1. Introduction
Maize (
Zea mays L.) is an essential and deliberate commodity that can be cultivated in various agro-ecological systems. Maize is a necessary food for most people worldwide [
1]. Furthermore, maize is also useful as animal feed, especially for cattle, and as an industrial raw material [
2,
3,
4]. To satisfy the predicted rise in food demand, maize production and productivity must improve by 2.40% per year [
5]. However, maize yields have plateaued, with an average yield of 2 t·ha
−1 in numerous nations [
6]. Yields have recently decreased, endangering future food security [
7,
8,
9]. The yield decline was caused by a genetic decline in maize germplasm [
6]. Assembling new genetic variations of germplasm via crosses that involve different natural resources can increase superior maize yields [
10]. Germplasm of the tropical maize has the potential to help the maize seed grow [
6]. Nevertheless, the tropical germplasm of maize has more undesirable genes than the subtropical germplasm, which is particularly beneficial for improvement [
11]. The introgression of subtropical into tropical genes of maize has been shown to improve diversity in genes and heterosis in breeding schemes of maize [
12,
13]. However, the hybrids developed from cross-breeding have different adaptability to environmental change [
14]. As a result, a rise in grain yields is required to meet national and worldwide food demands; thus, it must be investigated in a particular environment of production to discover superior and stable hybrids.
Climate change is a major factor that influences productivity in agriculture and is unpredictable. Meanwhile, the climate conditions nearly involved rainfed agriculture, such as staple food commodities (rice, maize, and wheat) [
15]. The climate will affect water availability, humidity, and sunlight intensity. The ecosystem will fluctuate in temperature and rainfall due to climate change scenarios [
16]. Climate change will also affect food safety and diminish crop production since it is affected by environmental conditions such as nutrition and water availability [
17].
Multiple cropping systems are an excellent tool to test multiple cultivars or maize lines. A cropping technique known as a multiple cropping system combines two or more crops in a single crop area. Through this technique, crop production can use water, fertilizer, and light more efficiently [
18]. The multiple-cropping system can also increase the stability of crop yields because it can form an optimal and suitable micro-climate for plant commodities [
17].
Regarding breeding programs, the major selection criteria are high yield potential, stability, and adaptability [
19,
20]. Through multi-environment experiments, adaptable and stable hybrids with high potential for productivity were discovered. When evaluating genotypes in various environments, breeders must contend with genotype-by-environment interactions (GEIs) [
21]. A comprehensive assessment of GEIs necessitates more complex statistical approaches than ordinary analysis of variance (ANOVA) [
6]. Parametric and non-parametric stability [
22], AMMI [
23], and GGE biplot [
24,
25] have been widely used to assess the GEI’s effects. Therefore, it is crucial to measure GEIs to identify high-performing hybrids in the final selection cycle and predict their potential performance in various environments.
The selection of superior maize hybrids involves many statistical measurements. The selection of high-yielding varieties using a single stability measurement was considered inaccurate, so it is necessary to combine various stability measurements [
14]. Various studies have reported the combined stability measurements, including those of black soybeans [
26,
27], durum wheat [
28,
29], sweet potatoes [
30], stevia [
31], and rice [
32]. In combining stability measurements, biplot visualization is very important to use. Currently, two biplot models are used, namely AMMI and GGE biplots. These two models can help select genotypes with broad and specific adaptations by supporting superiority and stability indices [
23,
24]. In addition, both of them can also identify the performance of genotypes and GEIs. This allows the identification of hybrids in several test environments based on their environmental conditions to be able to distinguish the genotypes being tested from the ideal environment [
20,
33]. In the GGE-biplot, the potential and stability of yields were evaluated using the average environment [
34], which is determined by the average principal component scores for all environments [
35,
36]. GGE biplot allows the identification of superior hybrids evaluated in multiple environments, which is important for the development of maize plant breeding programs. The purpose of this study was to select maize hybrids within favorable traits such as grain yield and yield attributes; identify genotype and environmental interaction (GEIs) within maize yield; evaluate maize hybrids adaptability to four different locations in West Java; and identify a representative environment for testing and developing new maize hybrids. The results of this research can be used as a reference in selecting maize hybrids and representative environments in Indonesia.
4. Discussion
One of the functions of the GT biplot method is to evaluate maize hybrids that are superior in specific traits. Apart from that, GT biplot can also be used to identify relationships between traits evaluated [
49]. This method cannot ascertain how the combined value under various environmental conditions will affect other traits; it can only estimate the relationships between each hybrid and trait tested [
50,
51]. GT biplot analysis on the maize hybrids tested showed quite strong results because they represented 56.82% of the total variation (
Figure 1). Several researchers also reported GT biplot measurements above 50.00%, such as wheat [
50] and stevia [
49]. This shows that the GT biplot results on the traits tested in maize hybrids in West Java are quite good.
In the population tested, the G3 hybrid has a high value (superior) in sector 1 and correlates with the EW, EWoH, EWP, PH, ED, and OSW traits (
Figure 1). The G22 excels in sector 2 and correlates with EWoP, RSEW, Y, SW, and SWP compared to other hybrids in the sector. The maize hybrids that top the sector are superior in the sector and have a close relationship to the traits within them [
49,
51,
52]. Currently, plant breeding programs are not only focused on high yields. Early maturity, disease resistance, and stability in various environments [
19,
53,
54,
55], as well as good quality and other agronomic traits [
49,
50], are the ultimate goals of breeding superior varieties. The GT biplot shows that the yields are significantly and positively correlated with SWP and SW because they are positioned very close together and have very sharp angles (
Figure 2). These traits show a fairly strong relationship [
51]. Other traits that are interconnected (positively correlated) are EL with RN, EWoH with PH, and EWP with OSW (
Figure 2). The relationship between each trait can be used to indirectly select the expected traits. In addition, in
Figure 3, the maize hybrids tested are divided by mean value (vertical line) and trait stability (horizontal line). Hybrids that approach the small arrow, are close to the line of stability (horizontal), and have values higher than the average are the expected hybrids (superior) [
50]. In this study, G16, G2, and G3 were identified as being close to the small arrow (ideal point) (
Figure 3), so they have the highest value of all traits evaluated.
Quantitative traits of crops are significantly influenced by environmental factors, so they are difficult to predict [
21,
56]. In addition, testing maize hybrids in different agro-ecological conditions is needed to select the representative environment for each hybrid. So, it is necessary to test hybrids in different agro-ecologies and seasons to indicate their adaptability. This study only carried out GEIs and stability tests on crop yields because yield is one of the main characteristics that determine whether a variety is superior.
Significant variation was presented in the combined ANOVA for maize hybrid yields (
p < 0.01). The environment has the most significant influence on yield variation, namely 85.63%, followed by GEIs at 12.13% and genotype at 2.24% (
Table 3). The emergence of GEIs in multi-environmental testing indicates that there are stable and unstable (adaptive to specific environments) maize hybrids. Similar findings regarding the importance of GEIs have been widely reported, such as in maize [
20,
33], sweet potatoes [
30], rice [
57,
58], and soybeans [
27,
59]. The strong environmental influence on the yield variation indicates that external factors (the cropping system and environment) have a more significant influence than genetics. This is because yield is a quantitative trait, and many genes strongly influence that. Statistically, these results indicate that the stability order of the maize hybrids tested was not consistent in all environments. According to several researchers, environmental factors strongly influence crop yields [
33,
60,
61]. The heatmap GEIs (
Figure 4) also show the groupings and interaction patterns between the environments and the maize hybrids tested. The heatmap divided environments into two main groups. The first environmental cluster consists of KRW CS, KRW M, CKD CS, ARJ CS, and LMB CS, while the second environmental cluster consists of CKD M, ARJ M, and LMB M. The first cluster is an environmental group with a low average yield (2.56–4.19 t·ha
−1), while the second cluster is an environmental group with an average high yield (5.74–6.23 t·ha
−1). The GEI response pattern, illustrated in
Figure 4, shows that the hybrids tested have varied and dispersed colors. This indicates an interaction with the environment tested. The difference in resulting color indicates the magnitude of the simulated GEIs; the darker the color, the more positive the interaction [
62,
63]. In this study, G22 showed a darker color at the LMB M and CS locations. This means that G22 has the highest score in both environments compared to the other hybrids. To illustrate the distribution of maize hybrids among GEIs, a stability analysis was performed using various stability measures.
The results of stability measures of maize hybrid yields under various environmental conditions using parametric and non-parametric methods are presented in
Table 4. The average yield of maize hybrids is in the range of 3.90–4.96 t·ha
−1. The measurement results showed that
KR,
NP(1),
NP(2), and
NP(3) indicated that G2 was the most consistent (stable) hybrid. Measurements of
KR,
S(6), and
NP(4) identified G5 as the most consistent (stable). The
CVi measurement identified the G7 hybrid as the most consistent (stable). Measurements of
S(1),
S(2),
S(3),
Wi2,
σi2, S
2di, and
θi identified G10 as the most consistent (stable) hybrid. The regression coefficient (
bi) identifies G12 as a hybrid with a value of
bi = 1 and is declared the most stable. The
θ(i) measurements identified G18 as the most stable hybrid (
Table 5). The measurement results show that G2 has the smallest AR value and is declared the most consistent by all stability measurements [
30,
61], followed by G10, G6, and G5. In contrast, G18 was the hybrid that had the highest AR value and was declared the most inconsistent hybrid in all environments, followed by G16, G13, and G1.
To classify the maize hybrids tested, we used a Hierarchical Cluster Analysis/HCA (dendrogram) based on the stability rank of each measurement (
Figure 5). This has also been reported in several previous studies, such as maize [
33], barley [
61], and butterfly pea [
64]. The dendrogram classifies maize hybrids into several groups, namely unstable low yield, unstable high yield, stable low yield, and stable high yield. This grouping is based on the stability rank of all measurements. The stable high-yield group is the most expected because it can adapt to various environments with the best performance [
20]. The unstable high-yield group can be used as an environment-specific genotype because it can adapt to a supportive environment [
30]. The unstable low-yield group is the worst because it has low yields and needs to be adaptable to environmental changes.
GGE biplot analysis of maize hybrids was carried out in sole-crop, multiple-crop, and mixed environments. The visualization of the GGE biplot is presented in
Figure 6,
Figure 7 and
Figure 8. Hybrids closer to the axis points were identified as having stable yields [
35]. These hybrids had small GEIs [
6,
10]. Almost all hybrids that are at the top of the sole-crop sector (
Figure 6) show the same position in the multiple-crop portion (
Figure 7) and also a combination of both patterns (
Figure 8). This is because the hybrids tested were the result of selection that had fairly consistent yields at the previous stage. The hybrids at the top of the sector had the highest yields in the environments within that sector [
65]. In addition,
Figure 8 also generates three mega-environments. The hybrids at the top of the mega-environment sector are the best hybrids. The hybrids at the top of the mega-environment sector are the best hybrids. On the other hand, hybrids at the top of the sector, which have no environment, are the worst. G11 and G13 were the peak hybrids in the no-environment sector, indicating that their yield performance was poor in all environments in this study [
33,
35,
60]. Thus, the hybrids identified at the top of the sector containing the environment are environment-specific hybrids. This study’s selection is directed at stable high-yield and unstable high-yield hybrids. The measurement results show that combined stability identifies two stable high-yield hybrids and seven unstable high-yield hybrids. The GGE biplot identified five stable high-yield hybrids and four unstable high-yield hybrids. The slice of all measurements shows that G2 is a stable high-yield hybrid, and G4 and G16 are unstable high-yield hybrids (
Table 6).
The visualization of the GGE biplot pattern of ‘representative vs. discriminative’, presented in
Figure 9, shows that ARJ M was identified as the most representative environment. A representative environment was indicated by a long vector and the resulting angle to the abscissa [
10]. In addition, ARJ M also has high representation, so this environment is very suitable for selecting superior maize hybrids. The CKD M has a large angle to the abscissa and a long vector, making it suitable for selecting adaptive hybrids [
20]. LMB CS and KRW CS are the worst environments because they have short vectors. These conditions indicate that they are not suitable for use as test environments, as they provide biased information about the hybrids being evaluated [
35,
66,
67]. The results shown by this pattern indicate that multiple-cropping selection should be carried out in different seasons and agro-ecologies. Thus, to obtain an ideal intercropping pattern, it is necessary to consider the environment and planting season.