	0.98	−0.13	0.85	0.83
KR	0.65	0.33	0.17	0.63	0.75	0.20	0.63	0.76	0.73
Wi2	−0.40	0.40	0.75	0.07	−0.03	0.76	−0.33	−0.17	−0.10	0.43
σ2i	−0.40	0.40	0.75	0.07	−0.03	0.76	−0.33	−0.17	−0.10	0.43	1.00
s2di	−0.53	0.48	0.85	0.07	−0.12	0.87	−0.44	−0.25	−0.17	0.25	0.93	0.93
bi	0.60	0.25	−0.23	0.40	0.38	−0.51	0.46	0.35	0.43	0.47	−0.10	−0.10	−0.23
CVi	0.60	−0.05	−0.03	0.33	0.57	0.08	0.61	0.67	0.53	0.88	0.35	0.35	0.08	0.30
θ(i)	−0.40	0.40	0.75	0.07	−0.03	0.76	−0.33	−0.17	−0.10	0.43	1.00	1.00	0.93	−0.10	0.35
θi	0.40	−0.40	−0.75	−0.07	0.03	−0.76	0.33	0.17	0.10	−0.43	−1.00	−1.00	−0.93	0.10	−0.35	−1.00
Di	−0.43	0.47	0.80	0.12	−0.03	0.90	−0.33	−0.12	−0.08	0.34	0.92	0.92	0.98	−0.27	0.17	0.92	−0.92
TOP	0.96	0.03	−0.49	0.57	0.73	−0.57	0.95	0.81	0.78	0.60	−0.40	−0.40	−0.55	0.66	0.60	−0.40	0.40	−0.48

Numbers in bold have a significant correlation (p < 0.05); Y = Grain yield.

). Other positive and significant correlations were S⁽¹⁾ against S⁽²⁾, S⁽³⁾, S⁽³⁾, and NP⁽⁴⁾; S⁽²⁾ against NP⁽¹⁾, Wi², σ²_i, s²di, θ_(i), and Di; S⁽³⁾ against S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾, KR, and TOP; S⁽⁶⁾ against NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾, KR, CV_i, and TOP; NP⁽¹⁾ against W_i², σ²_i, and s²di, θ_(i), and Di; NP⁽²⁾ against NP⁽³⁾, NP⁽⁴⁾, KR, CV_i, and TOP; NP⁽³⁾ against NP⁽⁴⁾, KR, CV_i, and TOP; NP⁽⁴⁾ against KR, CV_i, and TOP; KR against CV_i, and TOP; W_i² against σ²_i, s²di, θ_(i), and Di; σ²_i against s²di, θ_(i), and Di; s²di against θ_(i), and Di; bi and CV_i against TOP; and θ_(i) against Di; while θ_(i) was negatively and significantly correlated with θ_i.

Graphical visualization based on PCA biplots was used to understand the relationship between the measurement and the stability concept (Figure 3). PCA biplots were taken from the highest values of the first two PCs (Table 7). Based on PCA analysis, all stability measures were classified into four groups. The first group consisted of NP⁽¹⁾, S⁽²⁾, D_i, W_i², S²d_i, θ_(i), and σ²_i; the second group consisted of S⁽¹⁾; the third group consisted of yields (Y) with TOP, S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, KR, NP⁽⁴⁾, CV_i, and b_i measurements; and the fourth group consisted of θ_i measurement. The first two and the fourth groups represent the concept of static stability, so they can be used to select hybrids in less favorable environments [52]. The third group showed that the measures were positively and significantly correlated based on Spearman’s rank correlation to maize hybrid yields, providing a measure of dynamic stability. They can be used to recommend ideal maize hybrids under favorable environmental conditions [27,28].

3.4. Mega-Environment of Maize Hybrid and Identification of the Best Locations

GGE biplot analysis for dry, rainy, and combined seasons are presented in Figure 4, Figure 5 and Figure 6, respectively. Based on the GGE biplot of the dry season (Figure 4), 60.92% of the total variation for grain yield was explained by PC1 (40.19%) and PC2 (20.73%). The ‘which won where/what’ pattern showed five sectors for ten locations with different winning (vertex) hybrids. The vertex hybrids were SC2 in KADS, LBDS, BBDS, NKDS, PPDS, and GJDS; SC3 hybrid in NNDS and ASDS; SC8 hybrid in JKDS and JTDS, while other vertex hybrids, namely SC1 and SC5 did not have locations that fall in the sector.

For the rainy season, with two PCs accounting for 63.30% of the total variation for grain yield (PC1 = 43.18% and PC2 = 20.12%), the GGE biplot revealed three mega-environments (Figure 5). The first mega-environment consisted of NNRS, PPRS, GJRS, KARS, ASRS, JKRS, and BBRS, with the winning hybrid SC2. The second mega-environment includes LBRS and NKRS, with SC7 as the winning hybrid. The third mega-environment includes JTRS, with the SC4 and SC8 as the winning hybrids. SC6 is the vertex hybrid in the sector without an environment.

For the combined data in dry and rainy seasons, GGE biplots of ten locations in two growing seasons revealed that the first two PCs accounted for 54.14% of the total variation (PC1 = 37.61% and PC = 16.53%) (Figure 6). Based on the biplot, there were five sectors with different winning hybrids (vertex). The vertex hybrids were SC1, SC7, SC2, and SC4. Figure 6 represents the three mega-environments. The first mega-environment consisted of JTDS and JTRS with the winning hybrid SC4. The second mega environment included BBRS, JKRS, GJRS, KARS, PPRS, NNRS, ASDS, NNDS, GJDS, NKDS, JKDS, KADS, LBDS, PPDS, and BBDS, with SC2 as the winning hybrid. The third mega-environment included ASRS, LBRS, and NKRS, with the SC1 and SC7 as the winning hybrids. SC5 and SC6 were vertex hybrids in the sector without environment, indicating that their yield performance was poor in all test environments in this study.

According to the ‘ranking environment’ pattern of the GGE biplot presented in Figure 7, the GJRS and KARS environments were ideal for testing because they were at the ideal point (small arrow). These two locations were ideal for selecting superior hybrids because they had high discriminating power and representation. The JTRS environment was farthest from the ideal point and close to the center of the biplot axis. This location provides little information about the maize hybrids tested, so they are unsuitable for testing. Other environments were close to the ideal point but were outside the first circle, so it was useful for selecting hybrids in specific environments.

The GGE biplot can provide an overview of the differences between hybrids and environmental characteristics tested in multi-environment experiments. One advantage of the biplot that showed the distribution pattern of hybrids and environments was “which won where/what biplot” [53]. One of the characteristics of this biplot was the presence of polygons that indicate the location of the hybrid being tested. Hybrids at the top of the polygon (vertex) have the highest yields in the environment in that sector. Another important feature of this pattern was the grouping of environments, which suggested the possibility of different mega-environments [1,29,54]. In this current study, it was shown that within each growing season, the sites fall into different groups, and the pattern of site grouping varied throughout the seasons. The first two PCs explained 54.14–63.30% of the total variability due to the effects of hybrid (G), environment (location and growing season), and their interactions (Figure 4, Figure 5 and Figure 6). The GGE biplot depicted the distribution of hybrids and environments in each season, and the average of the two seasons showed two and three mega-environments.

In the main mega-environment, SC2 was at the peak of the vertex in both dry, rainy, and combined seasons. Meanwhile, mega-environment (single) showed the difference between vertex hybrids. SC9 hybrids were always close to the center of the axis in each growing season and the combined one. This showed that SC9 tends to be stable in various environmental conditions; in other words, it has a small GEIs response. The two strategies for evaluating mega-environmental data (analysis of each growing season and its combination) showed that there was more than one mega-environment for maize breeding programs in various regions of Java island (Indonesia) and divided them into certain sub-regions. However, based on the combined data during two growing seasons, it was found that three mega-environments with different winning hybrids indicated the presence of maize hybrids specific to the mega-environment and the presence of substantial GEIs. The ideal hybrid is the hybrid with high yield and stability in multi-environment testing [27,28,33]. The hybrid SC2, followed by SC1, SC4, SC7, and SC9, were identified as ideal hybrids compared to others. This was confirmed by numerical measurements (parametric and non-parametric), where HCA separated SC2, SC7, and SC9 in the stable and high-yield groups (Figure 1). Based on these results, both measurement steps (numerical and graphical) produced the same pattern in selecting stable and high-yielding maize hybrids. This finding is similar to previous studies, which reported that stability measurements based on parametric, non-parametric, and GGE biplots resulted in the similar result for selection of stable and high-yielding genotypes, including sweet potato [27,54] and safflower [55].

In multi-environment evaluation, discriminative and representative locations were very important. The ideal environment (location) should differentiate the hybrids being evaluated and the representation of all environments [53,56]. In this current study, the GGE biplot revealed that the environments of GJRS and KARS were the discriminative location(s) and were at the ideal point (small arrow) of testing location(s) for multi-environment evaluation in Java Island (Figure 7). Contrary to that result, the JTRS was the farthest location from the ideal point and close to the center point of the biplot axis. This location provided small information about the maize hybrids tested; therefore, it was not suitable for multi-environment evaluation on Java island. Overall, the maize hybrids trial in mega-environments selected the five best genotypes (stable and high-yielding); were SC1, SC2, SC4, SC7, and SC9. In addition, both the dry season, the rainy season, and the combination of the two seasons produce three mega-environments. The GGE biplot has also succeeded in determining two representative environments for testing that can be used for large-scale development in the future, namely GJRS and KARS.

3.5. Stability Maize Hybrids Based on Sustainability Index (SI)

The Sustainability Index (SI) evaluation is presented in

Table 9. Estimation for sustainability Index (SI) on maize hybrids in 10 locations during two growing seasons.

Hybrid Code	Y	σn	YM	SI (%)
SC1	8.35	2.13	12.56	49.51	Moderate
SC2	9.73	2.02	13.16	58.54	Moderate
SC3	8.43	1.70	11.06	60.77	High
SC4	8.56	1.72	12.90	53.01	Moderate
SC5	7.86	1.83	12.08	49.86	Moderate
SC6	7.90	1.67	11.00	56.57	Moderate
SC7	8.68	1.66	12.08	58.14	Moderate
SC8	8.48	1.59	11.73	58.78	Moderate
SC9	8.69	1.65	12.27	57.43	Moderate

. Some researchers revealed that estimates of high SI indicate stability levels in certain genotype(s) [33,43,57]. In this current study, SI was divided into five groups: very low, low, medium, high, and very high [33,43]. Estimating SI for grain yield in maize ranged from 49.51% (moderate) to 60.77% (high). The small range of SI was due to the genetic background of the planting materials originating from the selected hybrid.

The moderate SI was shown by hybrid SC1 (49.51%), SC2 (58.54%), SC4 (53.01%), SC5 (49.86%), SC6 (56.57%), SC7 (58.14%), SC8 (58.78%), and SC9 (57.43%). Only the maize hybrid of SC3 showed high SI (60.77%). The maize hybrid of SC3 showed a high average yield at 8.43 ton.ha⁻¹ with high SI of 60.77%, indicating the highest performance and stability of this hybrid (Table 8). On the contrary to this result, maize hybrids of SC5 and SC6 showed medium SI at 49.86% and 56.57%, respectively, with a low yield at 7.86 tons per ha and 7.90 tons per ha. This result of SI for the two low-yield maize hybrids indicated the stability of grain yield. This result is similar to the previous combined analysis, as presented in Figure 2, wherein these two maize hybrids were grouped into a stable low-yield hybrid. The other maize hybrid showing high yield and SI nearly high were maize hybrids of SC2, SC7, SC8, and SC9. Generally, maize hybrids with moderate to high SI and performed yield above average can be categorized as ideal genotypes. Ruswandi et al. (2022) [33] reported a similar strategy to select high-yield maize hybrids by using SI.

Information of selected maize hybrids based on different stability analyses was summarized in

Table 10. Comparison of maize genotypes selection results based on each measurement.

Stability Measurements	Selected Genotypes	Percentage (%)
Combined analysis	SC2, SC7, SC8, SC9	44.44
GGE Biplot	SC1, SC2, SC4, SC7, SC9	55.56
SI	SC2, SC3, SC7, SC8, SC9	55.56
Slice of all measurements	SC2, SC7, SC9	33.33

. Based on this Table, three maize hybrids were selected, namely maize hybrids of SC2, SC7, and SC9. These three maize hybrids have high yields and are stable in different Java island environments, so that they can be recommended for maize development programs in Indonesia.

4. Conclusions

The results of the analysis showed that the main effects of the growing season, location, hybrid (G), and their interactions had a significant influence (p < 0.01) on the variation of maize hybrid yields in Java Island, Indonesia. Stability measurements NP⁽¹⁾, Wi², S²di, θ_(i), σ²_i, S⁽¹⁾, S⁽²⁾, and Di were included in the concept of static stability, while TOP, S⁽³⁾, S⁽⁶⁾, NP⁽²⁾, NP⁽³⁾, KR, NP⁽⁴⁾, CV_i, and b_i measurements were included in the concept of dynamic stability. SC2, SC7, and SC9 were identified as the most stable and high-yielding yields, so they can be recommended for maize development programs in Indonesia. The dry season, the rainy season, and the combination of the two seasons produce three mega-environments. GJRS and KARS were the most representative environments with high discriminatory power, so they can be used as favorable environments for selecting the ideal maize hybrid.